A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis

Jiang, Yaozhi; Yang, Kun; Shao, Chong; Zhou, Xu; Zhao, Long; Chen, Yingying; Wu, Hui

doi:10.1016/j.atmosres.2021.105574

Cited by 78 publications

(37 citation statements)

References 51 publications

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“…ERA5 has a better performance than other reanalysis data, 29 contains global long‐term data from 1979 to the present, and is easily accessible. Therefore, many previous downscaling studies 17,30–32 have used ERA5 as input data. As ERA5 includes hundreds of hourly weather variables, it is necessary to select and process the wind‐related variables.…”

Section: Methodsmentioning

confidence: 99%

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Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Lee

Kim

et al. 2022

Wind Energy

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The need for high-resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography-derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi-resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.

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Section: Methodsmentioning

confidence: 99%

“…ERA5 has a better performance than other reanalysis data, 29 contains global long-term data from 1979 to the present, and is easily accessible. Therefore, many previous downscaling studies 17,[30][31][32] have used ERA5 as…”

Section: Low-resolution Wind Resource Datamentioning

confidence: 99%

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Lee

Kim

et al. 2022

Wind Energy

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“…3 of 19 with a complex terrain (Jiang et al, 2021), while the geographically weighted regression (GWR) method is more effective when analyzing nonstationary spatial parameters and has been widely employed (Zhou et al, 2019). GWR allows the relationships between dependent and explanatory variables to vary over space and directly deals with non-stationarity.…”

Section: 1029/2021jd035542mentioning

confidence: 99%

“…Spatial interpolation methodologies, such as the inverse distance weighted (IDW) method and kriging technique, are now widely used. However, large uncertainties arise when applying interpolation methods (such as inverse distance weighted method and kriging technique) to areas with a complex terrain (Jiang et al., 2021), while the geographically weighted regression (GWR) method is more effective when analyzing nonstationary spatial parameters and has been widely employed (Zhou et al., 2019). GWR allows the relationships between dependent and explanatory variables to vary over space and directly deals with non‐stationarity.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Hourly Air Temperature of WRF Simulations Over Complex Topography: A Case Study of Chongli District in Hebei Province, China

et al. 2022

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The air temperature is one of the most frequently measured meteorological parameters. Air temperature forecasting is a crucial climatic factor-related task required in many different applications in areas such as agriculture, industry, energy, the environment, and tourism (Abdel-Aal, 2004;Cifuentes et al., 2020). Certain applications include short-term load forecasting for power utilities (Li et al., 2016), protection against freezing injury of various fruits (Chung et al., 2006), adaptive temperature control in greenhouses (Dombaycı & Gölcü, 2009), prediction of cooling and energy consumption in residential buildings (Ben-Nakhi & Mahmoud, 2004), and establishment of a planning horizon for infrastructure upgrades, insurance, energy policy, and business development purposes (Smith et al., 2007). Therefore, accurate forecasting of the air temperature with high spatiotemporal resolution has always been an important goal for meteorologists and weather forecasters.Mountainous areas also represent important areas of human activities, such as fruit cultivation, tourism, and skiing in the winter season. Therefore, hourly air temperature forecasting and high-resolution prediction in mountainous areas is necessary and important for human activities. In contrast to plain areas, the topography of a mountainous area is very complex, which can cause the air temperature to vary within small terrain units. For example, in a 1-km grid, the difference in altitude between the slope top and foot can reach as much as 500 m, and the temperature difference as high as 3°C, assuming that the slope of the grid is 0.5. It is a difficult task for air temperature forecasting and modeling in complex geographical zones because the temperature is influenced by several nonlinear processes, such as the interaction between large-scale air mass circulation and local airflow, airflow-topography interactions, and the interplay between radiation and topographic shading.Currently, many numerical weather prediction (NWP) models are available for air temperature prediction, for example, the European Centre for Medium-Range Weather Forecasts (ECMWF) model, the fifth-generation mesoscale model (MM5), and the Weather Research and Forecasting (WRF) model. All these NWP models allow day-ahead air temperature prediction, which is usually adopted in practice. However, uncertainties in model

