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, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23025
Abstract:
Intraseasonal circulation characteristics in the summer of 2022 and the influences from tropical oceans were studied by using the NCEP/DOE monthly reanalysis data and NOAA observational datasets during 1979–2022 with the composite and correlation analysis methods. The results show that there are significant spatial variations in temperatures in Central and Eastern China, the center of high temperature anomalies located in Central China, Southwest China, and the entire Yangtze River Basin in June, July, and August, respectively. The simultaneously strengthened Western Pacific Subtropical High (WPSH) and South Asian High (SAH), as well as their overlap, ultimately formed a rare Northern Hemisphere subtropical high, which is the direct cause of the high temperature in Central and Eastern China in the summer of 2022. However, the East Asian summer circulation in 2022 is distinct from a classic response to La Niña. The weak convection anomalies in the South China Sea and east of the Philippines and the insignificant East Asia–Pacific teleconnection pattern are both different from the typical La Niña years. The tropical Sea Surface Temperature (SST) anomalies in the summer of 2022 contributed to the formation of these extreme high temperature processes. The cold SST anomalies in the tropical West Indian Ocean and tropical Central Pacific Ocean are conducive to the occurrence of high temperatures in the Yangtze River Basin and Central China, respectively. The cold SST anomalies in the tropical West Indian Ocean contribute to the strengthening of the SAH, whereas the cold SST anomalies in the tropical Central Pacific Ocean may be an important cause of the strengthened WPSH.
Intraseasonal circulation characteristics in the summer of 2022 and the influences from tropical oceans were studied by using the NCEP/DOE monthly reanalysis data and NOAA observational datasets during 1979–2022 with the composite and correlation analysis methods. The results show that there are significant spatial variations in temperatures in Central and Eastern China, the center of high temperature anomalies located in Central China, Southwest China, and the entire Yangtze River Basin in June, July, and August, respectively. The simultaneously strengthened Western Pacific Subtropical High (WPSH) and South Asian High (SAH), as well as their overlap, ultimately formed a rare Northern Hemisphere subtropical high, which is the direct cause of the high temperature in Central and Eastern China in the summer of 2022. However, the East Asian summer circulation in 2022 is distinct from a classic response to La Niña. The weak convection anomalies in the South China Sea and east of the Philippines and the insignificant East Asia–Pacific teleconnection pattern are both different from the typical La Niña years. The tropical Sea Surface Temperature (SST) anomalies in the summer of 2022 contributed to the formation of these extreme high temperature processes. The cold SST anomalies in the tropical West Indian Ocean and tropical Central Pacific Ocean are conducive to the occurrence of high temperatures in the Yangtze River Basin and Central China, respectively. The cold SST anomalies in the tropical West Indian Ocean contribute to the strengthening of the SAH, whereas the cold SST anomalies in the tropical Central Pacific Ocean may be an important cause of the strengthened WPSH.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23011
Abstract:
For nowcasting models based on the Convolutional Neural Networks (CNNs) used in radar echo extrapolation, different strategies are commonly applied to forecasting the radar echo of future (usually within two hours) multiple time steps. In this work, using the nowcasting of the atmospheric vertical integrated liquid water content, the forecast performances of models with two types of strategies, namely Recursive Forecast Strategy (RFS) and Direct Forecast Strategies (DFSs), were compared. CNN-based models were constructed for spatiotemporal forecasting with the UNet architecture as the backbone. Results exhibited the significantly better performance of the two DFS models than the RFS model on the overall forecast horizon with a roughly 19% lower root-mean-square error. With increasing forecast time steps, the forecast estimation errors of the RFS model accumulated much faster than those of the two DFS models. For the DFS models, the multioutput DFS (DFS-M) was more robust and performed better than the single-output DFS (DFS-S) on the overall forecast horizon; for a short forecast horizon, DFS-S has a slightly better forecast (approximately the first two time steps in the future). Moreover, a neural network interpretation method (i.e., DeepLIFT) was applied to the two DFS models to find the relative importance of each input time step. It was revealed that for both DFS models, approximately 80% of the mean importance score was in the last two input time steps and early input time steps have an increasing impact on longer forecast times. The input importance of DFS-M at each output step was much more stable than that of DFS-S due to the self-constrain effect exerted by the stochastic dependencies between output steps. By combining the forecast of two direct models at different time steps, a more balanced forecast can be performed on the whole forecast horizon.
For nowcasting models based on the Convolutional Neural Networks (CNNs) used in radar echo extrapolation, different strategies are commonly applied to forecasting the radar echo of future (usually within two hours) multiple time steps. In this work, using the nowcasting of the atmospheric vertical integrated liquid water content, the forecast performances of models with two types of strategies, namely Recursive Forecast Strategy (RFS) and Direct Forecast Strategies (DFSs), were compared. CNN-based models were constructed for spatiotemporal forecasting with the UNet architecture as the backbone. Results exhibited the significantly better performance of the two DFS models than the RFS model on the overall forecast horizon with a roughly 19% lower root-mean-square error. With increasing forecast time steps, the forecast estimation errors of the RFS model accumulated much faster than those of the two DFS models. For the DFS models, the multioutput DFS (DFS-M) was more robust and performed better than the single-output DFS (DFS-S) on the overall forecast horizon; for a short forecast horizon, DFS-S has a slightly better forecast (approximately the first two time steps in the future). Moreover, a neural network interpretation method (i.e., DeepLIFT) was applied to the two DFS models to find the relative importance of each input time step. It was revealed that for both DFS models, approximately 80% of the mean importance score was in the last two input time steps and early input time steps have an increasing impact on longer forecast times. The input importance of DFS-M at each output step was much more stable than that of DFS-S due to the self-constrain effect exerted by the stochastic dependencies between output steps. By combining the forecast of two direct models at different time steps, a more balanced forecast can be performed on the whole forecast horizon.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23007
Abstract:
The weather research and forecasting model was adopted to simulate precipitation processes on 13 June and 14 June 2020, in the Ailao Mountain region of Yunnan Province. A discussion of the influence of the Ailao Mountain topography on the spatiotemporal distribution of heavy precipitation and its possible physical mechanism is presented through the comparative analysis of terrain sensitivity tests at different mountain heights. The following results were obtained: 1) Terrain sensitivity tests at different heights showed that terrain height affects the location of the low vortex shear line. 2) At high terrain elevation, the pseudo-equivalent potential temperature lines in the middle and low levels were denser, the gradient was larger, and water vapor and unstable energy rapidly accumulated. The accompanying strong upward motion may trigger severe convective weather in advance. With decreasing topographic height, the pseudo-equivalent potential temperature line was flat and evacuated. The local uplift and unstable energy of the Ailao Mountains were low and inadequate to trigger medium or small-scale severe convective weather. 3) In the WSM6 microphysics scheme, the variation of topographic height also has an obvious impact on the cloud microphysics process. At high terrain elevation, the forced uplift was strengthened, which made the ice crystal and snow mixture stay in the air longer and expand the range gradually. Consequently, a downdraft of secondary circulation was generated, and the cloud water and raindrops collided and strengthened in the middle and lower layers, leading to a decrease in the cloud water mixing ratio and an increase in the rain mixing ratio.
The weather research and forecasting model was adopted to simulate precipitation processes on 13 June and 14 June 2020, in the Ailao Mountain region of Yunnan Province. A discussion of the influence of the Ailao Mountain topography on the spatiotemporal distribution of heavy precipitation and its possible physical mechanism is presented through the comparative analysis of terrain sensitivity tests at different mountain heights. The following results were obtained: 1) Terrain sensitivity tests at different heights showed that terrain height affects the location of the low vortex shear line. 2) At high terrain elevation, the pseudo-equivalent potential temperature lines in the middle and low levels were denser, the gradient was larger, and water vapor and unstable energy rapidly accumulated. The accompanying strong upward motion may trigger severe convective weather in advance. With decreasing topographic height, the pseudo-equivalent potential temperature line was flat and evacuated. The local uplift and unstable energy of the Ailao Mountains were low and inadequate to trigger medium or small-scale severe convective weather. 3) In the WSM6 microphysics scheme, the variation of topographic height also has an obvious impact on the cloud microphysics process. At high terrain elevation, the forced uplift was strengthened, which made the ice crystal and snow mixture stay in the air longer and expand the range gradually. Consequently, a downdraft of secondary circulation was generated, and the cloud water and raindrops collided and strengthened in the middle and lower layers, leading to a decrease in the cloud water mixing ratio and an increase in the rain mixing ratio.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.22095
Abstract:
The effects of temperature stratification on the wind and turbulence characteristics of different local climate zones were numerically investigated using high-resolution large-eddy simulation methods. Four local climate zones (open highrise climate zone, compact highrise climate zone, compact midrise climate zone, and sparsely built climate zone) in Mentougou, Beijing, were selected as research objects. The simulation results on 7 Nov 2019, under light windy and sunny conditions, reveal the following results: 1) Temperature stratification remarkably impacts the shape and extent of turbulent eddies. In the near-surface horizontal profile, the number of vortices decreases under stable stratification compared with that under neutral stratification, but the longitudinal extension of vortices can increase by 67%. However, under unstable stratification, the number of vortices increases compared with that under neutral stratification, and the longitudinal extension of vortices can be reduced by 60%. In the vertical profile, the circulation structure is weakened, and the longitudinal extent of vortices can be reduced by 40% under stable stratification compared with that under neutral stratification; in the case of unstable stratification, the circulation structure is enhanced, and the longitudinal extent of vortices can be increased by 20%. This phenomenon is most pronounced in the compact highrise climate zone. 2) The high wind speeds in the four local climate zones are predominantly located on either side of the buildings that run parallel to the prevailing wind direction and near the rooftops. The thermal effect amplifies the total wind speed, resulting in the wind speed near the ground increasing to 1.27–2.18 times in comparison with the incoming wind speed. 3) The high turbulence energies in the four local climate zones are primarily located at the bottom corners of buildings and roofs. The turbulence energy near the ground under unstable stratification is 1.2–1.5 times higher than that under neutral stratification, while that under stable stratification is 0.5–0.8 times higher than that under neutral stratification. The buoyancy-induced thermal turbulence enhances the mixing efficiency under unstable stratification, while the turbulent motion is suppressed under stable stratification. 4) Compared with other local climate zones, the wind speeds at the bottom of the buildings in highrise-dense areas are larger. A stronger narrow-tube effect is easily formed under unstable stratification compared to other stratification. The maximum wind speed in its neighborhood canyon is 1.5 times higher than that in the compact midrise climate zone.
