2022 Vol. 28, No. 1
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2022, 28(1): .
Abstract:
2022, 28(1): 1-11.
doi: 10.46267/j.1006-8775.2022.001
Abstract:
Environmental conditions determining the timing of the lifetime maximum intensities of tropical cyclones (TCs) are investigated for the TCs over the western North Pacific during the period 2008-2017. The results show that the land controls the timings of the lifetime maximum intensities in 42% of the TCs over this basin, indicating that accurate track forecasts are beneficial for TC intensity forecasts. With respect to other TCs that are not affected by the land (i.e., Ocean-TCs), the timings of their lifetime maximum intensities are determined by multiple oceanic factors. In particular, interactions between TCs and cold-core eddies occur in a large proportion (nearly 60%) of Ocean-TCs at or shortly after the times of their lifetime maximum intensities, especially in strong TCs (categories 4 and 5), suggesting that a consideration of the above interactions is necessary for improving TC intensity forecasting skills. In addition, unfavorable oceanic heat content conditions become common as the latitude increases over 25°N, influencing half of the Ocean-TCs. Strong vertical wind shear contributes detrimentally to the atmospheric environment in 17% of the TCs over this basin, especially in moderate and weak TCs. In contrast, neither the maximum potential intensity nor the humidity in the middle level of the atmosphere plays dominant roles when TCs turn from their peak intensities to weakening.
Environmental conditions determining the timing of the lifetime maximum intensities of tropical cyclones (TCs) are investigated for the TCs over the western North Pacific during the period 2008-2017. The results show that the land controls the timings of the lifetime maximum intensities in 42% of the TCs over this basin, indicating that accurate track forecasts are beneficial for TC intensity forecasts. With respect to other TCs that are not affected by the land (i.e., Ocean-TCs), the timings of their lifetime maximum intensities are determined by multiple oceanic factors. In particular, interactions between TCs and cold-core eddies occur in a large proportion (nearly 60%) of Ocean-TCs at or shortly after the times of their lifetime maximum intensities, especially in strong TCs (categories 4 and 5), suggesting that a consideration of the above interactions is necessary for improving TC intensity forecasting skills. In addition, unfavorable oceanic heat content conditions become common as the latitude increases over 25°N, influencing half of the Ocean-TCs. Strong vertical wind shear contributes detrimentally to the atmospheric environment in 17% of the TCs over this basin, especially in moderate and weak TCs. In contrast, neither the maximum potential intensity nor the humidity in the middle level of the atmosphere plays dominant roles when TCs turn from their peak intensities to weakening.
2022, 28(1): 12-28.
doi: 10.46267/j.1006-8775.2022.002
Abstract:
By deriving the discrete equation of the parameterized equation for the New Medium-Range Forecast (NMRF) boundary layer scheme in the GRAPES model, the adjusted discrete equation for temperature is obviously different from the original equation under the background of hydrostatic equilibrium and adiabatic hypothesis. In the present research, three discrete equations for temperature in the NMRF boundary layer scheme are applied, namely the original (hereafter NMRF), the adjustment (hereafter NMRF-gocp), and the one in the YSU boundary-layer scheme (hereafter NMRF-TZ). The results show that the deviations of height, temperature, U and V wind in the boundary layer in the NMRF-gocp and NMRF-TZ experiments are smaller than those in the NMRF experiment and the deviations in the NMRF-gocp experiment are the smallest. The deviations of humidity are complex for the different forecasting lead time in the three experiments. Moreover, there are obvious diurnal variations of deviations from these variables, where the diurnal variations of deviations from height and temperature are similar and those from U and V wind are also similar. However, the diurnal variation of humidity is relatively complicated. The root means square errors of 2m temperature (T2m) and 10m speed (V10m) from the three experiments show that the error of NMRF-gocp is the smallest and that of NMRF is the biggest. There is also a diurnal variation of T2m and V10m, where T2m has double peaks and V10m has only one peak. Comparison of the discrete equations between NMRF and NMRF-gocp experiments shows that the deviation of temperature is likely to be caused by the calculation of vertical eddy diffusive coefficients of heating, which also leads to the deviations of other elements.
