Artículos de investigación

The probability density function of drops is difficult to model. Current approaches make assumptions that are often problematic, as they allow negative values for the mean of the distribution. While the statistical goodness of fit of those models might be reasonable for precipitation radar estimation, the situation is unsatisfactory if a fully consistent physical modeling of precipitation across scales is desired. This is the case of weather and climate models. This paper discusses a model that satisfies mathematical and physical consistency. The model can be seamlessly integrated into the parameterizations of the microphysics of precipitation and is tested on an extensive disdrometer data set. Comparison with existing models shows that the new method has substantial practical and theoretical advantages. The research has implications in elucidating the role of clouds in the climate sensitivity of climate models.

Climate models are fundamental tools for the study of global change. They are used not only to test scientific hypotheses about the physical processes operating in planetary atmospheres, but also to advise society about expected changes in the climate of our planet. Current Earth System Models have begun to incorporate biological, chemical and geological processes (biogeochemical cycles, water chemistry) as feedback between the surface and the atmosphere. However, due to the many challenges involved, the integration of ‘human dynamics’ remains to be fully addressed. This perspective outlines a path towards two-way feedback Human Earth Systems Models. Major challenges in the plan include how to deal with the uncertainty inherent in human behavior and our interaction with the environment, the limits of predictability, and the scale of computational resources required to model human behavior with sufficient accuracy (i.e. close to observed measurements) and precision (i.e. with low dispersion). This paper provides a roadmap towards a tool that could revolutionize Earth system modeling by fully integrating the physical and human systems.

A new method to analyze the potential for rapid intensity change in tropical cyclones (TC) is presented. The method is based on satellite observations of precipitation derived from microwave (MW) radiometers. The approach is intended to condense the information in the environment and in the vortex using a low wavenumber representation of the rain index (RaIn, a multichannel nonlinear combination of passive MW observations), and train a deep-learning, multilayer neural network (NN) with the RaIn and the changes in the wind over the next 24 h. The resulting NN exhibits a near-perfect ability to identify rapid intensification (RI: changes in the hurricane wind speed in excess of 30 knots within a 24-h period). It is found that the spatial structure and amounts of the columnar water condensate within the extended environment is necessary to capture the most important information regarding the RI process. Analyses of the NN structure provide new insight into the physics of TC and can help improve model forecasting. Environmental conditions as far as 1050 km from the TC center might affect the process of RI by at least three physical processes: absolute angular momentum inflow, wind shear stabilization, and steering the outflow jets in the upper troposphere. The findings can be used to build a RI discriminant (RID) for real-time operations.

Major floods in Spain in September 9–13, 2019 resulted in seven casualties and massive losses to agriculture, property and infrastructure. This paper investigates the utility of satellite data to: (1) characterize the event when input into a hydrological model, and to provide an accurate picture of the evolution of the floods; and (2) inform meteorologists in real time in order to complement model forecasts. It is shown that the precipitation estimates from the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO, available since 2014) and the merged satellite estimates provide an extraordinary improvement over previous technologies to monitor severe hydrometeorological episodes in near real time. In spite of known biases and errors, these new satellite precipitation estimates can be of broad practical interest to deal with emergencies and long-term readiness, especially for semi-arid areas potentially affected by ongoing global warming. Comparisons of satellite data of the September event with model outputs and more direct observations such as rain gauges and ground radars reinforce the idea that satellites are fundamental for an appropriate management of hydrometeorological events.

Precipitation science is a growing research field. It is concerned with the study of the water cycle from a broad perspective, from tropical to polar research and from solid precipitation to humidity and microphysics. It includes both modeling and observations. Drawing on the results of several meetings within the International Collaborative Experiments for the PyeongChang 2018 Olympics and Paralympic Winter Games (ICE-POP 2018), and on two Special Issues hosted by Remote Sensing starting with “Winter weather research in complex terrain during ICE-POP 2018”, this paper completes the “Precipitation and Water Cycle” Special Issue by providing a perspective on the future research directions in the field.

Sustainable Development Goals (SDGs) include a subset of targets that can be advanced through standard urban management activities. In particular, routine urban vegetation management comprises a number of activities with potential impact on Goal #4 (quality education), #11 (sustainable cities and communities), #13 (protect the planet), #15 (life on land), and, perhaps less obviously, but equally important, on Goal #8 (good jobs and economic growth). This paper discusses how urban vegetation management can help achieve the SDGs at a local level. Drawing on a case study (Talavera de la Reina, Spain), it is shown that an intelligent approach to urban vegetation management can leverage resources towards the SDGs at little or no cost to municipalities. Minor modifications and conceptual changes in how standard practices are carried out can make a difference. Including this dimension can even result in a positive balance for the municipal budget. Our analyses and proposals are of broad and direct applicability for urban areas worldwide and can help city authorities and officials to align their cities with the SDGs simply by making minor adjustments to how they currently deal with urban vegetation.

Evaluating satellite ability in capturing sudden natural disasters such as heavy snowstorms is a topic of societal interest. This paper presents a rapid qualitative analysis of an intense snowfall in Madrid using data from the Global Precipitation Measurement (GPM) mission, specifically the GPM IMERG (Integrated Multi-satellitE Retrievals for GPM) Late Precipitation L3 Half Hourly 0.1◦ × 0.1◦ V06 estimates of precipitation (IMERG-Late), and Sentinel-2 imagery. The main research question addressed is the consistency of ground observations, model outputs and satellite data, a topic of major interest for an appropriate and timely societal response to severe weather episodes. Indeed, the choice of the ‘Late’ product over the IMERG ‘Final’ or other GPM datasets was motivated by the availability of data for near real-time response to the storm. Additionally, the 30-min temporal resolution of the product would in principle allow for a detailed analysis of the dynamic processes involved in the snowstorm. Using several complementary data sources, it is shown that optical remote sensing sensors (Sentinel) add value to existing ground data and that is invaluable for rapid response to severe meteorological events such as Filomena. Regarding the GPM precipitation radar, the sampling of the GPM-core satellite was insufficient to provide the IMERG algorithm with enough quality data to correctly represent the actual sequence of precipitation. Without corrections, the total precipitation differs from observations by a factor of two. The difficulties of retrieving precipitation with radiometers over snow-covered surfaces was a major factor for the mismatch. Thus, the calibrated precipitation product did not fully capture the historic storm, and neither did the IR-based element of the IMERG-Late product, which is a neural network merging of microwave and infrared data. It follows that increased temporal resolution of spaceborne microwave sensors and improved retrieval of precipitation from radiometers are critical in order to provide a complete account of these sorts of extreme, significant, short-duration cases. Otherwise, the high-quality, radar and radiometer data feeding the high temporal resolution algorithms simply slip through the grasp of the ascending and descending orbits, leaving little quality data to be interpolated into successive overpasses.

The validation of climate models using satellite precipitation measurements presents some particularities. By construction, climate models are different from reanalyses and numerical weather prediction models, and several specificities affect the methods of validation and the interpretation of the results. Thus, for instance, an important topic to consider is that climate models are not always intended to provide the temporal succession of the weather but the statistics of the weather over long periods, and that fact affects the validation strategy. In addition, there is a set of quality standards that need to be fulfilled even before analyzing the statistics or the qualitative agreement with observations.

Regional Climate Models (RCMs) emerged 30 years ago as a transient tool to provide detailed estimates of meteorological parameters (temperature, precipitation, humidity, wind, and others) for regional applications. Their dynamic downscaling approach was intended to fill the gap between the global but coarse estimates of Global Climate/Circulation Models (GCMs), which typically had a 2.5° resolution, and practical requirements such as estimating precipitation for hydrologic operations in small basins under conditions of increased greenhouse gas emissions. Over the three decades, RCMs provided data to inform policies and helped to increase knowledge of the present climate and the impacts of global warming at regional level. This paper describes the major achievements of RCMs, critically reviewing the main issues and limitations that have been featured in the literature. It puts forward a controversial claim aimed at starting a debate in the climate community, namely, that the cycle of RCM research has reached an end for informing policies. This is because these models have recently been superseded for that purpose by high-resolution GCMs and Earth System Models (ESM).

After 5 years in orbit, the Global Precipitation Measurement (GPM) mission has produced enough quality-controlled data to allow the first validation of their precipitation estimates over Spain. High-quality gauge data from the meteorological network of the Spanish Meteorological Agency (AEMET) are used here to validate Integrated Multisatellite Retrievals for GPM (IMERG) level 3 estimates of surface precipitation. While aggregated values compare notably well, some differences are found in specific locations. The research investigates the sources of these discrepancies, which are found to be primarily related to the underestimation of orographic precipitation in the IMERG satellite products, as well as to the number of available gauges in the GPCC gauges used for calibrating IMERG. It is shown that IMERG provides suboptimal performance in poorly instrumented areas but that the estimate improves greatly when at least one rain gauge is available for the calibration process. A main, generally applicable conclusion from this research is that the IMERG satellite-derived estimates of precipitation are more useful (r2 > 0.80) for hydrology than interpolated fields of rain gauge measurements when at least one gauge is available for calibrating the satellite product. If no rain gauges were used, the results are still useful but with decreased mean performance (r2 ≈ 0.65). Such figures, however, are greatly improved if no coastal areas are included in the comparison. Removing them is a minor issue in terms of hydrologic impacts, as most rivers in Spain have their sources far from the coast.

The microphysics of precipitation has to be parameterized in Earth System Models (ESM), Global Circulation/Climate Models (GCMs), Cloud Resolving Models (CRMs), Regional Climate Models (RCMs), and Numerical Weather Prediction (NWP) models since the relevant physical processes operate at centimeter scale, thus well below the finest model grid size. While more than 20 bulk microphysics schemes have been described in the literature, they all have a number of empirical values and work under a set of reasonable assumptions that treat the problems in a simplified way. This paper discusses these choices in order to present a homogenous account of the physics within the parameterizations, and illustrates how observations can help improve our present understanding of precipitation physics. The Weather and Forecasting Research model (WRF), and the Community Atmosphere Model (CAM) in the Community Earth System Model (CESM) are used as prototypes of full-confidence models. This contribution can also help frame and advance research into the human-induced, ongoing climate change as those parameterizations are instrumental for the appropriate ESM modeling of future climates.

Climate classifications of climate models’ outputs have been used to assess environmental changes but systematic analyses of the differences between models, scenarios and classification methods are scarce. Here, the results of applying the most commonly used climate classifications to the outputs of 47 Global Climate Models (GCM) of different physical parameterizations and varied grid size are presented. The extent and intensity of changes for present climate, three different Representative Pathways Scenarios (RCP26, RCP45 and RCP85) and three increasingly-fine classification methods show that there is a consensus between models, and that climate classifications are indeed useful tools to translate physical climatology variables into environmental changes. The main conclusions are that climate classifications can indeed be used to gauge model performance at several grid sizes and that the classification method does not decisively affects the potential global changes in future climates under increasing greenhouse gas emissions. The analyses also reveal that there are several uncertainties that are not attributable to model grid size or to limitations in the reference datasets but more likely to deficiencies in the physics of the models.

The absence of systematic estimates of vertical transport of water by convective storms is one of the main causes of uncertainties in weather forecasting in the tropics, in severe weather forecasting, and in the climate-scale analysis and prediction of global circulation. The difficulty of providing such estimates lies in that the only instruments capable of providing such estimates at present, radiometers onboard satellites, operate at short wavelengths, and that makes difficult the retrievals. This paper describes a method capable of providing robust estimates of the maximum height of precipitation and the 3-D structure of condensed water in convective systems using a constellation of radiometers. Using hurricane Winston and the Global Precipitation Measurement (GPM) mission Core Observatory satellite dual precipitation radar as a reference, it is shown that the method is suitable to improve the parameterization of convection in numerical models.

The convective rainfall rate from cloud physical properties (CRPh) algorithm for Meteosat second-generation satellites is a day-only precipitation algorithm developed at the Spanish Meteorological Agency (AEMET) for EUMETSAT’ Satellite Application Facility in support of nowcasting and very short-range forecasting (NWC SAF). It is therefore mainly intended to provide input for monitoring and near-real-time forecasts for a few hours. This letter critically discusses the theoretical basis of the algorithm with special emphasis on the empirical values and assumptions in the microphysics of precipitation, and compares the qualitative performances of the CRPh with its antecessor, the convective rainfall rate algorithm (CRR), using an object-based method applied to a case-study. The analyses show that AEMET’s CRPh is physically consistent and outperforms the CRR. The applicability of the algorithm for nowcasting and the challenges of improving the product to an all-day algorithm are also presented.

Climate classifications based on temperature and precipitation measurements are increasingly being used for environmental and climate change studies. Using three classification methods (Köppen, Extended Köppen, and Holdridge) and one observational dataset for present climate (CRU, Climate Research Unit), we show that GCMs have bridged the gap that led to the emergence of RCMs thirty years ago, as GCMs can now provide global climate classifications whose accuracy and precision are comparable to those of regional outputs of the RCMs. Projections of high-resolution GCMs for future climates under the assumptions of three Representative Concentration Pathways (RCP26, RCP45 and RCP85) can therefore be used as a primary source for climate change and global warming studies at high resolution. This paper provides comprehensive, model-derived climate classifications for the entire planet, using RCMs and two GCMs for present and future climate-change scenarios, and discusses how well the models actually represent the climates of the world when compared with reference, ground validation data. It turns out that both GCMs and RCMs appear still limited to provide practical estimates of the world climates even for present climate conditions. The modeling of precipitation remains the Achilles’ heel of models and thus of multidimensional indices, which are very sensitive to this variable. The conclusion is that model outputs at regional scale need to be taken with extreme caution without venturing into informing policies presenting potentially large societal impacts. Nonetheless, the role of models as privileged tools to advance our scientific knowledge of the Earth’s system remains undisputed.

This paper presents a consensus estimate of the changes in oceanic precipitation off the coast of Europe under increasing greenhouse gas emissions. An ensemble of regional climate models (RCMs) and three gauge and satellite-derived observational precipitation datasets are compared. While the fit between the RCMs’ simulation of current climate and the observations shows the consistency of the future-climate projections, uncertainties in both the models and the measurements need to be considered to generate a consensus estimate of the potential changes. Since oceanic precipitation is one of the factors affecting the thermohaline circulation, the feedback mechanisms of the changes in the net influx of freshwater from precipitation are relevant not only for improving oceanic-atmospheric coupled models but also to ascertain the climate signal in a global warming scenario.

One of the applications of satellite-derived precipitation datasets, such as Global Precipitation Climatology Project (GPCP) or Climate Prediction Centre Merged Analysis of Precipitation (CMAP), is to validate output from numerical models, either numerical weather prediction (NWP) models, Regional Climate Models (RCMs), Global Climate Models (GCMs) or Earth System Models (ESMs). A qualitative comparison of total annual precipitation and climatology is the first step in detecting model deficiencies and thus improving our understanding of the world’s climate. However, spatial (or association) analysis of the precipitation fields offers new insights into model performance and their ability to provide realistic predictions of rain and snowfall in both current and future climates. Here we analyse the spatial structure of precipitation according to 40 GCMs for 20 years (January 1980 to December 1999), quantitatively comparing the modelled precipitation against five observational datasets: three land-only (CRU, PRECL and GPCC) and two global (GPCP and CMAP). We found discrepancies between the GCMs’ predictions and the observational datasets and noted that satellite-derived datasets are essential for pinpointing areas that require attention. The analyses also revealed a consistent trend towards less spatially correlated fields. This trend is not apparent in aggregated, traditional validation exercises but arises when spatial association indices are applied. So long as the trend is not an artefact in the observational datasets, then we suggest the tendency could be attributed to changes in stratiform/convective partitioning or the result of increasingly intense convection.

Measuring precipitation from space is of paramount importance for improving our scientific understanding of the global hydrological cycle, to develop, improve, and tune numerical models and to provide valuable information for many human activities, including energy production. This chapter briefly summarizes this topic.

Precise estimates of precipitation are required for many environmental tasks, including water resources management, improvement of numerical model outputs, nowcasting and evaluation of anthropogenic impacts on global climate. Nonetheless, the availability of such estimates is hindered by technical limitations. Rain gauge and ground radar measurements are limited to land, and the retrieval of quantitative precipitation estimates from satellite has several problems including the indirectness of infrared-based geostationary estimates, and the low orbit of those microwave instruments capable of providing a more precise measurement but suffering from poor temporal sampling. To overcome such problems, data fusion methods have been devised to take advantage of synergisms between available data, but these methods also present issues and limitations. Future improvements in satellite technology are likely to follow two strategies. One is to develop geostationary millimeter-submillimeter wave soundings, and the other is to deploy a constellation of improved polar microwave sensors. Here, we compare both strategies using a simulated precipitation field. Our results show that spatial correlation and RMSE would be little affected at the monthly scale in the constellation, but that the precise location of the maximum of precipitation could be compromised; depending on the application, this may be an issue.

While it has been proved that multiple scattering in the microwave frequencies has to be accounted for in precipitation retrieval algorithms, the effects of the random arrangements of drops in space has seldom been investigated. The fact is, a single rain drop size distribution (RDSD) corresponds with many actual 3D distributions of those rain drops and each of those may a priori absorb and scatter radiation in a different way. Each spatial configuration is equivalent to any other in terms of the RDSD function, but not in terms of radiometric characteristics, both near and far from field, because of changes in the relative phases among the particles. Here, using the T-matrix formalism, we investigate the radiometric variability of two ensembles of 50 different 3D, stochastically-derived configurations from two consecutive measured RDSDs with 30 and 31 drops, respectively. The results show that the random distribution of drops in space has a measurable but apparently small effect in the scattering calculations with the exception of the asymmetry factor.

Improving the atmospheric component of hydrological models is beneficial for applications such as water resources assessment and hydropower operations. Within this goal, precise characterization of rain microphysics is key for climate and weather modeling, and thus for hydrometeorological applications. Such characterization can be achieved by analyzing the evolution in time of the particle size distribution (PSD) of hydrometeors, which can be measured at ground using disdrometers for validation. The estimation, however, depends on the choice of the PSD form (the shape) and on the parameters to define the exact shape. In the case of modeling rain microphysics, two approaches compete: the use of the number concentration of drops decoupled from the shape of the distribution (the [NT, E(D), E(D2)] and the (NT, E(D), E[log(D)]) models), and the (N0, Λ, μ) model that embeds in N0 both the shape of the distribution and the number concentration of drops. Here we use a comprehensive dataset of disdrometer measurements to show that the NT-based approaches allow a more precise characterization of the drop size distribution (DSD) and also a physically based modeling of the microphysical processes of rain since NT is analytically independent of the shape of the DSD (parameterized by E(D), and E(D2) or E[log(D)]). The implication is that numerical models would benefit from decoupling the number of drops from the shape of distribution in their modules of precipitation microphysics in order to improve outputs that eventually feed hydrological models.

Laser disdrometers measure the particle size distribution (PSD) of hydrometeors through a small cross-sectional (tens of square centimeters) surface. Such a limited area induces a sampling effect in the estimates of the PSD, which translates to error in the reflectivity-rain-rate (Z-R) relationship used for ground radar estimates of rainfall, estimates of kinetic energy of precipitation, and any other hydrometeorological application relying on particle size information. Here, the results of a dedicated experiment to estimate the extent of the effect of limited area sampling of rainfall are presented. Using 14 Parsivel, version 1 (Parsivel-1), disdrometers placed within 6 m2, it was found that the combined area of at least seven disdrometers is required for the estimates to start converging to a stable value. The results can be used to quantify the degree of over-/underestimation of precipitation parameters for a single instrument due to the limited collecting area effect. It has been found that a single disdrometer may underestimate instantaneous rain rate by 70%.

