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Advances to space-based observing systems and data processing techniques have made precipitation datasets quickly and easily available via various data portals and widely used in Earth sciences. The increasingly lengthy time span of space-based precipitation data records has enabled cross discipline investigations and applications that would otherwise not be possible, revealing discoveries related to hydrological and land processes, climate, atmospheric composition, and ocean freshwater budget and proving a vital element in addressing societal issues. The purpose of this article is to demonstrate how the availability and continuity of precipitation data records from recent and upcoming space missions is transforming the ways that scientific and societal issues are addressed, in ways that would not be otherwise possible. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties' influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm's performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions. [ABSTRACT FROM AUTHOR]

Since early March 2014, the NASA/JAXA Global Precipitation Measurement Core- Observatory (GPM-CO) satellite has allowed analysis of precipitation systems around the globe, thanks to the capabilities of the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). In this work, we demonstrate how GPM-CO measurements obtained from 4 years of observations over the Mediterranean area can be used as an extremely effective tool to study the main climatological characteristics of the most intense Mediterranean storm structures. DPR and GMI-based Precipitation Features (PFs) parameters are used as proxies of the vertical structure and microphysical properties of these events, and their statistical distribution is analyzed to identify extremes. The analysis of annual, seasonal and geographical distribution of the identified deep convective systems highlights substantial differences in their diurnal cycle and in the distribution between land-sea and summer-winter. There is a general shift of the convective systems from the south (mostly over the sea) in the cold season, to the north (mostly over land) in the warm season. The analysis shows also that the inferred convective intensity is not always related to heavy precipitation. Known DPR and GMI-based criteria were adopted to identify overshooting top events and potential hailstorms, identify extreme deep convection signatures, like those observed for tropical and subtropical systems, and the most intense occur mostly over the sea. Although the analysis is limited to four years, the results show that the GPM-CO offers unprecedented measurements to identify and characterize extreme weather events in the Mediterranean region, with unique potentials for future long-term climatology and interannual variability analysis. [ABSTRACT FROM AUTHOR]

Water vapor transport affects regional precipitation and climate change. The measurement of precipitable water (PW) and water vapor flux (WVF) is of great importance for the study of precipitation and water vapor transport. This study presented a new method of computing PW and estimating WVF using the water vapor vertical column density (VCD) and profile retrieved from multi-axis differential optical absorption spectroscopy (MAX-DOAS), combined with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 wind profiles. We applied our method to MAX-DOAS observations in the coastal (Qingdao) and inland (Xi'an) cities of China from June 2019 to May 2020 and compared the results to the ERA5 reanalysis datasets. Good agreement with ERA5 datasets was found; the correlation coefficient (r) of the PW and the zonal and meridional WVFs were r ≥ 0.92, r = 0.77, and r ≥ 0.89, respectively. The comparison results showed the feasibility and reliability of estimating PW and WVF using MAX-DOAS. Then, we analyzed the seasonal and diurnal climatology of the PW and WVFs in Qingdao and Xi'an. The results indicated that the seasonal and diurnal variations of the PW in the two cities were similar. The zonal water vapor transport of the two cities mainly involved eastward transport, Qingdao's meridional water vapor mainly involved southward transport, and that of Xi'an mainly involved northward transport. The WVFs of the two cities were higher in the afternoon than in the morning, which may be related to wind speed. The results also indicated that the WVF transmitting belts appeared at around 2 and 1.4 km above the surface in Qingdao and around 2.8, 2.6, 1.6, and 1.0 km above the surface in Xi'an. Before precipitation, the WVF transmitting belt moved from near the ground to a high level, reaching its maximum at about 2 km, and the PW and meridional vertically integrated WVF increased. Finally, the sources and transports of water vapor during continuous precipitation and torrential rain were analyzed according to a 24 h backward trajectory. The air mass from the southeast accounted for more than 84% during continuous precipitation in Xi'an, while the air mass from the ocean accounted for more than 75% during torrential rain in Qingdao and was accompanied by a high-level ocean jet stream. As an optical remote sensing instrument, MAX-DOAS has the advantages of high spatiotemporal resolution, low cost, and easy maintenance. The application of MAX-DOAS to meteorological remote sensing provides a better method for evaluating the PW and WVF. [ABSTRACT FROM AUTHOR]

