May 22 - July 28
Impact of Different Microphysical Schemes and Additional Surface Observations on NEWS-e Forecasts
Francesca Lappin, Dusty Wheatley, and Kent Knopfmeier
What is already known:
- The NOAA Warn-on-Forecast (WoF) Project seeks to enhance warning lead times and reduce false alarm rates for hazardous weather using ensemble storm-scale data assimilation and prediction systems such as the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e).
- The NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) assimilates radar, satellite, and conventional (e.g., surface) data, and the resultant storm-scale analyses are used to generated new 0-3 h probabilistic forecasts two times an hour.
- In 2017, the NEWS-e began assimilating surface observations from ASOS sites (in addition to Oklahoma Mesonet). Also, the microphysical scheme was changed from the Thompson (partial two-moment) to the NSSL two-moment.
What this study adds:
- In this study, when verifying simulated thunderstorm rotation objects, the assimilation of surface observations from ASOS sites produced forecasts with consistently higher probability of detection (POD) scores, by as much as 20% at early forecast times.
- The change from a partial two-moment (Thompson) to a full two-moment microphysical scheme provided similar improvements to the POD scores.
- Using different choices for microphysics show that the Thompson simulations produce too many model grid points with accumulated rainfall in the 0-.5 in range, while the NSSL 2-moment simulations produce too many grid points for rainfall amounts exceeding 0.5 in.
Abstract:
The National Weather Service issues hundreds of hazardous weather advisories, such as tornado warnings each year. However, the current warning paradigm has led to false alarm rates of 70% - 80% (Simmons and Sutter, 2011) and a stagnancy in warning lead times. In an attempt to mitigate these issues, the Warn-on-Forecast (WoF) project is developing a regional, high-resolution, storm-scale prediction system capable of predicting hazardous weather phenomena on the 0-3 h time scale. A prototype system, the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) combines, surface observations, radar data, and satellite retrievals in an ensemble data assimilation framework to create rapidly-updating probabilistic forecasts. Testing of the real-time configuration of the NEWS-e was completed during the 2017 Hazardous Weather Testbed Spring Forecast Experiment (HWT-SFE).
Two separate experiments were performed on three events to evaluate how changes in the configuration affect the performance of the NEWS-e. The first was a data denial experiment where the METAR surface observations were removed from the data assimilation. The second was a switch from the NSSL two-moment to the Thompson microphysics scheme. Quantitative metrics, namely the probability of detection (POD) and false alarm rate (FAR) for rotational objects, were computed to examine the overall performance of each NEWS-e forecast. The real-time run, which includes the assimilation of all surface observations and employs the NSSL two-moment microphysics scheme, produced POD values ~ 20% higher compared to both experimental runs. Qualitative comparisons of reflectivity values and updraft speed between the real-time and Thompson runs are also presented. Lastly, the impacts of removing the METAR observations from the 16 May 2017 run are discussed.