The advent of global precipitation data sets with increasing temporal span has made it possible to use them for validating climate models. In order to fulfill the requirement of global coverage, existing products integrate satellite-derived retrievals from many sensors with direct ground observations (gauges, disdrometers, radars), which are used as reference for the satellites. While the resulting product can be deemed as the best-available source of quality validation data, awareness of the limitations of such data sets is important to avoid extracting wrong or unsubstantiated conclusions when assessing climate model abilities. This paper provides guidance on the use of precipitation data sets for climate research, including model validation and verification for improving physical parameterizations. The strengths and limitations of the data sets for climate modeling applications are presented, and a protocol for quality assurance of both observational databases and models is discussed. The paper helps elaborating the recent IPCC AR5 acknowledgment of large observational uncertainties in precipitation observations for climate model validation.

Disruptions of the spatiotemporal distribution of surface precipitation that are induced by global warming may affect Earth’s climate more significantly than changes in the total precipitation amount. Identifying such disruptions at global scales is not straightforward, as it requires disentangling a weak signal from comprehensive, gapless data in a 5-D configuration space whose dimensions are latitude, longitude, time, power, and period. Drawing on reliable, state-of-the-art climate model simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) experiments and using well-tested analytical methods, clear changes in the global precipitation cycles have been found for the simulated period 1862–2003. It has also been found that the disruptions may be attributable to anthropogenic forcing. The disruptions are relevant enough to envision significant changes in precipitation timing if human greenhouse gas emissions continue to accumulate in the future. It is noteworthy that the effects of anthropogenic forcings have been found not predominantly in the intra-annual cycles, i.e., in the short-term weather patterns that would be indicative of local effects, but rather in the interannual planetary long-term variability of the atmosphere. This suggests a global, distributed effect of the anthropogenic forcings on precipitation, which in turn is indicative of changes in the precipitation patterns linked with changes in the thermodynamics of the precipitation microphysics and to a lesser extent with the dynamical aspects of the precipitation processes.

The raindrop size distribution (RDSD) is defined as the relative frequency of raindrops per given diameter in a volume. This paper describes a mathematically consistent modeling of the RDSD drawing on probability theory. It is shown that this approach is simplerthan the use of empirical fits and that it provides a more consistent procedure to estimate the rainfall rate (R) from reflectivity (Z) measurements without resorting to statistical regressions between both parameters. If the gamma distribution form is selected, th modeling expresses the integral parameters Z and R in terms of only the total number of drops per volume (NT), the sample mean [m=E(D)], and the sample variance [σ2=E(m-D)2]of the drop diameters (D) or, alternatively, in termsof NT, E(D), and E[log(D)]. Statistical analyses indicate that (NT, m) are independent, as are (NT, s2). The Z-Rrelationship that arises from this model is a linear R=T×Z expression (or Z=T-1R), with T a factor depending on m and σ2 only and thus independent of NT. The Z-R so described isinstantaneous, in contrast with the operational calculation of the RDSD in radar meteorology, where the Z-R arises from a regression line over a usually large number of measurements. Theprobabilistic approach eliminates the need of intercept parameters N0 or Nz.ast;0, whichare often used in statistical approaches but lack physical meaning. The modeling presented here preserves a well-defined and consistent set of units across all the equations,alsotaking into account the effects of RDSD truncation. It is also shown that the rain microphysical processes such as coalescence, breakup, or evaporation can then be easily described in terms of two parameters-the sample mean and the sample variance-and that eachof those processes have a straightforward translation in changes of the instantaneous Z-R relationship.

This paper explores the many aspects of precipitation measurement that are relevant to providing an accurate global assessment of this important environmental parameter. Methods discussed include ground data, satellite estimates and numerical models. First, the methods for measuring, estimating, and modeling precipitation are discussed. Then, the most relevant datasets gathering precipitation information from those three sources are presented. The third part of the paper illustrates a number of the many applications of those measurements and databases, namely hydropower, data assimilation and validation of Regional Climate Models (RCM). The aim of the paper is to organize the many links and feedbacks between precipitation measurement, estimation and modeling, indicating the uncertainties and limitations of each technique in order to identify areas requiring further attention, and to show the limits within which datasets can be used. Special emphasis is put on the central role of the upcoming Global Precipitation Measurement (GPM) mission in precipitation science.

Ensembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address moist physics deficiencies to realize the full benefits of the ensemble approach, in addition to optimizing initial conditions. The results also provide insights into differences in simulations arising from ensembles of weather models using several combinations of different physical parameterizations.

Hydroelectric plants require precise and timely estimates of rain, snow and other hydrometeors for operations. However, it is far from being a trivial task to measure and predict precipitation. This paper presents the linkages between precipitation science and hydroelectricity, and in doing so it provides insight into current research directions that are relevant for this renewable energy. Methods described include radars, disdrometers, satellites and numerical models. Two recent advances that have the potential of being highly beneficial for hydropower operations are featured: the Global Precipitation Measuring (GPM) mission, which represents an important leap forward in precipitation observations from space, and high performance computing (HPC) and grid technology, that allows building ensembles of numerical weather and climate models.

This paper discusses the potential of Regional Climate Models (RCMs) as reanalysis tools by presenting a reconstruction of the European climate using several RCMs with diverse physical parameterizations. The use of RCMs is intended to increase the spatial resolution of the analysis provided by Global Models through dynamic downscaling. At the same time, the use of several models allows us to characterize the uncertainties, as these can be estimated from the spread of the ensemble. When the RCMs are nested in reanalyses instead of in a Global Model it is possible to create climatologies of unprecedented robustness for variables such as temperature, precipitation, wind speed, and humidity, among others. While these climatologies are subject to further improvement as methods and computing power evolve, they point the way forward to the development of atmospheric information products suitable for a variety of studies including education, agriculture, renewable energies and climate change research, biogeography, insurance, risk assessment, hydrology, and regional planning.

This article presents a new approach to derive fine-scale socioeconomic information of urban areas using very high resolution satellite data. The rationale behind the method is to use high resolution satellite data, capable of resolving urban morphology details, to derive a classification of the image. Thus, it is assumed that there is a relationship between the socioeconomic profile and the urban morphology of an area in terms of availability of green areas, sport facilities, private swimming pools or pavement conditions. The method is tested using a case study of Lima, Peru. Using a sample of ground data, a neural network classifier was applied to a pre-classified image in which entropy had been used to mask extensive, non-built up areas that would otherwise have inserted spurious information into the classifier. The result shows a high correlation (0.70 R2) when compared with validation data. The good performances also show that a physiographic satellite view of the city reflects the socioeconomic layout of their inhabitants, thus making remote sensing a complementary tool for social research and urban planning. While the parameterization of the problem may differ from one area to another, it is shown that an a priori choice of a few parameters may help to automatically characterize large areas in social terms, thus allowing social inequality and its evolution to be mapped in those areas with limited availability of data. In order to make the method widely applicable, the possibilities and limitations of applying the procedure to other large cities are discussed.

This paper presents an analysis of the precipitation climate signal in Europe emerging from a simulation of heterogeneous regional climate models (RCMs) using five observational datasets as the reference for present day climate conditions. Current climate simulations, as well as those from the A2 family of scenarios from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES-A2), from eight RCMs involved in the Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects (PRUDENCE) project have been cross-compared with data from the Climate Research Unit (CRU), the Global Precipitation Climatology Project (GPCP), the Global Precipitation Climatology Centre (GPCC), the Climate Prediction Center (CPC), and the CPC Merged Analysis of Precipitation (CMAP) databases for Europe. The RCMs used areHIRHAM, the ClimateHigh ResolutionModel (CHRM), the Rossby Centre Atmosphere-Ocean (RCAO) model, the GKSS Climate Version of the Local Model (CLM), the Hadley Center RCM (HadRM3H), the Atmospheric Hydrostatic Regional Model (REMO), the Prognostic Model at the Mesoscale (PROMES), and the regional coupled ocean-atmosphere-ice model (RACMO). The comparison shows that the climate signal has to be interpreted depending on the reference data used. Although each validation dataset has its own relative merits and shortcomings, it is known that all of the datasets present variable uncertainties and error sources, which impedes consideration of a single dataset as the only valid representation of actual precipitation.Hence, it is suggested that a robust joint estimate of changes in future precipitation might include the uncertainties of both the RCMs and those of the observational datasets. After accounting for the difference between observed and simulated precipitation in the present climate, the analysis of such joint estimates reveals significant agreement in the climate signal formost of Europe. This lends confidence to the idea that the RCMs are able to correctly simulate future changes in precipitation.

This paper reports on the results of an experiment designed to measure the spatial variability of rain drop size distribution (RDSD) at kilometer scale. Eight dual instruments (16 Parsivel disdrometers) were used to record the RDSD from October 2009 to January 2010. The spatial variability of the RDSD in terms of cross-correlation and changes in the reflectivity-rainfall (Z-R) relationship was calculated. The results provide an estimate of the variability range at spatial scales relevant for spatial radars such as TRMM-PR and GPM-DPR. It was found that the spatial variability of the RDSD in a single episode can exceed the inter-episode variability. This implies that estimates of the RDSD using a few disdrometers are not enough to capture the evolution of the RDSD, and that more detailed areal estimates are needed in order to fully analyze the RDSD.

There is an increasing use of satellite data and numerical models to assess the environmental constraints and energy potential of several renewable energies, namely solar, wind, hydropower, biomass and geothermal. This paper presents an overview of state of the art spatial technology and quantitative models in renewable energy research, with an emphasis on applications.