By deriving the discrete equation of the parameterized equation for the New Medium-Range Forecast (NMRF) boundary layer scheme in the GRAPES model, the adjusted discrete equation for temperature is obviously different from the original equation under the background of hydrostatic equilibrium and adiabatic hypothesis. In the present research, three discrete equations for temperature in the NMRF boundary layer scheme are applied, namely the original (hereafter NMRF), the adjustment (hereafter NMRF-gocp), and the one in the YSU boundary-layer scheme (hereafter NMRF-TZ). The results show that the deviations of height, temperature, U and V wind in the boundary layer in the NMRF-gocp and NMRF-TZ experiments are smaller than those in the NMRF experiment and the deviations in the NMRF-gocp experiment are the smallest. The deviations of humidity are complex for the different forecasting lead time in the three experiments. Moreover, there are obvious diurnal variations of deviations from these variables, where the diurnal variations of deviations from height and temperature are similar and those from U and V wind are also similar. However, the diurnal variation of humidity is relatively complicated. The root means square errors of 2m temperature (T2m) and 10m speed (V10m) from the three experiments show that the error of NMRF-gocp is the smallest and that of NMRF is the biggest. There is also a diurnal variation of T2m and V10m, where T2m has double peaks and V10m has only one peak. Comparison of the discrete equations between NMRF and NMRF-gocp experiments shows that the deviation of temperature is likely to be caused by the calculation of vertical eddy diffusive coefficients of heating, which also leads to the deviations of other elements.
2022, 28(1): 29-44.
doi: 10.46267/j.1006-8775.2022.003
Abstract:
In this study, we assess the prediction for May rainfall over southern China (SC) by using the NCEP CFSv2 outputs. Results show that the CFSv2 is able to depict the climatology of May rainfall and associated circulations. However, the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations. In observation, the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China, respectively, with a low-pressure convergence in between. In the CFSv2, however, the anomalous circulations exhibit the patterns in response to the El Ni?o-Southern Oscillation (ENSO), demonstrating that the model overestimates the relationship between May SC rainfall and ENSO. Because of the onset of the South China Sea monsoon, the atmospheric circulation in May over SC is more complex, so the prediction for May SC rainfall is more challenging. In this study, we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the CFSv2. The sea surface temperature anomalies (SSTAs) in the northeastern Pacific and the centraleastern equatorial Pacific, and the 500-hPa geopotential height anomalies over western Siberia in previous April, which exert great influence on the SC rainfall in May, are chosen as predictors. Furthermore, multiple linear regression is employed between the predictors obtained from the CFSv2 and observed May SC rainfall. Both cross validation and independent test show that the hybrid model significantly improve the model's skill in predicting the interannual variation of May SC rainfall by two months in advance.
In this study, we assess the prediction for May rainfall over southern China (SC) by using the NCEP CFSv2 outputs. Results show that the CFSv2 is able to depict the climatology of May rainfall and associated circulations. However, the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations. In observation, the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China, respectively, with a low-pressure convergence in between. In the CFSv2, however, the anomalous circulations exhibit the patterns in response to the El Ni?o-Southern Oscillation (ENSO), demonstrating that the model overestimates the relationship between May SC rainfall and ENSO. Because of the onset of the South China Sea monsoon, the atmospheric circulation in May over SC is more complex, so the prediction for May SC rainfall is more challenging. In this study, we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the CFSv2. The sea surface temperature anomalies (SSTAs) in the northeastern Pacific and the centraleastern equatorial Pacific, and the 500-hPa geopotential height anomalies over western Siberia in previous April, which exert great influence on the SC rainfall in May, are chosen as predictors. Furthermore, multiple linear regression is employed between the predictors obtained from the CFSv2 and observed May SC rainfall. Both cross validation and independent test show that the hybrid model significantly improve the model's skill in predicting the interannual variation of May SC rainfall by two months in advance.
