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

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.

Impact of Microphysics Parameterization Schemes on the Assimilation of GOES‐16 ABI All‐Sky Infrared Radiances for a Bow Echo Analysis and Prediction

“…The initial and boundary conditions for 40 ensemble members are derived from the lagged NCEP Global Ensemble Forecast System (GEFS) (Zhou et al., 2017). This follows various studies (e.g., Gasperoni et al., 2020, 2022, 2023, Tong et al., 2020; Y. Wang & Wang, 2021; Johnson et al., 2022; Chandramouli et al., 2022; Yang & Wang, 2023; Park et al., 2023). Conventional data, such as surface and upper‐level observations, are assimilated hourly from 0700 UTC to 1500 UTC during the spin‐up of convective scale features (Figure 3).…”

“…The initial and boundary conditions for 40 ensemble members are derived from the lagged NCEP Global Ensemble Forecast System (GEFS) (Zhou et al, 2017). This follows various studies (e.g., Gasperoni et al, 2020, Tong et al, 2020Y. Wang & Wang, 2021;Johnson et al, 2022;Chandramouli et al, 2022;Yang & Wang, 2023;Park et al, 2023).…”

Impact of Direct Radar Reflectivity Data Assimilation on the Simulation of Mesoscale Descending Inflow and Secondary Eyewall Formation in Hurricane Matthew (2016)

“…Both experiments have hourly DA cycling from 1500 to 1800 UTC and a 12‐hr free forecast initialized at 1800 UTC 6 October. The assimilation of radar reflectivity follows the GSI EnVar direct reflectivity DA approach proposed and developed by Wang and Wang (2017, 2021). For other details and information on the GBR observations please refer to Green et al.…”

Improving CONUS Convective‐Scale Forecasting With Simultaneous Multiscale Data Assimilation

“…Observations between 5 and 10 dBZ are not assimilated because storms at these thresholds are likely unable to be distinguished from nonweather phenomena, for example, ground clutter, birds, and insects (Aksoy et al., 2009). The reflectivity observation error of 5 dBZ is used in this study, as in other studies (e.g., Gasperoni et al., 2023; Johnson et al., 2015; Yussouf et al., 2013; Wang & Wang, 2017, 2020, 2021a, 2021b, 2023a, 2023b).…”

“…To address this challenge, a commonly used approach is to assimilate observations by sampling larger scales first. The resultant analysis is used as the first guess to assimilate convective scale radar observations (e.g., Degelia et al, 2018;Dowell et al, 2022;Gasperoni et al, 2022Gasperoni et al, , 2023Johnson et al, 2015;Sun et al, 2022;Wang & Wang, 2021b;Zhang et al, 2009). In each step a different influence radius or localization radius is implemented, reflecting the underlying updated scales.…”