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“…ERA5 reanalysis data comprise an hourly collection of atmospheric and land-surface meteorological elements that has taken place since 1979 that the European Centre for Medium-Range Weather Forecasts (ECMWF) has used with its prediction model and data assimilation system to reanalyze archived observations (Jiang et al, 2021). Data used in this paper include surface relative humidity (RH, expressed as a percentage), air temperature at a height of 2 m (T 2 m , in K), wind speed (U 10 , V 10 ; in m s −1 ), surface pressure (SP, in Pa), boundary layer height (BLH, in m), and cumulative precipitation (RAIN, in m) at 10 m above the ground.…”

Section: Meteorological Datamentioning

confidence: 99%

Estimation of PM<sub>2.5</sub> concentration in China using linear hybrid machine learning model

et al. 2021

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Abstract. Satellite remote sensing aerosol optical depth (AOD) and meteorological elements were employed to invert PM2.5 (the fine particulate matter with a diameter below 2.5 µm) in order to control air pollution more effectively. This paper proposes a restricted gradient-descent linear hybrid machine learning model (RGD-LHMLM) by integrating a random forest (RF), a gradient boosting regression tree (GBRT), and a deep neural network (DNN) to estimate the concentration of PM2.5 in China in 2019. The research data included Himawari-8 AOD with high spatiotemporal resolution, ERA5 meteorological data, and geographic information. The results showed that, in the hybrid model developed by linear fitting, the DNN accounted for the largest proportion, and the weight coefficient was 0.62. The R2 values of RF, GBRT, and DNN were reported as 0.79, 0.81, and 0.8, respectively. Preferably, the generalization ability of the mixed model was better than that of each sub-model, and R2 (determination coefficient) reached 0.84, and RMSE (root mean square error) and MAE (mean absolute error) were reported as 12.92 and 8.01 µg m−3, respectively. For the RGD-LHMLM, R2 was above 0.7 in more than 70 % of the sites and RMSE and MAE were below 20 and 15 µg m−3, respectively, in more than 70 % of the sites due to the correlation coefficient having a seasonal difference between the meteorological factor and PM2.5. Furthermore, the hybrid model performed best in winter (mean R2 was 0.84) and worst in summer (mean R2 was 0.71). The spatiotemporal distribution characteristics of PM2.5 in China were then estimated and analyzed. According to the results, there was severe pollution in winter with an average concentration of PM2.5 being reported as 62.10 µg m−3. However, there was only slight pollution in summer with an average concentration of PM2.5 being reported as 47.39 µg m−3. The period from 10:00 to 15:00 LT (Beijing time, UTC+8 every day is the best time for model inversion; at this time the pollution is also high. The findings also indicate that North China and East China are more polluted than other areas, and their average annual concentration of PM2.5 was reported as 82.68 µg m−3. Moreover, there was relatively low pollution in Inner Mongolia, Qinghai, and Tibet, for their average PM2.5 concentrations were reported below 40 µg m−3.

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A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis

Cited by 78 publications

References 51 publications

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Downscaling Hourly Air Temperature of WRF Simulations Over Complex Topography: A Case Study of Chongli District in Hebei Province, China

Estimation of PM<sub>2.5</sub> concentration in China using linear hybrid machine learning model

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A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis

Cited by 78 publications

References 51 publications

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data

Downscaling Hourly Air Temperature of WRF Simulations Over Complex Topography: A Case Study of Chongli District in Hebei Province, China

Estimation of PM&lt;sub&gt;2.5&lt;/sub&gt; concentration in China using linear hybrid machine learning model

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Estimation of PM<sub>2.5</sub> concentration in China using linear hybrid machine learning model