Regional Climate–Weather Research and Forecasting Model

Abstract

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model. This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

The cloud–aerosol–radiation (CAR) ensemble modeling system

“…Only three GCMs use a prognostic approach to predict cloud cover, and CWRF is implementing one such scheme following Tiedtke (1993), Tompkins (2002), and Watanabe et al (2009). CAR incorporates both kinds of diagnostic cloud cover schemes and, when coupled with CWRF that includes a dozen of explicit cloud microphysics schemes (Liang et al, 2012) and the future prognostic cloud cover scheme, provides a comprehensive simulator to address the structured uncertainty that may result from cloud-radiation effects and 8342 X.-Z. Liang and F. Zhang: The cloud-aerosol-radiation (CAR) ensemble modeling system their interactions with climate.…”

“…This experiment includes regional climate simulations of the fully coupled CWRF/CAR over the extended US domain at 30 km grid spacing (Liang et al, 2012) as driven by lateral boundary conditions from the ERI data. For each of the seven major radiation packages with their original cloud configurations, CWRF was integrated continuously from 1 December 2003 to 31 December 2004, using the initial month as a model spin-up.…”

“…The differences among these satellite data form the range of observational uncertainties. To depict the climatic effects, surface (2 m) air temperature and precipitation over the contiguous US domain are assessed against the best available observational analysis from dense station measurements (see details in Liang et al, 2012).…”

“…The evaluation is based on several experiments to highlight some key features of CAR. These include specific cases for the conventional radiation code intercomparison, GHG radiative forcing estimates for the recent IPCC future projections, clear and cloud radiative forcing distributions over the globe with and without aerosol effects as driven by the observational reanalysis of the climate in 2004, and climate effects due to cloud-aerosol-radiation interactions using a continuous integration during 2004 of CAR as fully coupled with the regional Climate-Weather Research Forecast model (CWRF) (Liang et al, 2012) over the US. Each experiment compares a large number of alternate schemes, up to 896 combinations.…”

Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin

“…The emerging of Regional Climate Models (RCMs) as a dynamical downscaling technique has been considered to bridge the gap between regional scale estimates and the current limited resolution in GCMs. Compared to GCMs, RCMs have a finer spatial-temporal resolution and can describe the complex underlying surface in detail, subject to the available computer resources [11].…”