The effects of temperature stratification on the wind and turbulence characteristics of different local climate zones were numerically investigated using high-resolution large-eddy simulation methods. Four local climate zones (open highrise climate zone, compact highrise climate zone, compact midrise climate zone, and sparsely built climate zone) in Mentougou, Beijing, were selected as research objects. The simulation results on 7 Nov 2019, under light windy and sunny conditions, reveal the following results: 1) Temperature stratification remarkably impacts the shape and extent of turbulent eddies. In the near-surface horizontal profile, the number of vortices decreases under stable stratification compared with that under neutral stratification, but the longitudinal extension of vortices can increase by 67%. However, under unstable stratification, the number of vortices increases compared with that under neutral stratification, and the longitudinal extension of vortices can be reduced by 60%. In the vertical profile, the circulation structure is weakened, and the longitudinal extent of vortices can be reduced by 40% under stable stratification compared with that under neutral stratification; in the case of unstable stratification, the circulation structure is enhanced, and the longitudinal extent of vortices can be increased by 20%. This phenomenon is most pronounced in the compact highrise climate zone. 2) The high wind speeds in the four local climate zones are predominantly located on either side of the buildings that run parallel to the prevailing wind direction and near the rooftops. The thermal effect amplifies the total wind speed, resulting in the wind speed near the ground increasing to 1.27–2.18 times in comparison with the incoming wind speed. 3) The high turbulence energies in the four local climate zones are primarily located at the bottom corners of buildings and roofs. The turbulence energy near the ground under unstable stratification is 1.2–1.5 times higher than that under neutral stratification, while that under stable stratification is 0.5–0.8 times higher than that under neutral stratification. The buoyancy-induced thermal turbulence enhances the mixing efficiency under unstable stratification, while the turbulent motion is suppressed under stable stratification. 4) Compared with other local climate zones, the wind speeds at the bottom of the buildings in highrise-dense areas are larger. A stronger narrow-tube effect is easily formed under unstable stratification compared to other stratification. The maximum wind speed in its neighborhood canyon is 1.5 times higher than that in the compact midrise climate zone.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.22054
Abstract:
Yunnan Province is located in a low-latitude plateau area adjacent to Southeast Asia. The local emissions and air quality of this province are affected by cross-border transportation from Southeast Asia. This study analyzes the observational PM2.5 concentration data from 2017 to 2021 at 40 sites in 16 cities of Yunnan Province to understand the characteristics and trends of PM2.5 pollution. Cluster analysis with the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) backward trajectory model and concentration weighted trajectory are used to analyze potential PM2.5 sources. The results show that the annual mean PM2.5 concentration in Yunnan Province decreases by 0.91±0.23 μg m−3 a−1. PM2.5 concentration varies seasonally, and its concentration reaches the highest value in spring with 31.92±9.08 μg m−3 and lowest in summer with 13.50±2.69 μg m−3. The PM2.5 pollution is severe in spring due to biomass burning in Southeast Asia, which contributes to ＜40 μg m−3 of PM2.5 concentration. The high-potential source areas in spring include the southwest of Guangxi. The diurnal PM2.5 variations reveal a bimodal pattern with the value peaking between 0900 LST–1200 LST and 2100 LST–0100 LST, which is mainly attributed to human activities and meteorological factors, such as the lower boundary layer height and wind speed. In conclusion, PM2.5 during spring in Yunnan Province mostly comes from cross-border transportation. Thus, this study provides new insight for air pollutant control in the province.
Yunnan Province is located in a low-latitude plateau area adjacent to Southeast Asia. The local emissions and air quality of this province are affected by cross-border transportation from Southeast Asia. This study analyzes the observational PM2.5 concentration data from 2017 to 2021 at 40 sites in 16 cities of Yunnan Province to understand the characteristics and trends of PM2.5 pollution. Cluster analysis with the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) backward trajectory model and concentration weighted trajectory are used to analyze potential PM2.5 sources. The results show that the annual mean PM2.5 concentration in Yunnan Province decreases by 0.91±0.23 μg m−3 a−1. PM2.5 concentration varies seasonally, and its concentration reaches the highest value in spring with 31.92±9.08 μg m−3 and lowest in summer with 13.50±2.69 μg m−3. The PM2.5 pollution is severe in spring due to biomass burning in Southeast Asia, which contributes to ＜40 μg m−3 of PM2.5 concentration. The high-potential source areas in spring include the southwest of Guangxi. The diurnal PM2.5 variations reveal a bimodal pattern with the value peaking between 0900 LST–1200 LST and 2100 LST–0100 LST, which is mainly attributed to human activities and meteorological factors, such as the lower boundary layer height and wind speed. In conclusion, PM2.5 during spring in Yunnan Province mostly comes from cross-border transportation. Thus, this study provides new insight for air pollutant control in the province.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2022.22052
Abstract:
Biomass burning emits large amounts of trace gases and particulate matter into the atmosphere. Emission inventories are important datasets for studying the environmental and climatic effects of biomass burning. The spatial distribution, seasonal changes, and interannual variations in biomass burning emissions over China during 2008–2017 were investigated based on three biomass burning inventories, namely, the Global Fire Emissions Database (GFED), the Fire INventory from NCAR (FINN), and the emission inventory of open biomass burning in China (MEIC). The similarities and differences among the inventories were also compared and analyzed. The inventories consistently exhibited high amounts of black carbon (BC), organic carbon (OC), aerodynamic particulate matter with a size of ＜2.5 µm (PM2.5), and carbon monoxide (CO) from biomass burning over Northeast China, the regions between the lower reaches of the Yellow River and Yangtze River, and southern China, where agriculture and forest areas concentrated. The emission amounts in the FINN inventory were larger in southern and Southwest China than those in the other two inventories, whereas the emission amounts in the GFED inventory were higher in the Yangtze River Delta. Averaged over China, the peak of biomass burning emissions was in spring, whereas the peak emission amount occurred in different seasons for typical subregions of China, which could be related to different crop-sowing seasons and seeding habits. Between 2008 and 2017, the domain average annual biomass burning emissions peaked in 2014 in China. However, the period corresponding to maximum emissions was different for the subregions. Maximum emissions occurred in 2015, 2013, 2008, and 2010 in Northeast China, East China, South China, and Southwest China, respectively. For BC, OC, and PM2.5, the estimated domain average emissions over China were similar between GFED and MEIC, whereas the emissions in FINN were 2–3 times higher than those in the other two inventories. The CO emissions were similar in the three inventories. The annual and domain average OC and PM2.5 emissions from biomass burning in the three emission inventories accounted for 9%–24% and 5%–16%, respectively, of anthropogenic emissions over China in 2014, indicating that OC and primary PM2.5 from biomass burning are important sources of primary aerosols over China.
