Assimilation of Himawari‐8 All‐Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction

Abstract

Japan's new geostationary satellite “Himawari‐8” enables updating precipitation and flood predictions as frequently as every 10 min and to capture as early as possible the flood risk associated with rapidly changing severe weather. This study focuses on the advantage of the frequent update of precipitation and flood predictions by assimilating all‐sky Himawari‐8 infrared radiances in the case of September 2015 Kanto‐Tohoku heavy rainfall, a major flooding event in Japan. The analyzed tropical cyclone representation and moisture transport are improved by the Himawari‐8 data assimilation. Deterministic runs from the analyses with the Himawari‐8 data provide much improved forecasts of a strong precipitation band. Moreover, every 10 min updates of river discharge forecasts driven by these improved precipitation forecasts show a large improvement in forecast skill with longer lead times. The results demonstrate that the frequent refresh aids to capture a signature of flooding at an earlier stage.

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

“…The horizontal localization radius was set to 50 km. This horizontal localization radius is similar to the previous studies (30 km for hydrometeors in Zhang et al, ; 60 km in Honda, Miyoshi, et al, ; and 40 km in Honda, Kotsuki, et al, ). No vertical localization was applied to assimilate Himawari‐8 radiances, following Zhang et al ().…”

“…We used the method proposed by Tompkins and Janiskova () to estimate the cloud fraction profile for input to RTTOV. The configuration of RTTOV used in this study has thoroughly been evaluated by the previous studies (e.g., Baran et al, ; Okamoto, ; Vidot et al, ) and has been applied to assimilate real all‐sky Himawari‐8 radiances into a mesoscale model (Honda, Miyoshi, et al, ; Honda, Kotsuki, et al, ). We applied no parallax correction, which may bring some errors in estimating radiances especially at the edge of cloud systems (see Kostka et al ()).…”

“…The conventional observations are assimilated only in D1 for all experiments. While only clear‐sky radiances of Himawari‐8 are assimilated in the JMA's operational numerical weather prediction system, we assimilated all‐sky Himawari‐8 radiances as done by Honda, Miyoshi, et al () and Honda, Kotsuki, et al (). In this study, the length of the assimilation window was set to 10 min in the D2 domain of the Him8 and Him8‐RIP experiments, so that the atmospheric state variables were analyzed every 10 min.…”

“…This threshold (i.e., 1.0 K) has been applied in the previous literatures (Harnisch et al, ; Honda, Miyoshi, et al, ) to identify clear‐sky radiances. This quality control has not been applied in the previous all‐sky infrared radiance data assimilation studies (e.g., Honda, Miyoshi, et al, ; Honda, Kotsuki, et al, ; Zhang et al, ) since they mainly focused on the effect of assimilating radiances over ocean. Most operational centers currently avoid directly using clear‐sky infrared radiances over land (e.g., Kazumori, ; Ma et al, ), so that it is appropriate not to use them in this preliminary trial of assimilating cloudy sky infrared radiances in the convective scale.…”

“…Honda, Miyoshi, et al () improved the TC analysis and forecast by assimilating the real Himawari‐8 all‐sky infrared radiances. Kunii et al () and Honda, Kotsuki, et al () revealed that Himawari‐8 data assimilation can improve the forecast of TC‐induced rainfall. The new generation geostationary satellites have a capability to frequently monitor the entire lifecycle of individual convective cells as well as large‐scale instabilities so that they may potentially improve the analysis and forecast of the isolated and unorganized convective systems induced by much weaker large‐scale forcing than TCs or organized convective systems.…”