This paper presents a quantitative method for characterizing high speed train stations in terms of passenger intermodality. The aim of the procedure is two-fold: firstly, the method is to be used as an objective measure for comparing stations in order to detect suboptimal points and improve the performance of stations as nodal points; secondly, the method provides a means to embed intermodality into regional accessibility models, allowing comprehensive modelling at lower scales. The empirical base of the work comprises data from 27 European high speed rail stations, which is deemed an appropriate statistical sample for the whole European rail network. Using the entropy metric, we found that several different patterns emerged: there was a clear hierarchy of stations which was linked to their respective roles within the system, while strong constraints impeded stations from performing optimally as true intermodal nodes.

This paper presents a spatial analysis of the results in the general elections of contemporary Spain (1977-2007). By using electoral data at municipal level, we have traced back the evolution of the vote at high spatial resolution, thus providing a more accurate overview of the evolution of modern Spanish politics and offering empirical evidence on the nationalization thesis. We demonstrate that this approach reveals several processes that remain hidden when aggregated data are used, especially the clustering patterns in the smooth transition from dictatorship to full democracy (the so-called Spanish Transition). We found a statistical normalization process of the left/right dynamics with a growing number of balanced municipalities with a similar number of votes for left and right parties. The tendency towards a Gaussian histogram centered on a value of 50% for the left vote is an indication of political normalization after 40 years of dictatorship.

Regional climate models (RCMs) are dynamical downscaling tools aimed to improve the modelling of local physical processes. Ensembles of RCMs are widely used to improve the coarse-grain estimates of global climate models (GCMs) since the use of several RCMs helps to palliate uncertainties arising from different dynamical cores and numerical schemes methods. In this paper, we analyse the differences and similarities in the climate change response for an ensemble of heterogeneous RCMs forced by one GCM (HadAM3H), and one emissions scenario (IPCC’s SRES-A2 scenario). As a difference with previous approaches using PRUDENCE database, the statistical description of climate characteristics is made through the spatial and temporal aggregation of the RCMs outputs into probability distribution functions (PDF) of monthly values. This procedure is a complementary approach to conventional seasonal analyses. Our results provide new, stronger evidence on expected marked regional differences in Europe in the A2 scenario in terms of precipitation and temperature changes. While we found an overall increase in the mean temperature and extreme values, we also found mixed regional differences for precipitation.

This handbook presents the foundations of modern rural analysis. The first part of the book presents a comprehensive description of the elements of rural analysis, providing the basis for a synthetic view of rural landscapes in the second part. Included is a comprehensive description and explanation of the rural landscapes from throughout the world, which leads to a complete management scheme for rural landscapes. The text covers topics such as global change, the role of information technologies in rural areas, gender relations and how to bridge the gap between scientific analysis and decision-making processes. A fully-worked example is provided, illustrating how all the analyses and techniques shown can be applied in a real case.

Previous studies of the role of regional airports in the Spanish economy have focused on calculating efficiency using air traffic as the output and airport infrastructure data as the input. We present an alternative quantitative analysis based upon the geographical structure within the airport’s market basin. We formulate a ‘geographical efficiency’ model that considers territorial variables within the airport hinterland. Input data for our model include the socio-economic structure of the population, intermodal transport links, industrial and tourism potential and existing leisure-related services. The output is the annual number of passengers associated with such constraints. The result is a relative efficiency estimate that shows uneven patterns of geographical efficiency amongst Spain’s regional airports and also provides insights into opportunities for expanding some of these critical items of infrastructure. They suggest that some of Spain’s regional airports may be better placed than others to compete in a liberalized market that exhibits a clear tendency in favor of coastal, tourism-based airports.

This paper analyzes the changes in the precipitation climatologies of Europe for the periods 1960-90 and 2070-2100 using a heterogeneous set of regional climate models (RCMs). The authors used the Climatic Research Unit (CRU) database to define a precipitation climatology for current climate conditions (1960-90), then compare the estimates with the RCMs’ simulations for the same period using spectral analysis. After the authors evaluated the performance of the models compared with validation data for current climate, they calculated the future climate spectra (2070-2100). Changes in the future climate have been evaluated in terms of differences in the phase and amplitude of the annual cycle with respect to present conditions. The results show that models provide consistent results and that under the A2 scenario (increased greenhouse gases conditions) precipitation climatologies in Europe are expected to suffer noticeable changes, the most important being a strengthening of the annual cycle in most of the Atlantic coastal areas of the continent. While total amounts of rainfall might undergo little change, the consequences of changes in the seasonal distribution of precipitation will strongly affect both ecosystems and human activities. Differences were also found in the probability distribution function (pdf) of precipitation, indicating an overall increase in the frequency of precipitation-related hazards in Europe.

This paper addresses the identification of hurricanes in low-resolution global climate models (GCM). As hurricanes are not fully resolvable at the coarse resolution of the GCMs (typically 2.5 × 2.5 deg), indirect methods such as analyzing the environmental conditions favoring hurricane formation have to be sought. Nonetheless, the dynamical cores of the models have limitations in simulating hurricane formation, which is a far from fully understood process. Here, it is shown that variations in the specific entropy rather than in dynamical variables can be used as a proxy of the hurricane intensity as estimated by the Accumulated Cyclone Energy (ACE). The main application of this research is to ascertain the changes in the hurricane frequency and intensity in future climates.

A cloud motion winds (CMW) method is presented for improving quantitative rainfall estimation advection schemes that use both infrared (IR) and passive microwave (PMW) satellite data. Advection schemes are used to provide quantitative rainfall estimates by combining more direct PMW rainfall estimates with more frequent IR cloud top temperature measures using a two-step technique: (1) PMW estimates are transported along CMW trajectories calculated with an advection scheme at subpixel resolution; and (2) PMW estimates are calibrated using the IR gradient along those trajectories. These schemes outperform traditional methods of satellite rainfall estimation but no clear physical basis for the procedure has yet been described. Here, the physical basis for the image processing techniques used in advection techniques is described. It is shown that geostationary satellite-derived CMW from IR sensors can be modelled in terms of fluid dynamics using Navier-Stokes equations. This approach allows for modelling the problem as equivalent to the flow of a brightness temperature field, also providing subpixel resolution and unlimited rotation/deformation possibilities. The method is illustrated with rainfall estimates from a numerical weather prediction (NWP) model and with 3-hourly PMW products as simulation data, obtaining consistent results.

This Letter presents the results of analysing a global, satellite-based monthly precipitation database on a 2.5° latitude-longitude grid for the 22-year period from 1979 to 2001 using a direct maximum entropy spectral analysis method. The NOAA Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) database is used to calculate the rainfall cycles for ∼10 000 time series covering the whole Earth. It is shown that several cycles other than the annual or seasonal cycles affect the rainfall distribution of many areas, in particular western Europe.

The first tropical cyclone recorded to landfall in continental Spain was on 2005 and was named Hurricane Vince. The storm developed 800km east-southeast of the Azores with a cold sea surface temperature (SST). The initial development of Vince was caused by the cumulative low pressure between 5 and 8 of October 2005, according to the analysis data from the Global Forecast System and the European Center for Medium Range Weather Forecasts (ECMWF). The QuikSCAT data was used to calculate the vorticity value in the vicinity of Vince and as a result, the extent of the cyclonic circulation around Vince is at 1954 UTC on 7 October. Using QuikSCAT satellite data, the vorticity analysis helps to quantitatively determine the radius appropriate for the phase-space analyses.

This paper presents an analysis of the current situation of Spanish geography in higher education. The authors show that Spanish geography has undergone a profound transformation in the new democratic era, resulting in a thriving community struggling to find its place amongst other disciplines. To assess the current health of the discipline in higher education, they discuss the two ends of the spectrum as perceived by many Spanish geographers: either geography is losing its place in Spanish academe (the worst of times, in Dickens’s words), or it is starting a new golden age (the best of times). What was found is that the current situation does not necessarily involve a weighted combination of these extreme views, as one would be tempted to think: it might be that geography is actually at one of the poles. It was also found that the divide between classical physical and human geography is widening, with very different views on what geography is about, its societal role, and the future strategies to advance the discipline. In view of this diversity and the need to implicate themselves in this debate, the authors end up providing some suggestions from their own perspective. Their proposals to strengthen geography in higher education are both strategic and methodological, without forgetting that the problems geography faces in higher education are not particular to geography – but are shared by the arts and social sciences in technocratic societies.

Regional multi-model ensembles are used to both increase the spatial resolution of the global simulations and to palliate uncertainties arising from different parameterizations and dynamical cores. Here, we present the simulated current (1960-1990) and future (2070-2100) precipitation climatologies using eight Regional Climate Models (RCM) over Europe for an increased greenhouse gases scenario. Analysis of the current climate simulations in terms of the Probability Distribution Functions (PDF) shows noticeable regional differences in the type of precipitation which are in agreement with known precipitation climatologies. For future climate we observe an overall decrease of mean precipitation in most of the Mediterranean regions. A rise in high monthly precipitation amounts appear for all the regions, except for the Iberian peninsula and the Alps. As our analysis embed both spatial and temporal uncertainties in the modeling, our results provide further evidence of a variety of regional patterns within Europe under an increased greenhouse emissions scenario. Copyright 2007 by the American Geophysical Union.

We present a method for choosing a member in a global circulation model (GCM) ensemble forecasting. We show that the r2 correlation between members and independent data is related to the flux of information entropy contained into the forecasts. This allows to classify the members in terms of goodness-of-fit. To validate the method we use several ECMWF-ensemble forecast from the DEMETER project, and the ERA-40 database. We compare ERA-40 actual estimates with our entropy-based choice of the DEMETER members in each of the 180 days, obtaining consistent results. Our hypothesis is based on energy-balance, maximum entropy production considerations so that the method might only be applicable to global simulations. Copyright 2006 by the American Geophysical Union.

