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The paper is focused on the problem of weather radar precipitation compositing and aims to produce the data for the whole measurement domain in a way that ensures the quality of the composite is as high as possible. This is achieved by employing information about the data quality in the form of a quality index (QI) map. Different techniques that take the QI values as the compositing criterion are investigated; they apply the maximum or the weighted mean from particular radars. The proposed method employs a combination of the QI value and the distance from the radar site as a criterion in a weighted mean approach. Different versions of the quality‐based techniques are compared with standard techniques that are based on the maximum of estimated precipitation and distance from the nearest radar site. Verification of the techniques was performed employing statistical metrics by comparison against rain gauge observations. Moreover qualitative verification with assessment of seamlessness and the presence of contamination was carried out on long‐term accumulations of the composites generated by means of different methods. The proposed method based on QI value and distance from the radar site was found to be the most suitable for radar precipitation compositing. [ABSTRACT FROM AUTHOR]

In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R( A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011-13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R( A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R( A) rain estimates are compared to R( Z) and R( KDP) retrievals using specific phase shift KDP. PBB and calibration corrections derived in Part I made the R( Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R( KDP) is used and weaker impact on R( A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R( KDP) or R( A) estimates with R( Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R( A) complemented by R( Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R( KDP) substituted with R( Z) at rain rates below 8 mm h−1. [ABSTRACT FROM AUTHOR]

Convective precipitation is the main cause of extreme rainfall events in small areas. Its primary characteristics are both large spatial and temporal variability. For this reason, the monitoring of accumulated precipitation fields (liquid and solid components) at the surface is difficult to carry out through the use of rain gauge networks or remote sensing observations. Alternatively, numerical models may be a useful tool to simulate convective precipitation for various analyses and predictions. This paper focuses on improving quantitative convective precipitation estimates that are obtained with a cloud-resolving model. This aim is attained by using the appropriate cloud drop size distribution and modified single sounding data. The authors perform comparisons between observations and three model samples of the areal-accumulated convective precipitation for a 15-yr period over mountainous and flat land areas with 45 and 29 convective events, respectively. They compare the results from a numerical cloud model that uses 2 different microphysical schemes--the unified Khrgian--Mazin size distribution of cloud drops--and an alternative scheme that is a combination of a monodispersed cloud droplet spectrum and the Marshall--Palmer size distribution for raindrops. The authors'' statistical analysis shows that the model version with the Khrgian--Mazin size distribution and the new initial conditions better simulates the observed areal-accumulated convective precipitation than the alternative microphysical approach for both study areas. The model simulations with the Khrgian--Mazin size distribution most closely match observations for the flat land area with a correlation coefficient of 0.94, while it is somewhat lower (0.89) for the mountainous area. Use of the alternative microphysical approach, on the other hand, underestimates the observed precipitation, and has the lowest correlation coefficient among the methods, 0.82 for the mountainous area and 0.85 for the flat land. [ABSTRACT FROM AUTHOR]

Improving the quality of atmospheric precipitation measurements is crucial in the view of minimizing the uncertainty in weather forecasting, climate change impact assessment, water resource assessment and management, and drought and flood prediction. Remote sensing technology has considerably improved the spatio-temporal assessment of precipitation. Despite the advancement in the remote sensing technology, there is a need to investigate the robust approach towards integrating ground-based-measured and satellite-product precipitation to better understand the hydrologic process of any basin. Several data-merging methods have been proposed; however, the application of merged precipitation products for hydrological simulation has rarely been investigated. Thus, in this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, the limitations of eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized Weighting, (4) Inverse Root-Mean-Square Error Weighting, (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, and (8) the Kriging Method, and their advances with respect to hydrological simulation are discussed. Finally, future research directions towards improving data merging approaches are recommended. [ABSTRACT FROM AUTHOR]

This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor. [ABSTRACT FROM AUTHOR]

Abstract: The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present a statistical downscaling algorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying a downscaling methodology, TRMM 3B43 0.25°×0.25° precipitation fields were downscaled to 1×1km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscaling algorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r 2 ranging from 0.72 to 0.96 at six different raingauge stations. [Copyright &y& Elsevier]

