ALBERT

Description: An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network.

Description: This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May–October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to represent spatial variability of precipitation in the area. Results show that both IMERG products have a better bias ratio and correlation coefficient on both time scales as compared to CMORPH. Comparison statistics show a slight improvement in the skill of detecting rainfall events in IMERG products compared to CMORPH. Results also show a decreasing trend in the detection ability of satellite products for increasing threshold values, highlighting the need to further improve detection during heavy precipitation.

Description: The objective of this study was to evaluate the performance of satellite rainfall estimates (Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2) and Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEPv2) from 1981 to 2018 for monthly meteorological drought analysis over the Upper Blue Nile (UBN) basin. The reference for the performance evaluation was rainfall measured in situ selected with reference to the elevation zones of the basin: Highland, midland, and lowland. Both the measured and estimated rainfall datasets were aggregated by month at a spatial resolution of 10 km × 10 km with a temporal coverage of 38 years from 1981 to 2018 and evaluated with respect to raw precipitation statistics and the standardized precipitation index (SPI). The values of SPI were validated with reference to documented meteorological drought records of the country. The mean bias, correlation coefficient, probability of bias (PBias, %), mean error (ME, mm), and root mean square error (RMSE, mm) values across the elevation zones for CHIRPSv2 were found to be 1.07, 0.91, 6.75, 7.74, and 122.34, respectively. The corresponding values were 1.19, 0.87, 18.56, 19.54, and 130.26 for MSWEPv2. Based on this result, CHIRPSv2 was employed to analyze the magnitude of drought in the different elevation zones of the UBN. The magnitude (SPI) of monthly meteorological drought over the entire UBN basin from 1981 to 2018 ranged from 0 to −3.74. The strongest negative SPI value (−3.74) was observed in August 1984 in midland areas. The highest magnitude of drought was −3.0 in July 2015 over the highland and −3.03 in June 2015 over the lowland during 2014–2017. The observed drought was characterized by extreme, severe, and moderate levels. The mean frequency of severe/extreme meteorological drought in the 38-year period over the highland, midland, and lowland parts of the UBN ranged from 7% to 11%. The average of severe/extreme drought events in each of the elevation zones of the basin was 9%, that is, drought occurred almost every 10 years for all elevation zones of the basin. Over the 38-year period, severe/extreme drought occurred at the onset and/or offset time of rainy season over all elevation zones of the basin. The UBN is characterized as a drought-prone basin. However, the frequency and magnitude of drought could neither be described as a decreasing nor as an increasing linear trend. Thus, the farming practices in the basin need to be enhanced with an improved early warning system and drought-resistant seed technologies.

Description: The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B.

Description: The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons.