GFDL researchers have developed a regional
dynamical downscaling model for Atlantic hurricanes and tested it by comparing with observed hurricane activity since 1980.
Not exact matches
Rockel, B., C.L. Castro, R.A. Pielke Sr., H. von Storch, and G. Leoncini, 2008:
Dynamical downscaling: Assessment of
model system dependent retained and added variability for two different regional climate
models.
In sensitivity experiments the influence of removed orography of Greenland on the Arctic flow patterns and cyclone tracks during winter have been determined using a global coupled
model and a
dynamical downscaling with the regional atmospheric
model HIRHAM.
Yang, and R.A. Pielke Sr., 2008: Assessment of three
dynamical climate
downscaling methods using the Weather Research and Forecasting (WRF)
Model.
Dynamical downscaling uses high - resolution climate
models to represent global or regional sub-domains, and uses either observed or lower - resolution AOGCM data as their boundary conditions.
Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier, 2004: Hydrologic implications of
dynamical and statistical approaches to
downscaling climate
model outputs (link is external).
Wood, A.W., L.R Leung, V. Sridhar, and D.P. Lettenmaier, 2004: Hydrologic implications of
dynamical and statistical approaches to
downscaling climate
model outputs.
These results are obtained from 16 global general circulation
models downscaled with different combinations of
dynamical methods... http://dx.doi.org/10.1175/JCLI-D-12-00766.1
Dynamical downscaling, or the use of Regional Climate
Models (RCMs), does not share the limitations of statistical
downscaling.
Knutson, T. R., J. J. Sirutis, G. A. Vecchi, S. Garner, M. Zhao, H. - S. Kim, M. Bender, R. E. Tuleya, I. M. Held, and G. Villarini, 2013:
Dynamical downscaling projections of twenty - first - century Atlantic hurricane activity: CMIP3 and CMIP5
model - based scenarios.
Dynamical downscaling projections of twenty - first - century Atlantic hurricane activity: CMIP3 and CMIP5
model - based scenarios
This study evaluates the hydrologic prediction skill of a
dynamical climate
model - driven hydrologic prediction system (CM - HPS), based on an ensemble of statistically -
downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS).
In the last 10 years,
downscaling techniques, both
dynamical (i.e. Regional Climate
Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios.
In a series of Atlantic basin - specific
dynamical downscaling studies (Bender et al. 2010; Knutson et al. 2013), we attempted to address both of these limitations by letting the Atlantic basin regional
model of Knutson et al. (2008) provide the overall storm frequency information, and then
downscaling each individual storm from the regional
model study into the GFDL hurricane prediction system.