Biomass burning emits large amounts of trace gases and particulate matter into the atmosphere. Emission inventories are important datasets for studying the environmental and climatic effects of biomass burning. The spatial distribution, seasonal changes, and interannual variations in biomass burning emissions over China during 2008–2017 were investigated based on three biomass burning inventories, namely, the Global Fire Emissions Database (GFED), the Fire INventory from NCAR (FINN), and the emission inventory of open biomass burning in China (MEIC). The similarities and differences among the inventories were also compared and analyzed. The inventories consistently exhibited high amounts of black carbon (BC), organic carbon (OC), aerodynamic particulate matter with a size of ＜2.5 µm (PM2.5), and carbon monoxide (CO) from biomass burning over Northeast China, the regions between the lower reaches of the Yellow River and Yangtze River, and southern China, where agriculture and forest areas concentrated. The emission amounts in the FINN inventory were larger in southern and Southwest China than those in the other two inventories, whereas the emission amounts in the GFED inventory were higher in the Yangtze River Delta. Averaged over China, the peak of biomass burning emissions was in spring, whereas the peak emission amount occurred in different seasons for typical subregions of China, which could be related to different crop-sowing seasons and seeding habits. Between 2008 and 2017, the domain average annual biomass burning emissions peaked in 2014 in China. However, the period corresponding to maximum emissions was different for the subregions. Maximum emissions occurred in 2015, 2013, 2008, and 2010 in Northeast China, East China, South China, and Southwest China, respectively. For BC, OC, and PM2.5, the estimated domain average emissions over China were similar between GFED and MEIC, whereas the emissions in FINN were 2–3 times higher than those in the other two inventories. The CO emissions were similar in the three inventories. The annual and domain average OC and PM2.5 emissions from biomass burning in the three emission inventories accounted for 9%–24% and 5%–16%, respectively, of anthropogenic emissions over China in 2014, indicating that OC and primary PM2.5 from biomass burning are important sources of primary aerosols over China.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.22050
Abstract:
In this study, the characteristics and applicability of wind resources in offshore China are analyzed using the reanalysis data of ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis version 5), CFSR (Climate Forecast System Reanalysis), and MERRA2 (Modern-Era Retrospective analysis for Research and Applications version 2) and the observation data of 13 ocean stations and three wind towers in offshore China. Results showed that the weak wind frequency of reanalysis data in the East China Sea and South China Sea is higher than that in the Bohai Sea and Yellow Sea. The cumulative probabilities of weak wind (＜6 m/s) of ERA5 in all sea areas are 38.99% (44.95%), 43.63% (47.54%), 60.74% (41.63%), and 47.94% (38.19%), higher than that of CFSR (MERRA2). In all three reanalyses, the distributions of effective wind time (EW) in offshore China are similar (84%–95%). The value of mean wind energy density (WPD) showed the “low–high–low” spatial distribution from north to south; the Taiwan Strait had a high WPD value (＞4000 W/m2). Regarding the stability of wind energy, the daily variation of ERA5 and CFSR is weak in the south and strong in the north, while the daily coefficient of variation (CV) of MERRA2 is between 1.03 and 4. The performance of ERA5 is better than that of CFSR and MERRA2. The MERRA2 reproduces the daily fluctuation of wind energy in the Bohai Sea and the South China Sea. Moreover, CFSR has certain advantages with respect to the EW and WPD of the Yellow Sea and the CV of the East China Sea and the Yellow Sea. According to the above-mentioned conclusions, the applicability of reanalysis data to different indicators of offshore China wind resources varies. Consequently, based on the requirements and data conditions, using different types of reanalysis data for different sea areas in wind resource assessment research and work.
In this study, the characteristics and applicability of wind resources in offshore China are analyzed using the reanalysis data of ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis version 5), CFSR (Climate Forecast System Reanalysis), and MERRA2 (Modern-Era Retrospective analysis for Research and Applications version 2) and the observation data of 13 ocean stations and three wind towers in offshore China. Results showed that the weak wind frequency of reanalysis data in the East China Sea and South China Sea is higher than that in the Bohai Sea and Yellow Sea. The cumulative probabilities of weak wind (＜6 m/s) of ERA5 in all sea areas are 38.99% (44.95%), 43.63% (47.54%), 60.74% (41.63%), and 47.94% (38.19%), higher than that of CFSR (MERRA2). In all three reanalyses, the distributions of effective wind time (EW) in offshore China are similar (84%–95%). The value of mean wind energy density (WPD) showed the “low–high–low” spatial distribution from north to south; the Taiwan Strait had a high WPD value (＞4000 W/m2). Regarding the stability of wind energy, the daily variation of ERA5 and CFSR is weak in the south and strong in the north, while the daily coefficient of variation (CV) of MERRA2 is between 1.03 and 4. The performance of ERA5 is better than that of CFSR and MERRA2. The MERRA2 reproduces the daily fluctuation of wind energy in the Bohai Sea and the South China Sea. Moreover, CFSR has certain advantages with respect to the EW and WPD of the Yellow Sea and the CV of the East China Sea and the Yellow Sea. According to the above-mentioned conclusions, the applicability of reanalysis data to different indicators of offshore China wind resources varies. Consequently, based on the requirements and data conditions, using different types of reanalysis data for different sea areas in wind resource assessment research and work.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2022.22044
Abstract:
In this paper, the field observation and new generation geostationary meteorological satellite data are utilized to conduct the low visibility training experiments on Zhejiang Jinliwen Expressway with six machine learning methods, including logistic regression, linear discriminant analysis, K-nearest neighbor algorithm (KNN), classification and regression tree, Gaussian naive Bayes, and support vector machine (SVM). In addition, this paper also incorporates a variety of methods to evaluate the training results. The SVM algorithm demonstrated a good training effect, particularly for recognizing visibility of ＜1000 m. Further, the integration of the ground observation and satellite data to establish the recognition model exhibits that the modeling results are better than a single source. Generally, the KNN algorithm has improved the modeling effect. Thus, to distinguish between dense fog and strong dense fog, the model combining the ground and satellite data has enhanced the recognition results. The SVM algorithm was employed for single source data, whereas the KNN algorithm was utilized for ground as well as satellite data to distinguish the fogging process on the Jinliwen Expressway. The results demonstrate that the recognition effect of the new generation geostationary meteorological satellite data is not only equivalent to the field observation recognition but also capable of distinguishing between morning and night fog, which can be utilized as an effective functional extension to the field observation recognition. Therefore, this research will provide an auxiliary reference for the identification and prediction of low visibility, thereby eliminating the requirement for field meteorological observations in the province.
In this paper, the field observation and new generation geostationary meteorological satellite data are utilized to conduct the low visibility training experiments on Zhejiang Jinliwen Expressway with six machine learning methods, including logistic regression, linear discriminant analysis, K-nearest neighbor algorithm (KNN), classification and regression tree, Gaussian naive Bayes, and support vector machine (SVM). In addition, this paper also incorporates a variety of methods to evaluate the training results. The SVM algorithm demonstrated a good training effect, particularly for recognizing visibility of ＜1000 m. Further, the integration of the ground observation and satellite data to establish the recognition model exhibits that the modeling results are better than a single source. Generally, the KNN algorithm has improved the modeling effect. Thus, to distinguish between dense fog and strong dense fog, the model combining the ground and satellite data has enhanced the recognition results. The SVM algorithm was employed for single source data, whereas the KNN algorithm was utilized for ground as well as satellite data to distinguish the fogging process on the Jinliwen Expressway. The results demonstrate that the recognition effect of the new generation geostationary meteorological satellite data is not only equivalent to the field observation recognition but also capable of distinguishing between morning and night fog, which can be utilized as an effective functional extension to the field observation recognition. Therefore, this research will provide an auxiliary reference for the identification and prediction of low visibility, thereby eliminating the requirement for field meteorological observations in the province.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2022.21146
Abstract:
Using comprehensive metadata, homogeneity test and corrections were conducted on daily mean, maximum, and minimum temperatures at 83 stations in Heilongjiang from 1951 to 2019.The penalized maximal t-test (PMT) and penalized maximal F-test (PMF) methods from the RH test software package developed by Environment Canada were employed. The adjusted series were compared to the CHTM dataset, with a focus on how changes in observation instruments affected data homogeneity over the past decade. From the homogenized daily temperature data spanning from 1951 to 2019, the following climate extreme indices in Heilongjiang were calculated: Cold spell duration index (CSDI), frost days (FD), icing days (ID), and daily temperature range (DTR). The analysis identified 40, 20, and 57 changepoints in daily mean, maximum, and minimum temperatures, respectively. The primary reasons for data discontinuity were station relocations, instrument changes, and automatic observation software upgrades. Spatial consistency improved after corrections were applied. After adjustment, the long-term trends for mean and minimum temperatures increased from 0.27°C/10 a and 0.25°C/10 a to 0.29°C/10 a and 0.27°C/10 a, respectively. The CSDI, FD, and ID indices in Heilongjiang demonstrated a significant downward trend, aligning with the national climate warming pattern. The lowest FD index value occurred in 1998, coinciding with China’s second hottest recorded mean temperature. The warming trend of the annual mean maximum temperature in Heilongjiang was slightly less than that of the annual mean minimum temperature, resulting in a decreasing trend for DTR.
