Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

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

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation

Comprehensive Evaluation of Global Precipitation Products and Their Accuracy in Drought Detection in Mainland China

Abstract: Thorough evaluations of satellite precipitation products are necessary for accurately detecting meteorological drought. A comprehensive assessment of 15 state-of-the-art precipitation products (i.e., IMERG_cal, IMERG_uncal, GSMaP-G, CPC-Global, TRMM3B42, CMORPH-CRT, PERSIANN-CDR, PERSIANN, PERSIANN-CCS, SM2RAIN, CHIRPS, ERA5, ERA-Interim, MERRA-2, and GLDAS) is herein conducted for the period 2010–19 giving special attention to their performance in detecting meteorological drought over mainland China at 0.25° spatial resolution. The cited precipitation products are compared against China’s gridded gauge-based Daily Precipitation Analysis (CGDPA) product, derived from 2400 meteorological stations, and their quality is assessed at daily, seasonal, and annual precipitation time scales. Meteorological droughts in the datasets are determined by calculating the standardized precipitation evapotranspiration index (SPEI). The performance of the precipitation products for drought detection with respect to the SPEI is assessed at three time scales (1, 3, and 12 months). The results show that the GSMaP-G outperforms other satellite-based datasets in drought detection and precipitation estimation. The MERRA-2 and the ERA5 are on average closer to the CGDPA reference data than other reanalysis products for precipitation estimation and drought detection. These products capture well the spatial and temporal pattern of the SPEI in southern and eastern China having a probability of detection (POD) above 0.6 and a correlation coefficient (CC) above 0.65. CPC-Global, IMERG, and the ERA5 reanalysis product are ideal candidates for application in western China, especially in the Qinghai–Tibetan Plateau and the Xinjiang Province. Generally, the accuracy of precipitation products for drought detection is improved with longer time scales of the SPEI (i.e., SPEI-12). This study contributes to drought-hazard detection and hydrometeorological applications of satellite precipitation products. Significance Statement: The purpose of this study is to comprehensively evaluate the quality of 15 global satellite-based, gauge-based, and reanalysis precipitation products for meteorological drought detection at 1-, 3-, and 12-month time scales. This work systematically evaluates these products’ capacity to capture precipitation occurrence and intensity in different seasons. This is followed by a comparison of the precipitation products’ performance in drought detection. This work’s findings and evaluation results will improve the ability of those who develop precipitation products in identifying error sources and further improving retrieval algorithms. This paper’s results will serve as a valuable reference for end users seeking to better understand the application of precipitation products to drought detection

MSWEP : 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3hourly temporal and 0.25 degrees ffi spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0% of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km(2)) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byrans Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9% of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Performance Assessment of GPM IMERG Products at Different Time Resolutions, Climatic Areas and Topographic Conditions in Catalonia

Quantitative Precipitation Estimates (QPEs) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in semiarid regions with complex orography, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to assess the performance of three IMERG products (Early, Late and Final) at different time scales, ranging from yearly to sub-daily periods. The analysis at a half-hourly scale also considered three different orographic features (valley, flat and ridgetop), diverse climatic conditions (BSk, Csa, Cf and Df) and five categories related to rainfall intensity (light, moderate, intense, very intense and torrential). While IMERG_E and IMERG_L overestimate precipitation, IMERG_F reduces the error at all temporal scales. However, the calibration to which a Final run is subjected causes underestimation regardless in some areas, such as the Pyrenees mountains. The proportion of false alarms is a problem for IMERG, especially during the summer, mainly associated with the detection of false precipitation in the form of light rainfall. At sub-daily scales, IMERG showed high bias and very low correlation values, indicating the remaining challenge for satellite sensors to estimate precipitation at high temporal resolution. This behaviour was more evident in flat areas and cold semi-arid climates, wherein overestimates of more than 30% were found. In contrast, rainfall classified as very heavy and torrential showed significant underestimates, higher than 80%, reflecting the inability of IMERG to detect extreme sub-daily precipitation events

Evaluating the latest IMERG products in a subtropical climate : the case of Paraná state, Brazil

The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Paraná, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis