Short-Term Explicit Prediction (STEP)

Short-term forecasting of High-impact weather

While our capability in prediction large-scale flow structure for the medium range has experienced marked advances in the last few decades, the short-term forecast (less than one day) of high impact weather at the county/city scale lags seriously behind. The Short Term Explicit Prediction (STEP) program was established in 2005 to tackle the challenging problem of accurate (location and timing specific) prediction of hazardous weather via an collaborative effort across several NCAR labs. In the recent few years, the main focus of STEP is to improve heavy precipitation and flash flood prediction by developing an integrated hydro-meteorological system that is able to produce quantitative streamflow forecast with improved rainfall estimate and nowcast/forecasting. That objective is being supported by four research topics: mesoscale processes and predictability, multi-scale data assimilation, convection-permitting NWP modeling, and QPE/QPF and their its impact on hydromet prediction, as well as a cross-topic theme that features real-time demonstration and evaluation of STEP modeling systems. STEP currently funds seven projects in support of these research areas with participants from three research Labs: RAL, MMM, and EOL.

Conceptual diagram of STEP program’s research topics and cross-topic theme
Conceptual diagram of STEP program’s research topics and cross-topic theme

High-resolution, short-term forecasts of high-impact weather provide critical information for a wide range of users, including the aviation community, ground transportation, urban emergency and water resources management groups, recreation facilities, construction industries, and the military, that assists them to safely and efficiently deploy resources.  However, achieving reliable and accurate convective weather forecasts remains a scientific challenge due to uncertainty in grasping initial conditions, shortcomings in model physics and computational capabilities, and limitations of our understanding of how nature works.  The forecast skill of observation-driven expert systems decreases rapidly with increasing lead-time, while numerical weather prediction models exhibit a limited forecast ability within the first few hours after initialization primarily due to spin-up problems. For explicit flash flood forecast, the coupled hydro-meteorological model requires accurate precipitation forecast that is beyond the capability of the current operational models can provide. STEP’s mission is to address the challenge of short-term high impact weather prediction through an broad end-to-end approach and a broad across-NCAR collaborative effort.

Seven projects, listed below with lead lab and PI, are being conducted to tackle various research problems in the four STEP research areas led by PIs from three different NCAR labs:

  • Understanding the Evolution and Predictability (QPN) of Heavy Rainfall using the STEP Hydromet and PECAN datasets (RAL, Rita Roberts)
  • Advancing Real-time Hydrologic Predictions as Part of the Short Term Explicit Prediction Experiment (RAL, Dave Gochis)
  • Improving WRF physics schemes to improve short-term forecasts of convective storm structure, evolution, and QPF (RAL, Sarah Tessendorf)
  • Toward an ensemble high-resolution data assimilation system for improved short-term high-impact weather prediction (MMM, Jenny Sun)
  • Convection Resolving Ensemble Forecasting with the Weather Research and Forecasting (WRF) Model (MMM, Morris Weisman)
  • Examination of the Mesoscale Forcing of Nocturnal Convection in the WRF Ensemble Modeling System (MMM, Stan Trier)
  • Toward Improved Understanding of Convective Precipitation Through Basic Research and Enhanced Observational Products (EOL, Tammy Weckwerth)
  • Convective Initiation with PECAN Data: Analyzed elevated convective initiation events with PECAN data
  • Improving WRF Physics: Improved WRF Physics by studying short-term forecasts of convective initiation, evolution, and quantitative precipitation forecasts (QPF) using a squall line case study
  • Quantitative Precipitation Nowcasting (QPN): Used Quantitative Precipitation Nowcasting (QPN) to document the percentage of storms that form and dissipate over the Rockies.
  • STEP Hydromet Experiment: Advanced STEP's goals of improving short-term prediction of high-impact weather, particularly heavy rainfall and flash flooding.
  • Streamflow Prediction with WRF-Hydro: Real-time, high-resolution hydrologic streamflow predictions were produced using the community WRF-Hydro® modeling system.


Please direct questions/comments about this page to:

Jenny Sun

Senior Scientist