TY - JOUR
T1 - Assimilating Sentinel-3 All-Sky PWV Retrievals to Improve the WRF Forecasting Performance Over the South China
AU - Gong, Yangzhao
AU - Liu, Zhizhao
AU - Chan, Pak Wai
AU - Hon, Kai Kwong
N1 - Funding Information:
This work was funded by the Hong Kong Research Grants Council (RGC) (Q73B, PolyU 15211919 and Q80Q, PolyU 15221620) and the Emerging Frontier Area (EFA) Scheme of Research Institute for Sustainable Urban Development (RISUD) of the Hong Kong Polytechnic University (Grant 1-BBWJ). The authors thank European Space Agency for providing Snetinel-3 PWV data. The National Centers for Environmental Prediction (NCEP) is thanked for providing the Global Forecast System (GFS) data. The authors thank GNSS data product service platform of China Earthquake Administration for providing GNSS water vapor data of the Crustal Movement Observation Network Of China (CMONOC). We are grateful for the China Meteorological Administration GNSS Network (CMAGN) water vapor products provided by the China Meteorological Administration. We thank the National Oceanic and Atmospheric Administration (NOAA) for providing the Integrated Global Radiosonde Archive (IGRA) radiosonde data. We thank the China Meteorological Data Service Center for providing rainfall data. The Goddard Earth Sciences Data and Information Services Center (GES DISC) is thanked for providing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) rainfall data. The United States of America National Center for Atmospheric Research (NCAR) is thanked for providing the WRF model.
Funding Information:
This work was funded by the Hong Kong Research Grants Council (RGC) (Q73B, PolyU 15211919 and Q80Q, PolyU 15221620) and the Emerging Frontier Area (EFA) Scheme of Research Institute for Sustainable Urban Development (RISUD) of the Hong Kong Polytechnic University (Grant 1‐BBWJ). The authors thank European Space Agency for providing Snetinel‐3 PWV data. The National Centers for Environmental Prediction (NCEP) is thanked for providing the Global Forecast System (GFS) data. The authors thank GNSS data product service platform of China Earthquake Administration for providing GNSS water vapor data of the Crustal Movement Observation Network Of China (CMONOC). We are grateful for the China Meteorological Administration GNSS Network (CMAGN) water vapor products provided by the China Meteorological Administration. We thank the National Oceanic and Atmospheric Administration (NOAA) for providing the Integrated Global Radiosonde Archive (IGRA) radiosonde data. We thank the China Meteorological Data Service Center for providing rainfall data. The Goddard Earth Sciences Data and Information Services Center (GES DISC) is thanked for providing the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) rainfall data. The United States of America National Center for Atmospheric Research (NCAR) is thanked for providing the WRF model.
Publisher Copyright:
© 2023. The Authors.
PY - 2023/4/27
Y1 - 2023/4/27
N2 - Water vapor is a key driver for the evolution of weather system. To investigate the impact of assimilating Sentinel-3 precipitable water vapor (PWV) on weather forecasting, Sentinel-3 PWV retrievals over the South China with two different assimilation schemes are assimilated into the Weather Research and Forecasting (WRF) model. In the first assimilation scheme, only Sentinel-3 clear-sky PWV are assimilated, while Sentinel-3 all-sky PWV are assimilated for the second assimilation scheme. For both data assimilation schemes, we totally conduct 28 WRF data assimilation runs and forecasts for 28 selected days over two periods, that is, 14 days in March 2020 and 14 days in June 2020. The weather condition in June 2020 is much wetter than March 2020. Generally, assimilating Sentinel-3 PWV improves the WRF forecasting performance, particularly for June 2020. Assimilation of all-sky PWV outperforms assimilation of clear-sky PWV. The comparison results with radiosonde profiles show that assimilating Sentinel-3 PWV appreciably corrects the bias of WRF water vapor mixing ratio forecasting results for June 2020. The rainfall validation results show that both assimilation schemes show a positive impact in June 2020, but a neutral impact in March 2020. For June 2020, assimilating Sentinel-3 all-sky PWV improves rainfall forecast skill score by 2.4%, while the rainfall forecast score is improved by 1.0% after assimilating clear-sky PWV. Additionally, assimilation of Sentinel-3 PWV can modify the WRF moisture field, which further improves the rainfall spatial pattern.
AB - Water vapor is a key driver for the evolution of weather system. To investigate the impact of assimilating Sentinel-3 precipitable water vapor (PWV) on weather forecasting, Sentinel-3 PWV retrievals over the South China with two different assimilation schemes are assimilated into the Weather Research and Forecasting (WRF) model. In the first assimilation scheme, only Sentinel-3 clear-sky PWV are assimilated, while Sentinel-3 all-sky PWV are assimilated for the second assimilation scheme. For both data assimilation schemes, we totally conduct 28 WRF data assimilation runs and forecasts for 28 selected days over two periods, that is, 14 days in March 2020 and 14 days in June 2020. The weather condition in June 2020 is much wetter than March 2020. Generally, assimilating Sentinel-3 PWV improves the WRF forecasting performance, particularly for June 2020. Assimilation of all-sky PWV outperforms assimilation of clear-sky PWV. The comparison results with radiosonde profiles show that assimilating Sentinel-3 PWV appreciably corrects the bias of WRF water vapor mixing ratio forecasting results for June 2020. The rainfall validation results show that both assimilation schemes show a positive impact in June 2020, but a neutral impact in March 2020. For June 2020, assimilating Sentinel-3 all-sky PWV improves rainfall forecast skill score by 2.4%, while the rainfall forecast score is improved by 1.0% after assimilating clear-sky PWV. Additionally, assimilation of Sentinel-3 PWV can modify the WRF moisture field, which further improves the rainfall spatial pattern.
KW - data assimilation
KW - numerical weather prediction
KW - precipitable water vapor
KW - Sentinel-3
KW - Weather Research and Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85158953643&partnerID=8YFLogxK
U2 - 10.1029/2022JD037979
DO - 10.1029/2022JD037979
M3 - Journal article
AN - SCOPUS:85158953643
SN - 2169-897X
VL - 128
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
IS - 8
M1 - e2022JD037979
ER -