Although there have been various recent improvements in formulating the dynamics, thermodynamics, and microphysics of mesoscale models, as well as computer advances which allow the use of high resolution cloud-resolving grids and explicit latent heating over regional domains, spinup remains at the forefront of unresolved mesoscale modeling problems. In general, non-realistic spinup limits the skill in predicting the spatial-temporal distribution of convection and precipitation, primarily in the early hours of a forecast, stemming from standard prognostic variables not representing the initial diabatic heating field produced by the ambient convection and cloud fields.
The long-term goal of this research is to improve short-range (12-hour) quantitative precipitation forecasting (QPF) over the Korean peninsula through the use of innovative data assimilation methods based on geosynchronous satellite measurements. As a step in this direction, a non-standard data assimilation experiment in conjunction with GMS-retrieved nowcasted rainfall information introduced to the mesoscale model is conducted. The 3-hourly precipitation forecast information is assimilated through nudging the associated diabatic heating during the early stages of a forecast period. This procedure is expected to enhance details in the moisture field during model integration, and thus improve spinup performance, assuming the errors in the “future time” latent heating data ate less than intrinsic model background errors.
To incorporate the forecast rainrates, a relatively new approach in nudging-based rainfall data initialization using six hours of accumulated mean rain rate is applied to obtain vertical profiles of moisture consistent with the assimilated rainrates. The rain assimilation process is then performed with the reconstructed fields for the first three hours of each 12-hour forecast period based on “future time” rain rate information to acquire a 3-hour gain on real time insofar as data assimilation. To invoke this process more effectively, Newtonian relaxation is applied before performing the rain rate assimilation, a process referred as dynamic nudging. The dynamic nudging limits the large–scale error growth and allows development of a large-scale balance by guiding the relaxation toward the large-scale analysis.
The following numerical experiments are then performed: (1) control (CTL) – without any nudging, (2) rain assimilation (RAIN) – nudging only to the 3-hour mean nowcasted rain rates, and (3) rain assimilation with dynamic nudging (DYRAIN) – nudging wind and temperature to the large-scale analysis, and then nudging the model rain rates to the 3-hour mean nowcasted rain rates. The integration cycle of the experiments consists of a 12-hour preforecast period prefacing a 12-hour forecast period -- defining a complete 24-hour model integration period. For the RAIN and DRYRAIN experiments, “future time” rain nudging is performed for the first three hours of the 12-hour forecast period, while the dynamic nudging is performed for the last six hours of the 12-hour preforecast period.
These experiments help shed light on how operational precipitation forecasts made during the Korean rainy season could be modified and possibly improved by applying a GEO satellite-based data assimilation procedure in which “future time” precipitation conditions can be represented at initial time. The above methods are tested on three severe storm cases that took place in South Korea during the summer seasons of 1998, 1999, and 2000. For purpose of validating the satellite precipitation nowcasts, one-minute sampled rain gage measurements are used. These data were acquired from the dense operational Automatic Weather Station (AWS) network (i.e., some 530 rain gages distributed over South Korea) maintained by the Korean Meteorological Agency (KMA).
It is found that the use of mean nowcasted rain rates as target rain rates for assimilation during the early hours of a forecast period produce better precipitation forecasts for low to medium rainrates, as well as better organized vertical velocity fields than generated by the CTL experiments. Application of the dynamic nudging procedure during the preforecast period produces greatly improved precipitation forecasts vis-ŕ-vis rain location and intensity factors, especially for medium to heavy rain rates. Combined used of the dynamic nudging during the preforecast period and rain assimilation during the forecast period produces superior forecasts relative to the CTL, RAIN nudging, or dynamic nudging only (i.e., DRYRAIN without follow on RAIN nudging). Forecast skill quantified by threat and skill scores for heavy rain rate are greatly improved in the DYRAIN experiment, although the bias score for the DYRAIN experiment is only slightly larger than for the RAIN experiment.
The impact of the assimilation scheme depends to some degree on the characteristics of the precipitation events. The 2000 case study undergoes a greater combined impact of dynamic nudging during the preforecast period and rain assimilation during the forecast period, relative to associated impacts for the 1998 and 1999 case studies. Overall, the analysis suggests that the combined nudging procedure, as embodied in the DRYRAIN scheme, would lead to measurable improvements in mesoscale model-based QPF over the Korean Peninsula.