Satellite Precipitation Uncertainty

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (25 November 2021) | Viewed by 3959

Special Issue Editors

Department of Earth Sciences, National Taiwan Normal University, Taipei 11677, Taiwan
Interests: regional climate changes; diurnal rainfall; remote sensing of precipitation; multiple timescale interactions; climate model simulation and projection
Department of Physics, National and Kapodistrian University of Athens, 157 72 Athens, Greece
Interests: atmospheric modeling; remote sensing; satellite precipitation
1. BEYOND Center of Earth Observation Research and Satellite Remote Sensing, Space Applications and Remote Sensing, Institute for Astronomy and Astrophysics, National Observatory of Athens, 118 10 Athens, Greece
2. Department of Physics, National and Kapodistrian University of Athens, 157 72 Athens, Greece
Interests: numerical weather prediction; satellite rainfall estimates; data fusion

Special Issue Information

Dear Colleagues,

Precipitation is an important element in the global water cycle. Free and accessible satellite-based estimates of precipitation covering a nearly global domain are an important data source for various research studies. It is important to assess and understand the uncertainty of satellite precipitation prior to applying the satellite precipitation to various research subjects.

This Special Issue aims to publish research helping to clarify satellite precipitation uncertainty from a broad perspective. We invite researchers to contribute papers dealing with all aspects of satellite precipitation development, assessment, and application over regional or global domains. In particular, original research articles or review articles exploring the performance of various satellite precipitation products (CMORPH, CHIRPS, CloudSat, MSWEP, PERSIANN, GSMaP, IMERG, TMPA, etc.) over complex terrain are welcome. Topics of interest include, but are not limited to:

  • Quantitative precipitation estimation;
  • Spatial and temporal characteristics of satellite precipitation;
  • Extreme precipitation events (front, tropical cyclone, etc.);
  • Validation of precipitation simulation (global climate models, regional climate models, weather forecasting models, reanalyses, etc.) using satellite precipitation products;
  • New methods applied to reduce satellite precipitation uncertainty.

Dr. Wan-Ru Huang
Dr. George Kallos
Dr. Nikolaos S. Bartsotas
Guest Editors

Manuscript Submission Information

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Keywords

  • remote sensing of precipitation
  • quantitative precipitation estimation
  • spatio-temporal characteristics
  • performance skills
  • validation and application

Published Papers (1 paper)

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Research

20 pages, 8347 KiB  
Article
Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China
by Yiming An, Wenwu Zhao, Changjia Li and Yanxu Liu
Atmosphere 2020, 11(11), 1223; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111223 - 13 Nov 2020
Cited by 22 | Viewed by 2806
Abstract
Satellite-based and reanalysis products are precipitation data sources with high potential, which may exhibit high uncertainties over areas with a complex climate and terrain. This study aimed to evaluate the accuracy of the latest versions of six precipitation products (i.e., Climate Hazards Group [...] Read more.
Satellite-based and reanalysis products are precipitation data sources with high potential, which may exhibit high uncertainties over areas with a complex climate and terrain. This study aimed to evaluate the accuracy of the latest versions of six precipitation products (i.e., Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) V2.0, gauge-satellite blended (BLD) Climate Prediction Center Morphing technique (CMORPH) V1.0, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V6 Final, Global Satellite Mapping of Precipitation (GSMaP) near-real-time product (NRT) V6, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-CDR) over the Yellow River Basin, China. The daily precipitation amounts determined by these products were evaluated against gauge observations using continuous and categorical indices to reflect their quantitative accuracy and capability to detect rainfall events, respectively. The evaluation was first performed at different time scales (i.e., daily, monthly, and seasonal scales), and indices were then calculated at different precipitation grades and elevation levels. The results show that CMORPH outperforms the other products in terms of the quantitative accuracy and rainfall detection capability, while CHIRPS performs the worst. The mean absolute error (MAE), root mean square error (RMSE), probability of detection (POD), and equitable threat score (ETS) increase from northwest to southeast, which is similar to the spatial pattern of precipitation amount. The correlation coefficient (CC) exhibits a decreasing trend with increasing precipitation, and the mean error (ME), MAE, RMSE, POD and BIAS reveal an increasing trend. CHIRPS demonstrates the highest capability to detect no-rain events and the lowest capability to detect rain events, while ERA5 has the opposite performance. This study suggests that CMORPH is the most reliable among the six precipitation products over the Yellow River Basin considering both the quantitative accuracy and rainfall detection capability. ME, MAE, RMSE, POD (except for ERA5) and BIAS (except for ERA5) increase with the daily precipitation grade, and CC, RMSE, POD, false alarm ratio (FAR), BIAS, and ETS exhibit a negative correlation with elevation. The results of this study could be beneficial for both developers and users of satellite and reanalysis precipitation products in regions with a complex climate and terrain. Full article
(This article belongs to the Special Issue Satellite Precipitation Uncertainty)
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