Summary: Deriving high quality daily rainfall estimates are required not only for successful hydrological modelling but also for its application in ungauged basins. At present, there are two resources to estimate rainfall fields: raingauge stations and remote sensing devices (such as satellites and radar). The observations obtained from raingauges are accurate and reliable, but are usually limited to interpolate daily rainfall as input into hydrological models by spatial coverage. Hence, the areal precipitation estimates constructed solely by raingauges exhibit a great deal of uncertainty where the raingauge network is sparse, especially for local convective events. Satellite-based gridded precipitation estimates are produced at high temporal (3h) and reasonably high spatial resolution (0.25°×0.25°). However, the accuracy of these surfaces is lower than using ground based observations. The main aim of this paper is to present a novel method of generating a synthesis of a discrete set of point raingauge observations and a satellite derived rainfall product. This method stems from some ideas of data assimilation and focuses on the difference between gridded and point data. A nonparametric kernel smoothing method is employed in this merging strategy with emphasis on discontinuity correction and spatial interpolation adapting for sparse design. A cross-validation study was undertaken to blend observations from the Australian raingauge network and satellite derived TRMM Multisatellite Precipitation Analysis (TMPA) 3B42. In order to test the proposed method, several methods including ordinary kriging and cokriging were trialled. The comparison showed that the bias results produced by the proposed method were far superior to the other methods trialled and other error statistics such as root mean square error were comparable or better (although the kriging methods tended to perform better in densely gauged areas). In particular, the bias of the proposed method was reduced consistently over the whole country, whilst the kriging methods trialled produced relatively large biases across the country leading to near overall zero bias. In addition to improving quantitative efficiency, the estimated rainfall field showed good visual performance in the presence of sparse gauge stations. [Copyright &y& Elsevier]

Abstract: The observation and modeling of the indirect effects of aerosols on clouds remain an enormous challenge. Aerosols have a significant yet complicated impact on the precipitation processes. They can either enhance or suppress precipitation depending upon type of aerosol, seasonality, climate regime, cloud type or orographic profile of a region, particularly over populated areas. In order to observe and examine both cloud and precipitation processes, a combination of both satellite and ground-based remote sensing techniques can be employed. This paper presents the results from three years of data collection in Birmingham, United Kingdom. It describes and explains the application of a range of complimentary techniques: fluorescence spectrophotometry to examine dissolved organic carbon compounds in rainwater samples; satellite analysis tools are used to assess cloud-top microphysics; and an array of vertically-pointing micro-rain radars (MRRs) are used to assess variations in drop size distribution (DSD) for categorized events. Events are classified as microphysically ‘maritime’ or ‘continental’, showing that full development of the ice phase was reached at relatively warm temperatures for microphysically ‘maritime’ events, but at colder temperatures for microphysically ‘continental’ events. The importance of updrafts in severe thunderstorms and tornadic events is highlighted. High rainwater content of tyrosine-like substances (TYLIS) and tryptophan-like substances (TRYLIS) is found to be associated mainly with microphysically ‘maritime’ events, providing evidence for these substances acting as ice nuclei at relatively warm temperatures. High rainwater content of humic-like substances (HULIS) is associated with both microphysically ‘maritime’ and ‘continental’ events due to the complexity of such substances. As might be expected, continentally-sourced events had a similar structure to microphysically ‘continental’ events, whereas maritime-sourced events differed in their microphysical structure, indicating the local impacts on their microstructure. The DSD appears to vary between different events — for example, continentally-sourced, microphysically ‘continental’, convective events with low rainwater TRYLIS have a DSD containing fewer smaller droplets, whereas maritime-sourced, microphysically ‘maritime’, stratiform events with high TRYLIS had a DSD containing a greater number of smaller droplets. Satellite observations and vertically-pointing radars were found to be useful for analyzing clouds and precipitation since they provide a wealth of information to allow microphysical parameters to be investigated in detail. [Copyright &y& Elsevier]

Abstract: This review paper deals with four aspects of precipitation: measurement, remote sensing, climatology and modeling. The measurement of precipitation is summarized in terms of the instruments that count and measure drop sizes (defined as disdrometers) and the instruments that measure an average quantity proportional to the integrated volume of an ensemble of raindrops (these instruments are normally called rain gauges). Remote sensing of precipitation is accomplished with ground based radar and from satellite retrievals and these two approaches are separately discussed. The climatology of precipitation has evolved through the years from the traditional rain gauge data analyses to the more sophisticated data bases that result from a coalescence of data and information on precipitation that is available from several sources into amalgamated products. Recently, rain observations from both ground and space have been assimilated into regional and global numerical weather prediction models aiming at improved moisture analysis and better forecasts of extreme weather events. The current status and the main outstanding issues related to precipitation forecasting are discussed, providing a basic structure for research coordination aimed at the improvement of modeling, observation and data assimilation applicable to global and regional scales. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

