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This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges. [ABSTRACT FROM AUTHOR]

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection. [ABSTRACT FROM AUTHOR]

Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014–2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product's performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to –Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product's performance and open a door to future studies regarding hydrometeorological applications of these products in Iran. [ABSTRACT FROM AUTHOR]

Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global Satellite Mapping of Precipitation (GSMaP) constellation over an arid environment like the Sultanate of Oman that is characterized by a complex topography and extremely variable rainfall patterns. Global Satellite-based Precipitation Estimates (GSPEs) can provide wide coverage and high spatial and temporal resolutions, but evaluating their accuracy is a mandatory step before involving them in different hydrological applications. In this paper, the reliability of the Integrated Multi-satellitE Retrievals for the GPM (IMERG) V04 and GSMaP V06 products were evaluated using the reference in-situ rain gauges at sub-daily (e.g., 6, 12, and 18 h) and daily time scales during the period of March 2014–December 2016. A set of continuous difference statistical indices (e.g., mean absolute difference, root mean square error, mean difference, and unconditional bias), and categorical metrics (e.g., probability of detection, critical success index, false alarm ratio, and frequency bias index) were used to evaluate recorded precipitation occurrences. The results showed that the five GSPEs could generally delineate the spatial and temporal patterns of rainfall while they might have over- and under-estimations of in-situ gauge measurements. The overall quality of the GSMaP runs was superior to the IMERG products; however, it also encountered an exaggeration in case of light rain and an underestimation for heavy rain. The effects of the gauge calibration algorithm (GCA) used in the final IMERG (IMERG-F) were investigated by comparison with early and late runs. The IMERG-F V04 product did not show a significant improvement over the early (i.e., after 4 h of rainfall observations) and late (i.e., after 12 h of rainfall observations) products. The results indicated that GCA could not reduce the missed precipitation records considerably. [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 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]

This paper presents a geostatistical downscaling procedure to improve the spatial resolution of precipitation data. The kriging method with external drift has been applied to surface rain intensity (SRI) data obtained through the Operative Precipitation Estimation at Microwave Frequencies (OPEMW), which is an algorithm for rain rate retrieval based on Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations. SRI data have been downscaled from coarse initial resolution of AMSU-B/MHS radiometers to the fine resolution of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) flying on board the Meteosat Second Generation (MSG) satellite. Orographic variables, such as slope, aspect and elevation, are used as auxiliary data in kriging with external drift, together with observations from Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI) in the water vapor band (6.2 µm and 7.3 µm) and in thermal-infrared (10.8 µm and 8.7 µm). The validation is performed against measurements from a network of ground-based rain gauges in Southern Italy. It is shown that the approach provides higher accuracy with respect to ordinary kriging, given a choice of auxiliary variables that depends on precipitation type, here classified as convective or stratiform. Mean values of correlation (0.52), bias (0.91 mm/h) and root mean square error (2.38 mm/h) demonstrate an improvement by +13%, −37%, and −8%, respectively, for estimates derived by kriging with external drift with respect to the ordinary kriging. [ABSTRACT FROM AUTHOR]

Remote sensing allows for the continuous and repetitive measurement of rainfall values. Satellite rainfall products such as Tropical Rainfall Measurement Mission (TRMM) 3B42 and the Hydroestimator (Hydroe) can be potential sources of data for hydrologic applications, mainly in areas with irregular and sparse spatial distributions of traditional rain gauge stations. However, the accuracy of these satellite rainfall products over different spatial and temporal scales is unknown. In this study, we examined the potential of the TRMM 3B42 and Hydroe rainfall products to provide reliable rainfall estimates for a mountainous watershed in a humid subtropical climate region of Brazil. The purpose was to develop useful guidelines for future hydrologic studies on the potential and uncertainties of the rainfall products at different spatial and temporal resolutions. We compared the satellite products to reference rainfall data collected at 11 rain gauge stations irregularly distributed in the area. The results showed different levels of accuracy for each temporal scale evaluated. TRMM 3B42 performed better at the daily, monthly, and seasonal scales than Hydroe, while Hydroe presented a better correlation at the annual scale. In general, TRMM 3B42 overestimated the rainfall over the watershed at all evaluated temporal scales, whereas Hydroe underestimated it except for June-August at the seasonal scale. An evaluation based on contingency tables indicated that TRM 3B42 was better able to represent the local rainfall than Hydroe. The findings of this study indicate that satellite rainfall products are better suited for applications at the monthly and annual scales rather than the daily scale. [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]

