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We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.

Previous total lightning climatology studies using Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) observations were reported at coarse resolution (0.5°) and employed significant spatial and temporal smoothing to account for sampling limitations of TRMM’s tropical to subtropical low-Earth-orbit coverage. The analysis reported here uses a 16-yr reprocessed dataset to create a very high-resolution (0.1°) climatology with no further spatial averaging. This analysis reveals that Earth’s principal lightning hotspot occurs over Lake Maracaibo in Venezuela, while the highest flash rate density hotspot previously found at the lower 0.5°-resolution sampling was found in the Congo basin in Africa. Lake Maracaibo’s pattern of convergent windflow (mountain–valley, lake, and sea breezes) occurs over the warm lake waters nearly year-round and contributes to nocturnal thunderstorm development 297 days per year on average. These thunderstorms are very localized, and their persistent development anchored in one location accounts for the high flash rate density. Several other inland lakes with similar conditions, that is, deep nocturnal convection driven by locally forced convergent flow over a warm lake surface, are also revealed. Africa is the continent with the most lightning hotspots, followed by Asia, South America, North America, and Australia. A climatological map of the local hour of maximum flash rate density reveals that most oceanic total lightning maxima are related to nocturnal thunderstorms, while continental lightning tends to occur during the afternoon. Most of the principal continental maxima are located near major mountain ranges, revealing the importance of local topography in thunderstorm development.

Tropical cyclone (TC) rainfall, convection, and latent heating distributions are compiled from 14 years of Tropical Rainfall Measuring Mission (TRMM) precipitation radar overpasses. The dataset of 818 Northern Hemisphere tropical storms through category 2 hurricanes is divided by future 24-h intensity change and exclusively includes storms with at least moderately favorable environmental conditions. The rapidly intensifying (RI) category is further subdivided into an initial [RI (initial)] and continuing [RI (continuing)] category based on whether the storm is near the beginning of an RI event or has already been undergoing RI for 12 or more hours prior to the TRMM overpass. TCs in each intensity change category are combined into composite diagrams orientated relative to the environmental vertical wind shear direction. Rainfall frequency, defined as the shear-relative occurrence of PR near-surface reflectivity >20 dBZ, is most strongly correlated with future intensity change. The rainfall frequency is also higher in RI (continuing) TCs than RI (initial). Moderate-to-deep convection and latent heating only increase significantly after RI is underway for at least 12 h in the innermost 50 km relative to the TC center. The additional precipitation in rapidly intensifying TCs is composed primarily of a mixture of weak convective and stratiform rain, especially in the upshear quadrants. The rainfall frequency and latent heating distributions are more symmetric near the onset of RI and continue to become more symmetric as RI continues and the rainfall coverage expands upshear. The relationship between rainfall distributions and future TC intensity highlights the potential of 37-GHz satellite imagery to improve real-time intensity forecasting.

Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Available observations generated by automated weather stations or meteorological observatories are often limited in spatial resolution resulting in misrepresentation or absence of rainfall information at these levels. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex spatio-temporal process leading to non-linear or chaotic spatio-temporal variations, no single downscaling method can be considered efficient enough. In the domains dominated by complex topographies, quasi-periodicities, and non-linearities, deep learning (DL)–based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. We employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods in this work. Summer monsoon season data from India Meteorological Department (IMD) and the tropical rainfall measuring mission (TRMM) data set were downscaled up to 4 times higher resolution using these methods. High-resolution data derived from deep learning-based models provide better results than linear interpolation for up to 4 times higher resolution. Among the three algorithms, namely, SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD-based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data post-processing, in particular, ERA5 reanalysis data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation. This study is the first step towards developing deep learning-based weather data downscaling model for Indian summer monsoon rainfall data.

A great deal of expertise in satellite precipitation estimation has been developed during the Tropical Rainfall Measuring Mission (TRMM) era (1998–2015). The quantification of errors associated with satellite precipitation products (SPPs) is crucial for a correct use of these datasets in hydrological applications, climate studies, and water resources management. This study presents a review of previous work that focused on validating SPPs for liquid precipitation during the TRMM era through comparisons with surface observations, both in terms of mean errors and detection capabilities across different regions of the world. Several SPPs have been considered: TMPA 3B42 (research and real-time products), CPC morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP; both the near-real-time and the Motion Vector Kalman filter products), PERSIANN, and PERSIANN–Cloud Classification System (PERSIANN-CCS). Topography, seasonality, and climatology were shown to play a role in the SPP’s performance, especially in terms of detection probability and bias. Regions with complex terrain exhibited poor rain detection and magnitude-dependent mean errors; low probability of detection was reported in semiarid areas. Winter seasons, usually associated with lighter rain events, snow, and mixed-phase precipitation, showed larger biases.

