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Downscaling Techniques (downscaling + techniques)
Selected AbstractsStatistical downscaling of daily precipitation from observed and modelled atmospheric fieldsHYDROLOGICAL PROCESSES, Issue 8 2004Stephen P. Charles Abstract Statistical downscaling techniques have been developed to address the spatial scale disparity between the horizontal computational grids of general circulation models (GCMs), typically 300,500 km, and point-scale meteorological observations. This has been driven, predominantly, by the need to determine how enhanced greenhouse projections of future climate may impact at regional and local scales. As point-scale precipitation is a common input to hydrological models, there is a need for techniques that reproduce the characteristics of multi-site, daily gauge precipitation. This paper investigates the ability of the extended nonhomogeneous hidden Markov model (extended-NHMM) to reproduce observed interannual and interdecadal precipitation variability when driven by observed and modelled atmospheric fields. Previous studies have shown that the extended-NHMM can successfully reproduce the at-site and intersite statistics of daily gauge precipitation, such as the frequency characteristics of wet days, dry- and wet-spell length distributions, amount distributions, and intersite correlations in occurrence and amounts. Here, the extended-NHMM, as fitted to 1978,92 observed ,winter' (May,October) daily precipitation and atmospheric data for 30 rain gauge sites in southwest Western Australia, is driven by atmospheric predictor sets extracted from National Centers for Environmental Prediction,National Center for Atmospheric Research reanalysis data for 1958,98 and an atmospheric GCM hindcast run forced by observed 1955,91 sea-surface temperatures (SSTs). Downscaling from the reanalysis-derived predictors reproduces the 1958,98 interannual and interdecadal variability of winter precipitation. Downscaling from the SST-forced GCM hindcast only reproduces the precipitation probabilities of the recent 1978,91 period, with poor performance for earlier periods attributed to inadequacies in the forcing SST data. Copyright © 2004 John Wiley & Sons, Ltd. [source] Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspectiveINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 11 2009Willem A. Landman Abstract The ability of advanced state-of-the-art methods of downscaling large-scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December-January-February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea-surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large-scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead-time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10-year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post-processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied. Copyright © 2008 Royal Meteorological Society [source] Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modellingINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2007H. J. Fowler Abstract There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts. Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the ,downscaling for hydrological impacts' community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision-making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system. Copyright © 2007 Royal Meteorological Society [source] Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South AfricaINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 10 2006B. C. Hewitson Abstract This paper discusses issues that surround the development of empirical downscaling techniques as context for presenting a new approach based on self-organizing maps (SOMs). The technique is applied to the downscaling of daily precipitation over South Africa. SOMs are used to characterize the state of the atmosphere on a localized domain surrounding each target location on the basis of NCEP 6-hourly reanalysis data from 1979 to 2002, and using surface and 700-hPa u and v wind vectors, specific and relative humidities, and surface temperature. Each unique atmospheric state is associated with an observed precipitation probability density function (PDF). Future climate states are derived from three global climate models (GCMs): HadAM3, ECHAM4.5, CSIRO Mk2. In each case, the GCM data are mapped to the NCEP SOMs for each target location and a precipitation value is drawn at random from the associated precipitation PDF. The downscaling approach combines the advantages of a direct transfer function and a stochastic weather generator, and provides an indication of the strength of the regional versus stochastic forcing, as well as a measure of stationarity in the atmosphere,precipitation relationship. The methodology is applied to South Africa. The downscaling reveals a similarity in the projected climate change between the models. Each GCM projects similar changes in atmospheric state and they converge on a downscaled solution that points to increased summer rainfall in the interior and the eastern part of the country, and a decrease in winter rainfall in the Western Cape. The actual GCM precipitation projections from the three models show large areas of intermodel disagreement, suggesting that the model differences may be due to their precipitation parameterization schemes, rather than to basic disagreements in their projections of the changing atmospheric state over South Africa. Copyright © 2006 Royal Meteorological Society. [source] Global Daily Reference Evapotranspiration Modeling and Evaluation,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2008G.B. Senay Abstract:, Accurate and reliable evapotranspiration (ET) datasets are crucial in regional water and energy balance studies. Due to the complex instrumentation requirements, actual ET values are generally estimated from reference ET values by adjustment factors using coefficients for water stress and vegetation conditions, commonly referred to as crop coefficients. Until recently, the modeling of reference ET has been solely based on important weather variables collected from weather stations that are generally located in selected agro-climatic locations. Since 2001, the National Oceanic and Atmospheric Administration's Global Data Assimilation System (GDAS) has been producing six-hourly climate parameter datasets that are used to calculate daily reference ET for the whole globe at 1-degree spatial resolution. The U.S. Geological Survey Center for Earth Resources Observation and Science has been producing daily reference ET (ETo) since 2001, and it has been used on a variety of operational hydrological models for drought and streamflow monitoring all over the world. With the increasing availability of local station-based reference ET estimates, we evaluated the GDAS-based reference ET estimates using data from the California Irrigation Management Information System (CIMIS). Daily CIMIS reference ET estimates from 85 stations were compared with GDAS-based reference ET at different spatial and temporal scales using five-year daily data from 2002 through 2006. Despite the large difference in spatial scale (point vs. ,100 km grid cell) between the two datasets, the correlations between station-based ET and GDAS-ET were very high, exceeding 0.97 on a daily basis to more than 0.99 on time scales of more than 10 days. Both the temporal and spatial correspondences in trend/pattern and magnitudes between the two datasets were satisfactory, suggesting the reliability of using GDAS parameter-based reference ET for regional water and energy balance studies in many parts of the world. While the study revealed the potential of GDAS ETo for large-scale hydrological applications, site-specific use of GDAS ETo in complex hydro-climatic regions such as coastal areas and rugged terrain may require the application of bias correction and/or disaggregation of the GDAS ETo using downscaling techniques. [source] | ||||||||||