Improving short-term forecast of severe and high-impact weather events using a weather-dependent hybrid ensemble-variational data assimilation system with radar and satellite derived observations

Pan, Sijie

Improving short-term forecast of severe and high-impact weather events using a weather-dependent hybrid ensemble-variational data assimilation system with radar and satellite derived observations

dc.contributor.advisor	Wang, Xuguang
dc.contributor.advisor	Gao, Jidong
dc.contributor.author	Pan, Sijie
dc.contributor.committeeMember	Goodman, Nathan
dc.contributor.committeeMember	Homeyer, Cameron
dc.contributor.committeeMember	Jones, Thomas
dc.contributor.committeeMember	Mcfarquhar, Greg
dc.date.accessioned	2023-05-16T20:02:49Z
dc.date.available	2023-05-16T20:02:49Z
dc.date.issued	2023-05-12
dc.date.manuscript	2023-05-03
dc.description.abstract	A hybrid ensemble-variational (EnVAR) data assimilation and forecast system, namely WoF-Hybrid, was initially developed at the National Severe Storms Laboratory (NSSL) supported by the NOAA Warn-on-Forecast project (WoF). It is a real-time, deterministic, weather adaptive, dual-resolution analysis and forecasting system in parallel with the WoF ensemble data assimilation and forecast system (WoFS), which provides an on-demand, short-term, rapidly updating, convection-allowing ensemble forecasts to the end users. Some new components for the WoF-Hybrid system are developed and tested in this dissertation to address a few scientific and technical challenges in assimilating the conventional, radar and satellite derived observations to improve the short-term numerical weather prediction (NWP) of convective-scale severe and high-impact weather events. First of all, a novel component is introduced into the WoF-Hybrid system, which extends the observation forward operators to include cloud water path (CWP) and layered precipitable water (LPW). The impact of assimilating observations from several platforms on the estimation of initial states and the short-term forecasts using the WoF-Hybrid system is firstly investigated. The findings of this research reveal that (1) assimilating CWP improves the analysis of cloud properties, downward shortwave radiation over cloudy areas, surface temperature, and atmospheric instability, (2) assimilating the LPW product intensifies the moisture gradient at the surface and saturates the moisture fields surrounding ongoing convection, thereby enhancing the convection initiation (CI), and (3) low-level vertical wind shear is also improved through flow-dependent error covariances derived from the WoFS ensemble. Furthermore, a diagnostic analysis is conducted to explain why additional satellite-derived observations can improve the positioning and intensity forecast of storm cells, illustrated with three different hazardous severe weather events occurring in 2017. In the next part, the dissertation proceeds to further develop and evaluate an updated version of the variational component of the WoF-Hybrid system (named as WoF-Var hereafter) by incorporating weather-dependent static background error covariances derived from the WoFS ensemble forecast products. The impact of weather-dependent background error structure on WoF-Var analyses is evaluated using a single observation experiment, which reveals the heterogeneous nature of the analysis increment for the meridional wind field under different weather situations. Two severe weather events that occurred on 5 May 2020 and 10 Dec 2021 are used to evaluate the impact of the weather-dependent error covariances on the WoF-var analysis and forecast system in detail. Diagnostic analysis of both events indicates that the utilization of weather-dependent errors in the WoF-Var leads to improved analyses of in-storm structure, particularly for the three-dimensional wind, temperature, and humidity fields, resulting in significantly better subsequent 0- to 6-hour forecasts. The last part of the dissertation investigates the impact of weather-dependent error covariances on convective-scale NWP in the framework of WoF-Hybrid. In this, the flow-dependent ensemble error covariances are incorporated by using WoFS ensemble forecasts. Quantitative evaluations, aggregated over five selected severe and high-impact weather events, demonstrate that the experiments using weather-dependent covariances, instead of homogeneous covariances, in the hybrid system performs better in 0- to 6-hour reflectivity and rotational strength forecasts, particularly for strong convection with either higher reflectivity or higher updraft helicity. A more in-depth analysis of each event indicates that the adoption of weather-dependent covariances typically results in more skillful severe weather predictions regarding the areal coverage of reflectivity, the initiation and movement of storms, as well as the position and intensity of rotation tracks, even for the event that performed the worst out of the five selected events. The findings suggest that the weather-dependent error covariances within a hybrid system have added values to short-term convective-scale NWP.	en_US
dc.identifier.uri	https://shareok.org/handle/11244/337701
dc.language	en_US	en_US
dc.rights	Attribution-NonCommercial 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/	*
dc.subject	Data Assimilation	en_US
dc.subject	Convective Scale	en_US
dc.subject	Severe Weather Forecast	en_US
dc.thesis.degree	Ph.D.	en_US
dc.title	Improving short-term forecast of severe and high-impact weather events using a weather-dependent hybrid ensemble-variational data assimilation system with radar and satellite derived observations	en_US
ou.group	College of Atmospheric and Geographic Sciences::School of Meteorology	en_US
shareok.orcid	0000-0002-3608-8310	en_US