2022, 28(1): 45-56.
doi: 10.46267/j.1006-8775.2022.004
Abstract:
Tropical cyclone (TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature (SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied. During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution. 338 primary precursors are obtained from statistically significant correlation regions of SST, sea level pressure, 1000hPa air temperature, 850hPa specific humidity, 500hPa geopotential height and zonal wind shear in winter. Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis (PCA). Furthermore, the Multiple Linear Regression (MLR), the Gaussian Process Regression (GPR) and the Long Short-term Memory Networks and Fully Connected Layers (LSTM-FC) models are constructed relying on the above 19 factors. For three different kinds of test sets from 2010 to 2019, 2011 to 2019 and 2010 to 2019, the root mean square errors (RMSEs) of MLR, GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45, 1.00-1.93 and 0.71-0.95 as well as the average absolute errors (AAEs) 0.88-1.0, 0.75-1.36 and 0.50-0.70, respectively. As for the 2010-2019 experiment, the mean deviations of the three model outputs from the observation are 0.89, 0.78 and 0.56, together with the average evaluation scores 82.22, 84.44 and 88.89, separately. The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR. In conclusion, the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency. The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province.
Tropical cyclone (TC) annual frequency forecasting is significant for disaster prevention and mitigation in Guangdong Province. Based on the NCEP-NCAR reanalysis and NOAA Extended Reconstructed global sea surface temperature (SST) V5 data in winter, the TC frequency climatic features and prediction models have been studied. During 1951-2019, 353 TCs directly affected Guangdong with an annual average of about 5.1. TCs have experienced an abrupt change from abundance to deficiency in the mid to late 1980 with a slightly decreasing trend and a normal distribution. 338 primary precursors are obtained from statistically significant correlation regions of SST, sea level pressure, 1000hPa air temperature, 850hPa specific humidity, 500hPa geopotential height and zonal wind shear in winter. Then those 338 primary factors are reduced into 19 independent predictors by principal component analysis (PCA). Furthermore, the Multiple Linear Regression (MLR), the Gaussian Process Regression (GPR) and the Long Short-term Memory Networks and Fully Connected Layers (LSTM-FC) models are constructed relying on the above 19 factors. For three different kinds of test sets from 2010 to 2019, 2011 to 2019 and 2010 to 2019, the root mean square errors (RMSEs) of MLR, GPR and LSTM-FC between prediction and observations fluctuate within the range of 1.05-2.45, 1.00-1.93 and 0.71-0.95 as well as the average absolute errors (AAEs) 0.88-1.0, 0.75-1.36 and 0.50-0.70, respectively. As for the 2010-2019 experiment, the mean deviations of the three model outputs from the observation are 0.89, 0.78 and 0.56, together with the average evaluation scores 82.22, 84.44 and 88.89, separately. The prediction skill comparisons unveil that LSTM-FC model has a better performance than MLR and GPR. In conclusion, the deep learning model of LSTM-FC may shed light on improving the accuracy of short-term climate prediction about TC frequency. The current research can provide experience on the development of deep learning in this field and help to achieve further progress of TC disaster prevention and mitigation in Guangdong Province.
2022, 28(1): 57-70.
doi: 10.46267/j.1006-8775.2022.005
Abstract:
This study reveals that the interannual variability of the western edge of the western North Pacific (WNP) subtropical high (WNPSH) in early summer experienced an interdecadal decrease around 1990. Correspondingly, the zonal movement of the WNPSH and the zonal extension of the high-pressure anomaly over the WNP (WNPHA) in abnormal years possess smaller ranges after 1990. The different influences of the tropical SSTAs are important for this interdecadal change, which exhibit slow El Niño decaying pattern before 1990 while rapid transformation from El Niño to La Niña after 1990. The early summer tropical SSTAs and the relevant atmospheric circulation anomalies present obvious interdecadal differences. Before 1990, the warm SSTAs over the northern Indian Ocean and southern South China Sea favor the WNPHA through eastward-propagating Kelvin wave and meridional-vertical circulation, respectively. Meanwhile, the warm SSTA over the tropical central Pacific induces anomalous ascent to its northwest through the Gill response, which could strengthen the anomalous descent over the WNP through meridional-vertical circulation and further favor the eastward extension of the WNPHA to central Pacific. After 1990, the warm SSTAs over the Maritime Continent and northern Indian Ocean cause the WNPHA through meridional-vertical and zonal-vertical circulation, respectively. Overall, the anomalous warm SSTs and ascent and the resultant anomalous descent over the WNP are located more westward and southward after 1990 than before 1990. Consequently, the WNPHA features narrower zonal range and less eastward extension after 1990, corresponding to the interdecadal decease in the interannual variability of the western edge of the WNPSH. On the other hand, the dominant oscillation period of ENSO experienced an interdecadal reduction around 1990, contributing to the change of the El Niño SSTA associated with the anomalous WNPSH from slow decaying type to rapid transformation type.
