Short Term Explicit Prediction Program (STEP)

The Short Term Explicit Prediction (STEP) Program is a multi-NCAR laboratory activity to improve the short–term forecasting of high–impact weather such as severe thunderstorms, winter storms, and hurricanes. Improving short–term forecasts of such weather can have significant societal and economic benefits, including

  1. Reduced fatalities and injuries due to weather hazards
  2. Reduced private, public, and industrial property damage
  3. Improved efficiency and savings for industry, transportation, and agriculture.

The STEP Program is also being stimulated by the significant advancement in a number of fields that are required to make progress in this area. These include the ability to observe the four–dimensional structure of the atmosphere, the development of new data assimilation techniques, and the continuing development of numerical modeling systems that can be run at grid resolutions which properly represent the physical processes critical to the production of such hazardous weather.

In 2011, STEP’s RAL efforts were awarded six projects out of the total of 11 funded proposals. These RAL projects were primarily focused on the study of terrain-induced precipitation using Terrain-influenced Monsoon Rainfall Experiment (TiMREX) data over Taiwan and radar data over the Rocky Mountain Front Range. The 2011 STEP efforts also include

  • Front Range polarimetric radar quantitative precipitation estimates (QPE)
  • Land-surface modeling of warm season precipitation
  • Object-based verification – extended to use the time dimension.

The NSF–funded STEP program plays a pivotal role in the research, development, and technology transfer of various externally–funded projects that focus on high–resolution forecasting of high impact weather. >more

(a) Radar radial velocity from 25 NEXRAD radars over a domain that a STEP retrospective study was conducted on. These velocity data are used in a data assimilation experiment whose precipitation skill is shown in panel (b). (b) ETS from three data assimilation and forecast experiments using WRF: initialized by GFS analysis (GFS) , by RTFDDA (no radar), and by a hybrid of 3DVAR and RTFDDA (with radar). (c) Hovemuller diagram of rainfall simulated with Thompson microphysics scheme, showing a good agreement with the result from a detailed scheme in (d)