The South African Weather Service is mandated to issue warnings of hazardous weather events, including those related to heavy precipitation, in order to safeguard life and property. Flooding and flash flood events are common in South Africa. Frequent updates and real-time availability of precipitation data are crucial to support hydrometeorological warning services. Satellite rainfall estimation provides a very important data source for flash flood guidance systems as well as nowcasting of precipitation events for the data sparse regions of the African continent. Although low earth orbiting satellites with microwave instruments provide good quality rainfall estimates, their temporal and spatial resolution are not adequate for time-critical services. Precipitation estimation using geostationary satellites is less accurate, but provides excellent spatial coverage, is updated frequently and is available in real-time. This study compares different ways to use and combine satellite precipitation estimates and numerical weather prediction model fields over the South African domain in order to determine the optimal estimate of precipitation, which can also be applied in real-time to support flash flood guidance. [ABSTRACT FROM AUTHOR]

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy's Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occurrence consistency, and elevation dependency, from grid-cell to regional scales. The results show that the nine satellite-based products exhibited different agreement with gauge-based reference data with median correlation coefficients ranging from 0.15 to 0.95. Three products (climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA)) generally presented the best performance with the reference data, even in complex terrain regions, given their root mean square errors (RMSE) of less than 25 mm/mon. The climate prediction center merged analysis of precipitation (CMAP) product has relatively coarse spatial resolution, but it also exhibited good performance with a bias of less than 20% in watershed scale. Two other products (precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PER-CCS) and climate prediction center morphing technique-raw (CMORPH-RAW)) overestimated precipitation with median RMSEs of 87 mm/mon and 45 mm/mon, respectively. All the precipitation products generally exhibited better agreement with the reference data for rainy season and lower-elevation regions. All of the products captured precipitation occurrence well, with hit event over 60%, and similar percentages of missed and false event. According to the evaluation, the four products (CHIRPS, MSWEP, TMPA, and CMAP) revealed that the annual precipitation over the TP fluctuated between 333 mm/yr and 488 mm/yr during the period 2003 to 2015. The study indicates the importance of integration of multiple data sources and post-processing (e.g., gauge data fusion and elevation correction) for satellite-based products and have implications for selection of suitable precipitation products for hydrological modeling and water resources assessment for the TP. [ABSTRACT FROM AUTHOR]

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation. [ABSTRACT FROM AUTHOR]

Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based precipitation products with high spatial and temporal resolution and long records, as opposed to temporally and spatially sparse rain gauge networks, are a suitable alternative to analyze precipitation trends over Iran. This study analyzes the trends in annual, seasonal, and monthly precipitation along with the contribution of each season and month in the annual precipitation over Iran for the 1983–2018 period. For the analyses, the Mann–Kendall test is applied to the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) estimates. The results of annual, seasonal, and monthly precipitation trends indicate that the significant decreases in the monthly precipitation trends in February over the western (March over the western and central-eastern) regions of Iran cause significant effects on winter (spring) and total annual precipitation. Moreover, the increases in the amounts of precipitation during November in the south and south-east regions lead to a remarkable increase in the amount of precipitation during the fall season. The analysis of the contribution of each season and month to annual precipitation in wet and dry years shows that dry years have critical impacts on decreasing monthly precipitation over a particular region. For instance, a remarkable decrease in precipitation amounts is detectable during dry years over the eastern, northeastern, and southwestern regions of Iran during March, April, and December, respectively. The results of this study show that PERSIANN-CDR is a valuable source of information in low-density gauge network areas, capturing spatiotemporal variation of precipitation. [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]

Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the accuracy of satellite precipitation estimates. The first step is data merging based on optimum interpolation (OI), and the second step is downscaling based on geographically weighted regression (GWR); therefore, the framework is called OI-GWR. An Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used to demonstrate the effectiveness of OI-GWR in the Tianshan Mountains, China. First, the original IMERG precipitation data (OIMERG) are merged with rain gauge data using the OI method to produce corrected IMERG precipitation data (CIMERG). Then, using CIMERG as the first guess and the normalized difference vegetation index (NDVI) as the auxiliary variable, GWR is utilized for spatial downscaling. The two-step OI-GWR method is compared with several traditional methods, including GWR downscaling (Ori_GWR) and spline interpolation. The cross-validation results show that (1) the OI method noticeably improves the accuracy of OIMERG, and (2) the 1-km downscaled data obtained using OI-GWR are much better than those obtained from Ori_GWR, spline interpolation, and OIMERG. The proposed OI-GWR method can contribute to the development of high-resolution and high-accuracy regional precipitation datasets. However, it should be noted that the method proposed in this study cannot be applied in regions without any meteorological stations. In addition, further efforts will be needed to achieve daily- or hourly-scale downscaling of precipitation. [ABSTRACT FROM AUTHOR]

Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based precipitation estimations are a promising alternative to rain gauges for providing homogeneous precipitation information. Most satellite-based precipitation products suffer from short-term data records, which make them unsuitable for various climatological and hydrological applications. However, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides more than 35 years of precipitation records at 0.25° × 0.25° spatial and daily temporal resolutions. The PERSIANN-CDR algorithm uses monthly Global Precipitation Climatology Project (GPCP) data, which has been recently updated to version 2.3, for reducing the biases in the output of the PERSIANN model. In this study, we constructed PERSIANN-CDR using the newest version of GPCP (V2.3). We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 (from here on referred to as PERSIANN-CDR V2.3) with the PERSIANN-CDR constructed using GPCP V2.2 (from here on PERSIANN-CDR V2.2), at monthly and daily scales for the period from 2009 to 2013. First, we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean. Second, we evaluate the improvements in PERSIANN-CDR V2.3 with respect to the Climate Prediction Center (CPC) unified gauge-based analysis, a gauged-based reference, and Tropical Rainfall Measuring Mission (TRMM 3B42 V7), a commonly used satellite reference, at monthly and daily scales. The results show noticeable differences between PERSIANN-CDR V2.3 and V2.2 over oceans between 40° and 60° latitude in both the northern and southern hemispheres. Monthly and daily scale comparisons of the two bias-adjusted versions of PERSIANN-CDR with the above-mentioned references emphasize that PERSIANN-CDR V2.3 has improved mostly over the global land area, especially over the CONUS and Australia. The updated PERSIANN-CDR V2.3 data has replaced V2.2 data for the 2009–2013 period on CHRS data portal and NOAA National Centers for Environmental Information (NCEI) Program. [ABSTRACT FROM AUTHOR]

Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific differential phase (KDP), and double parameters that further integrate the differential reflectivity (ZDR), namely, R(ZH, ZDR), R(KDP, ZDR), and R(AH, ZDR), are investigated for an extreme precipitation event that occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution data observed by two disdrometers. The results show that (i) although R(AH, ZDR) underestimates in the light rain scenario, it performs the best among all radar QPE estimators according to the normalized mean error; (ii) the optimal radar rainfall relationship and consistency between radar measurements aloft and their surface counterparts are both required to obtain accurate rainfall estimates close to the ground. The contamination from melting layer on AH and KDP can make R(AH), R(AH, ZDR), R(KDP), and R(KDP, ZDR) less effective than R(ZH) and R(ZH,ZDR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior AH–based rainfall estimator; (iii) each radar QPE estimator may outperform others during some time intervals featured by particular rainfall characteristics, but they all tend to underestimate rainfall if radar fails to capture the rapid development of rainstorms. [ABSTRACT FROM AUTHOR]

In most Asian high mountain areas, ground-based observations of precipitation are sparse. It is urgent to assess and apply satellite precipitation products (SPPs). In recent years, relatively dense rain gauges have been established in five tributaries in Lhasa. Therefore, based on high-density rain gauges, two SPPs (GPM IMERG V05B, TRMM 3B42V7) were evaluated at the grid, region, and time scales with different statistical indices in the five tributaries. Besides, the dependence of SPPs performances on the precipitation intensities, elevation, and slope was investigated. The results indicate that: (1) both 3B42V7 and IMERG showed similarly low correlation with rain gauges at daily scale and high correlation at monthly scale, but 3B42V7 tended to suffer from systematic overestimation of monthly precipitation; (2) IMERG product outperformed 3B42V7 except for obvious overestimation of trace precipitation (0.1~1 mm day−1) and underestimation of torrential precipitation (>50 mm day−1); (3) the precipitation over the five tributaries showed significant spatial variability with difference of characteristic values (e.g., average daily precipitation) more than 20% in some IMERG grids and most 3B42V7 grids; (4) elevation had an obvious effect on the accuracy of 3B42V7 and IMERG, and the accuracy of the two SPPs decreased significantly with the increase of elevation. [ABSTRACT FROM AUTHOR]

