Impact of AERI Temperature and Moisture Retrievals on the Simulation of a Central Plains Severe Convective Weather Event

William E Lewis,Thomas A Jones,Jason A Otkin,Timothy J Wagner

doi:10.3390/atmos11070729

Abstract

In this study, bias-corrected temperature and moisture retrievals from the Atmospheric Emitted Radiance Interferometer (AERI) were assimilated using the Data Assimilation Research Testbed ensemble adjustment Kalman filter to assess their impact on Weather Research and Forecasting model analyses and forecasts of a severe convective weather (SCW) event that occurred on 18–19 May 2017. Relative to a control experiment that assimilated conventional observations only, the AERI assimilation experiment produced analyses that were better fit to surface temperature and moisture observations and which displayed sharper depiction of surface boundaries (cold front, dry line) known to be important in the initiation and development of SCW. Forecasts initiated from the AERI analyses also exhibited improved performance compared to the control forecasts using several metrics, including neighborhood maximum ensemble probabilities (NMEP) and fractions skill scores (FSS) computed using simulated and observed radar reflectivity factor. Though model analyses were impacted in a broader area around the AERI network, forecast improvements were generally confined to the relatively small area of the computational domain located downwind of the small cluster of AERI observing sites. A larger network would increase the spatial coverage of “downwind areas” and provide increased sampling of the lower atmosphere during both active and quiescent periods. This would in turn offer the potential for larger and more consistent improvements in model analyses and, in turn, improved short-range ensemble forecasts. Forecast improvements found during this and other recent studies provide motivation to develop a nationwide network of boundary layer profiling sensors.

Highlights

Severe thunderstorms and tornadoesare among the most spectacular of natural phenomena and have attracted the interest of observers for millennia
Atmospheric Emitted Radiance Interferometer (AERI) in operational numerical weather prediction (NWP), and to build upon the work presented in Coniglio et al [20], Hu et al [22], and Degelia et al [24], we examine the impact of AERI observations on the simulation of an severe convective weather (SCW)
Bias with respect to the meridional wind. These results provide confidence in the efficacy of the AERI bias-correction algorithm, and hereafter AERI refers to the simulation conducted with

Summary

Introduction

Severe thunderstorms and tornadoes (hereafter referred to as severe convective weather, SCW)are among the most spectacular of natural phenomena and have attracted the interest of observers for millennia. While advances have been made in the prediction of SCW over the past decade, due in large part to advances in numerical weather prediction (NWP) models and data assimilation (DA), significant challenges remain. Forecasters increasingly depend on guidance from NWP models, and continued improvements in the sophistication, accuracy and computational efficiency of these models will be necessary if forecasts are to continue to improve. Progress toward these ends can be met in numerous ways, from increases in model resolution and more realistic physics [1], through the development of new DA algorithms [2], and through the inclusion of new observation types in the data stream that drives the DA.

Methods

Results

Conclusion