An NDVI-Based Statistical ET Downscaling Method

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

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Optimising predictor domains for spatially coherent precipitation downscaling

Hydrology and Earth System Sciences ◽

10.5194/hess-17-4189-2013 ◽

2013 ◽

Vol 17 (10) ◽

pp. 4189-4208 ◽

Cited By ~ 30

Author(s):

S. Radanovics ◽

J.-P. Vidal ◽

E. Sauquet ◽

A. Ben Daoud ◽

G. Bontron

Keyword(s):

Statistical Downscaling ◽

Prediction Models ◽

Target Location ◽

Weather Prediction ◽

Target Zones ◽

Southeastern Part ◽

Global Circulation Models ◽

Downscaling Method ◽

Starting Point ◽

East West

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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A new statistical precipitation downscaling method with Bayesian model averaging: a case study in China

Climate Dynamics ◽

10.1007/s00382-015-2491-7 ◽

2015 ◽

Vol 45 (9-10) ◽

pp. 2541-2555 ◽

Cited By ~ 20

Author(s):

Xianliang Zhang ◽

Xiaodong Yan

Keyword(s):

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Downscaling Method

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Lake stratification and temperature profiles simulated using downscaled GCM output

Water Science & Technology ◽

10.2166/wst.1998.0471 ◽

1998 ◽

Vol 38 (11) ◽

pp. 217-226 ◽

Cited By ~ 11

Author(s):

Hany Hassan ◽

Toshiya Aramaki ◽

Keisuke Hanaki ◽

Tomonori Matsuo ◽

Robert Wilby

Keyword(s):

Water Temperature ◽

Flow Direction ◽

Future Climate Change ◽

Temperature Profiles ◽

Downscaling Method ◽

Surface Climate ◽

Statistical Relationships ◽

June July ◽

The Uk ◽

Lake Stratification

A mathematical in-lake water temperature model (WATEMP-Lake) was developed to investigate future responses of lake stratification and temperature profiles to future climate change due to rising concentrations of atmospheric greenhouse gases (GHGs). The model was used to simulate daily water temperature profiles and stratification characteristics in summer (June, July, and August-JJA) for Suwa Lake in Japan as a case study. For future assessments, the model uses surface climate variables obtained from a downscaling method that was applied to the UK Hadley Centre's coupled ocean/atmosphere model forced by combined CO2 and sulphate aerosol changes (HadCM2SUL). The downscaling method employed mean sea level surface pressure to derive three airflow indices identified as: the total shear vorticity (Z) -a measure of cyclonicity -, the strength of the resultant flow (F), and the overall flow direction (D). Statistical relationships between these indices and seven daily meteorological time series were formulated to represent climate variable series at sites around Suwa Lake. These relationships were used to downscale the observed climatology of 1979-1995 and that of 2080-2099 using HadCM2SUL outputs.

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Uncovering the shortcomings of a weather typing based statistical downscaling method

10.5194/hess-2019-40 ◽

2019 ◽

Cited By ~ 1

Author(s):

Els Van Uytven ◽

Jan De Niel ◽

Patrick Willems

Keyword(s):

Statistical Downscaling ◽

Impact Analysis ◽

Climate Model ◽

Weather Type ◽

User Needs ◽

End User ◽

Statistical Downscaling Method ◽

Downscaling Method ◽

Weather Typing ◽

General Statistical

Abstract. In recent years many methods for statistical downscaling of the climate model outputs have been developed. Each statistical downscaling method (SDM) has strengths and limitations, but those are rarely evaluated. This paper proposes an approach to evaluate the skill of SDMs for the specific purpose of impact analysis in hydrology. The skill is evaluated by the verification of the general statistical downscaling assumptions, and by the perfect predictor experiment that includes hydrological impact analysis. The approach has been tested for an advanced weather typing based SDM and for impact analysis on river peak flows in a Belgian river catchment. Significant shortcomings of the selected SDM were uncovered such as biases in the frequency of weather types and non-stationarities in the extreme precipitation distribution per weather type. Such evaluation of SDMs becomes of use for future tailoring of SDM ensembles to end user needs.

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