Using comprehensive metadata, homogeneity test and corrections were conducted on daily mean, maximum, and minimum temperatures at 83 stations in Heilongjiang from 1951 to 2019.The penalized maximal t-test (PMT) and penalized maximal F-test (PMF) methods from the RH test software package developed by Environment Canada were employed. The adjusted series were compared to the CHTM dataset, with a focus on how changes in observation instruments affected data homogeneity over the past decade. From the homogenized daily temperature data spanning from 1951 to 2019, the following climate extreme indices in Heilongjiang were calculated: Cold spell duration index (CSDI), frost days (FD), icing days (ID), and daily temperature range (DTR). The analysis identified 40, 20, and 57 changepoints in daily mean, maximum, and minimum temperatures, respectively. The primary reasons for data discontinuity were station relocations, instrument changes, and automatic observation software upgrades. Spatial consistency improved after corrections were applied. After adjustment, the long-term trends for mean and minimum temperatures increased from 0.27°C/10 a and 0.25°C/10 a to 0.29°C/10 a and 0.27°C/10 a, respectively. The CSDI, FD, and ID indices in Heilongjiang demonstrated a significant downward trend, aligning with the national climate warming pattern. The lowest FD index value occurred in 1998, coinciding with China’s second hottest recorded mean temperature. The warming trend of the annual mean maximum temperature in Heilongjiang was slightly less than that of the annual mean minimum temperature, resulting in a decreasing trend for DTR.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.22147
Abstract:
Through the measurement results of the Beijing Nanjiao radiosonde in January–October 2021 and measurement data of the automatic station of the Alpine Skiing Venue in Yanqing during the Winter Olympic Games, the accuracies of temperature and humidity profiles retrieved by a ground-based MicroWave Radiometer (MWR) were assessed. The results revealed that the MWR-retrieved temperature profile exhibited a good correlation with the observations from the radiosonde and the automatic station, with relatively small errors and good reliability. However, the MWR-retrieved humidity profile exhibited poor correlation with the observations from the radiosonde and the automatic station (correlation coefficient=0.81). Comparison of the results from the MWR and those of different automatic stations and the radiosonde revealed that at levels of 0.15–0.6 km, the MWR-retrieved temperature profile exhibited a good correlation with the observations from the radiosonde and the automatic station. Furthermore, the Root-Mean-Square Error (RMSE) and Average Deviation (AD) of the MWR-retrieved temperature profile concerning the observation from the automatic station increased with increasing height and nevertheless those of the MWR-retrieved temperature profile with respect to the observation from the radiosonde decreased with increasing height at levels of 0.15–0.6 km. The correlation, RMSE, and AD of the MWR-retrieved relative humidity profile with respect to the observation from the radiosonde and the automatic station increased with increasing height. Based on the comparison between the MWR and the radiosonde, the authors observed a significant positive correlation between the MWR-retrieved temperature and radiosonde observations over the entire sounding height, which is higher in the lower atmosphere than in the upper atmosphere. In contrast, the correlation of the MWR-retrieved humidity profile with the observation from the radiosonde was significantly lower than that of the temperature profile and exhibited a negative correlation at levels of 2.75–3.5 km. The Mean Error (ME) of the MWR-retrieved temperature profile at each sounding height, except at 10 km, was within 2°C. Meanwhile, the RMSE and AD of the MWR-retrieved temperature profile were about 3.4°C and 2.5°C, respectively, at levels of ＜3 km. Furthermore, the RMSE and AD values of all other levels increased with increasing height. The RMSE and AD of the MWR-retrieved humidity profile were expectedly higher than those of the temperature profile at all levels. Meanwhile, the ME of the MWR-retrieved humidity profile was large at most levels, with a maximum of 23.67%. Precipitation caused the error in the MWR-retrieved temperature profile to increase at the most levels for the duration of 0800 LST and 2000 LST, in which the RMSE and AD under precipitation were expectedly higher than those under the no-rain condition at levels ＞0.5 km. However, the RMSE and AD of the MWR-retrieved humidity profile during 0800 LST and 2000 LST in rainy days (at most levels in the lower atmosphere) were expectedly lower than those during clear days, in which the RMSE and AD at 2000 LST degraded dramatically.
Through the measurement results of the Beijing Nanjiao radiosonde in January–October 2021 and measurement data of the automatic station of the Alpine Skiing Venue in Yanqing during the Winter Olympic Games, the accuracies of temperature and humidity profiles retrieved by a ground-based MicroWave Radiometer (MWR) were assessed. The results revealed that the MWR-retrieved temperature profile exhibited a good correlation with the observations from the radiosonde and the automatic station, with relatively small errors and good reliability. However, the MWR-retrieved humidity profile exhibited poor correlation with the observations from the radiosonde and the automatic station (correlation coefficient=0.81). Comparison of the results from the MWR and those of different automatic stations and the radiosonde revealed that at levels of 0.15–0.6 km, the MWR-retrieved temperature profile exhibited a good correlation with the observations from the radiosonde and the automatic station. Furthermore, the Root-Mean-Square Error (RMSE) and Average Deviation (AD) of the MWR-retrieved temperature profile concerning the observation from the automatic station increased with increasing height and nevertheless those of the MWR-retrieved temperature profile with respect to the observation from the radiosonde decreased with increasing height at levels of 0.15–0.6 km. The correlation, RMSE, and AD of the MWR-retrieved relative humidity profile with respect to the observation from the radiosonde and the automatic station increased with increasing height. Based on the comparison between the MWR and the radiosonde, the authors observed a significant positive correlation between the MWR-retrieved temperature and radiosonde observations over the entire sounding height, which is higher in the lower atmosphere than in the upper atmosphere. In contrast, the correlation of the MWR-retrieved humidity profile with the observation from the radiosonde was significantly lower than that of the temperature profile and exhibited a negative correlation at levels of 2.75–3.5 km. The Mean Error (ME) of the MWR-retrieved temperature profile at each sounding height, except at 10 km, was within 2°C. Meanwhile, the RMSE and AD of the MWR-retrieved temperature profile were about 3.4°C and 2.5°C, respectively, at levels of ＜3 km. Furthermore, the RMSE and AD values of all other levels increased with increasing height. The RMSE and AD of the MWR-retrieved humidity profile were expectedly higher than those of the temperature profile at all levels. Meanwhile, the ME of the MWR-retrieved humidity profile was large at most levels, with a maximum of 23.67%. Precipitation caused the error in the MWR-retrieved temperature profile to increase at the most levels for the duration of 0800 LST and 2000 LST, in which the RMSE and AD under precipitation were expectedly higher than those under the no-rain condition at levels ＞0.5 km. However, the RMSE and AD of the MWR-retrieved humidity profile during 0800 LST and 2000 LST in rainy days (at most levels in the lower atmosphere) were expectedly lower than those during clear days, in which the RMSE and AD at 2000 LST degraded dramatically.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.22072
Abstract:
Hazardous/lethal compound temperature–humidity heatwaves with a wet bulb temperature (i.e., ≥33°C/35°C) can severely affect human health. The middle and lower reaches of the Yangtze River always experience high-frequency compound temperature–humidity heatwaves. This study investigates the population exposed to these heatwaves in the middle and lower reaches of the Yangtze River for the near- (2021–2040), medium- (2041–2060), and long-term (2081–2100) periods using the five-climate model outputs under seven Shared Socioeconomic Pathways-based scenarios (i.e., SSP1-1.9, SSP1-2.6, SSP4-3.4, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5). These scenarios are obtained from the Coupled Model Intercomparison Project Phase 6 in combination with the demographic characteristics under the Shared Socioeconomic Pathways (i.e., SSP1-5). The results show that during the baseline period of 1995–2014, hazardous/lethal compound temperature–humidity heatwaves occurred at frequencies of approximately 6 and 3 d in the middle and lower reaches of the Yangtze River, where the longest duration was approximately 10 and 4 d, respectively. In the future, the frequency and duration of such heatwaves are projected to increase in the long-term period, where the frequency is expected to be approximately 12–39 and 7–24 d, and the longest duration is as long as 30 and 14 d, respectively. In the baseline period, the hazardous/lethal compound temperature–humidity heatwaves affected an area of approximately 74.8×104 and 22.3×104 km2 in the middle and lower reaches of the Yangtze River, exposing the populations of 170 and 20 million people of the hazardous/lethal compound temperature–humidity heatwaves, respectively. The impact range and maximum exposed population in the 21st century were observed in the long-term period, accounting for approximately 83%–100% and 32%–98% of the study area, respectively. The exposed population was approximately 1.2–2.5 and 2.5–20.5 times higher than that during the base period in the middle and lower reaches of the Yangtze River, respectively. The population exposed to the lethal compound temperature–humidity heatwaves increased considerably by approximately 40–370 million. Spatially, the main areas affected by these heatwaves in the 21st century are Shanghai, northern Zhejiang, southern Jiangsu, southern Anhui, eastern Hunan, and eastern Jiangxi. Overall, forecasting, early warning, and risk prevention of lethal compound temperature–humidity heatwaves must be urgently improved.
