Impact of all‐sky water vapour channel radiance from INSAT‐3D/3DR satellite over South Asia region using WRF model

Abstract

The objective of this study is to explore and present a methodology to assimilate all-sky water vapour (WV) radiance from Indian geostationary satellites in the Weather Research and Forecasting (WRF) model. For all-sky assimilation, hydrometeors are considered as control variables by adding in background error covariance. Additionally, this study uses the application of Global Satellite based Inter-Calibration System (GSICS) based bias correction mechanism on WV radiances before their assimilation. To fulfil these objectives, three sets of experiments have been performed with and without WV radiance assimilation for the month of July 2018 over the South Asia region. The impact assessment of assimilation has been performed by comparing radiative transfer (RT) model-simulated analysed and predicted brightness temperature against independent satellite observations from SAPHIR (Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiométrie) and MHS (Microwave Humidity Sounder) sensors. Results not only show the number of assimilated observations increased significantly (∼250%) in all-sky assimilation compared to clear-sky assimilation but also present that the all-sky analyses are closer to actual satellite observations. Due to the multivariate nature of variational data assimilation, noteworthy changes are also noticed in hydrometeors analyses in all-sky assimilation. The short-range forecasts confirm the positive impact of all-sky assimilation as compared to clear-sky assimilation when verified against SAPHIR and MHS measurements.

Assimilating Sentinel‐3 All‐Sky PWV Retrievals to Improve the WRF Forecasting Performance Over the South China

“…Our work differs from the previous studies in two aspects. First, in the data assimilation community, most previous studies focused on assimilating water vapor radiance data instead of retrieved PWV data (Kumar et al., 2022; Y. Wang et al., 2018; Xian et al., 2019). In our study, the retrieved water vapor data (i.e., PWV) are assimilated.…”

“…We have demonstrated the effectiveness of Sentinel‐3 all‐sky PWV data though their observation is affected by cloud. In the previous IR data assimilation studies, the IR data were normally assimilated in the form of water vapor radiance using a variational assimilation method (Kumar et al., 2022; J. Li et al., 2022) or ensemble Kalman filter (EnKF) (Okamoto et al., 2019; Zhang et al., 2021). Different from previous IR data assimilation methods, in this work we assimilate the water vapor data in the form of PWV.…”

“…Most of previous data assimilation studies normally assimilated satellite clear‐sky radiance only. Several studies have attempted to assimilate all‐sky water vapor IR radiance into WRF model (Kumar et al., 2022; Y. Wang et al., 2022; Zhang et al., 2021). These studies reported that assimilating satellite all‐sky IR radiance indicated a superior performance, for example, better spatial distribution of water vapor field and better precipitation forecasting results, to assimilation of clear‐sky IR radiance only.…”

“…Most of previous data assimilation studies normally assimilated satellite clear-sky radiance only. Several studies have attempted to assimilate all-sky water vapor IR radiance into WRF model (Kumar et al, 2022;Y. Wang et al, 2022;Zhang et al, 2021).…”

Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

“…And Kumar et al. (2022) found all‐sky assimilation experiments with IR imagers data from Indian geostationary satellites provided better forecast results than clear‐sky assimilation experiments for short‐range forecasts.…”