To address the need to map precipitation on a global scale, a collection of satellites carrying passive microwave (PMW) radiometers has grown over the last 20 years to form a constellation of about 10–12 sensors at any one time. Over the same period, a broad range of science and user communities has become increasingly dependent on the precipitation products provided by these sensors. The constellation presently consists of both conical and cross-track-scanning precipitation-capable multichannel instruments, many of which are beyond their operational and design lifetime but continue to operate through the cooperation of the responsible agencies. The Group on Earth Observations and the Coordinating Group for Meteorological Satellites (CGMS), among other groups, have raised the issue of how a robust, future precipitation constellation should be constructed. The key issues of current and future requirements for the mapping of global precipitation from satellite sensors can be summarized as providing 1) sufficiently fine spatial resolutions to capture precipitation-scale systems and reduce the beam-filling effects of the observations; 2) a wide channel diversity for each sensor to cover the range of precipitation types, characteristics, and intensities observed across the globe; 3) an observation interval that provides temporal sampling commensurate with the variability of precipitation; and 4) precipitation radars and radiometers in low-inclination orbit to provide a consistent calibration source, as demonstrated by the first two spaceborne radar–radiometer combinations on the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission Core Observatory. These issues are critical in determining the direction of future constellation requirements while preserving the continuity of the existing constellation necessary for long-term climate-scale studies.

Because of the scarcity of meteorological observations, the precipitation climate on the Tibetan Plateau and surrounding regions (TP) has been insufficiently documented so far. In this study, the characteristics and basic features of precipitation on the TP during an 11-yr period (2001–11) are described on monthly-to-annual time scales. For this purpose, a new high-resolution atmospheric dataset is analyzed, the High Asia Reanalysis (HAR), generated by dynamical downscaling of global analysis data using the Weather Research and Forecasting (WRF) model. The HAR precipitation data at 30- and 10-km resolutions are compared with both rain gauge observations and satellite-based precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM). It is found that the HAR reproduces previously reported spatial patterns and seasonality of precipitation and that the high-resolution data add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation. It is demonstrated that this process-based approach, despite some unavoidable shortcomings, can improve the understanding of the processes that lead to precipitation on the TP. Analysis focuses on precipitation amounts, type, seasonality, and interannual variability. Special attention is given to the links between the observed patterns and regional atmospheric circulation. As an example of an application of the HAR, a new classification of glaciers on the TP according to their accumulation regimes is proposed, which illustrates the strong spatial variability of precipitation seasonality. Finally, directions for future research are identified based on the HAR, which has the potential to be a useful dataset for climate, glaciological, and hydrological impact studies.

The goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and Excess Storage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically, even with its limited data availability to date, in this well-gauged midlatitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed.

Using 16-yr Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) observations, rainfall properties in the inner-core region of tropical cyclones (TCs) and the relative importance of stratiform and convective precipitation are examined with respect to the evolution of rapid intensification (RI) events. The onset of RI follows a significant increase in the occurrence and azimuthal coverage of stratiform rainfall in all shear-relative quadrants, especially upshear left. The importance of the increased stratiform occurrence in RI storms is further confirmed by the comparison of two groups of slowly intensifying (SI) storms with one group that underwent RI and the other that did not. Statistically, SI storms that do not undergo RI during their life cycle have a much lower percent occurrence of stratiform rain within the inner core. The relatively greater areal coverage of stratiform rain in RI cases appears to be related to the moistening/humidification of the inner core, particularly in the upshear quadrants. In contrast to rainfall frequency, rainfall intensity and total volumetric rain do not increase much until several hours after RI onset, which is more likely a response or positive feedback rather than the trigger of RI. Despite a low frequency of occurrence, the overall contribution to total volumetric rain by convective precipitation is comparable to that of stratiform rain, owing to its intense rain rate.

Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental United States across a set of widely used QPE products. We examine five datasets over an 18-yr period (2002–19). The products include three satellite-based products, CPC morphing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Tropical Rainfall Measuring Mission–Multisatellite Precipitation Analysis (TRMM-TMPA); the ground-radar- and rain-gauge-based NCEP Stage IV; and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans, Louisiana, and Houston, Texas, metropolitan areas. Along the East Coast, TCs can contribute up to 20% of the warm season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM, and ERA5. Satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty in QPEs is further reflected in the TC flooding potential measured by the extreme rainfall multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced by the satellite and reanalysis QPEs compared to that using NCEP Stage IV.