Joint variable spatial downscaling

Feng, Zhang; Georgakakos, Aris P.

doi:10.1007/s10584-011-0167-9

Cited by 26 publications

(16 citation statements)

References 31 publications

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“…Analogue methods have been applied in different regions of the world with very diverse climates, e.g. Switzerland , Australia (Timbal and McAvaney, 2001), central Sweden (Wetterhall et al, 2005), Punjab (India) (Raje and Mujumdar, 2011), southeast USA (Zhang and Georgakakos, 2012), the Alpine region (Themeßl et al, 2011), and northeast Spain (Ibarra-Berastegi et al, 2011).…”

Section: Statistical Downscaling Methodsmentioning

confidence: 99%

Optimising predictor domains for spatially coherent precipitation downscaling

Radanovics¹,

Vidal²,

Sauquet³

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east-west band around 47 • N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east-west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.

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Section: Statistical Downscaling Methodsmentioning

confidence: 99%

Optimising predictor domains for spatially coherent precipitation downscaling

Radanovics¹,

Vidal²,

Sauquet³

et al. 2013

Hydrol. Earth Syst. Sci.

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show abstract

“…First, it smears finescale spatial features and increases the spatial coherence of the final downscaled field. This affects flooding, which is influenced by the spatial coherence of the precipitation field (the same problem has been found in BCSD; Zhang and Georgakakos 2012;Hwang and Graham 2014). Second, averaging tends to reduce the temporal variance of the final result (e.g., von Storch 1999).…”

Section: Introductionmentioning

confidence: 92%

“…Typically, ;30% of the points are edge cells. Figure c. Multivariate downscaling Abatzoglou and Brown (2012) and Zhang and Georgakakos (2012) have noted the importance of downscaling some quantities using information from multiple variables simultaneously. This is easily accomplished in LOCA.…”

Section: Fieldmentioning

confidence: 98%

Statistical Downscaling Using Localized Constructed Analogs (LOCA)*

Pierce

Cayan

Thrasher³

2014

Journal of Hydrometeorology

364

349

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A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.

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“…For example, where it is desired to maintain a joint distribution of multiple variables through bias correction, as opposed to individual variable downscaling as used here, joint downscaling methods have been developed (Abatzoglou and Brown, 2012; Mehrotra and Sharma, 2015;Zhang and Georgakakos, 2012). The probability transformations in quantile mapping are incapable of correcting for GCM biases in low-frequency variability, and autoregressive and spectral transformations have been developed to accommodate these biases where important (Mehrotra and Sharma, 2012;Pierce et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

Maurer

Ficklin

Wang

2016

Hydrol. Earth Syst. Sci.

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Abstract. Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantilemapping bias correction is performed from 2 • (∼ 200 km) to 1/8 • (∼ 12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5 • (∼ 50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis.

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Joint variable spatial downscaling

Cited by 26 publications

References 31 publications

Optimising predictor domains for spatially coherent precipitation downscaling

Optimising predictor domains for spatially coherent precipitation downscaling

Statistical Downscaling Using Localized Constructed Analogs (LOCA)*

Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

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