We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Evaluation of Short-Range Quantitative Precipitation Forecasts from a Time-Lagged Multimodel Ensemble.
- Authors
Yuan, Huiling; Lu, Chungu; McGinley, John A.; Schultz, Paul J.; Jamison, Brian D.; Wharton, Linda; Anderson, Christopher J.
- Abstract
Short-range quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) are investigated for a time-lagged multimodel ensemble forecast system. One of the advantages of such an ensemble forecast system is its low-cost generation of ensemble members. In conjunction with a frequently cycling data assimilation system using a diabatic initialization [such as the Local Analysis and Prediction System (LAPS)], the time-lagged multimodel ensemble system offers a particularly appealing approach for QPF and PQPF applications. Using the NCEP stage IV precipitation analyses for verification, 6-h QPFs and PQPFs from this system are assessed during the period of March–May 2005 over the west-central United States. The ensemble system was initialized by hourly LAPS runs at a horizontal resolution of 12 km using two mesoscale models, including the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecast (WRF) model with the Advanced Research WRF (ARW) dynamic core. The 6-h PQPFs from this system provide better performance than the NCEP operational North American Mesoscale (NAM) deterministic runs at 12-km resolution, even though individual members of the MM5 or WRF models perform comparatively worse than the NAM forecasts at higher thresholds and longer lead times. Recalibration was conducted to reduce the intensity errors in timelagged members. In spite of large biases and spatial displacement errors in the MM5 and WRF forecasts, statistical verification of QPFs and PQPFs shows more skill at longer lead times by adding more members from earlier initialized forecast cycles. Combing the two models only reduced the forecast biases. The results suggest that further studies on time-lagged multimodel ensembles for operational forecasts are needed.
- Subjects
PRECIPITATION forecasting; THERMODYNAMICS; METEOROLOGICAL precipitation; TROPICAL cyclones; SPACIAL distribution
- Publication
Weather & Forecasting, 2009, Vol 24, Issue 1, p18
- ISSN
0882-8156
- Publication type
Article
- DOI
10.1175/2008WAF2007053.1