Hazardous/lethal compound temperature–humidity heatwaves with a wet bulb temperature (i.e., ≥33°C/35°C) can severely affect human health. The middle and lower reaches of the Yangtze River always experience high-frequency compound temperature–humidity heatwaves. This study investigates the population exposed to these heatwaves in the middle and lower reaches of the Yangtze River for the near- (2021–2040), medium- (2041–2060), and long-term (2081–2100) periods using the five-climate model outputs under seven Shared Socioeconomic Pathways-based scenarios (i.e., SSP1-1.9, SSP1-2.6, SSP4-3.4, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5). These scenarios are obtained from the Coupled Model Intercomparison Project Phase 6 in combination with the demographic characteristics under the Shared Socioeconomic Pathways (i.e., SSP1-5). The results show that during the baseline period of 1995–2014, hazardous/lethal compound temperature–humidity heatwaves occurred at frequencies of approximately 6 and 3 d in the middle and lower reaches of the Yangtze River, where the longest duration was approximately 10 and 4 d, respectively. In the future, the frequency and duration of such heatwaves are projected to increase in the long-term period, where the frequency is expected to be approximately 12–39 and 7–24 d, and the longest duration is as long as 30 and 14 d, respectively. In the baseline period, the hazardous/lethal compound temperature–humidity heatwaves affected an area of approximately 74.8×104 and 22.3×104 km2 in the middle and lower reaches of the Yangtze River, exposing the populations of 170 and 20 million people of the hazardous/lethal compound temperature–humidity heatwaves, respectively. The impact range and maximum exposed population in the 21st century were observed in the long-term period, accounting for approximately 83%–100% and 32%–98% of the study area, respectively. The exposed population was approximately 1.2–2.5 and 2.5–20.5 times higher than that during the base period in the middle and lower reaches of the Yangtze River, respectively. The population exposed to the lethal compound temperature–humidity heatwaves increased considerably by approximately 40–370 million. Spatially, the main areas affected by these heatwaves in the 21st century are Shanghai, northern Zhejiang, southern Jiangsu, southern Anhui, eastern Hunan, and eastern Jiangxi. Overall, forecasting, early warning, and risk prevention of lethal compound temperature–humidity heatwaves must be urgently improved.
2023 Issue 4
Display Method:
2023, 28(4): 343-355.
doi: 10.3878/j.issn.1006-9585.2022.21049
Abstract:
Based on the 503 observed fog station data from 1958 to 2007, the temporal and spatial characteristics of fog days in autumn and winter were analyzed. It was found that the fog in autumn and winter occurs frequently over Southwest China. The annual average number of fog days in autumn and winter in Southwest China is more than 18 days, which is twice as many as that in eastern China in the same period. Moreover, there is a significant climate variability of fog days in autumn and winter in Southwest China, which is mainly reflected in the interannual and interdecadal scales, and there are significant differences in the meteorological conditions of fog days in different climate scales. On the interannual scale, the north wind anomaly in the middle and upper levels is more significant, which brings the cold air from the north to the southwest, causing the cold air anomaly in the middle and upper levels over the southwest. Moreover, due to the strong sinking movement over the region, the cold air in the upper levels is brought to the lower levels. At this time, because of the abnormally high temperature and humidity in the lower levels, the cold and warm air converges over Southwest China which is located in the north of the rain belt. In a result, the air is easily oversaturated, resulting the number of fog days increasing. On the interdecadal scale, the anomalous northerly wind in the lower level is more significant compared with the interannual scale, which brings the cold air in the lower level from the north to the southwest. This results in the decreasing of temperature, specific humidity and temperature dew point difference and further leads to the supersaturation of water vapor in the Southwest China because the temperature drops faster than the humidity. At the same time, the atmosphere is relatively stable, increasing the number of fog days.
Based on the 503 observed fog station data from 1958 to 2007, the temporal and spatial characteristics of fog days in autumn and winter were analyzed. It was found that the fog in autumn and winter occurs frequently over Southwest China. The annual average number of fog days in autumn and winter in Southwest China is more than 18 days, which is twice as many as that in eastern China in the same period. Moreover, there is a significant climate variability of fog days in autumn and winter in Southwest China, which is mainly reflected in the interannual and interdecadal scales, and there are significant differences in the meteorological conditions of fog days in different climate scales. On the interannual scale, the north wind anomaly in the middle and upper levels is more significant, which brings the cold air from the north to the southwest, causing the cold air anomaly in the middle and upper levels over the southwest. Moreover, due to the strong sinking movement over the region, the cold air in the upper levels is brought to the lower levels. At this time, because of the abnormally high temperature and humidity in the lower levels, the cold and warm air converges over Southwest China which is located in the north of the rain belt. In a result, the air is easily oversaturated, resulting the number of fog days increasing. On the interdecadal scale, the anomalous northerly wind in the lower level is more significant compared with the interannual scale, which brings the cold air in the lower level from the north to the southwest. This results in the decreasing of temperature, specific humidity and temperature dew point difference and further leads to the supersaturation of water vapor in the Southwest China because the temperature drops faster than the humidity. At the same time, the atmosphere is relatively stable, increasing the number of fog days.
2023, 28(4): 356-366.
doi: 10.3878/j.issn.1006-9585.2022.21196
Abstract:
Using reanalysis data from the National Center for Environmental Prediction/National Center for Atmospheric Research and the monthly precipitation data from the Global Precipitation Climatology Center for the period 1961–2016, this study investigates the decadal relationships between the subtropical westerly jet and summer rainfall over central Asia. Results indicate that the subtropical westerly jet experienced significant decadal change points in 1997/1998. Before 1997, when the jet shifted southward, an anomalous cyclone dominated central Asia, while the anomalous anticyclone over the Indian Peninsula and anomalous cyclone over central Asia transported water vapor from the tropical Indian Ocean into central Asia, causing increased summer rainfall. After 1997, the relationship between the subtropical westerly jet and summer rainfall weakened. Furthermore, when the jet shifted southward, the anomalous cyclone over central Asia weakened and shifted westward, while the anomalous anticyclone over the Indian Peninsula strengthened and shifted westward. This resulted in weakened meridional water vapor transport from the tropical Indian Ocean into central Asia, thus, no longer reaching the eastern and northern parts of the region. The East Atlantic/West Russia (EA-WR) pattern exhibited different decadal relationships with the meridional location of the Central Asian subtropical westerly jet, acting as an indirect influence. Before 1997, a negative EA-WR pattern corresponded to an anomalous anticyclone over the Ural Mountains region, with the northerly flow on the anticyclone’s eastern flank transporting cold air from high latitudes southward. This led to a cooling of the upper troposphere and the formation of an anomalous cyclone over Central Asia, corresponding to the jet shifting southward. After 1997, the anomalous anticyclone over the former Ural Mountains region shifted eastward, no longer causing upper troposphere cooling over central Asia, and the relationships between the subtropical westerly jet location and EA-WR decreased. Before and after the decadal change in subtropical westerly jet location, a positive Pacific–North America pattern could cause the anomalous cyclone to determine central Asia, corresponding to the jet shifting southward.
Using reanalysis data from the National Center for Environmental Prediction/National Center for Atmospheric Research and the monthly precipitation data from the Global Precipitation Climatology Center for the period 1961–2016, this study investigates the decadal relationships between the subtropical westerly jet and summer rainfall over central Asia. Results indicate that the subtropical westerly jet experienced significant decadal change points in 1997/1998. Before 1997, when the jet shifted southward, an anomalous cyclone dominated central Asia, while the anomalous anticyclone over the Indian Peninsula and anomalous cyclone over central Asia transported water vapor from the tropical Indian Ocean into central Asia, causing increased summer rainfall. After 1997, the relationship between the subtropical westerly jet and summer rainfall weakened. Furthermore, when the jet shifted southward, the anomalous cyclone over central Asia weakened and shifted westward, while the anomalous anticyclone over the Indian Peninsula strengthened and shifted westward. This resulted in weakened meridional water vapor transport from the tropical Indian Ocean into central Asia, thus, no longer reaching the eastern and northern parts of the region. The East Atlantic/West Russia (EA-WR) pattern exhibited different decadal relationships with the meridional location of the Central Asian subtropical westerly jet, acting as an indirect influence. Before 1997, a negative EA-WR pattern corresponded to an anomalous anticyclone over the Ural Mountains region, with the northerly flow on the anticyclone’s eastern flank transporting cold air from high latitudes southward. This led to a cooling of the upper troposphere and the formation of an anomalous cyclone over Central Asia, corresponding to the jet shifting southward. After 1997, the anomalous anticyclone over the former Ural Mountains region shifted eastward, no longer causing upper troposphere cooling over central Asia, and the relationships between the subtropical westerly jet location and EA-WR decreased. Before and after the decadal change in subtropical westerly jet location, a positive Pacific–North America pattern could cause the anomalous cyclone to determine central Asia, corresponding to the jet shifting southward.