Accurate estimation of precipitation from satellite precipitation products (PPs) over the complex topography and diverse climate of Pakistan with limited rain gauges (RGs) is an arduous task. In the current study, we assessed the performance of two PPs estimated from soil moisture (SM) using the SM2RAIN algorithm, SM2RAIN-CCI and SM2RAIN-ASCAT, on the daily scale across Pakistan during the periods 2000–2015 and 2007–2015, respectively. Several statistical metrics, i.e., Bias, unbiased root mean square error (ubRMSE), Theil's U, and the modified Kling–Gupta efficiency (KGE) score, and four categorical metrics, i.e., probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Bias score, were used to evaluate these two PPs against 102 RGs observations across four distinct climate regions, i.e., glacial, humid, arid and hyper-arid regions. Total mean square error (MSE) is decomposed into systematic (MSEs) and random (MSEr) error components. Moreover, the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TRMM TMPA 3B42v7) was used to assess the performance of SM2RAIN-based products at 0.25° scale during 2007–2015. Results shows that SM2RAIN-based product highly underestimated precipitation in north-east and hydraulically developed areas of the humid region. Maximum underestimation for SM2RAIN-CCI and SM2RIAN-ASCAT were 58.04% and 42.36%, respectively. Precipitation was also underestimated in mountainous areas of glacial and humid regions with maximum underestimations of 43.16% and 34.60% for SM2RAIN-CCI. Precipitation was overestimated along the coast of Arabian Sea in the hyper-arid region with maximum overestimations for SM2RAIN-CCI (SM2RAIN-ASCAT) of 59.59% (52.35%). Higher ubRMSE was observed in the vicinity of hydraulically developed areas. Theil's U depicted higher accuracy in the arid region with values of 0.23 (SM2RAIN-CCI) and 0.15 (SM2RAIN-ASCAT). Systematic error components have larger contribution than random error components. Overall, SM2RAIN-ASCAT dominates SM2RAIN-CCI across all climate regions, with average percentage improvements in bias (27.01% in humid, 5.94% in arid, and 6.05% in hyper-arid), ubRMSE (19.61% in humid, 20.16% in arid, and 25.56% in hyper-arid), Theil's U (9.80% in humid, 28.80% in arid, and 26.83% in hyper-arid), MSEs (24.55% in humid, 13.83% in arid, and 8.22% in hyper-arid), MSEr (19.41% in humid, 29.20% in arid, and 24.14% in hyper-arid) and KGE score (5.26% in humid, 28.12% in arid, and 24.72% in hyper-arid). Higher uncertainties were depicted in heavy and intense precipitation seasons, i.e., monsoon and pre-monsoon. Average values of statistical metrics during monsoon season for SM2RAIN-CCI (SM2RAIN-ASCAT) were 20.90% (17.82%), 10.52 mm/day (8.61 mm/day), 0.47 (0.43), and 0.47 (0.55) for bias, ubRMSE, Theil's U, and KGE score, respectively. TMPA outperformed SM2RAIN-based products across all climate regions. SM2RAIN-based datasets are recommended for agricultural water management, irrigation scheduling, flood simulation and early flood warning system (EFWS), drought monitoring, groundwater modeling, and rainwater harvesting, and vegetation and crop monitoring in plain areas of the arid region. [ABSTRACT FROM AUTHOR]

