Quadrangle statistical downscaling method application to Mascara-Matemore in Algeria

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Abderrahmane Hamimed ◽  
Oumeria Ouafrigh ◽  
Abdelkader Harizia ◽  
Benali Benzater ◽  
Mohamed Habi ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Statistical Downscaling Method ◽  
Downscaling Method

Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

2021 ◽  
Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Performance Criteria ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Support Vector ◽  
Seasonal Temperature ◽  
Statistical Downscaling Method ◽  
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.

scholarly journals Uncovering the shortcomings of a weather typing based statistical downscaling method

2019 ◽  
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.

scholarly journals Evaluation of the empirical–statistical downscaling method EPISODES

2018 ◽  
Vol 52 (1-2) ◽  
pp. 991-1026 ◽  
Author(s):  
Frank Kreienkamp ◽  
Andreas Paxian ◽  
Barbara Früh ◽  
Philip Lorenz ◽  
Christoph Matulla
Keyword(s):  
Statistical Downscaling ◽  
Statistical Downscaling Method ◽  
Downscaling Method

scholarly journals Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation

2017 ◽  
Vol 18 (9) ◽  
pp. 2385-2406 ◽  
Author(s):  
Yu-Kun Hou ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Jie Chen ◽  
Sheng-Lian Guo
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Support Vector ◽  
Statistical Downscaling Method ◽  
Calibration And Validation ◽  
Downscaling Method ◽  
Calibration Methods

Abstract Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

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