Global climate models (GCMs) are important tools for improving our understanding and prediction of climate on time scales ranging from months to centuries. Outputs from GCMs provide essential information for the assessments of the impacts of large-scale climate variation and change on natural resources and environments at regional and global scales. However, for impact assessments at local and finer scales, climate downscaling is needed. 

Climate downscaling is a practice whereby coarse-resolution climate data – either global reanalysis or GCM outputs – are fed to physically based models (dynamical downscaling) or empirically based formulas (statistical downscaling) to simulate regional- and local-scale climate at much finer spatial and temporal resolutions. The need to climate downscaling has grown tremendously in the past decades, as the scientific community endeavors to address an increasing number of weather and climate impacts issues related to agriculture, water resources, human health, agriculture, transportation, and urban planning.

RAL scientists and engineers have developed a multiple tools for climate modeling and climate downscaling including the WRF-RTFDDA (Weather Research and Forecasting – Real Time Four Dimensional Data Assimilation) system, the CFDDA (Climate Four Dimensional Data Assimilation) system, the WRF-Hydro (WRF-Hydrology) system, and the WRF-Chem-RTFDDA (WRF-Chemistry-RTFDDA) system. These tools have been applied over the U.S., the Middle East, Asia and Africa for the examination of the impacts of climate and climate change on water resources, dust, air quality and ecosystems.