2023, 28(4): 367-384.
doi: 10.3878/j.issn.1006-9585.2022.21180
Abstract:
The daily daytime/nighttime precipitation data of 81 National Meteorological Science Data Center stations on the Qinghai–Tibet Plateau and 80 stations in Southwest China were used to statistically analyze the daytime/nighttime rainy days of different magnitudes, differences in this number in the two regions from 1961 to 2020, and interannual change (trend) characteristics. The results showed the following: (1) The number of daytime/nighttime rainy days of different magnitudes on the Tibetan Plateau and in Southwest China increased rapidly in May and decreased significantly in November; large differences in the number of daytime/nighttime rainy days in the two regions were observed in May–October, and the number of heavy rain and rainstorm days was higher in May–October. The number of rainstorm days on the Tibetan Plateau accounted for ＞85% of the total number of rainstorm days in the year, heavy rain accounted for ＞90%, and the proportions of these two levels of precipitation were 93% and 86%, respectively, in Southwest China. The probability of extremely heavy precipitation (rainstorms) during the rainy season in both regions was substantially higher at night than during the day. (2) In addition to obvious regional differences in the number of daytime/nighttime rainy days of different magnitudes in the two regions during the rainy season, very significant differences were observed between daytime and nighttime. The Three-Rivers Source region and the southeastern part of the Tibetan Plateau have frequent light and moderate rains during the day and night. Heavy rain occurs exclusively at night in southeastern Tibet and the east of the Three-Rivers Source region; rainstorms occur only in the east of the Tibetan Plateau, and there are visibly more sites receiving precipitation at night than during the day. Central Sichuan, western Sichuan marginal mountains, and southwestern Yunnan in Southwest China have a high incidence of daytime/nighttime light rain and moderate rain; heavy rain is more likely to occur during the day in southwestern Yunnan, whereas it is more likely to occur at night in central Sichuan. Typical areas with rainstorms are central and eastern Sichuan, western Chongqing, and central and southern Guizhou. (3) The overall trends of interannual changes in the number of rainy seasons in the rainy season are significantly different between daytime/nighttime-graded rain days. In the Tibetan Plateau, except for the significant decrease in the number of light rainy days at night, the rest of the rainy days of different magnitudes increased significantly during the daytime and nighttime, with the increasing trend at night being greater than that during the day, and the increasing trend of heavy rain and rainstorm at night was almost twice that of the day. Unlike the Tibetan Plateau, the number of days with light precipitation (light rain, moderate rain, and heavy rain) decreased significantly in Southwest China. Among them, the number of light rainy days at night decreased the most, approximately twice as much as during the daytime; the number of days with heavy precipitation (rainstorm or torrential rain) increased in the opposite direction, and the increasing trend at night is greater than that during the daytime.
The daily daytime/nighttime precipitation data of 81 National Meteorological Science Data Center stations on the Qinghai–Tibet Plateau and 80 stations in Southwest China were used to statistically analyze the daytime/nighttime rainy days of different magnitudes, differences in this number in the two regions from 1961 to 2020, and interannual change (trend) characteristics. The results showed the following: (1) The number of daytime/nighttime rainy days of different magnitudes on the Tibetan Plateau and in Southwest China increased rapidly in May and decreased significantly in November; large differences in the number of daytime/nighttime rainy days in the two regions were observed in May–October, and the number of heavy rain and rainstorm days was higher in May–October. The number of rainstorm days on the Tibetan Plateau accounted for ＞85% of the total number of rainstorm days in the year, heavy rain accounted for ＞90%, and the proportions of these two levels of precipitation were 93% and 86%, respectively, in Southwest China. The probability of extremely heavy precipitation (rainstorms) during the rainy season in both regions was substantially higher at night than during the day. (2) In addition to obvious regional differences in the number of daytime/nighttime rainy days of different magnitudes in the two regions during the rainy season, very significant differences were observed between daytime and nighttime. The Three-Rivers Source region and the southeastern part of the Tibetan Plateau have frequent light and moderate rains during the day and night. Heavy rain occurs exclusively at night in southeastern Tibet and the east of the Three-Rivers Source region; rainstorms occur only in the east of the Tibetan Plateau, and there are visibly more sites receiving precipitation at night than during the day. Central Sichuan, western Sichuan marginal mountains, and southwestern Yunnan in Southwest China have a high incidence of daytime/nighttime light rain and moderate rain; heavy rain is more likely to occur during the day in southwestern Yunnan, whereas it is more likely to occur at night in central Sichuan. Typical areas with rainstorms are central and eastern Sichuan, western Chongqing, and central and southern Guizhou. (3) The overall trends of interannual changes in the number of rainy seasons in the rainy season are significantly different between daytime/nighttime-graded rain days. In the Tibetan Plateau, except for the significant decrease in the number of light rainy days at night, the rest of the rainy days of different magnitudes increased significantly during the daytime and nighttime, with the increasing trend at night being greater than that during the day, and the increasing trend of heavy rain and rainstorm at night was almost twice that of the day. Unlike the Tibetan Plateau, the number of days with light precipitation (light rain, moderate rain, and heavy rain) decreased significantly in Southwest China. Among them, the number of light rainy days at night decreased the most, approximately twice as much as during the daytime; the number of days with heavy precipitation (rainstorm or torrential rain) increased in the opposite direction, and the increasing trend at night is greater than that during the daytime.
2023, 28(4): 385-397.
doi: 10.3878/j.issn.1006-9585.2022.22016
Abstract:
To comprehensively explore the local differences of urban microclimates, a one-year observational experiment was conducted utilizing a smart micro weather station in the urban area of Mentougou, Beijing, China. The local climate zoning was implemented to analyze the effects of the local environment on the near-surface temperature and wind field. The results demonstrated the following: (1) Daytime temperature is governed by solar radiation, building shading, land use, and anthropogenic heat emissions. On the other hand, night temperature is primarily influenced by urban canopy characteristics as well as anthropogenic heat emissions (traffic, life). The average daily temperature in summer is 0.68°C higher in the middle-level dense plots and 0.66°C higher in winter in the high-level dense plots (compared with sparse building plots, the same below). The cooling effect of dense tree plots in summer (0.3°C) is stronger than in winter (0.07°C). The cooling effect of the water body is primarily reflected during the summer days, which is 0.29°C lower on average, while in winter, the water body is predominantly warmed with a daily average of 0.38°C higher. (2) Due to the low roughness of the water block, the daily average wind speeds in summer and winter are higher by 0.5 and 0.37 m/s, respectively. On the other hand, the effects of wind barriers can be attributed to the reduced daily average wind speeds in summer and winter by 0.13 and 0.23 m/s, respectively. Owing to the blocking effect of buildings, the average daily wind speed of the middle-level dense plots is 0.54 and 0.48 m/s lower in summer and winter, respectively. This paper reveals the quantitative impact of urban complexes on local air temperature and wind. Research shows that local climate zoning can better reflect the local differences in temperature and wind in urban blocks and provide a scientific basis for the construction of livable cities.
To comprehensively explore the local differences of urban microclimates, a one-year observational experiment was conducted utilizing a smart micro weather station in the urban area of Mentougou, Beijing, China. The local climate zoning was implemented to analyze the effects of the local environment on the near-surface temperature and wind field. The results demonstrated the following: (1) Daytime temperature is governed by solar radiation, building shading, land use, and anthropogenic heat emissions. On the other hand, night temperature is primarily influenced by urban canopy characteristics as well as anthropogenic heat emissions (traffic, life). The average daily temperature in summer is 0.68°C higher in the middle-level dense plots and 0.66°C higher in winter in the high-level dense plots (compared with sparse building plots, the same below). The cooling effect of dense tree plots in summer (0.3°C) is stronger than in winter (0.07°C). The cooling effect of the water body is primarily reflected during the summer days, which is 0.29°C lower on average, while in winter, the water body is predominantly warmed with a daily average of 0.38°C higher. (2) Due to the low roughness of the water block, the daily average wind speeds in summer and winter are higher by 0.5 and 0.37 m/s, respectively. On the other hand, the effects of wind barriers can be attributed to the reduced daily average wind speeds in summer and winter by 0.13 and 0.23 m/s, respectively. Owing to the blocking effect of buildings, the average daily wind speed of the middle-level dense plots is 0.54 and 0.48 m/s lower in summer and winter, respectively. This paper reveals the quantitative impact of urban complexes on local air temperature and wind. Research shows that local climate zoning can better reflect the local differences in temperature and wind in urban blocks and provide a scientific basis for the construction of livable cities.
2023, 28(4): 398-408.
doi: 10.3878/j.issn.1006-9585.2023.22066
Abstract:
The safe operation of the ropeway was necessary for the Alpine skiing event during the 2022 Beijing Winter Olympics, and the peak gust was the key meteorological parameter that can affect the ropeway operation. In the winter 2019 and winter 2020, continuous observations of hourly peak gust at ten brackets of three ropeways in National Alpine Skiing Center were recorded using appropriate equipment. The obtained data showed the following characteristics of peak gust: (1) The ropeway peak gust increased with increasing altitude, and the directions of the primary peak gust of different ropeways varied. (2) When the peak gust speed reached a certain threshold and the included angle between the wind direction and the ropeway was 90°, the ropeways were affected with a maximum probability of 48.9%. (3) The peak gust speed affecting the ropeway was mainly concentrated in the 12–20 m/s range, and the included angle between the wind direction and the ropeway was 45° or 90°. Using the Lamb–Jenkinson classification method, the weather in Yanqing was classified into six categories, with F ropeway and B1 ropeway at lower altitudes mainly affected by weather type N and ropeway C at a high altitude mainly affected by weather type E–SE–NE. The forecast models for ropeway peak gust were established using a machine learning algorithm, and the results for ropeway bracket C8 were the most accurate: a forecast accuracy of up to 62.1%, mean absolute error of 2.2 m/s, and suprathreshold (＞12 m/s) forecast accuracy of up to 84%. This research supported the forecast of ropeway peak gust at the National Alpine Ski Center and provided a scientific foundation for the safe operation of ropeways in ski resorts during the post–Winter Olympics period.
