Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method

Baghanam, Aida Hosseini; Norouzi, E.; Nourani, Vahid

doi:10.2166/nh.2022.094

Cited by 9 publications

(3 citation statements)

References 71 publications

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“…There are different SD approaches, for example, regression, weather classifications, and weather generators. Regression approaches are very popular, such as multi linear regression (MLR) 20 , generalized linear model (GLM) 21 , and machine learning (ML) method including support vector machine (SVM) 22 , random forests (RF) 23 , and artificial neural networks (ANN) 24 , 25 . Many studies have compared the performance among different regression approaches 26 – 29 .…”

Section: Background and Summarymentioning

confidence: 99%

Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia

Lin,

Tang,

Wang

et al. 2023

Sci Data

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U-Net, a deep-learning convolutional neural network, is used to downscale coarse meteorological data. Based on 19 models from the Coupled Model Intercomparison Project Phase 6 and the Multi-Source Weather (MSWX) dataset, bias correction and UNet downscaling approaches are used to develop high resolution dataset over the East Asian region, referred to as Climate Change for East Asia with Bias corrected UNet Dataset (CLIMEA-BCUD). CLIMEA-BCUD provides nine meteorological variables including 2-m air temperature, 2-m daily maximum air temperature, 2-m daily minimum air temperature, precipitation, 10-m wind speed, 2-m relative humidity, 2-m specific humidity, downward shortwave radiation and downward longwave radiation with 0.1° horizontal resolution at daily intervals over the historical period of 1950–2014 and three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of 2015–2100. Validation against MSWX indicates that CLIMEA-BCUD shows reasonable performance in terms of climatology, and it is capable of simulating seasonal cycles and future changes well. It is suggested that CLIMEA-BCUD can promote the application of deep learning in climate research in the areas of climate change, hydrology, etc.

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Section: Background and Summarymentioning

confidence: 99%

Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia

Lin,

Tang,

Wang

et al. 2023

Sci Data

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“…In various studies, wavelet analysis has been successfully applied to investigate nonstationary signals in hydrological processes [28]. Continuous wavelet transform (CWT) was used by Rana and Moradkhani to analyze downscaled precipitation and temperature data [29].…”

Section: Introductionmentioning

confidence: 99%

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

Hosseini Baghanam,

Nourani,

Norouzi

et al. 2023

Sustainability

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Climate model (CM) statistical downscaling requires quality and quantity modifications of the CM’s outputs to increase further modeling accuracy. In this respect, multi-resolution wavelet transform (WT) was employed to determine the hidden resolutions of climate signals and eliminate bias in a CM. The results revealed that the newly developed discrete wavelet transform (DWT)-based bias correction method can outperform the quantile mapping (QM) method. In this study, wavelet coherence analysis was utilized to assess the high common powers and the multi-scale correlation between the predictors and predictand as a function of time and frequency. Thereafter, to rate the most contributing predictors based on potential periodicity, the average variance was calculated, which is named the Scaled Average (SA) measure. Consequently, WT along with Artificial Neural Network (ANN) were applied for bias correction and identifying the dominant predictors for statistical downscaling. The CAN-ESM5 data of Canadian climate models and INM-CM5 data of Russian climate models over two climatic areas of Iran with semi-arid (Tabriz) and humid (Rasht) weather were applied. The projection of future precipitation revealed that Tabriz will experience a 3.4–6.1% decrease in precipitation, while Rasht’s precipitation will decrease by 1.5–2.5%. These findings underscore the importance of refining CM data and employing advanced techniques to assess the potential impacts of climate change on regional precipitation patterns.

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“…On the other hand, there are several types of statistical downscaling techniques that are commonly used in climate science. Some of the most well-known techniques include [1] (i) regression-based methods (bias-correction being one of them) [16][17][18][19][20][21][22][23]; (ii) weather typing approaches [24][25][26][27]; (iii) empirical statistical downscaling [28][29][30][31]; (iv) weather generator techniques [24,27,32,33]; and (v) neural network methods [33][34][35][36][37][38]. Note that there are different variations and combinations of these techniques, and as research in statistical downscaling progresses, new approaches may arise.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Precipitation in the South and Southeast of Mexico

Andrade-Velázquez,

Montero-Martínez

2023

Climate

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The advancements in global climate modeling achieved within the CMIP6 framework have led to notable enhancements in model performance, particularly with regard to spatial resolution. However, the persistent requirement for refined techniques, such as dynamically or statistically downscaled methods, remains evident, particularly in the context of precipitation variability. This study centered on the systematic application of a bias-correction technique (quantile mapping) to four designated CMIP6 models: CNRM-ESM2-6A, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0. The selection of these models was informed by a methodical approach grounded in previous research conducted within the southern–southeastern region of Mexico. Diverse performance evaluation metrics were employed, including root-mean-square difference (rmsd), normalized standard deviation (NSD), bias, and Pearson’s correlation (illustrated by Taylor diagrams). The study area was divided into two distinct domains: southern Mexico and the southeast region covering Tabasco and Chiapas, and the Yucatan Peninsula. The findings underscored the substantial improvement in model performance achieved through bias correction across the entire study area. The outcomes of rmsd and NSD not only exhibited variations among different climate models but also manifested sensitivity to the specific geographical region under examination. In the southern region, CNRM-ESM2-1 emerged as the most adept model following bias correction. In the southeastern domain, including only Tabasco and Chiapas, the optimal model was again CNRM-ESM2-1 after bias-correction. However, for the Yucatan Peninsula, the IPSL-CM6A-LR model yielded the most favorable results. This study emphasizes the significance of tailored bias-correction techniques in refining the performance of climate models and highlights the spatially nuanced responses of different models within the study area’s distinct geographical regions.

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Wavelet-based predictor screening for statistical downscaling of precipitation and temperature using the artificial neural network method

Cited by 9 publications

References 71 publications

Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia

Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

Statistical Downscaling of Precipitation in the South and Southeast of Mexico

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