Evaluation of an experimental Warn‐on‐Forecast 3DVAR analysis and forecast system on quasi‐real‐time short‐term forecasts of high‐impact weather events

Abstract

The purpose of this study is to demonstrate the capability of an experimental, weather-adaptive, high-resolution, deterministic Warn-on-Forecast (WoF) analysis and forecast system (WoF3DVAR-AFS) for predicting high-impact severe weather events that occurred during the Hazardous Weather Testbed 2019 Spring Forecast Experiments. WoF3DVAR-AFS uses a three-dimensional variational (3DVAR) method as its core data assimilation system and the Advanced Research Version of the Weather Research and Forecasting (WRF-ARW) model as its forward model. Surface measurements provided in meteorological aviation reports and the Oklahoma Mesonet, Doppler radar data, and spaceborne total lightning observations provided by the Geostationary Lightning Mapper are assimilated at 15-min frequency over a target domain determined by the "Day 1" Convective Outlook product from the Storm Prediction Center. The chief goal of this system is to complement probabilistic forecasts generated by ensemble analysis and forecast systems, such as the experimental Warn-on-Forecast System (WoFS) with a higher-resolution deterministic member to aid forecasters' decision-making. We performed both qualitative and quantitative evaluations on 0-6 hr forecasts launched hourly from 1900 to 0300 UTC the next day for each of the 12 cases. Aggregated subjective forecast evaluation metrics from each individual case, as well as detailed comparison against available verification datasets, suggest that the forecasts are generally skillful in terms of composite reflectivity fields, quantitative precipitation forecasts, and the strength and location of rotation tracks and damaging winds. This study presents initial efforts to assess the performance of WoF3DVAR-AFS and provides possible directions for further improvements, including the development of a weather-adaptive, dual-resolution analysis and forecast system hybrid with an ensemble system, such as the experimental Warn-on-Forecast system.

Impact of Assimilating High‐Resolution Atmospheric Motion Vectors on Convective Scale Short‐Term Forecasts: 3. Experiments With Radar Reflectivity and Radial Velocity

“…This indicates that the model microphysics may have difficulty in properly resolving convective clouds, which is also concluded by Hu et al. (2021). Overall, the result shows that all DA experiments outperform NoDA with larger values of POD, SR, and CSI.…”

“…BIAS is close to unity at 30 dBZ (Figure 5a), while overprediction is seen at higher thresholds from all experiments, for example, 40 and 50 dBZ (Figures 5c and 5e). This indicates that the model microphysics may have difficulty in properly resolving convective clouds, which is also concluded by Hu et al (2021). Overall, the result shows that all DA experiments outperform NoDA with larger values of POD, SR, and CSI.…”

“…However, AMVs have difficulty in discerning an isolated storm or a smaller cluster of cells. Doppler radar data, which samples the three-dimensional storm structures with high frequency and density, including wind and some hydrometeor information, has been playing an important role in convective scale DA and exhibits many encouraging results (Gao & Stensrud, 2012;Gao et al, 2004Gao et al, , 2016Hu et al, 2021;Stensrud & Gao, 2010;Sun, 2005;Wang & Wang, 2017). However, conventional weather radar cannot provide observations outside thunderstorms after formation of precipitation, limiting its utilization after convection initiation or in clear-air regions (Huang et al, 2022;Markowski et al, 2006).…”

Prediction of Meso-γ-Scale Local Heavy Rain by Ground-Based Cloud Radar Assimilation with Water Vapor Nudging

“…This method is widely used for predicting LHR (Kigawa 2014;Kato et al 2017a), and has been researched extensively (Otsuka et al 2016;Iwanami et al 2019). Second, numerical weather prediction (NWP) with storm-scale data assimilation (DA), in which information obtained from the precipitation radar is assimilated (Sun et al 2014;Yussouf et al 2016;Yussouf and Knopfmeier 2019;Hu et al 2021). Recently, it has been shown that assimilating precipitation radar information using NWP (e.g., radial velocity and reflectivity) can predict LHR (Miyoshi et al 2016;Kato et al 2017b;Maejima et al 2017Maejima et al , 2019Shimizu et al 2019).…”

A Method for Assimilating Pseudo Dewpoint Temperature as a Function of GLM Flash Extent Density in GSI‐Based EnKF Data Assimilation System—A Proof of Concept Study

“…According to NOAA National Centers for Environmental Information (NCEI, 2021) statistics on the 2021 U.S. Billion‐Dollar Weather Disasters, severe storms have caused, on average over the past decade, about 84 fatalities and $16.8 billion economic losses each year. Scientists have made significant strides toward improving the accuracy of convective‐scale forecasts (Clark et al., 2021; Hu et al., 2021; Skinner et al., 2018; Stensrud & Gao, 2010; Zhang, Minamide, et al., 2019; Zhang, Stensrud, & Zhang, 2019). However, the accuracy of severe weather forecasts still suffers due to the inaccurate initial conditions for numerical weather prediction (NWP) models and the complex non‐linear interactions between processes of different length scales.…”