Satellite‐enhanced dynamical downscaling for the analysis of extreme events

Abstract

The use of regional models in the downscaling of general circulation models provides a strategy to generate more detailed climate information. In that case, boundary‐forcing techniques can be useful to maintain the large‐scale features from the coarse‐resolution global models in agreement with the inner modes of the higher‐resolution regional models. Although those procedures might improve dynamics, downscaling via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional downscaling of global reanalysis, the Regional Spectral Model—originally developed at the National Centers for Environmental Prediction—employs a newly reformulated scale‐selective bias correction, together with the 3‐hourly assimilation of the satellite‐based precipitation estimates constructed from the Climate Prediction Center morphing technique. The two‐scheme technique for the dynamical downscaling of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satellite‐enhanced dynamical downscaling added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas.

Extreme Drought Events over the Amazon Basin: The Perspective from the Reconstruction of South American Hydroclimate

“…The results show that the product can identify similar and divergent spatiotemporal patterns of the two different extremes from 2005 and 2010 [8]. The SRDAS product makes use of the data assimilation improvements brought from the coarse-resolution R2, through the use of a new scale-selective bias correction, mainly applied to the rotational component of the horizontal wind [25]. Thus, the SRDAS concept of combining regional models and the assimilation of remote sensing products shows to be useful to the analyses of extreme climate events, such as drought is Amazon region.…”

“…Most of the features of larger synoptic to planetary waves from NCEP-DOE R2 are maintained in SRDAS through NSSBC, which is mainly applied to the rotational part of the wind field. The continuous assimilation of the three-hourly satellite-based precipitation estimates, obtained from the Climate Prediction Center Morphing technique (CMORPH) [31], is also included in RSM [25] to primary correct the representation of the deep convection in SRDAS, however atmosphere-land surface interactions are also expected to be enhanced.…”

“…In such context, the Satellite-Enhanced Regional Downscaling for Applied Studies (henceforth SRDAS) product has been used in the analysis of South American hydroclimate, with hourly to monthly outputs from January 1998 to the near present, with continuous assimilation of satellite-based precipitation estimates [25]. In the current study, SRDAS is used in the analyses of the drought episodes of 2005 and 2010 in the Amazon basin and corresponds to a 16-year numerical integration of a regional modeling system driven by the six hourly outputs a global reanalysis.…”

<strong>Extreme drought events over Amazon basin: the perspective from regional reconstruction of South American hydroclimate</strong>

“…It relies on a Regional Spectral Model integration that uses newly updated version of scale-selective bias correction and precipitation assimilation [7]. For this study, monthly means of precipitation, 2-m above the ground air temperature and relative humidity, and four-level integrated soil moisture from SRDAS are used from January 1998 to December 2012 in the region of 80°W -51°W and 6ºN -21°S.…”

“…Part of this breakthrough comes from increased spatial resolution and part comes from the inclusion of climate controls from new system components and their interaction. The use of regional downscaling of general circulation models provides a technique to generate more refined climatic information on extreme events through better representation of physical processes [7].…”