水蒸気量の鉛直分布のデータ同化による降水予測精度へのインパクト評価

Abstract

気象予報モデルを用いた短時間降水予測において、水蒸気量のデータ同化の重要性が増してきている。一方、GPS遅延量観測と鉛直方向の音波観測を組み合わせることで水蒸気量の鉛直分布を推定することが可能であるため、本研究では水蒸気量の鉛直分布をデータ同化し、降水予測への効果を調べた。データ同化システムCReSS-LETKFを用いた理想実験によって、水蒸気量の鉛直分布のデータ同化をおこなったところ2時間先の降水予測にまでも良い効果が確認された。また、CReSS-LTEKFに水蒸気のデータ同化モジュールを構築した。最後に、偏波レーダーのみから水蒸気量を推定する手法の素案を示した。

It is important for 0-6 hour nowcasting to provide for a high-quality initial condition in a meso-scale atmospheric model by a data assimilation of several observation data. It is getting more important to assimilate the water vapor for numerical weather prediction, because vapor is a source of any kind of precipitation. The vertical profile of water vapor can be estimated by the combination of GPS precipitable water and the echo profile of the sound. In this study, an impact of the assimilation of the vertical profile of vapor on accuracy for QPE is evaluated. As an implementation, the cloud-resolving non-hydrostatic atmospheric model, CReSS, which has detail microphysical processes, is employed as a forecast model. The local ensemble transform Kalman filter, LETKF, is used as a data assimilation method, which uses an ensemble of short-term forecasts to estimate the flow-dependent background error covariance required in data assimilation. A heavy rainfall event occurred in Okinawa in 2009 is chosen as an application. As the ideal experiment was carried out, the effect of that assimilation still continued for two hours forecast lead time.