During the last decade, satellite-based precipitation products have been believed to be a potential source for forcing inputs in hydro-meteorological and agricultural models, which are essential especially over the mountains area or in basins where ground gauges are generally sparse or nonexistent. This study comprehensively evaluates several newly released precipitation products, i.e., MSWEP-V2.2, IMERG-V05B, IMERG-V06A, IMERG-V05-RT, ERA5, and SM2RAIN-ASCAT, at daily and monthly time-scales over Austria. We show that all the examined products are able to reproduce the spatial precipitation distribution over the country. MSWEP, followed by IMERG-V05B and -V06A, show the strongest agreement with in situ observations and perform better than other products with respect to spatial patterns and statistical metrics. Both IMERG and ERA5 products seem to have systematic precipitation overestimation at the monthly time-scale. IMERG-V06A performs slightly better than IMERG-V05B. With respect to heavy precipitation (P > 10 mm/day), MSWEP compare to other products demonstrate advantages in detecting precipitation events with a higher spatial average of probability of detection (POD) and lower false alarm ratio (FAR) scores skill (0.74 and 0.28), while SM2RAIN and ERA5 reveal lower POD (0.35) and higher FAR (0.56) in this precipitation range in comparison with other products. However, the ERA5 and MSWEP indicate robust average POD and FAR values with respect to light/moderate precipitation (10 mm > P ≥ 0.1 mm) with 0.94 and 0.11, respectively. Such robustness of MSWEP may be rooted in applying the daily rain gauges in calibration processes. Moreover, although all products accurately map the spatial precipitation distribution they still have difficulty capturing the effects of topography on precipitation. The overall performance of the precipitation products was lower in the peripheries of the study area where most stations are situated in the mountainous area and was higher over the low altitude regions. However, according to our analysis of the considered products, MSWEP-V2.2, followed by IMERG-V06S and -V05B, are the most suitable for driving hydro-meteorological, agricultural, and other models over mountainous terrain. [ABSTRACT FROM AUTHOR]

This study simultaneously analyzed and evaluated the meteorological drought-monitoring utility of the following four satellite-based, quantitative precipitation estimation (QPE) products: the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B43V7 (TRMM-3B43), the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the Climate Prediction Center Morphing Technique gauge-satellite blended product (CMORPH-BLD), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). Data from 2000 to 2016 was used at global scale. The global Climate Research Unit (CRU) Version 4.02 was used as reference data to assess QPE products. The Standardized Precipitation Evapotranspiration Index (SPEI) drought index was chosen as an example to evaluate the drought utility of four QPE products. The results indicate that CHIRPS has the best performance in Europe, Oceania, and Africa; the PERSIANN-CDR has the best performance in North America, South America, and Asia; the CMORPH-BLD has the worst statistical indices in all continents. Although four QPE products showed satisfactory performance for most of the world according to SPEI statistics, poor drought monitoring ability occurred in Southeast Asia, Central Africa, the Tibetan plateau, the Himalayas, and Amazonia. The PERSIANN-CDR achieves the best performance of the four QPE products in most regions except for Africa; CHIRPS and TRMM-3B43 have comparable performances. According to the spatial probability of detection (POD) and false alarm ratio (FAR) of the SPEI, more than 50% of all drought events cannot be accurately identified by QPE products in regions with sparse gauge distribution. In other regions, such as the southeastern USA, southeastern China, and South Africa, QPE products capture more than 75% of drought events. Temporally, all datasets (except for CMORPH-BLD) can detect all typical drought events, namely, in the southeastern US in 2007, western Europe in 2003, Kenya in 2006, and Central Asia in 2008. The study concludes that CHIRPS and TRMM-3B43 can be used as near-real-time drought monitoring techniques whereas PERSIANN-CDR might be more suitable for long-term historical drought analysis. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard disaster that can deeply affect environments, economies, and societies around the world. Therefore, accurate monitoring of patterns in drought is important. Precipitation is the key variable to define the drought index. However, the spare and uneven distribution of rain gauges limit the access of long-term and reliable in situ observations. Remote sensing techniques enrich the precipitation data at different temporal–spatial resolutions. In this study, the climate prediction center morphing (CMORPH) technique (CMORPH-CRT), the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TRMM 3B42V7), and the integrated multi-satellite retrievals for global precipitation measurement (IMERG V05) were evaluated and compared with in situ observations for the drought monitoring in the Xiang River Basin, a humid region in China. A widely-used drought index, the standardized precipitation index (SPI), was chosen to evaluate the drought monitoring utility. The atmospheric water deficit (AWD) was used for comparison of the drought estimation with SPI. The results were as follows: (1) IMERG V05 precipitation products showed the highest accuracy against grid-based precipitation, followed by CMORPH-CRT, which performed better than TRMM 3B42V7; (2) IMERG V05 showed the best performance in SPI-1 (one-month SPI) estimations compared with CMORPH-CRT and TRMM 3B42V7; (3) SPI-1 was more suitable for drought monitoring than AWD in the Xiang River Basin, because its high R-values and low root mean square error (RMSE) compared with the corresponding index based on in situ observations; (4) drought conditions in 2015 were apparently more severe than that in 2016 and 2017, with the driest area mainly distributed in the southwest part of the Xiang River Basin. [ABSTRACT FROM AUTHOR]

