Evaluation of WRF and artificial intelligence models in short-term rainfall, temperature and flood forecast (case study)

Shirali, Emadeddin; Shahbazi, Afsaneh; Fathian, Hossein; Zohrabi, Narges; Hassan, Elham Mobarak

doi:10.1007/s12040-020-01450-9

Cited by 13 publications

(3 citation statements)

References 30 publications

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“…WRF is a mesoscale weather forecast model developed by the National Center for Atmospheric Research of the United States. This model has been successfully applied to data assimilation research [29,30], air quality modeling [31,32], and regional climate simulations, such as short−term rainfall forecasts [33] and typhoon simulation [34]. The WRF was dynamically scaled down using region nesting to improve the simulation resolution.…”

Section: Wrf−solar Modelmentioning

confidence: 99%

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

Fang

Zhou

et al. 2023

Remote Sensing

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Aerosols impact vegetation productivity by increasing diffuse radiation and changing temperature and humidity conditions. In this study, climate simulations of the Jinghe River Basin in 2020 based on aerosol and aerosol−free scenarios were carried out using the control variable method and the aerosol optical depth parameter as the external input data of Weather Report Forecast (WRF)−solar. These two output results were used as input data for the Carnegie Ames Stanford Approach (CASA) model to calculate the impact of aerosols on vegetation productivity. The results showed that WRF−solar accurately simulated changes in meteorological factors such as temperature, rainfall, solar radiation, and relative humidity in the Jinghe River Basin, with a correlation coefficient above 0.85. Aerosols significantly change the ratio of diffuse to direct radiation, act as a cooling function to reduce temperature, and affect rainfall by interacting with clouds. The scenario simulation results showed that under the influence of aerosols, the total solar radiation was reduced by 224.98 MJ/m2, accounting for 3.44% of the total annual radiation. Correspondingly, the average net primary productivity of vegetation in the Jinghe River Basin in 2020 decreased by 26.64 gC/m2, which was not conducive to vegetation photosynthesis and carbon fixation in the basin.

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Section: Wrf−solar Modelmentioning

confidence: 99%

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

Fang

Zhou

et al. 2023

Remote Sensing

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“…In addition to the simple statistical regression algorithms, some advanced ML algorithms have also been adopted for downscaling, and have been found to outperform other conventional approaches [23][24][25]. The support vector machine (SVM) was first used by Chen et al, (2010) [26] to spatially downscale the general circulation models (GCMs) of precipitation.…”

Section: Introductionmentioning

confidence: 99%

Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data

et al. 2023

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Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.

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“…Because of these computational difficulties, there is the possibility of applying artificial intelligence (AI) techniques to facilitate the processing of WRF-Hydro, seeking an improvement of the predictions made by modeling and post-processing, where it is possible to train the network with the measured data and even history of the WRF-Hydro itself to have a faster forecast and less computational computing costs. At this point, it is important to point out that AI has already been applied in the WRF model without the coupling of the water module, as [15][16][17][18][19][20] to improve the modeled results and predictability of future data. However, the application of AI together with WRF-Hydro has still been developed, as in the studies of [21,22].…”

Section: Introductionmentioning

confidence: 99%

Feasibility Analysis on the Construction of a Web Solution for Hydrometeorological Forecasting Considering Water Body Management and Indicators for the Pandemic of SARS-COV-2

Silva

Pedruzzi

Souza

et al. 2021

Preprint

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Currently, the NCAR (U.S. National Center for Atmospheric Research), the institution responsible for the WRF-Hydro (Weather Research and Forecasting - Hydro) model initiative, highlights four major global challenges: floods, pollution, droughts, and biodiversity. Thus, given the current scenario of a global pandemic caused by the Sars-CoV-2 virus (Covid19), the importance of hydrological studies, their correlation with contamination levels, and incidence of COVID-19 cases are also in the spotlight. Among the challenges around water resources management, the lack of good and representative data, especially for small water bodies and developing countries, to perform inferences and to manage these natural resources is critical. This situation applies not only to observational data and but also to input data for hydrological models. In this context, the WRF-Hydro system represents the state of the art for hydrometeorological modeling. Thus, the model emerges as a computational tool that becomes possible to provide auxiliary data for patterns analysis in time series and computational prediction. Also, with the evolution of artificial intelligence (AI), it is possible to consider the integration of this modern approach with the WRF-Hydro model simulations. Therefore, the main of this study is to analyze the feasibility of a web tool that integrates these functionalities. The coupled WRF-Hydro with AI will support the management and generate a water predictability analysis in the MATOPIBA region (Maranhão-Tocantins-Piauí-Bahia), northeastern Brazil, the focus area of this study. Although the WRF-Hydro system demonstrates efficiency in the hydrometeorological simulation for the region, the model has a range of subprocesses which has a high computational cost, especially for long-term studies. Therefore, due to the possibility of integrating these computational tools, it is proposed to develop and analyze the construction of a web tool using the WRF-Hydro system for the short and medium-term with AI tools for the short term (a few hours to a few days), to optimize the computational cost. Thus, the combined application of the WRF-Hydro and AI system can improve the water bodies management and assist the identification of contamination levels by Sars-CoV-2, given the presence of the virus in water bodies and the correlation of the pandemic with hydrological variables.

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Evaluation of WRF and artificial intelligence models in short-term rainfall, temperature and flood forecast (case study)

Cited by 13 publications

References 30 publications

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data

Feasibility Analysis on the Construction of a Web Solution for Hydrometeorological Forecasting Considering Water Body Management and Indicators for the Pandemic of SARS-COV-2

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