Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

A Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF

TitleA Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF
Publication TypeJournal Article
Year of Publication2019
JournalWeather and Forecasting
Volume34
Number4
Pages805-831
Abstract / Summary

A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April–September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions’ simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection.

URLhttp://dx.doi.org/10.1175/waf-d-19-0025.1
DOI10.1175/waf-d-19-0025.1
Journal: Weather and Forecasting
Year of Publication: 2019
Volume: 34
Number: 4
Pages: 805-831
Publication Date: 08/2019

A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April–September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions’ simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection.

DOI: 10.1175/waf-d-19-0025.1
Citation:
Wang, J, X Dong, A Kennedy, B Hagenhoff, and B Xi.  2019.  "A Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF."  Weather and Forecasting 34(4): 805-831.  https://doi.org/10.1175/waf-d-19-0025.1.