This paper presents a new algorithm to generate quantitative precipitation estimates from infrared (IR) satellite imagery using passive microwave (PMW) data from Special Sensor Microwave/Imager sensor (SSM/I) satellites as ancillary information. To generate the estimates, we model the probabilistic distribution function (PDF) of the rainfall rates through the maximum entropy method (MEM), applying a cumulative histogram matching (HM) technique to the IR brightness temperatures. This results in a straightforward algorithm that can be formulated as an algebraic expression, providing a simple method to derive rainfall estimates using only IR data. The main application of the method is the direct estimation of rainfall rates and accumulated rainfall from geostationary satellites, providing appropriate temporal and spatial resolutions (up to 15 min/4 km when the Meteosat Second Generation satellite becomes available). The proposed method can be easily applied at GOES or current Meteosat satellite reception stations to generate instantaneous rainfall rates estimates with little computational cost. Here we provide examples of applications using the Global Infrared Database and Meteosat images. Our results have been compared with GOES Precipitation Index (GPI) and validated against Global Precipitation Climatology Centre (GPCC)-land rain gauge measurements, at 5°, monthly accumulations. We have obtained correlations of 0.88 for the algorithm, while the GPI yields correlations of 0.85. Preliminary comparisons with other algorithms over Australia also show how the performances of the algorithm are similar to those of more complex models. Finally, we propose some improvements and fine-tuning procedures that can be applied to the algorithm.

The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1° spatial resolution. Rainfall is estimated using a neural networks (NN)-based approach utilizing passive microwave (PMW) and infrared satellite measurements. Several neural networks are tested, from multilayer perceptron to adaptative resonance theory architectures. The NN analytical selection process is explained. Half-hourly rain gauge data over Andalusia, Spain, are used for validation purposes. Several interpolation procedures are tested to transform point to areal measurements, including the maximum entropy estimation method. Rainfall estimations are also compared with Geostationary Operational Environmental Satellite precipitation index and histogram-matching results. Half-hourly rainfall estimates give ∼0.6 correlations with PMW data (∼0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5° and 0.1° monthly accumulated estimates, respectively.

Neural networks (NNs) have been successfully used in the environmental sciences over the last two decades. However, only a few review papers have been published, most of which cover image processing, classification, prediction and geophysical retrieval in general, while neglecting rainfall estimation issues. This paper reviews, without aiming to be exhaustive, NN approaches to satellite rainfall estimation (SRE) by providing an overview of some of the methodologies proposed. A basic introduction to NNs is provided and the advantages of using NNs in SRE are explained, illustrating how NNs can be used to complement more computational-expensive methods to generate quick and accurate results in near real time. The role of the NNs in statistical-empirical algorithms is also reviewed. The last section aims to generate some discussion through comparing the empirical and deterministic algorithmic approaches and contrasting some of the apparent drawbacks of using NNs with a statistically based view of the satellite geophysical parameter estimation.

This study presents the methodology followed in the land use mapping at scale 1:50,000 for the Functional Area of Segovia, Spain. The study is included in the regional planning directives for Segovia, and makes a comprehensive use of the remote sensing techniques developed during the second half of the 1990s. The methodological precedents in this respect are analyzed, mainly the CORINE land cover project, together with the data sources and the techniques that were used to obtain the thematic information searched for. The less known methods are described and a complete methodological sequence of the work is offered. The main characteristics of the methodology are the high degree of automation, objectivity, possibility of direct contrasting and its capacity for quick updating. Diverse data sources have been used, such as cartographic vector information and satellite imagery. LANDSAT-TM and IRS-1D Pan were used as well as aerial oblique photography. All information was integrated into a Geographic Information System (GIS; named SIGIM-TD). Data fusion methods were also widely used to improve the spatial resolution of the images. A neural network was generated to provide an appropriate classification method. Results of the neural network-based classification are shown and a classification-fieldwork correlation of 0.887 was obtained, in contrast with coefficients of 0.334, 0.432, 0.234 and 0.678 achieved through other techniques. Graphic results of the work are presented together with the data sources used for the map elaboration. Finally, a discussion on the advances that these techniques represent is done.

This work offers a new algorithm for the fusion of images for improving the spatial resolution of multispectral images, using the concepts of entropy of information and a priori probability. The algorithm is based on Jaynes’ maximum entropy method and starts from a previous modelling of the fusion of data as an operation in which a series of probabilities are designated at a micro-scale in terms of average statistical values at a macro-scale, which are those recorded by the image with the lowest spatial resolution. The theoretical base of the model is presented, its practical implementation is explained, a complete description allowing reproduction of the analysis is offered and the results of fusing Landsat Thematic Mapper (TM) images with IRS-ID Pan are shown.

The aim of this paper is to explain and discuss the role of GIS in quantitative geographical analysis. Special interest has been placed in offering a complete scheme of quantitative geographical analysis and GIS tools to develop this within the field of locational analysis as well as showing a theoretical framework for this kind of study. This theoretical framework comes from a mathematical conceptualisation of geographical space, and provides the appropriate numerical tools in each case. GIS-Remote Sensing-Statistical Methods relationships are studied, as is the probabilistic approach to some geographical problems, considering the connection with GIS. Finally, some GIS requirements in relation to geographical analysis are laid out, with some considerations on the prospects in the near future.

This study presents a proof of concept for a storm Detection, Delineation, and Tracking (DDT) method based on the vertical integral of condensed water and ice content—namely the Ice Water Path (IWP). Traditional DDT methods rely solely on regularly sampled variables, such as brightness temperatures, which do not directly capture storm evolution. In contrast, IWP is a direct indicator of storm presence and intensity, being zero when absent and high in convective cores. A machine learning approach is proposed to forecast IWP during gaps in passive microwave (PMW) observations. It integrates an initial IWP retrieval with the temporal evolution of geostationary IR data. The method was first evaluated using numerical weather model (NWP) simulations of convective episodes. Then, it was applied to 26 cases of near-coincident (20 min apart) PMW and IR observations of real storms. The prediction of IWP is addressed at three levels: (a) a cloud/no-cloud classification achieving probabilities of detection (POD) consistently above 0.60 and often exceeding 0.80; (b) a regression model with some challenges on the spatial structure forecast but normally achieving correlations above 0.60 (c) an intensity-based, three-class classification that outperformed simple variable persistence and demonstrated strong spatial coherence.

This study presents a continental-scale hail climatology for Europe (2014-2024) using the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR). To address the limitations of inhomogeneous surface observations, four hail detection algorithms were applied at radar-pixel level. Normalized detection frequency maps (0-1) were generated for the April-September period, individual months, and subregions, to identify and analyze European hail hotspots. The results identify the main hail epicenter near the Alpine range, with secondary axes extending over the Balkan peninsula and Apennines, confirming orography as the dominant influence. The analysis revealed a clear seasonal evolution. That is, activity began in April with scattered foci, shifted toward southeastern Europe in May, reached its maximum spatial extent and heterogeneity in June, consolidated over the Alpine system during the summer peak (July-August), and finally moved to the Mediterranean Sea in September. This derived climatology robustly validates documented patterns, provides new continental details, and addresses the observational gaps in terrestrial networks, thereby confirming the value of satellite radar for studying extreme events.

Satellite-based precipitation products (SPPs) have become a crucial source of quantitative global precipitation data. Geostationary Orbit (GEO) satellites provide high spatiotemporal resolution but tend to have lower accuracy, while Low Earth Orbit (LEO) satellites provide more precise precipitation estimates but suffer from lower temporal resolution due to their limited observation frequency. This study proposes an efficient algorithm for integrating and enhancing precipitation estimates from multiple satellite observations. The target domain includes the Full Disk (FD) and the extended East Asia (EA) regions, both of which are observable by GEO satellites, such as Himawari-8, serving as the GEO platform in this study. The algorithm involves four steps: pre-data preparation, LEO morphing, adjustment, and final merging. It produces Early and Late composite products with 10-min temporal and up to 2 km spatial resolution and significantly reduces latency compared to IMERG. Specifically, the Early and Late products can be generated with approximate latencies of 90 min and 270 min, respectively—much faster than Integrated Multi-satellite Retrievals for GPM (IMERG)’s Early (4-h) and Late (14-h) products. A key feature of the proposed method is the use of accuracy-based weighting derived from radar-based validation, enabling dynamic merging that reflects the reliability of each satellite observation. Statistical validation using Global Telecommunication System (GTS) precipitation data confirmed the positive impact of the proposed bias correction and merging method. In particular, the Late product achieved accuracy comparable to or higher than that of IMERG Early and IMERG Late, despite its significantly shorter latency. However, its accuracy was still lower than that of IMERG Final, which benefits from additional gauge-based correction but is released with a delay of several months.

Understanding and better predicting severe meteorological phenomena such as hailstorms is increasingly important because of their capacity to cause major socioeconomic losses. We analyzed seven case studies with confirmed hail on the ground. To do so, we used four microphysical parameterizations of the Weather Research and Forecasting (WRF) model (horizontal resolution 3 km) that incorporate hail formation. Predictive variables for hailstorm severity were extracted from the Dual-Frequency Precipitation Radar-Global Precipitation Measurement (DPR-GPM) sensor, a novel and effective tool for monitoring extreme events. The main objective was to evaluate WRF model data by comparing them with DPR-GPM observations during hailstorm days in southern France. For this, the SAL score was used, an object-oriented method that robustly evaluates the simulations. To deepen the analysis, other metrics were calculated. The selection of microphysical parameterizations for simulating hail is challenging because of their specific assumptions and dependence on other model parameterizations. However, for southern France, the best parameterization for simulating maximum reflectivity in the vertical profile and vertically integrated precipitation water in the solid phase was the Milbrandt-Yau Double-Moment 7-class. For vertically integrated precipitation water in the liquid phase, it was the WRF Single-Moment 7-class (WSM7), with WRF Double-Moment 7-class (WDM7) standing out in the localization of all three variables. In contrast, the NSSL 2–Moment 7-class scheme generally showed poorer performance in hail prediction and simulation of the analyzed variables. Identifying optimal parameterizations for hailstorm prediction in southern France will enhance future simulations of extreme weather in the Mediterranean.

