Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results

Yongming Wang,Xuguang Wang

doi:10.3390/atmos12101286

Yongming Wang, Xuguang Wang

Open Access

https://doi.org/10.3390/atmos12101286

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Journal: Atmosphere	Publication Date: Oct 2, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: University of Oklahoma

Abstract

This study first describes the extended Grid-Point Statistical Interpolation analysis system (GSI)-based ensemble-variational data assimilation (DA) system within the North American Mesoscale Rapid Refresh (NAMRR) system for the Nonhydrostatic Multiscale Model on the B grid (NMMB). Experiments were conducted to examine three critical aspects of data assimilation configuration in this system. Ten retrospective high-impact convective cases during the warm season of 2015–2016 were adopted for testing. A 10-member, 18 h ensemble forecast was launched for each experiment. Specifically, the experiment using horizontal (vertical) localization radii (Lr) of 300 km (0.55-scaled height measured in the nature log of pressure) overall had more skills than that of 500 km (1.1-scaled height) for conventional in-situ observation assimilation. Diagnostics suggest that the higher forecast skills could be attributed to applying smaller Lr in the boundary with large temperature and moisture gradients. For radar DA, the experiment was more skillful with horizontal (vertical) Lr of 15 km (1.1-scaled height) than that of 12 km (0.55-scaled height). Diagnostics suggest that the improved forecasts were achieved by using wider Lr to spread radar observations into unobserved areas more effectively. Slight forecast skill differences between the relaxation inflation factors of 95% and 65% are presented. The impact of varying inflation magnitudes primarily occurred in the upper-level spread.

Highlights

A hybrid ensemble variational (EnVar) method has increasingly attracted attention with expectations of preserving benefits and eliminating limitations of the traditional variational system (Var, or its specific implementations 3DVar and 4DVar) and ensemble Kalman filter (EnKF) [1,2,3,4,5,6,7,8,9,10,11,12,13,14]
Since the performance of a data assimilation (DA) system highly relies on its DA configuration [40,41], the primary goal of this study was to understand the impact of different DA configurations in terms of both statistical and physical processes
EnKF and EnVar for the dash green box when assimilating radar observations only.interfacing with the Nonhydrostatic Multiscale Model on the B grid (NMMB) model are extended with the capability of direct assimilation of radar reflectivity following [22,50]

Summary

Introduction

A hybrid ensemble variational (EnVar) method has increasingly attracted attention with expectations of preserving benefits and eliminating limitations of the traditional variational system (Var, or its specific implementations 3DVar and 4DVar) and ensemble Kalman filter (EnKF) [1,2,3,4,5,6,7,8,9,10,11,12,13,14]. Various hybrid EnVar methods, have been used for regional numerical weather prediction (NWP) applications [7,8,13,15,16,17,18,19,20,21,22,23] and adopted in several operational regional modeling and prediction systems, e.g., the North. Following [21,22], the flowdependent BECs incorporated in this system are estimated by an ensemble at a convectionallowing grid spacing (i.e., 3 km) in place of the GFS ensemble. Such a high-resolution ensemble is driven by the Nonhydrostatic Multiscale Model on the B grid (NMMB [31,32,33,34])

Objectives

Methods

Results