Importance of ocean prediction for heavy rainfall prediction over Japan in July 2020

Abstract

Hindcast experiments were performed for heavy rainfall events over Japan in July 2020 using a regional atmospheric model and a regional coupled model to examine the importance of ocean prediction for predicting heavy rainfall events. Both models were able to predict the first peak of accumulated rainfall over western Japan occurring in the first half of July. However, only the coupled model predicted the second peak that occurred in the second half of July. Sea level pressure (SLP) and low-level moisture inflow originating from an existing atmospheric river (AR) were found to differ in each model. In the regional atmospheric model, the error associated with the inaccurate low-level moisture inflow grew with rising excessive latent heat flux, which enhanced convection and resulted in incorrect SLP patterns. This trend seems to be enhanced by having a prescribed sea surface temperature (SST), which affects the surface heat flux. When ocean conditions are predicted as in the coupled model, such error growth is suppressed by changes in SST that adjust surface heat flux, and it leads to generation of the correct SLP patterns. With correct SLP especially for Pacific high in this case, favorable conditions for inflow from the AR can also be predicted, thus making it possible to predict the heavy rainfall. In conclusion, considering the atmospheric feedback on SST, ocean prediction can improve the predictability of heavy rainfall over Japan, the conditions for which are influenced by the nearby AR. Ocean prediction may therefore extend the range of weather forecasting.

The Merits of Ocean Prediction for the Prediction of 2010, 2016, and 2021 Summer Heavy Rainfall Events in Japan

“…The regional atmospheric and coupled models used for this study are identical to those used by Baba (2022). The atmospheric part of the model is based on a nonhydrostatic atmospheric model (Baba et al 2010) which employs a 30 km horizontal resolution and 34 vertical layers considering 25 km model top height.…”

“…This method is computationally efficient and has been used in ordinary numerical weather predictions (JMA 2019;Zhao, Held and Vecchi 2010). However, the air-sea coupling especially atmospheric feedback on SST is essentially neglected and only SST feedback on the atmosphere is considered by this method (Baba 2022). This is because the value of SST is prescribed as the boundary data.…”

“…The IVT differences due to the prediction model with a same lead time show that RUN-ATM predicted smaller IVT than RUN-CPL in the ocean regions southwest and south of Japan (third column in Figure 6). These correspond to the regions where rich precipitable water is frequently supplied during the heavy rainfall events in Japan (e.g., Araki et al 2021;Baba 2022;Takemi and Unuma 2019). Also, a part of this region overlaps with the Kyushu area, where RUN-ATM predicted smaller accumulated rainfall (Figure 2), and different low-level winds (Figure 4) and IWV (Figure 5) compared with RUN-CPL.…”

“…The purpose of this study is to examine the merits of ocean prediction (i.e., coupled model prediction) with respect to heavy rainfall prediction by conducting hindcast experiments for recent heavy rainfall events. Baba (2022) demonstrated the importance of ocean prediction for the prediction of the 2020 event where AR was dominant. Thus, other heavy rainfall events where AR was not dominant, i.e., Baiu or long-lasting front-derived rainfall events, should be considered.…”

“…However, the detailed mechanism underlying this difference has not yet been clarified. Also, although the importance of ocean prediction for heavy rainfall prediction in Japan has been discussed with regard to the events of 2020 in Baba (2022), the evaluation is considered insufficient because it was conducted for only one case. Japanese weather prediction has been operated using atmosphere-only models even recently (Japan Meteorological Agency 2019), so if the merits of ocean prediction with the mechanism of the prediction improvements are revealed, the findings will contribute to increase the prediction accuracy of Japanese weather prediction.…”