The safe operation of the ropeway was necessary for the Alpine skiing event during the 2022 Beijing Winter Olympics, and the peak gust was the key meteorological parameter that can affect the ropeway operation. In the winter 2019 and winter 2020, continuous observations of hourly peak gust at ten brackets of three ropeways in National Alpine Skiing Center were recorded using appropriate equipment. The obtained data showed the following characteristics of peak gust: (1) The ropeway peak gust increased with increasing altitude, and the directions of the primary peak gust of different ropeways varied. (2) When the peak gust speed reached a certain threshold and the included angle between the wind direction and the ropeway was 90°, the ropeways were affected with a maximum probability of 48.9%. (3) The peak gust speed affecting the ropeway was mainly concentrated in the 12–20 m/s range, and the included angle between the wind direction and the ropeway was 45° or 90°. Using the Lamb–Jenkinson classification method, the weather in Yanqing was classified into six categories, with F ropeway and B1 ropeway at lower altitudes mainly affected by weather type N and ropeway C at a high altitude mainly affected by weather type E–SE–NE. The forecast models for ropeway peak gust were established using a machine learning algorithm, and the results for ropeway bracket C8 were the most accurate: a forecast accuracy of up to 62.1%, mean absolute error of 2.2 m/s, and suprathreshold (＞12 m/s) forecast accuracy of up to 84%. This research supported the forecast of ropeway peak gust at the National Alpine Ski Center and provided a scientific foundation for the safe operation of ropeways in ski resorts during the post–Winter Olympics period.
2023, 28(4): 409-419.
doi: 10.3878/j.issn.1006-9585.2023.22089
Abstract:
Based on the daily observational data of air temperature and the soil temperature at a depth of 0–320 cm at the Shijiazhuang urban meteorological station and two nearby rural stations from 2009 to 2012, the urban heat island (UHI) effect from the canopy to the surface and deep layers of soil at Shijiazhuang station and its differences were compared and analyzed. The results revealed that: 1) The annual average air temperature UHI intensity from 2009 to 2012 was 0.9°C, the UHI intensity of the soil temperature at a depth of 0–320 cm was between −0.5°C and 0.2°C, and the air temperature UHI intensity was substantially stronger than that of the soil temperature. The surface (0 cm) and shallow (5–40 cm) soil temperatures exhibited a heat island effect, the deep (80–320 cm) soil temperature exhibited a cold island effect, and the soil temperature at a depth of 40–80 cm was the conversion horizon of the two. Deep soil temperature, which may be impacted by the local climate, demonstrated local characteristics. 2) The air temperature UHI intensities during spring, summer, autumn, and winter were 1.1°C, 0.6°C, 0.7°C, and 1.3°C, respectively; the seasons exhibiting the strongest and weakest UHI intensities were winter and summer, respectively. Furthermore, the soil temperatures at the surface layer and above 40 cm exhibited the heat island effect, and those below 80 cm exhibited the cold island effect. During autumn, the soil temperature at different depths exhibited the cold island effect, with the intensity of the cold island effect at a depth of 320 cm being the strongest. During winter, the soil temperatures at the surface layer and above 80 cm predominantly exhibited the heat island effect, whereas those at a depth of 320 cm exhibited the cold island effect. The seasonal variation of the soil surface UHI intensity was consistent with that of the air temperature, and its physical mechanism exhibited similar properties. 3) The air temperature UHI intensity in each month was between 0.5°C and 1.6°C, with the strongest intensity observed during January and the weakest intensity observed during July and October. The soil temperatures at the surface layer and above 40 cm generally exhibited the heat island effect from January to July and December, with the UHI intensity peaking during June, and exhibited the cold island effect from August to November. The soil temperature below 80 cm exhibited the cold island effect for the majority of the year. 4) The UHI intensities of the annual and seasonal average air temperatures clearly exhibited diurnal variation characteristics; the annual and seasonal surface soil temperatures exhibited similar characteristics. However, with the increase in soil depth, the diurnal variation of soil temperature UHI intensity gradually weakened and finally transformed into the cold island effect.
Based on the daily observational data of air temperature and the soil temperature at a depth of 0–320 cm at the Shijiazhuang urban meteorological station and two nearby rural stations from 2009 to 2012, the urban heat island (UHI) effect from the canopy to the surface and deep layers of soil at Shijiazhuang station and its differences were compared and analyzed. The results revealed that: 1) The annual average air temperature UHI intensity from 2009 to 2012 was 0.9°C, the UHI intensity of the soil temperature at a depth of 0–320 cm was between −0.5°C and 0.2°C, and the air temperature UHI intensity was substantially stronger than that of the soil temperature. The surface (0 cm) and shallow (5–40 cm) soil temperatures exhibited a heat island effect, the deep (80–320 cm) soil temperature exhibited a cold island effect, and the soil temperature at a depth of 40–80 cm was the conversion horizon of the two. Deep soil temperature, which may be impacted by the local climate, demonstrated local characteristics. 2) The air temperature UHI intensities during spring, summer, autumn, and winter were 1.1°C, 0.6°C, 0.7°C, and 1.3°C, respectively; the seasons exhibiting the strongest and weakest UHI intensities were winter and summer, respectively. Furthermore, the soil temperatures at the surface layer and above 40 cm exhibited the heat island effect, and those below 80 cm exhibited the cold island effect. During autumn, the soil temperature at different depths exhibited the cold island effect, with the intensity of the cold island effect at a depth of 320 cm being the strongest. During winter, the soil temperatures at the surface layer and above 80 cm predominantly exhibited the heat island effect, whereas those at a depth of 320 cm exhibited the cold island effect. The seasonal variation of the soil surface UHI intensity was consistent with that of the air temperature, and its physical mechanism exhibited similar properties. 3) The air temperature UHI intensity in each month was between 0.5°C and 1.6°C, with the strongest intensity observed during January and the weakest intensity observed during July and October. The soil temperatures at the surface layer and above 40 cm generally exhibited the heat island effect from January to July and December, with the UHI intensity peaking during June, and exhibited the cold island effect from August to November. The soil temperature below 80 cm exhibited the cold island effect for the majority of the year. 4) The UHI intensities of the annual and seasonal average air temperatures clearly exhibited diurnal variation characteristics; the annual and seasonal surface soil temperatures exhibited similar characteristics. However, with the increase in soil depth, the diurnal variation of soil temperature UHI intensity gradually weakened and finally transformed into the cold island effect.
2023, 28(4): 420-436.
doi: 10.3878/j.issn.1006-9585.2022.22101
Abstract:
The hourly precipitation data provided by the China Meteorological Administration, and the hourly 0.25o(latitude)×0.25o(longitude) reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to investigate the key weather systems that generated the extreme hourly precipitation in Zhengzhou, Henan Province ("21 · 7" torrential rainfall event during 17−22 Jul 2021), and to diagnose the kinetic energy budget of the Upper-Level Jet (ULJ) and sub jet. It was found that, the "21·7" torrential rainfall event in Zhengzhou showed significant phased characteristics. The heavy rainfall mainly occurred in the mountainous areas in the north of Henan during the period of the extreme hourly precipitation in Zhengzhou and around the windward slope of the mountainous areas, which were notably affected by the terrain features. Located over Henan Province, the high-pressure ridge in the upper troposphere, the low-pressure trough in the middle troposphere, the horizontal shear line and the mesoscale convective vortex in the lower troposphere were the key weather systems that caused the extreme hourly precipitation in Zhengzhou. The high-level jet and sub-jet in the northeast of the South Asia high (SAH) caused the rapid development of the upper-tropospheric short-wave trough over Shaanxi Province through the strong cold advection. The baroclinic development of the shortwave trough made the warm advection in its front significantly enhanced, which led to the rapid development of the high-pressure ridge in the upper troposphere over Henan Province. This maintained the strong high-level divergence, which provided a very favorable background circulation condition for the occurrence of extreme hourly precipitation in Zhengzhou. The ULJ and sub-jet in the northeast of the SAH showed significant horizontal unevenness. Among them, the wind speed enhancement trend and the strength of the cold advection both reach maximum values at the bifurcation point of the ULJ (Region 1). This provides the most favorable conditions for the rapid intensification of the upper tropospheric shortwave trough over Shaanxi. The kinetic energy budget showed that the horizontal kinetic energy transport by the westerly wind in the northeast of the SAH acted as the most favorable factor for the development/maintenance of the ULJ and sub-jet in this region, with the kinetic energy mainly come from the ULJ area on the northern edge of the SAH. In this stage, the pressure gradient force mainly did negative work, which was the most unfavorable factor for the development/maintenance of the upper jet and sub jet.
