Journal of the Meteorological Society of Japan. Ser. II
Online ISSN : 2186-9057
Print ISSN : 0026-1165
ISSN-L : 0026-1165
GSMaP RIKEN Nowcast: Global Precipitation Nowcasting with Data Assimilation
Shigenori OTSUKAShunji KOTSUKIMarimo OHHIGASHITakemasa MIYOSHI
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JOURNALS FREE ACCESS Advance online publication

Article ID: 2019-061

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Abstract

 Since January 2016, RIKEN has been running an extrapolation-based nowcasting system of global precipitation in real time. Although our previous paper reported its advantage of the use of data assimilation in a limited verification period, long-term stability of its forecast accuracy through different seasons has not been investigated. In addition, the algorithm was updated seven times between January 2016 and March 2018. Therefore, this paper aims to present how motion vectors can be derived more accurately, and how data assimilation can constrain an advection-diffusion model for extrapolation stably for the long-term operation. The Japan Aerospace Exploration Agency's Global Satellite Mapping of Precipitation (GSMaP) Near-Real-Time product is the only input to the nowcasting system. Motion vectors of precipitation areas are computed by a cross-correlation method, and the Local Ensemble Transform Kalman Filter generates a smooth, complete set of motion vectors. Precipitation areas are moved by the motion vectors up to 12 hours, and the product, called “GSMaP RIKEN Nowcast”, is disseminated on a webpage in real time. Most of the algorithmic updates were related to better estimating motion vectors, and the forecast accuracy was gradually and consistently improved by these updates. Particularly, the threat scores increased the most around 40°S and 40°N. A performance drop in the northern hemisphere winter was also reduced by reducing noise in advection. The time series of ensemble spread showed that an increase in the number of available motion vectors by a system update led to a decrease in the ensemble spread, and vice versa.

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© The Author(s) 2019. This is an open access article published by the Meteorological Society of Japan under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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