Design and Implementation of a GSI-Based Convection-Allowing Ensemble Data Assimilation and Forecast System for the PECAN Field Experiment. Part I: Optimal Configurations for Nocturnal Convection Prediction Using Retrospective Cases

Abstract

real-time GSI-based and ensemble-based data assimilation (DA) and forecast system was implemented at the University of Oklahoma during the 2015 Plains Elevated Convection at Night (PECAN) experiment. Extensive experiments on the configuration of the cycled DA and on both the DA and forecast physics ensembles were conducted using retrospective cases to optimize the system design for nocturnal convection. The impacts of radar DA between 1200 and 1300 UTC, as well as the frequency and number of DA cycles and the DA physics configuration, extend through the following night. Ten-minute cycling of radar DA leads to more skillful forecasts than both more and less frequent cycling. The Thompson microphysics scheme for DA better analyzes the effects of morning convection on environmental moisture than WSM6, which improves the convection forecast the following night. A multi-PBL configuration during DA leads to less skillful short-term forecasts than even a relatively poorly performing single-PBL scheme. Deterministic and ensemble forecast physics configurations are also evaluated. Thompson microphysics and the Mellor?Yamada?Nakanishi?Niino (MYNN) PBL provide the most skillful nocturnal precipitation forecasts. A well thought out multiphysics configuration is shown to provide advantages over evenly distributing three of the best-performing microphysics and PBL schemes or a fixed MYNN/Thompson ensemble. This is shown using objective and subjective verification of precipitation and nonprecipitation variables, including convective initiation. Predictions of the low-level jet are sensitive to the PBL scheme, with the best scheme being variable and time dependent. These results guided the implementation and verification of a real-time ensemble DA and forecast system for PECAN.