Fourteen-month precipitation measurements from a second-generation PARSIVEL disdrometer deployed in Beijing, northern China, were analyzed to investigate the microphysical structure of raindrop size distribution and its implications on polarimetric radar applications. Rainfall types are classified and analyzed in the domain of median volume diameter D 0 and the normalized intercept parameter N w . The separation line between convective and stratiform rain is almost equivalent to rain rate at 8.6 mm h−1 and radar reflectivity at 36.8 dBZ. Convective rain in Beijing shows distinct seasonal variations in log 10 N w – D 0 domain. X-band dual-polarization variables are simulated using the T-matrix method to derive radar-based quantitative precipitation estimation (QPE) estimators, and rainfall products at hourly scale are evaluated for four radar QPE estimators using collocated but independent rain gauge observations. This study also combines the advantages of individual estimators based on the thresholds on polarimetric variables. Results show that the blended QPE estimator has better performance than others. The rainfall microphysical analysis presented in this study is expected to facilitate the development of a high-resolution X-band radar network for urban QPE applications. [ABSTRACT FROM AUTHOR]

Extreme precipitation has received much attention because of its implications for hazard assessment and risk management. However, accurate precipitation information for extreme precipitation research from dense rain gauges is still difficult to obtain in developing countries or mountainous regions. Satellite-based precipitation products (SPPs) with high spatial and temporal resolution offer a new way of supplementing data from gauge-based observations. This study aims to evaluate the precision of six SPPs in detail at multiple temporal and spatial scales and explore their ability to capture the characteristics of extreme precipitation from 2009 to 2014 over a semi-arid to semi-humid climate transition area (Wei River basin) in China. The six products are TRMM 3B42RT, TRMM 3B42V7, PERSIANN, PERSIANN CDR, CMORPH RAW, and CRORPH CRT. China gauge-based daily precipitation analysis (CGDPA) provided by the China Meteorological Administration is used as the benchmark reference data. Various statistical evaluation techniques and extreme precipitation indices are used to evaluate and compare the performance of the selected products. The results show that the post real-time products (TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT) agreed better with the reference data than PERSIANN and CMORPH RAW. On a daily scale, TRMM 3B42V7, PERSIANN CDR, and CMORPH CRT displayed similarly good performance. However, at the monthly or annual scale, TRMM 3B42V7 was superior to the other products. With regard to the spatial distribution of precipitation, the datasets performed better over plains and were disappointing over mountainous areas. Additionally, TRMM 3B42V7 provided higher precision and less spatial uncertainty when monitoring extreme precipitation. This study provides a basis for selecting alternative precipitation data for climate transition basins. [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]

Tropical Rainfall Measurement Mission (TRMM) is one of the most popular global high resolution satellite-based precipitation products with a goal of measuring precipitation over the oceans and tropics. However, in recent years, the TRMM mission has come to an end. Its successor, Global Precipitation Measurement (GPM) mission was launched to measure the earth's precipitation structure, with an aim to improve upon the TRMM project. Both of the precipitation products have their own strengths and weaknesses in resolution, accuracy, and availability. The aim of this study is to evaluate the hydrologic utilization of the TRMM and GPM products in a humid basin of China. The main findings of this study can be summarized as follows: (1) 3B42V7 generally outperforms 3B42V6 in terms of hydrologic performance. Meanwhile, 3B42RTV7 significantly outperforms 3B42RTV6, and showed close performance with the bias-adjusted TRMM Multi-satellite Precipitation Analysis (TMPA) products. (2) The GPM showed better agreement with gauge observation than the TMPA products with lower RB and higher correlation coefficient (CC) values at different time scales. (3) The VIC hydrological model generally outperformed the XAJ hydrological model with lower RB, higher Nash–Sutcliffe Coefficient of Efficiency (NSCE) and CC values; though the 3B42RTV6 and 3B42RTV7 showed higher CC values in simulating the streamflow hydrograph by using the VIC and XAJ hydrological models. It can be found that the conceptual hydrological model was enough for the hydrologic evaluation of TRMM and GPM IMERG satellite-based precipitation in a humid basin of China. This study provides a reference for the comparison of multiple models on watershed scale. [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]

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]