GPS PWV Assimilation with the JMA Nonhydrostatic 4DVAR and Cloud Resolving Ensemble Forecast for the 2008 August Tokyo Metropolitan Area Local Heavy Rainfalls

Abstract

On 5th August 2008, scattering local heavy rainfalls occurred at various places over the Tokyo metropolitan area, and five drainage workers were claimed by an abrupt increase of water level. The Japan Meteorological Agency (JMA) operational mesoscale model of the day failed to predict occurrence of the local heavy rainfalls, which were brought about by deep convective cells developed on the unstable atmospheric condition without strong synoptic/orographic forcings. A 11-member mesoscale ensemble prediction with a horizontal resolution of 10 km was conducted using the operational mesoscale analysis of JMA and perturbations of the JMA global one-week ensemble prediction system as the initial condition and the initial and lateral boundary perturbations, but the intense rains exceeding 20 mm/3 h were hardly predicted. A downscaling ensemble forecast experiment with a horizontal resolution of 2 km was conducted using the 6 h forecast of the 10 km ensemble as the initial and boundary conditions. Scattered intense rains were predicted in some ensemble members, but their locations and distribution were insufficient. The total precipitatable water vapor (PWV ) observed by the GNSS Earth Observation Network System (GEONET) of Geospatial Information Authority of Japan showed that the JMA mesoscale analysis given by the hydrostatic Meso-4DVAR underestimated water vapor over the Tokyo metropolitan area. To modify the initial condition, a reanalysis data assimilation experiment was conducted with the JMA’s nonhydrostatic 4DVAR (JNoVA) , where PWV data from GEONET were assimilated 2.5 days with 3-h data assimilation cycles. The 2 km downscale ensemble run from the JNoVA analysis properly predicted the areas of scattering local heavy rains. Threat scores and ROC area skill scores suggest that even in the ensemble prediction, accuracy of initial condition is critical to numerically predict small scale convective rains. Fractions skill scores indicated the value of the cloud resolving ensemble forecast for such the unforced convective rain case.