Statistical downscaling of extreme daily precipitation, evaporation, and temperature and construction of future scenarios

2012 ◽  
Vol 26 (23) ◽  
pp. 3510-3523 ◽  
Author(s):  
Tao Yang ◽  
Huihui Li ◽  
Weiguang Wang ◽  
Chong-Yu Xu ◽  
Zhongbo Yu
Keyword(s):  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Future Scenarios

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

Statistical downscaling of daily precipitation over Greece

2008 ◽  
Vol 28 (5) ◽  
pp. 679-691 ◽  
Author(s):  
Ioannis Kioutsioukis ◽  
Dimitrios Melas ◽  
Prodromos Zanis
Keyword(s):  
Statistical Downscaling ◽  
Daily Precipitation

scholarly journals Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers

2019 ◽  
Vol 23 (3) ◽  
pp. 1741-1749
Author(s):  
Jan Hnilica ◽  
Martin Hanel ◽  
Vladimír Puš
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Dependence Structure ◽  
Climate Projections ◽  
Model Simulations ◽  
Correlation Structures ◽  
Climate Model Simulations ◽  
Dependence Structures

Abstract. Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making a distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by a few outliers even in large datasets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multivariate data and to quantify their impact on correlation structures.

Gridded Statistical Downscaling Based on Interpolation of Parameters and Predictor Locations for Summer Daily Precipitation in North China

2019 ◽  
Vol 58 (10) ◽  
pp. 2295-2311
Author(s):  
Yonghe Liu ◽  
Jinming Feng ◽  
Zongliang Yang ◽  
Yonghong Hu ◽  
Jianlin Li
Keyword(s):  
Distance Function ◽  
Statistical Downscaling ◽  
Spatial Dependence ◽  
Large Scale ◽  
North China ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Summer Precipitation ◽  
Single Station ◽  
Dense Grid

AbstractFew statistical downscaling applications have provided gridded products that can provide downscaled values for a no-gauge area as is done by dynamical downscaling. In this study, a gridded statistical downscaling scheme is presented to downscale summer precipitation to a dense grid that covers North China. The main innovation of this scheme is interpolating the parameters of single-station models to this dense grid and assigning optimal predictor values according to an interpolated predictand–predictor distance function. This method can produce spatial dependence (spatial autocorrelation) and transmit the spatial heterogeneity of predictor values from the large-scale predictors to the downscaled outputs. Such gridded output at no-gauge stations shows performances comparable to that at the gauged stations. The area mean precipitation of the downscaled results is comparable to other products. The main value of the downscaling scheme is that it can obtain reasonable outputs for no-gauge stations.

Downscaling and Projection of Winter Extreme Daily Precipitation over North America

2008 ◽  
Vol 21 (5) ◽  
pp. 923-937 ◽  
Author(s):  
Jiafeng Wang ◽  
Xuebin Zhang
Keyword(s):  
Climate Change ◽  
United States ◽  
North America ◽  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Generalized Extreme Value Distribution ◽  
Specific Humidity ◽  
Climate Modelling ◽  
Central United States ◽  
Distribution Parameters

Abstract Large-scale atmospheric variables have been statistically downscaled to derive winter (December–March) maximum daily precipitation at stations over North America using the generalized extreme value distribution (GEV). Here, the leading principal components of the sea level pressure field and local specific humidity are covariates of the distribution parameters. The GEV parameters are estimated using data from 1949 to 1999 and the r-largest method. This statistical downscaling procedure is found to yield skill over the southern and northern West Coast, central United States, and areas of western and eastern Canada when tested with independent data. The projected changes in covariates or predictors are obtained from transient climate change simulations conducted with the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 (CGCM3.1) forced by the Intergovernmental Panel on Climate Change (IPCC) A2 forcing scenario. They are then used to derive the GEV distribution parameters for the period 2050–99. The projected frequency of the current 20-yr return maximum daily precipitation for that period suggests that extreme precipitation risk will increase heavily over the south and central United States but decrease over the Canadian prairies. The difference between the statistical downscaling results and those estimated using GCM simulation is also discussed.

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