Short-range QPF over Korean peninsula using nonhydrostatic mesoscale model and 'future time' data assimilation based on rainfall nowcasting from GMS satellite measurements

This study investigates data assimilation impacts of near-term satellite-derived nowcasted rainfall information (i.e., 3-hourly independently forecasted rainrates) in the initialization phase of a nonhydrostatic mesoscale model used for predicting severe convective rainfall events over the Korean peninsula. Infrared (IR) window measurements from the Japanese Geostationary Meteorological Satellite (GMS) are used to specify downstream rainrates during a spinup period of the model---but in a "future time" framework since the independently acquired forecasted rainrates are used as assimilation control after the pre-forecast period has expired. This is tantamount to initializing in advance of real time in the context of operational forecasting. The main scientific objective of the study is to investigate the strengths and weaknesses of this data assimilation scheme insofar as its influence on quantitative precipitation forecasting (QPF) during the summertime Korean rainy season.; The long-term goal of the research is to improve short-range (12-hour) QPF over the Korean peninsula through the use of innovative data assimilation methods based on geosynchronous (GEO) and/or low earth orbit (LEO) satellite precipitation information---either IR or preferably time lapse microwave (MW) measurements, once the latter are introduced by the forthcoming Global Precipitation Measurement (GPM) Mission. As a step in this direction, a new type of data assimilation experiment is performed in conjunction with high-frequency GMS-retrieved nowcasted rainfall information introduced to a mesoscale model. The 3-hourly "future time" precipitation forecast information is assimilated through nudging the associated moisture field (and thus the latent heating) during the early stages of a forecast period. This procedure enhances details in the moisture field during model integration, and thus improves spinup performance, as long as the error statistics of the "future time" precipitation estimates are superior to those intrinsic to background error statistics of the model.; It is found that assimilation of nowcasted rainrates alone during the early hours of a forecast period, produces better precipitation forecasts for low to medium rainrates, as well as better organized vertical velocity fields, than generated in the CTL experiments. Application of the dynamic nudging procedure during the preforecast period produces even better precipitation forecasts vis-a-vis rain location and intensity, especially for medium to heavy rainrates. Thus, combined use of dynamic nudging during the preforecast period and "future time" rain assimilation during the forecast period produces superior forecasts relative to CTL, RAIN only, or dynamic nudging only (i.e., DYNRAIN assimilation without follow-on RAIN assimilation). Forecast skill, quantified by threat and skill scores for heavy rainrates, are improved in the DYNRAIN experiments, although the bias scores for the DYNRAIN experiments are only slightly larger than for the RAIN experiments. (Abstract shortened by UMI.)...