This study reveals that the interannual variability of the western edge of the western North Pacific (WNP) subtropical high (WNPSH) in early summer experienced an interdecadal decrease around 1990. Correspondingly, the zonal movement of the WNPSH and the zonal extension of the high-pressure anomaly over the WNP (WNPHA) in abnormal years possess smaller ranges after 1990. The different influences of the tropical SSTAs are important for this interdecadal change, which exhibit slow El Niño decaying pattern before 1990 while rapid transformation from El Niño to La Niña after 1990. The early summer tropical SSTAs and the relevant atmospheric circulation anomalies present obvious interdecadal differences. Before 1990, the warm SSTAs over the northern Indian Ocean and southern South China Sea favor the WNPHA through eastward-propagating Kelvin wave and meridional-vertical circulation, respectively. Meanwhile, the warm SSTA over the tropical central Pacific induces anomalous ascent to its northwest through the Gill response, which could strengthen the anomalous descent over the WNP through meridional-vertical circulation and further favor the eastward extension of the WNPHA to central Pacific. After 1990, the warm SSTAs over the Maritime Continent and northern Indian Ocean cause the WNPHA through meridional-vertical and zonal-vertical circulation, respectively. Overall, the anomalous warm SSTs and ascent and the resultant anomalous descent over the WNP are located more westward and southward after 1990 than before 1990. Consequently, the WNPHA features narrower zonal range and less eastward extension after 1990, corresponding to the interdecadal decease in the interannual variability of the western edge of the WNPSH. On the other hand, the dominant oscillation period of ENSO experienced an interdecadal reduction around 1990, contributing to the change of the El Niño SSTA associated with the anomalous WNPSH from slow decaying type to rapid transformation type.
2022, 28(1): 71-81.
doi: 10.46267/j.1006-8775.2022.006
Abstract:
Bases on the NCEP / NCAR reanalysis products, HadISST dataset, and data of tropical cyclone (TC) landfalling in the Chinese mainland during 1960-2019, the possible impacts of Indian Ocean Dipole (IOD) mode and Indian Ocean basin (IOB) mode on the last-TC-landfall date (LLD) and first-TC-landfall date (FLD), respectively, are investigated in this study. The LLD is in significantly negative correlation with autumn IOD on the interannual timescale and their association is independent of El Niño-Southern Oscillation (ENSO). The LLD tends to be earlier when the IOD is positive while becomes later when the IOD is negative. An anomalous lower-level anticyclone is located around the Philippines during October-November, resulting from the change of Walker circulation over the tropical Indo-west Pacific Ocean forced by sea surface temperature (SST) anomalies related to a positive IOD event. The Philippines anticyclone anomaly suppresses TC formation there and prevents TCs from landfalling in the Chinese mainland due to the anomalous westerly steering flows over southeast China during October-November, agreeing well with the earlier LLD. However, the robust connection between spring IOB and FLD depends on ENSO episodes in the preceding winter. There is an anticyclonic anomaly around the Philippines caused by the tropical SST anomalies through modulating the Walker circulation during May-June when the IOB is warming in the El Niño decaying phase. Correspondingly, the TC genesis is less frequent near the Philippines and the mid-level steering flows associated with the expanded western Pacific subtropical high are disadvantageous for TCs moving towards southeast China and making landfall during MayJune, in accordance with the later FLD. By contrast, cooling IOB condition in spring of a La Niña decaying year and negative IOD cases during autumn could produce a completely reversed atmospheric circulation response, leading to an earlier FLD and a later LLD over the Chinese mainland, respectively.
