Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for “7.19” Severe Storm Over North China

Abstract

Himawari‐8 is the first launched and operational new‐generation geostationary meteorological satellite. The Advanced Himawari Imager (AHI) on board Himawari‐8 provides continuous high‐resolution observations of severe weather phenomena in space and time. In this study, the capability to assimilate AHI radiances has been developed within the Weather Research and Forecasting (WRF) model's data assimilation system. As the first attempt to assimilate AHI using WRF data assimilation at convective scales, the added value of hourly AHI clear‐sky radiances from three water vapor channels on convection‐permitting (3 km) analyses and forecasts of the “7.19” severe rainstorm that occurred over north China during 18–21 July 2016 was investigated. Analyses were produced hourly, and 24 h forecasts were produced every 6 h. The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances when compared to the control experiment which assimilated only conventional observations. It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6 h lead time, especially for heavy rainfall exceeding 100 mm when verified against the observed rainfall. Furthermore, the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances, eventually contributing to a better forecast of the severe rainstorm.

Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

“…Although IR radiance assimilation has been operationally and successfully utilized at many centers, Migliorini () proved that sensors with more spectral channels are advantageous to assimilation of transformed retrievals. In a recent study, Wang et al () showed a positive impact from AHI radiance assimilation in predicting heavy precipitation from 19 to 20 July 2016 in Beijing. In another study, Wang, Li, et al () compared AHI radiance assimilation and LPW assimilation; they demonstrated that assimilation of the three derived LPWs yielded improved precipitation prediction.…”

Layer‐Wise Formation Mechanisms of an Entire‐Troposphere‐Thick Extratropical Cyclone That Induces a Record‐Breaking Catastrophic Rainstorm in Beijing

“…According to incomplete statistics (Sun et al, ), including the 20J event, since 2006, approximately 11 extreme severe rain events occurred in Beijing (an extreme severe rain event in Beijing must satisfy the condition that its precipitation is above 100 mm/day at any representative weather station among the 14 districts/counties of Beijing, with a rain intensity of 40 mm/hr appearing at one or more automatic weather stations; Sun et al, ). Compared to the recent 10 extreme severe rain events, three notable features were found in the 20J event (Lei et al, ; Sun et al, ; Wang et al, ; Zhao et al, ): (i) Its duration was long, and its area‐averaged precipitation (over Beijing) was the largest; (ii) its strongest precipitation rate was small (≤60 mm/hr), and the convection was relatively shallow (reflectivity above 15 dBZ was mainly located below 8 km; Lei et al, ); and (iii) having entered the mature stage, the cyclone extended from 950 hPa (near the surface) to up to 200 hPa, that is, the entire troposphere. This is the only special case (in terms of thickness) for the recent 11 extreme severe rain events over Beijing.…”

Efficient Regional Hybrid Ensemble-Variational Data Assimilation using the Global-Ensemble-Model-Augmented Error Covariance for Numerical Weather Prediction over Eastern China

“…It ranges from 0 to infinity with 1 representing the perfect score of BS. The BS measures the ratio of the frequency of forecast events to the frequency of observed events, indicating whether the forecast system has the tendency to overpredict (BS > 1) or underpredict (BS < 1) rainfall events [31]. Figure 9 shows the FSS, ETS, and BS as a function of threshold for 24 h accumulated rainfall.…”