Development of an Urban High-Resolution Air Temperature Forecast System for Local Weather Information Services Based on Statistical Downscaling

Yi, Chaeyeon; Shin, Yire; Roh, Jaehoon

doi:10.3390/atmos9050164

Cited by 31 publications

(27 citation statements)

References 28 publications

(25 reference statements)

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“…Most previous studies that have used machine learning approaches to correct the bias of the NWP model temperature forecasts only applied single machine learning methods (Marzban, 2003; Yi et al, 2018; Zjavka, 2016). In contrast, this study utilized three machine learning models (i.e., RF, SVR and ANN) and their ensemble (MME) for the bias correction of the LDAPS model's

T_{{boldmax}_{t + 1}}

and

T_{{boldmin}_{t + 1}}

forecasts.…”

Section: Resultsmentioning

confidence: 99%

“…That paper found that the neural network model shows superior accuracy to KF in error reduction for most validated stations. In addition to ANN, some other machine learning approaches (i.e., Support Vector Regression (SVR) and Random Forest (RF)) have been used to correct the bias of the NWP model's air temperature outputs (Eccel et al, 2007; Yi et al, 2018). Eccel et al (2007) tested various approaches, from simple correction (i.e., mean bias) to machine learning approaches—ANN and RF, to improve the minimum temperature forecasting skills of two NWP models, ECNWF and Local Area Model Italy (LAMI) in a region of the Italian Alps.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

113

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Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi-model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next-day maximum and minimum air temperatures (T maxtþ1 and T mintþ1 ) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in-situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R 2 of 0.69, a bias of −0.85°C and an RMSE of 2.08°C for T maxtþ1 forecast, whereas the proposed models resulted in the improvement with R 2 from 0.75 to 0.78, bias from −0.16 to −0.07°C and RMSE from 1.55 to 1.66°C by hindcast validation. For forecasting T mintþ1 , the LDAPS model had an R 2 of 0.77, a bias of 0.51°C and an RMSE of 1.43°C by hindcast, while the bias correction models showed R 2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03°C, and RMSEs from 0.98 to 1.02°C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave-one-station-out cross-validation.

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T_{{boldmax}_{t + 1}}

and

T_{{boldmin}_{t + 1}}

forecasts.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

113

View full text Add to dashboard Cite

show abstract

“…We also evaluated the temperature prediction accuracy in relation to precipitation episodes and seasonal fluctuations. In a comparable study by Yi et al [24], temperature overestimation occurred when an abrupt precipitation episode was simulated.…”

Section: Accuracy Evaluation Associated With Precipitation Episodes Amentioning

confidence: 78%

“…In a recent study, spatial downscaling from a resolution of 1.5 km to 25 m was accomplished by applying machine learning to the local data assimilation and prediction system (LDAPS) of the unified model (UM), an air temperature forecast (spatial resolution: 1.5 km) that receives its boundary fields from the global data assimilation and prediction system (GDAPS) [24]. In that study, the latest temperature trends in the prediction model were reflected using a training dataset consisting of temperature data for the last 30 days and a sliding window technique to calculate the maximum and minimum temperatures of the subsequent day.…”

Section: Introductionmentioning

confidence: 99%

Statistical Downscaling of Urban-scale Air Temperatures Using an Analog Model Output Statistics Technique

Shin

2019

Atmosphere

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This study was conducted to evaluate the suitability of an analog model output statistics (MOS) downscaling technique for urban-scale meteorology research and compares this MOS-Analog technique with the sliding window technique. We downscaled air temperatures forecasted for the Seoul metropolitan area from 1.5 km resolution (using data from the Unified Model-Local Data Assimilation and Prediction System, UM-LDAPS) to 25 m resolution using the analog MOS technique described in the paper. The support vector machine (SVM) technique was employed for empirical computational modeling, using urban surface parameters calculated using the Climate Analysis Seoul (CAS) workbench and automated weather station (AWS) observational data as training data. The comparison of the downscaled prediction results with the AWS observations for the periods of July/August 2016 and 2017 resulted in a lower root mean square error (RMSE) and higher correlation coefficients (CC) than those obtained for the LDAPS prediction results. The prediction performance was also stable for September, during which precipitation episodes and seasonal fluctuations occurred. The results of this study demonstrate that the proposed technique, which overcomes the limitations of the sliding window technique, is applicable to urban-scale meteorology research and potentially applicable other areas.

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“…Refs. [42,43] proposed a method to parameterize downscaled spatial data, such as buildings in urban areas and land cover, to produce high-resolution meteorological data at an urban scale. Recently, methods such as machine learning and geographically weighted regression analyses have also been employed.…”

Section: Introductionmentioning

confidence: 99%

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

Yang

2020

Atmosphere

Self Cite

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The importance of impact-based forecasting services, which can support decision-making, is being emphasized to reduce the damage of meteorological disasters, centered around the World Meteorological Organization. The Korea Meteorological Administration (KMA) began developing impact-based forecasting technology and warning services in 2018. This paper proposes statistical downscaling and bias correction methods for acquiring high-resolution meteorological data for the heat-wave impact forecast system operated by KMA. Hence, digital forecast data from KMA, with 5 km spatial resolution, were downscaled and corrected to a spatial resolution of 1 km using statistical interpolation methods. Cross-validation indicated the superior performance of the Gaussian process regression model (GPRM) technique with low root mean square error and percent bias values and high CC value. The GPRM technology had the lowest forecast error, especially during the hottest period in Korea. In addition, temperatures for land-use areas with low elevations and high activity, such as the urban, road, and agricultural areas, were high. It is essential to provide accurate heat exposure information at the screen level with high human activity. Spatiotemporally accurate heat exposure information can be used more realistically for risk management in agriculture, livestock and fishery, and for adjusting the working hours of outdoor workers in construction and shipbuilding.

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Development of an Urban High-Resolution Air Temperature Forecast System for Local Weather Information Services Based on Statistical Downscaling

Cited by 31 publications

References 28 publications

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Statistical Downscaling of Urban-scale Air Temperatures Using an Analog Model Output Statistics Technique

Heat Exposure Information at Screen Level for an Impact-Based Forecasting and Warning Service for Heat-Wave Disasters

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