The hourly precipitation data provided by the China Meteorological Administration, and the hourly 0.25o(latitude)×0.25o(longitude) reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to investigate the key weather systems that generated the extreme hourly precipitation in Zhengzhou, Henan Province ("21 · 7" torrential rainfall event during 17−22 Jul 2021), and to diagnose the kinetic energy budget of the Upper-Level Jet (ULJ) and sub jet. It was found that, the "21·7" torrential rainfall event in Zhengzhou showed significant phased characteristics. The heavy rainfall mainly occurred in the mountainous areas in the north of Henan during the period of the extreme hourly precipitation in Zhengzhou and around the windward slope of the mountainous areas, which were notably affected by the terrain features. Located over Henan Province, the high-pressure ridge in the upper troposphere, the low-pressure trough in the middle troposphere, the horizontal shear line and the mesoscale convective vortex in the lower troposphere were the key weather systems that caused the extreme hourly precipitation in Zhengzhou. The high-level jet and sub-jet in the northeast of the South Asia high (SAH) caused the rapid development of the upper-tropospheric short-wave trough over Shaanxi Province through the strong cold advection. The baroclinic development of the shortwave trough made the warm advection in its front significantly enhanced, which led to the rapid development of the high-pressure ridge in the upper troposphere over Henan Province. This maintained the strong high-level divergence, which provided a very favorable background circulation condition for the occurrence of extreme hourly precipitation in Zhengzhou. The ULJ and sub-jet in the northeast of the SAH showed significant horizontal unevenness. Among them, the wind speed enhancement trend and the strength of the cold advection both reach maximum values at the bifurcation point of the ULJ (Region 1). This provides the most favorable conditions for the rapid intensification of the upper tropospheric shortwave trough over Shaanxi. The kinetic energy budget showed that the horizontal kinetic energy transport by the westerly wind in the northeast of the SAH acted as the most favorable factor for the development/maintenance of the ULJ and sub-jet in this region, with the kinetic energy mainly come from the ULJ area on the northern edge of the SAH. In this stage, the pressure gradient force mainly did negative work, which was the most unfavorable factor for the development/maintenance of the upper jet and sub jet.
2023, 28(4): 437-449.
doi: 10.3878/j.issn.1006-9585.2023.22115
Abstract:
Based on the homogenized daily maximum and minimum temperature records during 1909−2021 in Changchun, the warming characteristics of temperature in Changchun over the recent 100 years were evaluated and the contribution rate of urbanization impact was quantified, the multi-scale variation characteristics of 16 extreme temperature indices were revealed, and then the relationship between extreme temperature indices on different scales and Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) was further discussed. The results showed that warm indices (SU25, TX90p, TN90p, and WSDI) showed upward trends in fluctuation; while cold indices (FD0, TX10p, TN10p, and CSDI) showed downward trends in fluctuation in recent 113 years. Trends of all indices were at 0.01 or 0.05 significance level, except for SU25, WSDI, and TX90p. The extreme temperature indices in Changchun revealed periodic changes at different scales, which are mainly determined by the first two intrinsic mode functions and the residual signal. Most extreme temperature indices have a 3-year or quasi-3-year main time scale revealing the interannual variations and a quasi-6-year time cycle dominating by decadal variations. Few indices are with significant longer time scales, such as quasi-31-year in SU25, reflecting the characteristics of multi-decadal variability. In the original signal and multi-decade variation, most warm indices (SU25, TX90p, TXx, and WSDI) were significantly negatively correlated with PDO in the same period but significantly positively correlated with AMO. It indicated there are obvious in-phase relationships between warm indices and AMO, revealing the significant modulating effect of AMO on interannual and multi-decade variations on extreme warm indices, but out-phase relationships between them and PDO. While, the situation is opposite for cold indices.
Based on the homogenized daily maximum and minimum temperature records during 1909−2021 in Changchun, the warming characteristics of temperature in Changchun over the recent 100 years were evaluated and the contribution rate of urbanization impact was quantified, the multi-scale variation characteristics of 16 extreme temperature indices were revealed, and then the relationship between extreme temperature indices on different scales and Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) was further discussed. The results showed that warm indices (SU25, TX90p, TN90p, and WSDI) showed upward trends in fluctuation; while cold indices (FD0, TX10p, TN10p, and CSDI) showed downward trends in fluctuation in recent 113 years. Trends of all indices were at 0.01 or 0.05 significance level, except for SU25, WSDI, and TX90p. The extreme temperature indices in Changchun revealed periodic changes at different scales, which are mainly determined by the first two intrinsic mode functions and the residual signal. Most extreme temperature indices have a 3-year or quasi-3-year main time scale revealing the interannual variations and a quasi-6-year time cycle dominating by decadal variations. Few indices are with significant longer time scales, such as quasi-31-year in SU25, reflecting the characteristics of multi-decadal variability. In the original signal and multi-decade variation, most warm indices (SU25, TX90p, TXx, and WSDI) were significantly negatively correlated with PDO in the same period but significantly positively correlated with AMO. It indicated there are obvious in-phase relationships between warm indices and AMO, revealing the significant modulating effect of AMO on interannual and multi-decade variations on extreme warm indices, but out-phase relationships between them and PDO. While, the situation is opposite for cold indices.
2023, 28(4): 450-460.
doi: 10.3878/j.issn.1006-9585.2023.23043
Abstract:
In March 2023, the Annual Symposium on China Climate Prediction for summer (June–August) was held at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. Under the background of a transition from three consecutive La Niña years to El Niño development in the next four to six months, climate anomalies in China for summer 2023 are predicted based on the results of various numerical and statistical models developed by the IAP. It has been predicted that in the 2023 flood season (June–August), seasonally averaged precipitation slightly exceeding the normal precipitation might occur in the northern and eastern parts of Northeast China, most parts of North China, middle and lower reaches of the Yellow River, Yellow River and Huaihe River basins, southeast coastal region of China, central Northwest China, western parts of Xinjiang and Tibet, and central part of Southwest China. Particularly, rainfall exceeding 20%–50% of its normal occurrence is expected in the northern and eastern parts of Northeast China and the Yellow River and Huaihe River basins, implying a high possibility of local flooding disasters. In contrast, other parts of China, including the middle and lower reaches of the Yangtze River, central and eastern Inner Mongolia, and the northern part of Xinjiang, may experience drier than normal conditions in the coming summer, and the amount of precipitation might be reduced by 20% to 50% in these regions. Furthermore, the number of landing typhoons may be close to normal this summer. Due to the uncertainty of El Niño/Southern Oscillation evolution and limited ability to predict intraseasonal variations of warm pool convection and mid-high latitude atmospheric circulations, this climate prediction for the 2023 flood season is uncertain to some extent. The authors will conduct supplementary forecasts based on the observed variations of atmospheric and oceanic processes in the late spring and early summer of 2023.
In March 2023, the Annual Symposium on China Climate Prediction for summer (June–August) was held at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. Under the background of a transition from three consecutive La Niña years to El Niño development in the next four to six months, climate anomalies in China for summer 2023 are predicted based on the results of various numerical and statistical models developed by the IAP. It has been predicted that in the 2023 flood season (June–August), seasonally averaged precipitation slightly exceeding the normal precipitation might occur in the northern and eastern parts of Northeast China, most parts of North China, middle and lower reaches of the Yellow River, Yellow River and Huaihe River basins, southeast coastal region of China, central Northwest China, western parts of Xinjiang and Tibet, and central part of Southwest China. Particularly, rainfall exceeding 20%–50% of its normal occurrence is expected in the northern and eastern parts of Northeast China and the Yellow River and Huaihe River basins, implying a high possibility of local flooding disasters. In contrast, other parts of China, including the middle and lower reaches of the Yangtze River, central and eastern Inner Mongolia, and the northern part of Xinjiang, may experience drier than normal conditions in the coming summer, and the amount of precipitation might be reduced by 20% to 50% in these regions. Furthermore, the number of landing typhoons may be close to normal this summer. Due to the uncertainty of El Niño/Southern Oscillation evolution and limited ability to predict intraseasonal variations of warm pool convection and mid-high latitude atmospheric circulations, this climate prediction for the 2023 flood season is uncertain to some extent. The authors will conduct supplementary forecasts based on the observed variations of atmospheric and oceanic processes in the late spring and early summer of 2023.
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23053
Abstract:
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23047
Abstract:
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23013
Abstract:
Evaluation and projection of the Eurasian winter snow water equivalent based on CMIP6 Coupled Models
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23005
Abstract:
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.22137
Abstract:
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.22097
Abstract:
, Available online ,
doi: 10.3878/j.issn.1006-9585.2023.23035
Abstract:
Since 1996 Bimonthly
Supervisor: Chinese Academy of Sciences
Sponsors by: Institute of Atmospheric Physics, Chinese Academy of Sciences/Chinese Meteorological Society
Editor: Wang Zifa
Email: qhhj@mail.iap.ac.cn
ISSN 1006-9585
CN 11-3693/P
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