Loading…
Evaluation and intercomparison of multiple satellite-derived and reanalysis downward shortwave radiation products in China
Downward shortwave radiation (DSR) is a critical variable in energy balance driving Earth's surface processes. Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades. However, as those products have different temporal resolutions, the...
Saved in:
Published in: | International journal of digital earth 2023-12, Vol.16 (1), p.1853-1884 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Downward shortwave radiation (DSR) is a critical variable in energy balance driving Earth's surface processes. Satellite-derived and reanalysis DSR products have been developed and continuously improved during the last decades. However, as those products have different temporal resolutions, their performances in different time scales have not been well-documented, particularly in China. This study intended to evaluate several DSR products across multiple time scales (i.e. instantaneous, 1-hourly, daily, and monthly average) and ecosystems in China. Six DSR products, including GLASS, BESS, CLARA-A2, MCD18A1, ERA5 and MERRA-2, were evaluated against ground measurements at Chinese Ecosystem Research Network (CERN) and integrated land-atmosphere interaction observation (TPDC) sites from 2009 to 2012. The instantaneous DSR of MCD18 showed a root mean square error (RMSE) of 146.02 W/m
2
. The hourly RMSE of ERA5 (155.52 W/m
2
) was largely smaller than MERRA-2 (188.53 W/m
2
). On the daily and monthly scale, BESS had the most optimized accuracy among the six products (RMSE of 36.82 W/m
2
). For the satellite-derived DSR products, the monthly accuracy at CERN can meet the threshold accuracy requirement set by World Meteorological Organization (WMO) for Global Numerical Weather Prediction (20 W/m
2
). |
---|---|
ISSN: | 1753-8947 1753-8955 |
DOI: | 10.1080/17538947.2023.2212918 |