Bases on the NCEP / NCAR reanalysis products, HadISST dataset, and data of tropical cyclone (TC) landfalling in the Chinese mainland during 1960-2019, the possible impacts of Indian Ocean Dipole (IOD) mode and Indian Ocean basin (IOB) mode on the last-TC-landfall date (LLD) and first-TC-landfall date (FLD), respectively, are investigated in this study. The LLD is in significantly negative correlation with autumn IOD on the interannual timescale and their association is independent of El Niño-Southern Oscillation (ENSO). The LLD tends to be earlier when the IOD is positive while becomes later when the IOD is negative. An anomalous lower-level anticyclone is located around the Philippines during October-November, resulting from the change of Walker circulation over the tropical Indo-west Pacific Ocean forced by sea surface temperature (SST) anomalies related to a positive IOD event. The Philippines anticyclone anomaly suppresses TC formation there and prevents TCs from landfalling in the Chinese mainland due to the anomalous westerly steering flows over southeast China during October-November, agreeing well with the earlier LLD. However, the robust connection between spring IOB and FLD depends on ENSO episodes in the preceding winter. There is an anticyclonic anomaly around the Philippines caused by the tropical SST anomalies through modulating the Walker circulation during May-June when the IOB is warming in the El Niño decaying phase. Correspondingly, the TC genesis is less frequent near the Philippines and the mid-level steering flows associated with the expanded western Pacific subtropical high are disadvantageous for TCs moving towards southeast China and making landfall during MayJune, in accordance with the later FLD. By contrast, cooling IOB condition in spring of a La Niña decaying year and negative IOD cases during autumn could produce a completely reversed atmospheric circulation response, leading to an earlier FLD and a later LLD over the Chinese mainland, respectively.
2022, 28(1): 82-94.
doi: 10.46267/j.1006-8775.2022.007
Abstract:
Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave (GW) analysis. To accurately and efficiently derive the threedimensional structure of the stratospheric GWs from the single-field-of-view (SFOV) Atmospheric InfraRed Sounder (AIRS) observations, this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach (ANN). The simulation experiments show that the retrieval bias is less than 0.5 K, and the root mean square error (RMSE) ranges from 1.8 to 4 K. Moreover, the retrieval results from 20 granules of the AIRS observations with the trained neural network (AIRS_SFOV) and the corresponding operational AIRS products (AIRS_L2) as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) (AIRS_DR) are compared respectively with ECMWF T799 data. The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR. Furthermore, the analysis of the typical GW events induced by the mountain Andes and the typhoon "Soulik" using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.
Satellite hyperspectral infrared sounder measurements have better horizontal resolution than other sounding techniques as it boasts the stratospheric gravity wave (GW) analysis. To accurately and efficiently derive the threedimensional structure of the stratospheric GWs from the single-field-of-view (SFOV) Atmospheric InfraRed Sounder (AIRS) observations, this paper firstly focuses on the retrieval of the atmospheric temperature profiles in the altitude range of 20-60 km with an artificial neural network approach (ANN). The simulation experiments show that the retrieval bias is less than 0.5 K, and the root mean square error (RMSE) ranges from 1.8 to 4 K. Moreover, the retrieval results from 20 granules of the AIRS observations with the trained neural network (AIRS_SFOV) and the corresponding operational AIRS products (AIRS_L2) as well as the dual-regression results from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) (AIRS_DR) are compared respectively with ECMWF T799 data. The comparison indicates that the standard deviation of the ANN retrieval errors is significantly less than that of the AIRS_DR. Furthermore, the analysis of the typical GW events induced by the mountain Andes and the typhoon "Soulik" using different data indicates that the AIRS_SFOV results capture more details of the stratospheric gravity waves in the perturbation amplitude and pattern than the operational AIRS products do.
2022, 28(1): 95-108.
doi: 10.46267/j.1006-8775.2022.008
Abstract:
The impact of warming and wetting on the ecological environment of the Qinghai-Tibet Plateau (TP) under the background of climate change has been a concern of the global scientific community. In this paper, the optimized interpolation variational correction approach is adopted for the analysis of monthly high-resolution satellite precipitation products and observations from meteorological stations during the past 20 years. As a result, the corrected precipitation products can not only supplement the"blank area"of precipitation observation stations on the TP, but also improve the accuracy of the original satellite precipitation products. The precipitation over the TP shows different spatial changes in the vegetation growing season, known as the time from May to September. The precipitation in the vegetation growing season and leaf area index (LAI) in the following month show a similar change pattern, indicating a"one-month lag" response of LAI to precipitation on the TP. Further analysis illustrates the influence of water vapor transport driven by the Asian summer monsoon. Water vapor derived from trans-equatorial air flows across the Indian Ocean and Arabian Sea is strengthened, leading to the increase of precipitation in the central and northern TP, where the trend of warming and wetting and the increase of vegetation tend to be more obvious. By contrast, as a result of the weakening trend of water vapor transport in the middle and low levels in southern TP, the precipitation decreases, and the LAI shows a downtrend, which inhibits the warming and wetting ecological environment in this area.
The impact of warming and wetting on the ecological environment of the Qinghai-Tibet Plateau (TP) under the background of climate change has been a concern of the global scientific community. In this paper, the optimized interpolation variational correction approach is adopted for the analysis of monthly high-resolution satellite precipitation products and observations from meteorological stations during the past 20 years. As a result, the corrected precipitation products can not only supplement the"blank area"of precipitation observation stations on the TP, but also improve the accuracy of the original satellite precipitation products. The precipitation over the TP shows different spatial changes in the vegetation growing season, known as the time from May to September. The precipitation in the vegetation growing season and leaf area index (LAI) in the following month show a similar change pattern, indicating a"one-month lag" response of LAI to precipitation on the TP. Further analysis illustrates the influence of water vapor transport driven by the Asian summer monsoon. Water vapor derived from trans-equatorial air flows across the Indian Ocean and Arabian Sea is strengthened, leading to the increase of precipitation in the central and northern TP, where the trend of warming and wetting and the increase of vegetation tend to be more obvious. By contrast, as a result of the weakening trend of water vapor transport in the middle and low levels in southern TP, the precipitation decreases, and the LAI shows a downtrend, which inhibits the warming and wetting ecological environment in this area.
2022, 28(1): 109-120.
doi: 10.46267/j.1006-8775.2022.009
Abstract:
Renewable energy sources, especially wind power, were believed to be able to slow down global warming; however, evidence in recent years shows that wind farms may also induce climate change. With the rapid development of wind power industry, the number of wind farms installed in mountains has gradually increased. Therefore, it is necessary to study the impact of wind farms in mountainous areas on local climate. The Suizhou and Dawu wind farms in northern Hubei Province were chosen for the present study on the impact of wind farm operations on the local climate in mountainous areas. The mesoscale meteorological numerical model Weather Research and Forecasting Model (WRF) and the Fitch model, together with turbulence correction factor, were used to simulate wind farm operations and study their effects on local climate. The results showed the characteristics of wind speed attenuation in mountainous wind farms: the amplitude and range of wind speed attenuation were stronger in the nighttime than in the daytime, and stronger in summer than in winter. The surface temperature increased and became more significant in summer. However, a cooling variation was observed above the surface warming center. The height of this center was higher in the daytime than it was in the nighttime. The latent heat flux in the wind farms decreased at night, accompanied by an increase in sensible heat flux. However, these changes were not significant. Some differences were observed between the impact of wind farms on the climate in the plains and the mountains. Such differences are more likely to be related to complex terrain conditions, climate conditions, and the density of wind turbines. The present study may provide support for the development and construction of wind farms in mountainous areas.
Renewable energy sources, especially wind power, were believed to be able to slow down global warming; however, evidence in recent years shows that wind farms may also induce climate change. With the rapid development of wind power industry, the number of wind farms installed in mountains has gradually increased. Therefore, it is necessary to study the impact of wind farms in mountainous areas on local climate. The Suizhou and Dawu wind farms in northern Hubei Province were chosen for the present study on the impact of wind farm operations on the local climate in mountainous areas. The mesoscale meteorological numerical model Weather Research and Forecasting Model (WRF) and the Fitch model, together with turbulence correction factor, were used to simulate wind farm operations and study their effects on local climate. The results showed the characteristics of wind speed attenuation in mountainous wind farms: the amplitude and range of wind speed attenuation were stronger in the nighttime than in the daytime, and stronger in summer than in winter. The surface temperature increased and became more significant in summer. However, a cooling variation was observed above the surface warming center. The height of this center was higher in the daytime than it was in the nighttime. The latent heat flux in the wind farms decreased at night, accompanied by an increase in sensible heat flux. However, these changes were not significant. Some differences were observed between the impact of wind farms on the climate in the plains and the mountains. Such differences are more likely to be related to complex terrain conditions, climate conditions, and the density of wind turbines. The present study may provide support for the development and construction of wind farms in mountainous areas.
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