Short-Range Precipitation Forecasts from Time-Lagged Multimodel Ensembles during the HMT-West-2006 Campaign

Abstract

High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). Shortrange (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of highresolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.

Changes of seasonal storm properties in California and Nevada from an ensemble of climate projections

“…We find that individual RCMs exhibit large range in projecting the percentage changes in storm duration, interstorm period, and average storm intensity especially in the summer period. As summer precipitation is mainly a cumulus convection process while winter precipitation mainly comes from large‐scale low pressure system [ Grubišic et al ., ; Jin et al ., ; Yang et al ., ; Yuan et al ., ], the larger range for both the simulation and projecting percentage changes of storm properties in summer season may be caused by the great differences in cumulus parameterization among the six RCMs and the different responses of these cumulus parameterizations to future climate change.…”

A Twenty-First-Century California Observing Network for Monitoring Extreme Weather Events

“…The physics packages used in the model include the Thompson microphysics scheme (Thompson et al 2004) and the nonlocal mixing Yonsei University (YSU) planetary boundary layer scheme (Noh et al 2003). These schemes were chosen based on 5 years of experience gained in running the WRF model over the western United States for HMT (Jankov et al 2007(Jankov et al , 2009(Jankov et al , 2011Yuan et al 2008Yuan et al , 2009). The analysis production starts 20 min after the hour in order to allow the latest data collected during the previous hour to arrive.…”

A Multiscale Observational Case Study of a Pacific Atmospheric River Exhibiting Tropical–Extratropical Connections and a Mesoscale Frontal Wave

“…This fact, and the related requirements, have led to the establishment of the Hydrometeorology Testbed (HMT), which has been focused on extreme precipitation events along the U.S. west coast (Ralph et al 2005b), and the challenge of making accurate QPFs (Ralph et al 2010). Several HMT-related studies of QPF methods have developed and evaluated advanced approaches for numerical models (Jankov et al 2007;Junker et al 2008;Yuan et al 2008;Ma et al 2011). In addition, detailed analyses of an extreme event in California by Ralph et al (2003) and Neiman et al (2004) have documented the presence of a meso-alpha-scale frontal wave of roughly 500-1000-km horizontal wavelength along the primary cold front.…”