Climate classification systems (CCSs) are emerging as essential tools in climate change science for mitigation and adaptation. However, their limitations are often misunderstood by non-specialists. This situation is especially acute when the CCSs are derived from Global Climate Model outputs (GCMs). We present a set of uncertainty maps of four widely used schemes -Whittaker-Ricklefs, Holdridge, Thornthwaite-Feddema and Köppen- for present (1980–2014) and future (2015–2100) climate based on 52 models from the Coupled Intercomparison Model Project Phase six (CMIP6). Together with the classification maps, the uncertainty maps provide essential guidance on where the models perform within limits, and where sources of error lie. We share a digital resource that can be readily and freely integrated into mitigation and adaptation studies and which is helpful for scientists and practitioners using climate classifications, minimizing the risk of pitfalls or unsubstantiated conclusions.

Imbalanced datasets pose significant challenges in machine learning (ML), often leading to catastrophic failures in models that are insensitive to their statistical particularities. In regression using neural networks (NN), imbalance is particularly problematic due to the potential undersampling of extreme but important values. Traditional classification methods are ineffective with imbalanced datasets and fail to capture the structure of the problem. This article presents a novel approach to handling imbalanced data in general regression. The new preprocessing technique, called “Sequence-Based Undersampling”, leverages the spatial structure of the data to selectively remove overrepresented instances. The method is tested using quantitative precipitation estimates (QPE), a well-known case of imbalance distribution in earth physics. The technique demonstrates consistent improvements in model performance compared to existing methods. The results suggest that sequence-aware undersampling improves regression models and ML algorithms, providing a practical solution to a prevalent issue in data-driven research. This method can enhance current satellite precipitation algorithms, as satellite retrievals often exhibit a leptokurtic distribution with few cases of high rainfall rates, many low rates, and numerous no-rain occurrences-a paradigmatic case of an imbalanced dataset built sequentially through radar and radiometer measurements.

Typical snowfall structure over the coastal mountainous region of the Korean Peninsula is investigated. East coast-type snowfall (ET) due to the lake-effect over the East Sea of Korea is dominant for snowfall intensity and duration. The ET can be divided by the high-pressure system over the Gaema Plateau (GH) and the extratropical low-pressure system passing southern part of the Korean Peninsula in addition to the GH pattern (GHSL). Composite analysis showed that the GHSL can allow a greater inflow of the snowfall from the sea into the land than the GH. The key factors for snowfall structure are 1) the wind-turning layer (WTL), which is the transition level from the lower-level easterly to the upper-level westerly; 2) vertical wind shear suppressing updrafts near the WTL and 3) the Froude number (Fr), which determines the snowfall penetration beyond the mountain. A higher WTL height indicates a deeper easterly layer, indicating favorable conditions for inland snowfall penetration. The strong vertical wind shear plays a role of suppressed updrafts near the WTL via downward momentum transport. It is presented that updraft limitation is mostly exerted by the wind shear. Fr indicates whether the weather system is blocked or unblocked by the mountains. It is shown that the larger Fr generally increases with height, which means that snow systems or flows near the mountain tops can easily to overcome the topography. It is shown that both dynamic and thermodynamic factors are important for understanding and predicting the structure and regions of snowfall.

Quantitative Precipitation Estimates (QPE) obtained from satellite data are essential for accurately assessing the hydrological cycle in both land and ocean. Early artificial Neural Networks (NN) methods were used previously either to merge infrared and microwave data or to derive better precipitation products from radar and radiometer measurements. Over the last 25 years, machine learning technology has advanced significantly, accompanied by the initiation of new satellites, such as the Global Precipitation Measurement Mission Core Observatory (GPM-CO). In addition, computing power has increased exponentially since the beginning of the 21st century. This paper compares the performance of a pure NN FORTRAN, originally designed to expedite the 2A12 TRMM (Tropical Rainfall Measuring Mission) algorithm, with a contemporary state-of-the-art NN in Python using the TensorFlow library (NN PYTHON). The performance of FORTRAN and Python approaches to QPE using GPM-CO data are compared with the goal of achieving a minimum NN architecture that at least matches the outcome of the Goddard Profiling Algorithm (GPROF) algorithm. Another conclusion is that the new NN PYTHON does not present significant advantages over the old FORTRAN code. The latter does not require dependencies, which has many practical advantages in operational use and therefore have an edge over more complex approaches in hydrometeorology.

Climate classification systems (CCSs) were developed in the 19th century to explain the global distribution of plants and animals, but were given a new life in the Earth physics community in the 21st century as a means to validate Earth System Models (ESMs). The purpose of this paper is to critically review the evolution of this approach to validation and to discuss the current status. It is shown that CCSs can not only measure model performance, but also help identify and deconstruct systematic model biases. CCSs provide a rational and comprehensive zonation of the global environment using quantitative estimates of physical variables such as precipitation, temperature, and humidity. They offer several advantages over single variables due to the high sensitivity of the resulting classes to small changes in input variables and dimensionality reduction. CCSs encompass model behavior on both energy and water balances, allowing for a more comprehensive assessment of ESM performance.

Hailstorms cause heavy losses, especially when their hailstones reach a large size. One of the European regions most affected by these severe atmospheric events is southern France, where a valuable and extensive hailpad network has been operational for more than three decades. These direct observations are extremely useful because they allow for the definitive verification of hailfall at the ground. Space-based sensors have seen increasing importance in hail monitoring. Global Precipitation Measurement (GPM) is an international mission designed to advance precipitation measurements from multispectral sensors. The GPM core satellite carries a powerful and unprecedented Dual-Frequency Precipitation Radar (DPR) for studying 3D precipitation characteristics. The objective of the present study is to evaluate the DPR sensor ability to identify hailstorms. We identified eight hailstorms over France where DPR data were coincident with ground-based observations from a hailpad network during 2014–2021. In addition, variables provided by the DPR sensor indicative of hail presence were studied and five detection algorithms were tested. This research serves as background for future work and the development of prediction algorithms based on empirical relationships with GPM data.

Assessing changes in the distribution of biological communities that share a climate (biomes) is essential for estimating their vulnerability to climate change. We use CMIP6 climate models to calculate biome changes as featuring in classifications such as Holdridge’s Life Zones (climate envelopes). We found that transitional zones between biomes (known as ecotones) are expected to decline under all climate change scenarios, but also that model consensus remains low. Accurate assessments of diversity loss are limited to certain areas of the globe, while model consensus is still poor for half of the planet. We identify where there are robust estimates of changes in biomes and ecotones, and where consensus is lacking. We argue that caution should be exercised in measuring biodiversity loss in the latter, but that greater confidence can be placed in the former. We find that shortcomings in the life zone classification are related to inter-model variability, which ultimately depends on a larger problem, namely the accurate estimation of precipitation compared to CRU. Application of the methodology to other climate classifications confirms the findings.

Social media platforms are becoming an increasingly important venue for public debate. Twitch, in particular, is a popular streaming platform that targets young adults. Originally created for online video game players, the platform also includes other types of content, such as politics, leisure, and science. Here, we present a study that examines climate change discourse on this understudied platform. Unlike previous studies, this work does not only focus on users’ reactions to streamer discourse but, using an ad-hoc methodology, also analyzes the content of the videos. Indeed, an added value of this research is the development of an objective evaluation based on automatic speech recognition (ASP) and natural language processing (NLP). We found that Twitch is an emergent locus for climate discussion with a thriving community of young users interested in the topic. Our findings challenge the understanding of social media discourse on climate change and suggest that platform architecture and intended commercial goals do not play a decisive role in shaping the topics circulating on them. In contrast, our findings support the idea that public discussion on climate change percolates through technology. In other words, the public debate finds its way across existing channels rather than being constrained by them. The research also contributes to the literature by expanding the empirical base for the study of online communication about climate change, especially among young audiences.

Precipitation is one of the most relevant fields in atmospheric modeling because of its environmental, social and economic implications. However, precipitation validation from weather model outputs presents substantial challenges, such as measurement uncertainties, use of gridded datasets vs. direct observations, and the selection of statistical goodness-of-fit measures. The main difficulty of working with precipitation is that it can be spatially irregular, especially in extreme events. High temporal aggregation smooths the field and reduces verification uncertainty. For this reason, validations are usually focused on a daily scale. However, many extreme events occur on shorter periods, for which a sub-daily precipitation assessment is required. In this paper, hourly precipitation verification of the Weather Research and Forecasting (WRF) model is explored for 45 extreme precipitation events (EPEs) recorded in northeastern Spain. For this, stations with recorded EPEs were classified according to the hourly distribution of precipitation. WRF simulations were established considering three microphysics and two planetary boundary layer (PBL) parameterizations. Finally, several statistical goodness-of-fit measures and spatial and temporal precipitation distributions were used for evaluating WRF performance. The results showed that microphysics were more important than PBL parameterizations. Goddard and Thompson together with Mellor-Yamada-Nakanishi and Nino PBL gave better results for most of the analyzed characteristics. However, an optimal combination of parameterizations was not obtained for all EPEs, because event characteristics had important effects on model performance.