The radar-based estimation of intense precipitation produced by convective storms is a challenging task and the verification through comparison with gauges is questionable due to the very high spatial variability of such types of precipitation. In this study, we explore the potential benefit of using a superconducting gravimeter as a new source of in situ observations for the evaluation of radar-based precipitation estimates. The superconducting gravimeter used in this study is installed in Membach (BE), 48 m underneath the surface, at 85 km distance from a C-band weather radar located in Wideumont (BE). The 15-year observation record 2003–2017 is available for both gravimeter and radar with 1 and 5 min time steps, respectively. Water mass increase at ground due to precipitation results in a decrease in underground measured gravity. The gravimeter integrates soil water in a radius of about 400 m around the instrument. This allows capture of rainfall at a larger spatial scale than traditional rain gauges. The precision of the gravimeter is a few tenths of nm s -2 , 1 nm s -2 corresponding to 2.6 mm of water. The comparison of reflectivity and gravity time series shows that short-duration intense rainfall events produce a rapid decrease in the underground measured gravity. A remarkable correspondence between radar and gravimeter time series is found. The precipitation amounts derived from gravity measurements and from radar observations are further compared for 505 rainfall events. A correlation coefficient of 0.58, a mean bias (radar–gravimeter)/gravimeter of 0.24 and a mean absolute difference (MAD) of 3.19 mm are obtained. A better agreement is reached when applying a hail correction by truncating reflectivity values to a given threshold. No bias, a correlation coefficient of 0.64 and a MAD of 2.3 mm are reached using a 48 dBZ threshold. The added value of underground gravity measurements as a verification dataset is discussed. The two main benefits are the spatial scale at which precipitation is captured and the interesting property that gravity measurements are directly influenced by water mass at ground no matter the type of precipitation: hail or rain. [ABSTRACT FROM AUTHOR]

Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual- Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5-15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for bothDPRKu and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products. [ABSTRACT FROM AUTHOR]

The diurnal variation of rainfall over China associated with landfalling tropical cyclones (TCs) is investigated using hourly rain gauge observations obtained from 2425 conventional meteorological stations in China. Records between 12 h prior to landfall and 12 h after landfall of 450 landfalling TCs in China from 1957 to 2014 are selected as samples. The harmonic analysis shows an obvious diurnal signal in TC rainfall with a rain-rate peak in the early morning and a minimum in the afternoon. The diurnal cycle in the outer region (between 400- and 900-km radii from the storm center) is found to be larger than in the core region (within 400 km of the storm center). This could be attributed to the effect of land on the inner core of the storms as the diurnal cycle is distinct in the core region well before landfall. As the result of this diurnal cycle, TCs making landfall at night tend to have cumulative precipitation, defined as the precipitation cumulated from the time at landfall to 12 h after landfall, about 30% larger than those making landfall around noon or afternoon. Moreover, the radial propagation of the diurnal cycle in TC rain rate, which has been a controversial phenomenon in some previous studies with remote sensing observations, was not present in this study that is based on rain gauge observations. Results also show that the diurnal signal has little dependence on the storm intensity 12 h prior to landfall. [ABSTRACT FROM AUTHOR]

This study evaluates the precipitation product of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) over the Mexican region during the period between April 2014 and October 2015 using three different time scales for cumulative precipitation (hourly, daily and seasonal). Also, the IMERG data have been analyzed as a function of elevation given the rain gauges from the automatic meteorological stations network, located within the area of study, which are used as a reference. In the present study, continuous and categorical statistics are used to evaluate IMERG. It was found that IMERG showed better performance at the daily and seasonal time scale resolutions. While hourly precipitation estimates reached a mean correlation coefficient of 0.35, the daily and seasonal precipitation estimates achieved correlations over 0.51. In addition, the IMERG precipitation product was able to reproduce the diurnal and daily cycles of the average precipitation with a trend towards overestimating rain gauges. However, extreme precipitation events were highly underestimated, as shown by relative biases of −61% and −46% for the hourly and daily precipitation analysis, respectively. It was also found that IMERG tends to improve precipitation detection and to decrease magnitude errors over the higher terrain elevations of Mexico. [ABSTRACT FROM AUTHOR]

Surface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces. [ABSTRACT FROM AUTHOR]

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day-1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates. [ABSTRACT FROM AUTHOR]

Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near-real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003-10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May-August) and cold (September-December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products. [ABSTRACT FROM AUTHOR]

No study has systematically evaluated streamflow modelling between monthly and daily time scales. This study examines streamflow from seven watersheds across the USA where five different precipitation products were used as primary input into the Soil and Water Assessment Tool (SWAT) to generate simulated streamflow. Time scales examined include monthly, dekad (10 days), pentad (5 days), triad (3 days), and daily. The seven basins studied are the San Pedro (Arizona), Cimarron (north-central Oklahoma), mid-Nueces (south Texas), mid-Rio Grande (south Texas and northern Mexico), Yocano (northern Mississippi), Alapaha (south Georgia), and mid-St. Francis (eastern Arkansas). The precipitation products used to drive simulations include rain gauge, NWS Multisensor Precipitation Estimator, Tropical Rainfall Measurement Mission (TRMM), Multi-Satellite Precipitation Analysis, TRMM 3B42-V6, and Climate Prediction Center Morphing Method (CMORPH). Understanding how streamflow varies at sub-monthly time scales is important because there are a host of hydrological applications such a flood forecast guidance and reservoir inflow forecasts that reside in a temporal domain between monthly and daily time scales. The major finding of this study is the quantification of a strong positive correlation between performance metrics and time step at which model performance deteriorates. Better performing simulations, with higher Nash-Sutcliffe values of 0.80 and above can support modeling at finer time scales to at least daily and perhaps beyond into the sub-daily realm. These findings are significant in that they clearly document the ability of SWAT to support modeling at sub-monthly time steps, which is beyond the capability for which SWAT was initially designed. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The focus of this study is the evaluation of the research-version Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall product at its finest spatial and temporal resolutions (3-hourly and 0.25°° ×× 0.25°°) over the Rome, Italy, metropolitan area during the period from October 2008 to January 2009. Accurate ground reference rainfall estimates for two satellite pixels are obtained from a dense rain gauge network (22 rain gauges in one pixel and 16 in the other one). The evaluation is based on examination of time series, scatterplots, and survival functions, as well as measures of agreement and disagreement. The results of this study point to the importance of using the TRMM satellite for rainfall estimation. Suggestions in terms of minimum number of rain gauges required to estimate ground reference rainfall are also provided. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation of multiple Artificial Intelligence (AI) techniques—decision tree, random forest, gradient boosting, and neural network—to generate improved precipitation estimates over the Upper Blue Nile Basin. All the AI methods merged multiple satellite and atmospheric reanalysis precipitation datasets to generate error-corrected precipitation estimates. The accuracy of the model predictions was evaluated using 13 years (2000–2012) of ground-based precipitation data derived from local rain gauge networks in the Upper Blue Nile Basin region. The results indicate that merging multiple sources of precipitation substantially reduced the systematic and random error statistics in the Upper Blue Nile Basin. The proposed methods have great potential in predicting precipitation over the complex terrain region. [ABSTRACT FROM AUTHOR]

A new generation of precipitation measurement products has emerged, and their performances have gained much attention from the scientific community, such as the Multi-Radar Multi-Sensor system (MRMS) from the National Severe Storm Laboratory (NSSL) and the Global Precipitation Measurement Mission (GPM) from the National Aeronautics and Space Administration (NASA). This study statistically evaluated the MRMS and GPM products and investigated their cascading hydrological response in August of 2017, when Hurricane Harvey brought historical and record-breaking precipitation to the Gulf Coast (>1500 mm), causing 107 fatalities along with about USD 125 billion worth of damage. Rain-gauge observations from Harris County Flood Control District (HCFCD) and stream-gauge measurements by the United States Geological Survey (USGS) were used as ground truths to evaluate MRMS, GPM and National Centers for Environmental Prediction (NCEP) gauge-only data by using statistical metrics and hydrological simulations using the Ensemble Framework for Flash Flooding Forecast (EF5) model. The results indicate that remote sensing technologies can accurately detect and estimate the unprecedented precipitation event with their near-real-time products, and all precipitation products produced good hydrological simulations, where the Nash–Sutcliff model efficiency coefficients (NSCE) were close to 0.9 for both the MRMS and GPM products. With the timeliness and seamless coverage of MRMS and GPM, the study also demonstrated the capability and efficiency of the EF5 framework for flash flood modeling over the United States and potentially additional international domains. [ABSTRACT FROM AUTHOR]

We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions. [ABSTRACT FROM AUTHOR]

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture. [ABSTRACT FROM AUTHOR]