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates. [ABSTRACT FROM AUTHOR]

The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to resolve the diurnal cycle properly. This study also proposes to evaluate different methodologies for partitioning gauge-adjusted daily precipitation products, i.e., a product made by the combination of satellite estimates and surface gauge observations, into 3-h precipitation accumulation. Two methodologies based on the calculation of a conversion factor F between a daily gauge-adjusted product, combined scheme (CoSch, hereafter), and a non-gauge-adjusted one, the integrated multi-satellite retrievals for GPM (IMERG)-Early (IMERG, hereafter) were tested for this research. Hourly rain gauge stations for the period of 2015–2018 over Brazil were used to assess the performance of the proposed methodologies over the whole region and five sub-regions with homogeneous precipitation regimes. Standard statistical metrics and categorical indices related with the capability to detect rainfall events were used to compare the ability of each product to represent the diurnal cycle. The results show that the new 3-h CoSch products show better agreement with rainfall gauge stations when compared with IMERG, better capturing the diurnal cycle of precipitation. The biggest improvement was over northeastern region close to the coast, where IMERG was not able to capture the diurnal cycle properly. One of the proposed methodologies (CoSchB) performed better on the critical success index and equitable threat score metrics, suggesting that this is the best product over the two. The downside, when compared with the other methodology (CoSchA), was a slight increase in the values of bias and mean absolute error, but still at acceptable levels. [ABSTRACT FROM AUTHOR]

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). • The paper reviews the main issues and achievements of RCMs since its inception 30 years ago. • Current high-resolution GCMs may supersede RCMs for informing policies. • RCMs may still be useful for benchmarking new parameterizations and rapid prototyping. [ABSTRACT FROM AUTHOR]

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. • RUSEM is a socio-economic model of rural dynamics based on system dynamics theory. • It offers computational support enabling robust decision-making in policy formulation. • It can be embedded into ESMs improving the representation of anthropogenic processes. • Output data showed the existence of 'critical mass' thresholds in small communities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • 2387 hailfall records between 2014 and 2021 were found. • Eight hailstorms were detected by DPR scans. • Hail forecast variables were extracted from the DPR sensor and analyzed. • Five hail-detection algorithms were tested on the storms. [ABSTRACT FROM AUTHOR]

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. • Hourly precipitation verification of the WRF model has been explored. • We selected 965 stations where extreme precipitation was recorded. • Three microphysics and two planetary boundary layer parameterizations were tested. • Goddard and Thompson together with Mellor-Yamada-Nakanishi and Nino gave better results. [ABSTRACT FROM AUTHOR]

• A comprehensive review and analysis of IMERG validation studies from 2016 to 2019. • There is robust representation of spatio-temporal patterns of precipitation. • Discrepancies can be found in extreme and light precipitation, and the winter season. • The 30-min scale has not yet been sufficiently evaluated. • Using IMERG in hydrological simulation results to high variance in their performance. 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. [ABSTRACT FROM AUTHOR]

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 performance of IMERG precipitation product in complex terrain areas is evaluated. • Seasonal variability and altitude play a key role in product performance. • IMERG compares well at aggregated level but underestimates precipitation maxima. • Best scores are at mid-elevations and the worst are at low and high elevations. • IMERG is useful in areas where rain gauges are sparse, even in complex regions. [ABSTRACT FROM AUTHOR]

Precipitation has often been used to gauge the performances of numerical weather and climate models, sometimes together with other variables such as temperature, humidity, geopotential, and clouds. Precipitation, however, is singular in that it can present a high spatial variability and probably the sharpest gradients amongst all meteorological fields. Moreover, its quantitative measurement is plagued with difficulties and there are even notable differences among different reference datasets. Several additional issues have yield to sometimes question its usefulness in model validation. This essay discusses the use of precipitation for model verification and validation, and the crucial role of highly precise and reliable satellite estimates, such as those from the core observatory of NASA's Global Precipitation Mission (GPM).

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.