Convection influences climate and weather events over a wide range of spatial and temporal scales. Therefore, accurate predictions of the time and location of convection and its development into severe weather are of great importance. Convection has to be parameterized in global climate models and Earth system models as the key physical processes occur at scales much lower than the model grid size. This parameterization is also used in some numerical weather prediction (NWP) models when convection is not explicitly resolved. The convection schemes described in the literature represent the physics by simplified models that require assumptions about the processes and the use of a number of parameters based on empirical values. These empirical values and assumptions are rarely discussed in the literature. The present paper examines these choices and their impacts on model outputs and emphasizes the importance of observations to improve our current understanding of the physics of convection. The focus is mainly on the empirical values and assumptions used in the activation of convection (trigger), the transport and microphysics (commonly referred to as the cloud model), and the intensity of convection (closure). Such information can assist satellite missions focused on elucidating convective processes (e.g., the INCUS mission) and the evaluation of model output uncertainties due to spatial and temporal variability of the empirical values embedded into the parameterizations.

Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high-quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended-Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended-Köppen classification methods (Cohen’s kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5-8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%).

Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG’s better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications.

Snowfall in the northeastern part of South Korea is the result of complex snowfall mechanisms due to a highly contrasting terrain combined with nearby warm waters and three synoptic pressure patterns. All these factors together create unique combinations, whose disentangling can provide new insights into the microphysics of snow on the planet. This study focuses on the impact of wind flow and topography on the microphysics drawing of 20 snowfall events during the ICE-POP 2018 (International Collaborative Experiment for PyeongChang 2018 Olympic and Paralympic winter games) field campaign in the Gangwon region. The vertical structure of precipitation and size distribution characteristics are investigated with collocated MRR (micro rain radar) and PARSIVEL (particle size velocity) disdrometers installed across the mountain range. The results indicate that wind shear and embedded turbulence were the cause of the riming process dominating the mountainous region. As the strength of these processes weakens from the mountainous region to the coastal region, riming became less significant and gave way to aggregation. This study specifically analyzes the microphysical characteristics under three major synoptic patterns: Air-sea interaction, cold low, and warm low. Air-sea interaction pattern is characterized by more frequent snowfall and vertically deeper precipitation systems on the windward side, resulting in significant aggregation in the coastal region, with riming featuring as a primary growth mechanism in both mountainous and coastal regions. The cold-low pattern is characterized by a higher snowfall rate and vertically deep systems in the mountainous region, with the precipitation system becoming shallower in the coastal region and strong turbulence being found in the layer below 2ĝ€¯km in the mountainous upstream region (linked with dominant aggregation). The warm-low pattern features the deepest system: precipitation here is enhanced by the seeder-feeder mechanism with two different precipitation systems divided by the transition zone (easterly below and westerly above). Overall, it is found that strong shear and turbulence in the transition zone is a likely reason for the dominant riming process in the mountainous region, with aggregation being important in both mountainous and coastal regions.

Gridded precipitation datasets have been developed for data assimilation and evaluation tasks of weather and climate models and for climate analyses. Gridded data uncertainty evaluation is crucial to understand the limitations and feasibility. The development of high-resolution daily gridded precipitation datasets is desirable, but several factors need to be considered, namely rain gauge station availability, their spatial distribution, and orographic and climate characteristics of a study area. Quality assessment of gridded datasets can present difficulties when the influence of these factors is not thoroughly analysed. The main objective of this study was a detailed validation of precipitation grids based on four factors, that is, station density used for grid construction, grid spatial resolution, station altitude, and climate type. To this end, 18 grids were built using six spatial resolutions (0.01°, 0.025°, 0.05°, 0.1°, 0.2° and 0.4°) and three station densities (25, 50 and 75% of all available stations). Results indicate larger differences among the grids as a function of analysed factors. Station density was found to be the main factor, whereas grid spatial resolution had minor importance. However, the latter factor becomes more relevant in areas with strong altitude gradients and when a high station density is available. In addition, weak and moderate precipitation is overestimated on daily grids, whereas heavy precipitation cells are less frequent, reducing data variability. On the contrary, monthly and annual aggregates present less deviation from the observed distribution than daily comparisons. These findings question the applicability of the daily grid datasets for validation studies and climate analysis on a grid cell level.

This study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice VI applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased VI . For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.

Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multiscale processes and nonlinear interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations due to the complexity associated with finding and bringing together semicoincident and semicontemporaneous multiparameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatiotemporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS)’a data analytic framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and online analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples of how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.

A gridded precipitation dataset derived from the high-density rain gauge network of the Ebro River Basin Authority is used to evaluate the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG) level-3 estimates. Although aggregated values compare well, several differences are found between climate regions. The research investigates the role of orography and gauge density on IMERG performance. There are important discrepancies over un-instrumented areas in the Pyrenees (R2 = 0.31) but the correlation dramatically increases (R2 > 0.71) when at least one rain gauge is available for calibration, even in complex, high-altitude terrain (>1500 m). IMERG overestimates precipitation at both lower altitudes (<500 m), especially in summer and autumn because of convective activity, and mid-altitudes (600–1200 m) in the northwestern study area, where weather is dominated by the advection of wet maritime air masses. The main conclusion is that IMERG performance strongly depends on altitude and the precipitation regime. IMERG is nonetheless a suitable alternative to gridded gauge-derived only products for hydrologic operations, especially in areas with a sparse rain gauge network.

The standard method used in the Weather Research and Forecasting (WRF) model for distributing MPI processes across the processors is not always optimal. This circumstance affects performance, i.e., execution times, but also energy consumption, especially if the application is to be extended to exascale. The authors found that the reason why the standard method for process distribution is not always optimal was an imbalance between the orthogonality of the communication and the proper cache usage, and this affects energy consumption. We present an improved MPI process distribution algorithm that increases the performance. Furthermore, scalability analyses for the new algorithm are presented and the energy use of the system is evaluated. A solution for balancing energy use with performance is also proposed for cases where the former is a concern.

Weather forecasting, especially snowfall prediction, was critical in the 2018 Winter Olympics, where the accuracy of the predictions was of key importance for the planning of the different Olympic events. It was a significant challenge for the authors to meet the requirements in time and forecast resolution, while doing their best to be as competitive as possible. All the forecasts were obtained using the Weather Research and Forecasting (WRF) model, executed on the GALGO supercomputer. In order to obtain the best performance and meet the required execution times, different combinations of compilers, Message Passing Interface (MPI) libraries and computing platforms were tested to seek the best combinations. This work proposes an empirical score of special interest to supercomputer maintainers, developers and scientists, which can be useful to obtain the best WRF configuration for their systems. Additionally, we found substantial performance differences when using different combinations of compilers, MPI libraries and hybrid shared memory paradigms, although these differences varied depending on the underlying platform. As conclusion, after all the tests we performed, we chose the combination with Intel compilers, Intel MPI library and OpenMP for the production system tasked to perform the weather forecasts for the Winter Olympic Games.

Performance, i.e., execution times, is one of the most important features of HPC software, but energy consumption is also growing in importance if we intend to extend application to Exascale. This is the case of HPC software used in weather forecasting, in which every ounce of performance is critical in order to increase the accuracy and precision of its results. In this work, we study the performance-energy balance of an OpenPOWER processor, which is designed for the high workloads typically seen on data servers and HPC environments. Our results show that the OpenPOWER processor is superior in performance in weather forecast workloads compared to other processors commonly used in HPC, but at the expense of consuming more energy. Furthermore, the highest hyperthreading modes available on OpenPOWER processors do not perform well with HPC workloads and are even detrimental to performance.

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014-2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data.

Uncertainties and non-linearities between processes involved in rural dynamics pose a challenge to planners and policymakers. Numerical models minimize the risk of making an inappropriate decision and assist planners with making informed policy decisions. In this paper, we present a quantitative analysis of the social, economic and demographic dimensions of rural dynamics using a system dynamics approach. The resulting model (RUSEM: RUral Socio-Economic Model) consists of four main modules that reproduce the main characteristics of the rural areas, with a focus for the European case. The economic module contains the most important variables in the dynamics of local economies – for example, the provision of key services, the availability of public services, and local economic structure. The social and demographic modules use large age groups to analyse the labour market and population dynamics. The attractiveness module summarises push-pull factors affecting rural migration. The model is used to evaluate thirty-six socio-economic scenarios of rural Spain. The results show that very small villages could be incapable to stop rural decline because the absence of “critical mass” while mid-sized villages require targeted actions to reinforce the positive feedbacks of local economy. RUSEM has two goals: to offer computational support enabling robust decision-making in policy formulation in conditions of deep uncertainty and to integrate the socio-economic dimension into Earth System Models (ESMs) providing a numerical model that can be embedded into ESMs at a relatively cheap computational cost.

Precise modeling of CO 2 emissions is important for environmental research. This paper presents a new model of human population dynamics that can be embedded into ESMs (Earth System Models) to improve climate modeling. Through a system dynamics approach, we develop a cohort-component model that successfully simulates historical population dynamics with fine spatial resolution (about 1°×1°). The population projections are used to improve the estimates of CO 2 emissions, thus transcending the bulk approach of existing models and allowing more realistic non-linear effects to feature in the simulations. The module, dubbed POPEM (from Population Parameterization for Earth Models), is compared with current emission inventories and validated against UN aggregated data. Finally, it is shown that the module can be used to advance toward fully coupling the social and natural components of the Earth system, an emerging research path for environmental science and pollution research.

Current computing platforms, including HPC, Grid and Cloud computing, offer many computational resources. These platforms can provide the scientific world with on-demand and scalable computing power. However, their use requires advanced computer skills. In our work, we summarize the main problems found by research scientists when they attempt to execute complex software on one or several of these platforms. As a solution to these problems, we propose the Science for Everyone (ScifE) framework, and an intuitive and easy-to-use web interface, to perform scientific experiments on HPC or Cloud platforms. The Community Earth System Model (CESM) has been used to test our proof-of-concept framework as it is a complex scientific software used by many climate researchers. Using our framework, a user can execute CESM with custom parameters with only a few clicks on the web interface. As ScifE is generic, other software can be imported relatively easy to our framework. Additional collaborative tools are being developed for successive versions of ScifE, enhancing the practice of science.

