Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation

Abstract

Space-time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space-time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency's Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

GSMaP RIKEN Nowcast: Global Precipitation Nowcasting with Data Assimilation

“…To obtain a smooth, complete set of motion vectors from noisy TREC-based motion vectors, the Local Ensemble Transform Kalman Filter (LETKF; Hunt et al 2007;Miyoshi and Yamane 2007) is used with 20 ensemble members. The covariance localization function is a Gaussian with a length scale of 100 km, while the covariance inflation method is a multiplicative inflation with a factor of 1.01 simultaneously with the relaxation to prior spread (Whitaker and Hamill 2012) so that the ensemble spread does not change in time (Otsuka et al 2016a).…”

“…These updates aimed to improve the quality of the motion vectors (V4, V10, V23), increase the number of available motion vectors (V4, V11, V23, V25), and stabilize the system (V1, V4a, V23, V25). The key goals of each update are as follows: • In the version presented in Otsuka et al (2016a), the advection occasionally exhibited numerical noise. To prevent this, V1 shortened the time interval of advection.…”

“…Ground-based radar precipitation nowcasting has a long history (e.g., Rinehart and Garvey 1978;Dixon and Wiener 1993;Bowler et al 2006), whereas satellite-based global precipitation nowcasting is a relatively new field. Recently, Otsuka et al (2016a) developed a global precipitation nowcasting system using GSMaP, and the system has run at RIKEN in real time since January 2016 (GSMaP RIKEN Nowcast, hereinafter referred to as RNC). The predictions are made open to the public with the permission of the Japan Meteorological Agency (JMA), as required by Japanese law.…”

“…The predictions are made open to the public with the permission of the Japan Meteorological Agency (JMA), as required by Japanese law. Although Otsuka et al (2016a) presented initial verification results for a particular month, the long-term stability of RNC in different seasons has not yet been investigated. Our real-time operations spanning more than two years have revealed its advantages and limitations on longer time scales.…”

Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere

“…However, many studies have used only either the RTPS or RTPP with manual tuning for real-world EnKF (e.g. Otsuka et al, 2016;Poterjoy and Zhang, 2016;Kotsuki et al, 2017). In contrast, this study focuses on the adaptive estimation of the relaxation parameters without using additive inflation.…”