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Information about Steenburgh

Published on May 2, 2008

Author: Sigfrid


Slide1:  Lessons learned during eight years of real-time mesoscale modeling at the University of Utah Jim Steenburgh, Ken Hart, Will Cheng, Daryl Onton, and Andy Siffert NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah Motivating questions:  Motivating questions Does decreasing grid spacing below 12 km produce measurable forecast improvements? How can we produce better point-specific and gridded forecasts over complex terrain? Is WRF any better than MM5? Models employed:  Models employed Olympic MM5 (Jan-Mar 2002) WRF (Jun-Aug 2003) Does decreasing grid spacing help?:  Does decreasing grid spacing help? For temperature: No, except at mountain sites Why not for temperature?:  Why not for temperature? Persistent cold pool example:  Persistent cold pool example Nocturnal cold pool example:  Nocturnal cold pool example Temp BE (°C) Temp MAE (°C) What about wind?:  What about wind? Precipitation gains more substantial:  Precipitation gains more substantial Bias Score Equitable Threat Score False Alarm Rate Probability of Detection 24-h Precipitation Bias Scores – 12 km:  24-h Precipitation Bias Scores – 12 km Underforecasted over Stansbury, Oquirrh and portions of Wasatch mountains Overforecasted in mountain valleys east of Wasatch Poor terrain representation led to strong association between bias score and model elevation bias Overforecast (≥ 160%) Underforecast (≤ 80%) 24-h Precipitation Bias Scores – 4 km:  24-h Precipitation Bias Scores – 4 km Local minima along and immediately to the lee of major mountain crests, but generally more than 12 km Local maxima along eastern (windward) bench Better terrain representation mitigated strong correlation between bias score and model elevation bias Overforecast (≥ 160%) Underforecast (≤ 80%) How can we do better?:  How can we do better? Wasatch Front Mountain Valley Mountain How do statistical approaches (MOS) compare to high res guidance? Major improvements for temperature and wind:  Major improvements for temperature and wind What about WRF?:  What about WRF? Must work on the land surface!:  Must work on the land surface! Lessons:  Lessons Native model sensible weather forecasts are “not good”, even at high resolution Persistent and nocturnal cold pools are a major Achilles heal for MM5 and WRF with “MM5 physics” Eta not great either Improvements possible with better land-surface model/initialization Over fine-scale Intermountain orography, model skill does improve at grid spacing is decreased to 4 km Impact of greatest for precipitation Wind to a lesser degree But, MOS more beneficial than resolution for T, RH, Wind Recommendations:  Recommendations The community must look beyond simply running local mesoscale modeling systems Land surface model/initialization improvements are needed More emphasis should be placed on developing integrated (numerical and statistical) predictive approaches Use our heads rather than computer cycles See next talk A broad, directed effort should be undertaken to improve the simulation of stable boundary layers in WRF Issues: Numerics (e.g., diffusion), initialization, parameterization, land surface, optimal vertical and horizontal resolution Slide18:  Hart et al. 2004: An evaluation of mesoscale model based model output statistics (MOS) during the 2002 Olympic and Paralympic Winter Games. Wea. Forecasting, 19, 200-218. Cheng and Steenburgh 2004: Evaluation of surface sensible weather forecasts by the WRF and Eta models over the western United States. Submitted to Wea. Forecasting. Hart et al. 2004: Model forecast improvements with decreased horizontal grid spacing over fine-scale Intermountain orography during the 2002 Olympic Winter Games. Submitted to Wea. Forecasting. Available at

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