ESMs (Earth system models) are important tools that help scientists understand the complexities of the Earth’s climate. Advances in computing power have permitted the development of increasingly complex ESMs and the introduction of better, more accurate parameterizations of processes that are too complex to be described in detail. One of the least well-controlled parameterizations involves human activities and their direct impact at local and regional scales. In order to improve the direct representation of human activities and climate, we have developed a simple, scalable approach that we have named the POPEM module (POpulation Parameterization for Earth Models). This module computes monthly fossil fuel emissions at grid-point scale using the modeled population projections. This paper shows how integrating POPEM parameterization into the CESM (Community Earth System Model) enhances the realism of global climate modeling, improving this beyond simpler approaches. The results show that it is indeed advantageous to model CO2 emissions and pollutants directly at model grid points rather than using the same mean value globally. A major bonus of this approach is the increased capacity to understand the potential effects of localized pollutant emissions on long-term global climate statistics, thus assisting adaptation and mitigation policies.

Attempts to interpret the measurements of millimeter-wave radiometers over tropical storms must overcome the difficulty of modeling the scattering signatures of hydrometeors at these frequencies. Approaches to date try to retrieve surface precipitation, to which the observations are not directly sensitive. Millimeter wavelengths are most sensitive to the scattering from hydrometeors in the cloud upper levels. Millimeter-wavelength radiometers have a definite advantage over the lower frequency radiometers in that they have finer spatial resolution to resolve deep convection. Preliminary analyses indicate that the measurements are indeed sensitive to the depth and intensity of convection. The challenge is to derive a robust approach to estimate the characteristics of the convection directly from the observations, and conversely to derive a robust forward representation of the dependence of the radiances on the underlying moisture fields. This is done in a two-step semi-empirical approach.

For the purposes of weather nowcasting, flood risk monitoring and water resources assessment, it is often difficult to achieve a reliable spatio-temporal representation of rainfall due to a low rain gauge network density. However, quantitative precipitation estimation (QPE) has acquired new prospects with the introduction of weather radars, thanks to their higher spatio-temporal resolution. Although a wide number of QPE algorithms are available for using C-band radar data, only a few studies have employed X-band radar. In this study the microscale rainfall variability in a small catchment is automatically measured using short-range X-band radar variograms and classifying precipitation into convective and stratiform types with a recently published index. The aim is to apply a straightforward geostatistical algorithm, named ordinary kriging of radar errors (OKRE), to integrate X-band radar and rain gauge measurements in a mountainous catchment (15 km2) in central Spain. As expected, convective events presented higher estimation errors due to their complex spatial and temporal variability. Despite this fact, errors are sufficiently small and results are reliable rainfall estimations. The two main contributions of this work are the adaptation of the OKRE method to small spatial scales and its application automatically differentiating between convective and stratiform events.

Several precipitation products use radiances and reflectances obtained from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to estimate convective precipitation. The direct use of these physical quantities in precipitation algorithms is known to generate an overestimation of the precipitation area and an underestimation of the rainfall rates. In order to extenuate these issues, the most recent Satellite Application Facility on Support to Nowcasting & Very Short Range Forecasting (NWC SAF/MSG) software package (version 2013) includes a new day-time algorithm that takes advantage of advances in cloud microphysics estimation, namely a better knowledge of Effective Radius (Reff), Cloud Optical Thickness (COT) and Water Phase. The improved algorithm, known as Convective Rainfall Rate from Cloud Physical Properties (CRPh), uses such Cloud Top Physical Properties (CPP) to estimate rainfall rates from convective clouds on a SEVIRI pixel basis (about 3 km at nadir). This paper presents the novelties of the new algorithm and provides both a comparison of the product with the previous versions in the NWC SAF/MSG software package, and a validation with independent ground radar data from the Spanish Radar Network operated by AEMET. Results obtained over 46 storms suggest that the CRPh provides more precise estimates than the previous algorithm, thus being more suitable for a number of quantitative applications.

In October 2012 Felix Baumgartner fell farther and faster than anyone before him. The forces he experienced during his flight can be readily analyzed, thanks to the GPS data collected during his jump.

Satellite simulators are used to calculate the brightness temperature T b that would be measured by a space borne sensor under a set of atmospheric conditions accounting for the radiometric characteristics of the sensor and the orbital parameters of the satellite. In this study, a simple approach is proposed for the parameterization of emissivity over land, a key parameter for the calculation of microwave T b. The rationale is to simulate a large ensemble of emissivity values for each frequency and surface characteristics and then relate the most likely observed value with soil characteristics. The derived emissivity values are used for the simulation of T b and simulated radiance is then compared with satellite observations. It is shown that this method improves the simulation of radiance and that it is suitable to provide a first guess of the emissivity value (a prior) that can then be refined using iterative procedures.

Experts suggest that the growth of long-term space-based precipitation datasets enables cross-disciplinary discoveries about hydrological and land processes, climate, atmospheric composition, and ocean freshwater budget and provides significant help in addressing societal issues. Many methods have been developed to merge passive microwave (PMW)-based precipitation data and geostationary satellite observations, such as the TRMM Multisatellite Precipitation Analysis and the Climate Prediction Center (CPC) morphing technique (CMORPH). The experts emphasize that precipitation observations provide an incomplete picture of the state of the atmosphere along. Precipitation products are critical for the development of applications that address a variety of scientific and societal needs despite these limitations.

One of the most interesting and most accessible opportunities of nanofillers is the reinforcement of fine structures in which conventional fillers cannot be readily accommodated, such as polymer foams. This paper reviews the progress to date towards the development of reactive foam nanocomposites, in particular polyurethane and silicone foams. The discussed systems are summarized based on the types of nanofillers used, i.e. nanoparticles, rod-like, and plate-like systems. The effect of nanofillers on the foaming process, cellular structure and properties is critically reported along with a summary of the measured improvements in the mechanical, electrical and thermal properties of the resulting nanocomposites.

This paper presents a maximum entropy approach to Rain Drop Size Distribution (RDSD) modelling. It is shown that this approach allows (1) to use a physically consistent rationale to select a particular probability density function (pdf) (2) to provide an alternative method for parameter estimation based on expectations of the population instead of sample moments and (3) to develop a progressive method of modelling by updating the pdf as new empirical information becomes available. The method is illustrated with both synthetic and real RDSD data, the latest coming from a laser disdrometer network specifically designed to measure the spatial variability of the RDSD.

The paper presents work that creates a geographical information system database of European census data from 1870 to 2000. The database is integrated over space and time. Spatially it consists of regional level data for most of Europe; temporally it covers every decade from 1870 to 2000. Crucially the data have been interpolated onto the administrative units that were available in 2000, thus allowing contemporary population patterns to be understood in the light of the changes that have occurred since the late 19th century. The effect of interpolation error on the resulting estimates is explored. This database will provide a framework for much future analysis on long-term Europewide demographic processes over space and time.

The physical properties of many multiphase systems are determined by coarsening phenomena. From raindrops to polycrystal grains and foams, the formation and stability of these systems continuously evolve towards lower-energy configurations through events such as coalescence, Ostwald ripening and drainage. Here we propose a procedure to identify and characterise key topological transformations of coarsening phenomena using a physically-based fluid dynamic framework. In situ, real-time foaming processes of a polymeric matrix reinforced with two morphologically different nanofillers, carbon nanotubes and graphene sheets were observed by synchrotron X-ray radioscopy. We obtained detailed information on the evolution of the growth patterns and coarsening events. Filled samples showed differences in both trend and speed compared with the unfilled sample. Furthermore, we found different dominating coarsening phenomena due to the wetting nature of carbon nanoparticles. Our procedure can be extended to sequences of any type of 2D projection or 3D images and to other multiphase systems.

The Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated.

The key objective of the project “Use of the MSG SEVIRI channels in a combined SSM/I, TRMM and geostationary IR method for rapid updates of rainfall” is the development of algorithms for rapid-update of satellite rainfall estimations at the geostationary (GEO) scale. The new channels available with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer in the visible (VIS), near infrared (NIR) and infrared (IR) portions of the spectrum provide new insights into the microphysical and dynamic structure of precipitating clouds thus allowing for a more precise identification of precipitation intensities. Passive microwave (PMW) radiometers on board low Earth orbiting (LEO) satellites are used to determine information on the vertical cloud structure. Key features of the new method(s) are:. 1. Microphysical characterization of precipitating clouds with VIS/IR sensors; 2. Creation of cloud microphysical and radiative databases from cloud model outputs and aircraft penetrations; 3. Tuning of PMW algorithms for different cloud systems (maritime, continental, convective, stratiform,…); 4. Combination of data from different algorithms and application to a rapid update cycle at the GEO scaleThe project provided the background for EURAINSAT “European Satellite Rainfall Estimation and Monitoring at the Geostationary Scale”, a research project co-funded by the Energy, Environment and Sustainable Development Programme of the European Commission within the topic “Development of generic Earth observation technologies”. The project web site is accessible at http://www.isac.cnr.it/~eurainsat/. Moreover, it has represented the European framework for the launch of the International Precipitation Working Group (IPWG). A brief account of the major research and organizational results will be given in the following.

The objective of this paper is to investigate how the synergy between Low-Earth-Orbit (LEO) microwave (MW) and Geostationary Earth Orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be sufficiently accurate, to calibrate spaceborne IR measurements on limited sub-regions and time windows. The proposed methodology is focused on a new statistical approaches, namely the multivariate probability matching (MPM). The MPM methods is rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy.