Volume 53,Issue 2,2025 Table of Contents
2025, 53(2):153-166.
DOI: 10.19517/j.1671-6345.20240135
Abstract:
Sea surface temperature (SST) is an important parameter for ocean and atmospheric forecasting systems and climate change research. The National Satellite Meteorological Centre (NSMC) develops the Fengyun-4A (FY-4A)/AGRI (advanced geostationary radiation imager) SST products using the split-window nonlinear SST (NLSST) algorithm. However, the traditional regression algorithm is difficult to meet the needs of higher accuracy SST retrieval. To solve this problem, this paper proposes a FY-4A/AGRI sea surface temperature retrieval method based on deep learning, aiming to improve the retrieval accuracy of SST and provide more accurate data support for meteorological research. FY-4A/AGRI satellite data, SST climatology data, and in situ SST observations are used to construct the retrieval dataset according to quality control standards and spatio-temporal matching rules. The NLSST algorithm is used to select features, including 10.7 μm band brightness temperature, 12 μm band brightness temperature, satellite zenith angle, and SST climatology data. According to the ratio of 8∶2, the feature data are divided into a training dataset and a validation dataset, which are used for training and validation respectively. A SST retrieval model based on a deep neural network is obtained through experiments. Finally, the FY-4A/AGRI satellite data are retrieved by the DNN model to generate SST products. The model-retrieved SST products are evaluated from two dimensions of accuracy and long-term series stability based on in situ SST, and also compared with the FY-4A/AGRI official SST products. By applying the quality levels of FY-4A/AGRI official SST products to the model-retrieved SST products, the performance of model-retrieved SST products under different quality levels (excellent, good, and bad) in three periods of day, night, and dawn is analysed. The statistical results show that when the quality level is excellent, the mean bias of the model-retrieved SST products is -0.19 ℃, the root mean square error (RMSE) is 0.67 ℃, and the correlation coefficient reaches 0.992. However, the mean bias of FY-4A/AGRI official SST products is -0.49 ℃, the RMSE is 0.99 ℃, and the correlation coefficient is 0.985. The mean bias and RMSE of the model-retrieved SST products are 0.3 ℃ smaller than those of the FY-4A/AGRI official SST products, and the correlation coefficient also indicates a good correlation between the model-retrieved SST products and in situ SST. In addition, the temporal stability of the model-retrieved SST products over an extended period outperforms that of the FY-4A/AGRI official SST products. This study provides a new approach for the SST retrieval from the next-generation geostationary satellite.
Sea surface temperature (SST) is an important parameter for ocean and atmospheric forecasting systems and climate change research. The National Satellite Meteorological Centre (NSMC) develops the Fengyun-4A (FY-4A)/AGRI (advanced geostationary radiation imager) SST products using the split-window nonlinear SST (NLSST) algorithm. However, the traditional regression algorithm is difficult to meet the needs of higher accuracy SST retrieval. To solve this problem, this paper proposes a FY-4A/AGRI sea surface temperature retrieval method based on deep learning, aiming to improve the retrieval accuracy of SST and provide more accurate data support for meteorological research. FY-4A/AGRI satellite data, SST climatology data, and in situ SST observations are used to construct the retrieval dataset according to quality control standards and spatio-temporal matching rules. The NLSST algorithm is used to select features, including 10.7 μm band brightness temperature, 12 μm band brightness temperature, satellite zenith angle, and SST climatology data. According to the ratio of 8∶2, the feature data are divided into a training dataset and a validation dataset, which are used for training and validation respectively. A SST retrieval model based on a deep neural network is obtained through experiments. Finally, the FY-4A/AGRI satellite data are retrieved by the DNN model to generate SST products. The model-retrieved SST products are evaluated from two dimensions of accuracy and long-term series stability based on in situ SST, and also compared with the FY-4A/AGRI official SST products. By applying the quality levels of FY-4A/AGRI official SST products to the model-retrieved SST products, the performance of model-retrieved SST products under different quality levels (excellent, good, and bad) in three periods of day, night, and dawn is analysed. The statistical results show that when the quality level is excellent, the mean bias of the model-retrieved SST products is -0.19 ℃, the root mean square error (RMSE) is 0.67 ℃, and the correlation coefficient reaches 0.992. However, the mean bias of FY-4A/AGRI official SST products is -0.49 ℃, the RMSE is 0.99 ℃, and the correlation coefficient is 0.985. The mean bias and RMSE of the model-retrieved SST products are 0.3 ℃ smaller than those of the FY-4A/AGRI official SST products, and the correlation coefficient also indicates a good correlation between the model-retrieved SST products and in situ SST. In addition, the temporal stability of the model-retrieved SST products over an extended period outperforms that of the FY-4A/AGRI official SST products. This study provides a new approach for the SST retrieval from the next-generation geostationary satellite.
2025, 53(2):167-176.
DOI: 10.19517/j.1671-6345.20240161
Abstract:
The global atmospheric temperature and humidity profiles are key datasets for studying extreme weather and climate change. However, the atmospheric profiles derived from satellite measurements include multiple error sources, such as instrument error, errors of retrieval algorithms, and quality control. Therefore, evaluating the errors of atmospheric profiles accurately is beneficial to understanding the features of derived atmospheric profiles and applying them in operations and research. The Three-cornered Hat (3CH) method, using the error model of Xi=truth+b+ei, estimates the errors of various datasets by solving linear equations. Unlike the single data evaluation method, the 3CH method uses uncorrelated datasets and does not assume that any reference dataset is error-free. In this study, the errors of atmospheric temperature and humidity profiles derived from the Fengyun-3D polar-orbiting satellite (FY-3D), the Suomi National Polar-Orbiting Partnership (NPP), and the Constellation Observing System for Meteorology, Ionosphere and Climate 2 (COSMIC2) in 2021 are evaluated by the 3CH method. The differences between the 3CH method and the single data evaluation method, using ECMWF Reanalysis v5 (ERA5) as the reference dataset, are also discussed. The results show that: (1) The errors of three temperature and humidity profiles present similar vertical distributions. Especially around 700-300 hPa, the errors of all temperature profiles are less than 2 K, illustrating all temperature profiles are consistent and accurate in the middle and lower troposphere. In comparison of three humidity profiles, the FY-3D and NPP profiles present the largest and smallest errors, respectively. (2) Compared with all-sky situations, the errors of temperature and humidity profiles derived from FY-3D decline obviously in clear-sky situations. The differences in NPP profile errors between all-sky and clear-sky are very small, illustrating the effects of clouds on NPP profiles are minute. The COSMIC2 profile errors become a little larger at lower layers, because the occultation observation is affected by the super-refraction phenomenon over the ocean. (3) The errors estimated by the 3CH method are different from the results estimated by the ERA5 dataset, because the error of the ERA5 dataset and the correlations between three atmospheric profiles and the ERA5 dataset are included in the 3CH method. For the COSMIC2, the errors of temperature and humidity profiles estimated by the 3CH method increase by about 0.4 K and 0.3 g kg-1 in the lower troposphere, respectively. In contrast, the errors of FY-3D and NPP profiles estimated by the 3CH method become smaller, which are more accurate than the results estimated by the ERA5 dataset.
The global atmospheric temperature and humidity profiles are key datasets for studying extreme weather and climate change. However, the atmospheric profiles derived from satellite measurements include multiple error sources, such as instrument error, errors of retrieval algorithms, and quality control. Therefore, evaluating the errors of atmospheric profiles accurately is beneficial to understanding the features of derived atmospheric profiles and applying them in operations and research. The Three-cornered Hat (3CH) method, using the error model of Xi=truth+b+ei, estimates the errors of various datasets by solving linear equations. Unlike the single data evaluation method, the 3CH method uses uncorrelated datasets and does not assume that any reference dataset is error-free. In this study, the errors of atmospheric temperature and humidity profiles derived from the Fengyun-3D polar-orbiting satellite (FY-3D), the Suomi National Polar-Orbiting Partnership (NPP), and the Constellation Observing System for Meteorology, Ionosphere and Climate 2 (COSMIC2) in 2021 are evaluated by the 3CH method. The differences between the 3CH method and the single data evaluation method, using ECMWF Reanalysis v5 (ERA5) as the reference dataset, are also discussed. The results show that: (1) The errors of three temperature and humidity profiles present similar vertical distributions. Especially around 700-300 hPa, the errors of all temperature profiles are less than 2 K, illustrating all temperature profiles are consistent and accurate in the middle and lower troposphere. In comparison of three humidity profiles, the FY-3D and NPP profiles present the largest and smallest errors, respectively. (2) Compared with all-sky situations, the errors of temperature and humidity profiles derived from FY-3D decline obviously in clear-sky situations. The differences in NPP profile errors between all-sky and clear-sky are very small, illustrating the effects of clouds on NPP profiles are minute. The COSMIC2 profile errors become a little larger at lower layers, because the occultation observation is affected by the super-refraction phenomenon over the ocean. (3) The errors estimated by the 3CH method are different from the results estimated by the ERA5 dataset, because the error of the ERA5 dataset and the correlations between three atmospheric profiles and the ERA5 dataset are included in the 3CH method. For the COSMIC2, the errors of temperature and humidity profiles estimated by the 3CH method increase by about 0.4 K and 0.3 g kg-1 in the lower troposphere, respectively. In contrast, the errors of FY-3D and NPP profiles estimated by the 3CH method become smaller, which are more accurate than the results estimated by the ERA5 dataset.
2025, 53(2):177-190.
DOI: 10.19517/j.1671-6345.20240187
Abstract:
Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations; however, there are still uncertainties and challenges in the applicability of LST retrieval based on satellite data. In this study, to assess the accuracy and applicability of LST products from the Advanced Geostationary Orbit Radiation Imager (AGRI) based on the Fengyun-4A (FY-4A) satellite in the Guangxi region, the FY-4A LST products are compared with the Moderate Resolution Imaging Spectrometer (MODIS) LST products, the surface temperature (0 cm) observed by 91 meteorological stations, and the surface temperature measured by a thermal infrared radiometer. By calculating statistical indicators such as bias, root mean square error, and correlation coefficient, the difference between two sets of remote sensing LST products and their potential relationship with surface conditions are comprehensively analysed, and the consistency between FY-4A LST products and ground-observed LST is deeply evaluated. In addition, a standardised anomaly analysis method is used to evaluate the monitoring ability of FY-4A LST products and station-observed LST on high-temperature and cold-wave events. The results show that the average difference in temperature between FY-4A LST and MODIS LST is about 2 ℃, and significant differences occur in areas with high elevation and sparse tree coverage. The correlation coefficients of the time series between FY-4A LST and MODIS LST exceed 0.9 in most areas. It is also found that there is a good consistency in temporal change between the FY-4A LST products and the LST obtained by the two ground observations. However, the FY-4A LST products exhibit systematic underestimation bias compared with the ground-observed LST due to the difference in observation methods, especially in the afternoon. Based on case analyses of extreme weather events such as high temperatures and cold waves, the FY-4A LST products have a close temporal and spatial relationship with the standardised anomalies of the station-observed LST, which can monitor large areas of abnormal temperature signals with standardised anomalies >2. In general, FY-4A LST products show good monitoring capabilities and applicability in the Guangxi region, especially in supplementing the observation of surface temperature in remote mountainous areas with sparse station distributions. The results of this study demonstrate that satellite observations can comprehensively characterise the spatio-temporal variation of the LST and allow us to better use satellite-based LST products in response to changing climate in the future.
Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations; however, there are still uncertainties and challenges in the applicability of LST retrieval based on satellite data. In this study, to assess the accuracy and applicability of LST products from the Advanced Geostationary Orbit Radiation Imager (AGRI) based on the Fengyun-4A (FY-4A) satellite in the Guangxi region, the FY-4A LST products are compared with the Moderate Resolution Imaging Spectrometer (MODIS) LST products, the surface temperature (0 cm) observed by 91 meteorological stations, and the surface temperature measured by a thermal infrared radiometer. By calculating statistical indicators such as bias, root mean square error, and correlation coefficient, the difference between two sets of remote sensing LST products and their potential relationship with surface conditions are comprehensively analysed, and the consistency between FY-4A LST products and ground-observed LST is deeply evaluated. In addition, a standardised anomaly analysis method is used to evaluate the monitoring ability of FY-4A LST products and station-observed LST on high-temperature and cold-wave events. The results show that the average difference in temperature between FY-4A LST and MODIS LST is about 2 ℃, and significant differences occur in areas with high elevation and sparse tree coverage. The correlation coefficients of the time series between FY-4A LST and MODIS LST exceed 0.9 in most areas. It is also found that there is a good consistency in temporal change between the FY-4A LST products and the LST obtained by the two ground observations. However, the FY-4A LST products exhibit systematic underestimation bias compared with the ground-observed LST due to the difference in observation methods, especially in the afternoon. Based on case analyses of extreme weather events such as high temperatures and cold waves, the FY-4A LST products have a close temporal and spatial relationship with the standardised anomalies of the station-observed LST, which can monitor large areas of abnormal temperature signals with standardised anomalies >2. In general, FY-4A LST products show good monitoring capabilities and applicability in the Guangxi region, especially in supplementing the observation of surface temperature in remote mountainous areas with sparse station distributions. The results of this study demonstrate that satellite observations can comprehensively characterise the spatio-temporal variation of the LST and allow us to better use satellite-based LST products in response to changing climate in the future.
2025, 53(2):191-200.
DOI: 10.19517/j.1671-6345.20240255
Abstract:
There is still a lack of automatic monitoring for freezing rain in the current meteorological observation field. The Random Forest (RF) machine learning method is introduced here, and an automatic recognition method for freezing rain is established using the data from Ka-band Millimetre-Wave Cloud Radar (MWCR). This provides a possibility to make up for the lack of automatic recognition and continuous observation of freezing rain. Firstly, the distribution characteristics of echo intensity and skewness values of Ka-band MWCR with different precipitation phenomena (rain, freezing rain, snow, and mixed rain and snow) during two freezing weather processes occurring in the Wuhan area in February 2024 are analysed. Significant differences in the value range and vertical height distribution are found. Then, echo intensity, skewness values, and near-surface temperature are determined as identification variables. Automatic recognition models for freezing rain using the RF machine learning method are established for several freezing processes in Wuhan and Guizhou respectively. After training and verification calculations, the test recognition accuracy rate (Acc) exceeds 90%, and the freezing rain hit rate (Pod) exceeds 80%. Independent freezing rain examples are used for verification, and the verifying Acc reaches 80%. Compared with wire icing observation, this method can automatically and continuously identify freezing rain phenomena in minutes, which is also feasible for business application. Since the echo intensity and skewness values of MWCR during the freezing rain process have obvious regional characteristics, freezing rain sample data from different regions should be collected to establish recognition models for different regions. The recognition accuracy can be improved by expanding samples and optimising model parameters and indicators, as well as reducing the False Alarm Rate (Far).
There is still a lack of automatic monitoring for freezing rain in the current meteorological observation field. The Random Forest (RF) machine learning method is introduced here, and an automatic recognition method for freezing rain is established using the data from Ka-band Millimetre-Wave Cloud Radar (MWCR). This provides a possibility to make up for the lack of automatic recognition and continuous observation of freezing rain. Firstly, the distribution characteristics of echo intensity and skewness values of Ka-band MWCR with different precipitation phenomena (rain, freezing rain, snow, and mixed rain and snow) during two freezing weather processes occurring in the Wuhan area in February 2024 are analysed. Significant differences in the value range and vertical height distribution are found. Then, echo intensity, skewness values, and near-surface temperature are determined as identification variables. Automatic recognition models for freezing rain using the RF machine learning method are established for several freezing processes in Wuhan and Guizhou respectively. After training and verification calculations, the test recognition accuracy rate (Acc) exceeds 90%, and the freezing rain hit rate (Pod) exceeds 80%. Independent freezing rain examples are used for verification, and the verifying Acc reaches 80%. Compared with wire icing observation, this method can automatically and continuously identify freezing rain phenomena in minutes, which is also feasible for business application. Since the echo intensity and skewness values of MWCR during the freezing rain process have obvious regional characteristics, freezing rain sample data from different regions should be collected to establish recognition models for different regions. The recognition accuracy can be improved by expanding samples and optimising model parameters and indicators, as well as reducing the False Alarm Rate (Far).
2025, 53(2):201-210.
DOI: 10.19517/j.1671-6345.20240102
Abstract:
This study delves into the relationship between lightning frequency and radar echo intensity across three distinct geomorphological regions in Sichuan Province (Sichuan Basin, Western Sichuan Plateau, and Panxi Region) based on lightning and radar echo data from 47 typical thunderstorm events occurring from June to August in 2020 and 2021. Employing boxplot analysis and logarithmic fitting methods, the study selects the maximum echo value within 0.03°×0.03° grid points from SWAN radar echo mosaics as the fitting factor for radar echoes and establishes a specific time window to accumulate lightning frequencies, thereby constructing a comprehensive sample dataset. The results reveal that the median and mean values of lightning frequency and radar echo intensity are relatively close in all three regions, with a generally symmetric data distribution. As lightning frequency increases, echo values tend to converge with reduced dispersion. Despite setting a minimum echo intensity threshold, the span of echo intensities corresponding to lightning frequencies in the Sichuan Basin remains relatively wide. A logarithmic distribution function is adopted to fit lightning frequency and radar echo, yielding satisfactory results in the Sichuan Basin and Western Sichuan Plateau, particularly in the Sichuan Basin where the goodness-of-fit for both the median and mean echo values versus lightning frequency reaches 0.909, indicating a strong correlation and high-precision fit. Conversely, the fit in the Panxi Region is less satisfactory, suggesting a weaker correlation between radar echo values and lightning frequencies in this area. The study further observes that as lightning frequency increases, the median and mean fitting curves of radar echo values in all three regions initially rise rapidly before levelling off, albeit with regional variations. These findings not only uncover the intricate relationship between lightning frequency and radar echo intensity but also provide a scientific basis for regional lightning warning and monitoring. From the perspective of individual case validation of fitted echoes, the fitting results in these three regions exhibit a tendency towards underestimation, likely due to the relatively low grid-point lightning density frequency used in calculations to reduce dispersion and ensure data stability. Nevertheless, the proxy echoes still accurately reflect high-value centre information consistent with observations, which holds significant value for thunderstorm forecasting and warning as well as the application of lightning data. In the future, further validation through more individual cases can be conducted to optimise the fitting algorithm, formula, and method, thereby enhancing the fitting results. Additionally, the fitting formula can be incorporated into model experiments to explore its application effects in different topographical and geomorphological regions.
This study delves into the relationship between lightning frequency and radar echo intensity across three distinct geomorphological regions in Sichuan Province (Sichuan Basin, Western Sichuan Plateau, and Panxi Region) based on lightning and radar echo data from 47 typical thunderstorm events occurring from June to August in 2020 and 2021. Employing boxplot analysis and logarithmic fitting methods, the study selects the maximum echo value within 0.03°×0.03° grid points from SWAN radar echo mosaics as the fitting factor for radar echoes and establishes a specific time window to accumulate lightning frequencies, thereby constructing a comprehensive sample dataset. The results reveal that the median and mean values of lightning frequency and radar echo intensity are relatively close in all three regions, with a generally symmetric data distribution. As lightning frequency increases, echo values tend to converge with reduced dispersion. Despite setting a minimum echo intensity threshold, the span of echo intensities corresponding to lightning frequencies in the Sichuan Basin remains relatively wide. A logarithmic distribution function is adopted to fit lightning frequency and radar echo, yielding satisfactory results in the Sichuan Basin and Western Sichuan Plateau, particularly in the Sichuan Basin where the goodness-of-fit for both the median and mean echo values versus lightning frequency reaches 0.909, indicating a strong correlation and high-precision fit. Conversely, the fit in the Panxi Region is less satisfactory, suggesting a weaker correlation between radar echo values and lightning frequencies in this area. The study further observes that as lightning frequency increases, the median and mean fitting curves of radar echo values in all three regions initially rise rapidly before levelling off, albeit with regional variations. These findings not only uncover the intricate relationship between lightning frequency and radar echo intensity but also provide a scientific basis for regional lightning warning and monitoring. From the perspective of individual case validation of fitted echoes, the fitting results in these three regions exhibit a tendency towards underestimation, likely due to the relatively low grid-point lightning density frequency used in calculations to reduce dispersion and ensure data stability. Nevertheless, the proxy echoes still accurately reflect high-value centre information consistent with observations, which holds significant value for thunderstorm forecasting and warning as well as the application of lightning data. In the future, further validation through more individual cases can be conducted to optimise the fitting algorithm, formula, and method, thereby enhancing the fitting results. Additionally, the fitting formula can be incorporated into model experiments to explore its application effects in different topographical and geomorphological regions.
6 Study on Trends of Extreme Temperature in China Considering Data Homogenization and Autocorrelation
2025, 53(2):211-221.
DOI: 10.19517/j.1671-6345.20240199
Abstract:
China frequently experiences extreme temperature events, which often have severe impacts on social production and daily life. Therefore, it is of great importance to study the long-term trends of extreme temperature changes. The homogenisation of the observation dataset is crucial for detecting temperature change trends. In the meantime, whether to consider time series autocorrelation can also affect the detection results. Failure to consider the homogenisation of the temperature dataset or the autocorrelation of the temperature time series brings about uncertainty in research conclusions. In addition, the higher the spatial coverage of observation sites, the more advantageous it is to reveal spatial differences in change characteristics. This study analyses the trends of extreme temperature changes in China during the period of 1961-2021 using a homogenised daily station temperature dataset with the most spatial coverage currently, while taking into account the impacts of time series autocorrelation. For China as a whole, the annual warm nights (days), where daily minimum (maximum) temperature is above its 90th percentile, have an increasing trend of 10.3 (5.9) d/10a, while the annual cold nights (days), where daily minimum (maximum) temperature is below its 10th percentile, have a decreasing trend of -7.8 (-3.6) d/10a on space average, respectively. The warming rates of the annual coldest night (TNn), warming night (TNx), coldest day (TXn), and warmest day (TXx) are 0.52, 0.30, 0.30, and 0.21 ℃/10a on space average, respectively. For the regional average time series of extreme temperature in China, the percentage differences between the original trend and the decorrelation trend are all less than 5%. For a single station, the impact of time series autocorrelation on the magnitude of long-term linear trend is less than 10% for most stations, but there are also some stations with impacts exceeding 50%. There are great differences between extreme temperature changes and average temperature changes. For example, although the summer warming trend is the weakest in terms of the regional average minimum and maximum temperatures in China, the increasing trend of the regional average warm nights and warm days is the strongest during summer, while the increasing trend is the weakest during winter. With higher spatial coverage of station datasets, this study reveals more details of extreme temperature changes in China. For example, TXx shows an especially pronounced warming trend in urban agglomerations such as the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing. Further research is needed to determine whether this is related to urbanisation.
China frequently experiences extreme temperature events, which often have severe impacts on social production and daily life. Therefore, it is of great importance to study the long-term trends of extreme temperature changes. The homogenisation of the observation dataset is crucial for detecting temperature change trends. In the meantime, whether to consider time series autocorrelation can also affect the detection results. Failure to consider the homogenisation of the temperature dataset or the autocorrelation of the temperature time series brings about uncertainty in research conclusions. In addition, the higher the spatial coverage of observation sites, the more advantageous it is to reveal spatial differences in change characteristics. This study analyses the trends of extreme temperature changes in China during the period of 1961-2021 using a homogenised daily station temperature dataset with the most spatial coverage currently, while taking into account the impacts of time series autocorrelation. For China as a whole, the annual warm nights (days), where daily minimum (maximum) temperature is above its 90th percentile, have an increasing trend of 10.3 (5.9) d/10a, while the annual cold nights (days), where daily minimum (maximum) temperature is below its 10th percentile, have a decreasing trend of -7.8 (-3.6) d/10a on space average, respectively. The warming rates of the annual coldest night (TNn), warming night (TNx), coldest day (TXn), and warmest day (TXx) are 0.52, 0.30, 0.30, and 0.21 ℃/10a on space average, respectively. For the regional average time series of extreme temperature in China, the percentage differences between the original trend and the decorrelation trend are all less than 5%. For a single station, the impact of time series autocorrelation on the magnitude of long-term linear trend is less than 10% for most stations, but there are also some stations with impacts exceeding 50%. There are great differences between extreme temperature changes and average temperature changes. For example, although the summer warming trend is the weakest in terms of the regional average minimum and maximum temperatures in China, the increasing trend of the regional average warm nights and warm days is the strongest during summer, while the increasing trend is the weakest during winter. With higher spatial coverage of station datasets, this study reveals more details of extreme temperature changes in China. For example, TXx shows an especially pronounced warming trend in urban agglomerations such as the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing. Further research is needed to determine whether this is related to urbanisation.
2025, 53(2):222-234.
DOI: 10.19517/j.1671-6345.20240143
Abstract:
There was an extreme thunderstorm gale weather event that occurred in central Hebei Province on 24 July 2023, which was missed in forecasting. The maximum wind speed at 6 national observation stations broke the record for the maximum value or ranked in the top two, with the maximum wind speed being 30.6 m/s, which appeared at the Yuanshi station. The large-scale weather background of this event was the weak northwest wind at the upper air weather chart, and the weather system was not obvious, which led to the failure of forecasting the thunderstorm gale disaster in the middle and short-term weather forecast. Using ERA5 reanalysis data, dual-polarisation weather radar, wind profile radar, L-band radiosonde, minute-level ground encryption observation station data, and other multi-source high spatial and temporal resolution observation data, the evolution characteristics and causes of the extreme thunderstorm gale weather event are analysed. The results show that: (1) The process of thunderstorm wind belonged to the high-altitude dry and cold advection weak forcing type, and there was an unstable atmospheric junction of low-level warm and upper-altitude weak cold. The CAPE value was 1544.8 J/kg, SI was -4.31 ℃, and the temperature difference between 850 hPa and 500 hPa exceeded 30 ℃, with the vertical wind shears of 0-3 km and 0-6 km being moderate-to-slightly weak. (2) The wind speed of the single-peak and the first stage of the double-peak were characterised by strong sudden onset, large instantaneous wind speed, and short duration. The corresponding radar echoes of the convection system included three categories: storm cells, bow echoes, and gust front, which were characterised by ZDR column, KDP column, low CC value, large wind core above 17 m/s, velocity ambiguity, and radial convergence of 3-8 km in the middle layer. The surface meteorological elements were thunderstorm high pressure with centre pressure above 1006 hPa and surface cold pool with temperature variations of one hour less than -3.5 ℃, corresponding to the steep rise of pressure, the sudden drop of temperature, the valley value of zero layer height, and weak precipitation. Compared with the first stage, the wind in the second stage of the double-peak type was significantly weaker, with a smaller impact range and a short duration, accompanied by no precipitation or only weak precipitation, corresponding to the weak radar echo type, which was caused by the regenerative weak echo after the main radar echo and the cold pool density current. (3) Extreme winds were also related to the new relocation station with few obstacles.
There was an extreme thunderstorm gale weather event that occurred in central Hebei Province on 24 July 2023, which was missed in forecasting. The maximum wind speed at 6 national observation stations broke the record for the maximum value or ranked in the top two, with the maximum wind speed being 30.6 m/s, which appeared at the Yuanshi station. The large-scale weather background of this event was the weak northwest wind at the upper air weather chart, and the weather system was not obvious, which led to the failure of forecasting the thunderstorm gale disaster in the middle and short-term weather forecast. Using ERA5 reanalysis data, dual-polarisation weather radar, wind profile radar, L-band radiosonde, minute-level ground encryption observation station data, and other multi-source high spatial and temporal resolution observation data, the evolution characteristics and causes of the extreme thunderstorm gale weather event are analysed. The results show that: (1) The process of thunderstorm wind belonged to the high-altitude dry and cold advection weak forcing type, and there was an unstable atmospheric junction of low-level warm and upper-altitude weak cold. The CAPE value was 1544.8 J/kg, SI was -4.31 ℃, and the temperature difference between 850 hPa and 500 hPa exceeded 30 ℃, with the vertical wind shears of 0-3 km and 0-6 km being moderate-to-slightly weak. (2) The wind speed of the single-peak and the first stage of the double-peak were characterised by strong sudden onset, large instantaneous wind speed, and short duration. The corresponding radar echoes of the convection system included three categories: storm cells, bow echoes, and gust front, which were characterised by ZDR column, KDP column, low CC value, large wind core above 17 m/s, velocity ambiguity, and radial convergence of 3-8 km in the middle layer. The surface meteorological elements were thunderstorm high pressure with centre pressure above 1006 hPa and surface cold pool with temperature variations of one hour less than -3.5 ℃, corresponding to the steep rise of pressure, the sudden drop of temperature, the valley value of zero layer height, and weak precipitation. Compared with the first stage, the wind in the second stage of the double-peak type was significantly weaker, with a smaller impact range and a short duration, accompanied by no precipitation or only weak precipitation, corresponding to the weak radar echo type, which was caused by the regenerative weak echo after the main radar echo and the cold pool density current. (3) Extreme winds were also related to the new relocation station with few obstacles.
2025, 53(2):235-246.
DOI: 10.19517/j.1671-6345.20240208
Abstract:
Two high-impact processes of advection fog on 31 December 2021 and radiation fog on 19 January 2022 occurred in Hunan. Using conventional weather observations, highway disaster data, and NCEP reanalysis data, the weather background, meteorological elements, physical quantity fields, their effects on highways, and the water vapour source of HYSPLIT backward trajectory simulation of the two processes are compared and analysed. The results show that during the two fog processes, the middle and high latitudes of 500 hPa show a circulation situation of two troughs and one ridge, there are multi-layer inversions, the atmospheric stratification is stable, and the relative humidity of the ground is above 98%. In terms of water vapour transport, there is a rapid sinking transport of water vapour in the north. The main and strongest water vapour transport in the near-surface channel is a short-distance and slow-moving channel entering Hunan through the mountainous area of western Hubei, and the contribution rate of water vapour flux accounts for more than 50%, indicating that the continuous and slow infiltration of cold and wet air near the ground layer is conducive to the cooling and condensation of the gas block to form droplets. There are obvious differences in the low-level, ground situation and meteorological elements during the two heavy fog processes. The first advection fog process has a strong warm advection development at 700-600 hPa, and the ground is controlled by weak cold high pressure, accompanied by the thickening of the inversion layer. The second radiation fog process is controlled by the northerly airflow behind the trough at 500 hPa, and the ground is a weak pressure field. During the fog, the whole layer turns to cold advection. The temperature during the advection fog process is lower than that during the radiation fog process. There are obvious differences in water vapour sources and the height of water vapour channels between the two processes. The radiation fog transports cold air in the northern channel, two in the upper and two in the lower layers. The dry cold air is quickly transported to the fog area with two high-level water vapour channels, and the wet cold air is slowly transported to the fog area with two low-level channels. The water vapour channels of advection fog input half from the north and half from the south. The southern channel transports warm and humid air, and the northern channel transports cold air. The analysis and research results are helpful for forecasters to improve the accuracy of forecasting and early warning of two types of fogs. At the same time, it provides a reference for the precise control of fog weather in the transportation department.
Two high-impact processes of advection fog on 31 December 2021 and radiation fog on 19 January 2022 occurred in Hunan. Using conventional weather observations, highway disaster data, and NCEP reanalysis data, the weather background, meteorological elements, physical quantity fields, their effects on highways, and the water vapour source of HYSPLIT backward trajectory simulation of the two processes are compared and analysed. The results show that during the two fog processes, the middle and high latitudes of 500 hPa show a circulation situation of two troughs and one ridge, there are multi-layer inversions, the atmospheric stratification is stable, and the relative humidity of the ground is above 98%. In terms of water vapour transport, there is a rapid sinking transport of water vapour in the north. The main and strongest water vapour transport in the near-surface channel is a short-distance and slow-moving channel entering Hunan through the mountainous area of western Hubei, and the contribution rate of water vapour flux accounts for more than 50%, indicating that the continuous and slow infiltration of cold and wet air near the ground layer is conducive to the cooling and condensation of the gas block to form droplets. There are obvious differences in the low-level, ground situation and meteorological elements during the two heavy fog processes. The first advection fog process has a strong warm advection development at 700-600 hPa, and the ground is controlled by weak cold high pressure, accompanied by the thickening of the inversion layer. The second radiation fog process is controlled by the northerly airflow behind the trough at 500 hPa, and the ground is a weak pressure field. During the fog, the whole layer turns to cold advection. The temperature during the advection fog process is lower than that during the radiation fog process. There are obvious differences in water vapour sources and the height of water vapour channels between the two processes. The radiation fog transports cold air in the northern channel, two in the upper and two in the lower layers. The dry cold air is quickly transported to the fog area with two high-level water vapour channels, and the wet cold air is slowly transported to the fog area with two low-level channels. The water vapour channels of advection fog input half from the north and half from the south. The southern channel transports warm and humid air, and the northern channel transports cold air. The analysis and research results are helpful for forecasters to improve the accuracy of forecasting and early warning of two types of fogs. At the same time, it provides a reference for the precise control of fog weather in the transportation department.
2025, 53(2):247-258.
DOI: 10.19517/j.1671-6345.20240167
Abstract:
Supercooled water is a crucial parameter for assessing the potential for weather modification in stratiform clouds. Utilising data from 32 aircrafts detected by the Airborne Meteorological Detection Device from 2017 to 2022, we conduct a statistical analysis of the altitude characteristics where supercooled water is present, along with temperature, humidity, vertical wind speed, and other atmospheric parameters. We also examine the variation of supercooled water content in relation to these environmental factors. Based on these findings, we develop a diagnostic forecasting method for supercooled water content in clouds over the Shanxi region. The study reveals the following insights: (1) The supercooled water content obtained by the Airborne Meteorological Detection Device shows good agreement with the liquid water content detected by the Cloud Particle Probe (when the temperature is below 0 ℃, the liquid water content detected by the CDP is the supercooled water content), which indicates that both instruments are better at detecting the supercooled water content in the cloud. (2) The supercooled water content predominantly ranges from 0.06 to 0.22 g·m-3, with an average value of 0.18 g·m-3. Its probability density function (PDF) exhibits a distinct single-peaked normal skewed distribution, and the cumulative distribution function (CDF) growth rate decreases as the supercooled water content increases. (3) The supercooled water region is predominantly found between 3589 and 4667 metres, approximately 1011 to 2316 metres above the 0 ℃ isotherm. The temperature range is from -8.52 ℃ to -3.52 ℃, humidity levels vary between 86.68% and 100%, and the supercooled water area is predominantly influenced by updrafts. (4) The supercooled water content tends to increase with decreasing altitude from the 0 ℃ isotherm, rising temperature, increasing relative humidity, and stronger updrafts within the clouds. (5) The relationships between the content of supercooled water and various factors such as temperature, relative humidity, altitude above the 0 ℃ isotherm, and vertical wind speed are determined through polynomial fitting. The analysis reveals that the inversion magnitude is substantial when the supercooled water content is low, and conversely, it is minimal when the content is high. However, when considering the overall distribution, there is a distinct positive correlation between the two variables. The retrieval results for six cases outside the statistical sample suggest that some supercooled water is also forecasted during periods when it is actually detected, with the trends of the two being largely consistent and accuracies exceeding 65%. However, the forecasted values are slightly higher than the observed values. This diagnostic method for forecasting supercooled water can, to a certain extent, qualitatively assess the magnitude of supercooled water content under specific conditions, locate areas rich in supercooled water, and offer guidance for the scientific advancement of artificial weather modification operations.
Supercooled water is a crucial parameter for assessing the potential for weather modification in stratiform clouds. Utilising data from 32 aircrafts detected by the Airborne Meteorological Detection Device from 2017 to 2022, we conduct a statistical analysis of the altitude characteristics where supercooled water is present, along with temperature, humidity, vertical wind speed, and other atmospheric parameters. We also examine the variation of supercooled water content in relation to these environmental factors. Based on these findings, we develop a diagnostic forecasting method for supercooled water content in clouds over the Shanxi region. The study reveals the following insights: (1) The supercooled water content obtained by the Airborne Meteorological Detection Device shows good agreement with the liquid water content detected by the Cloud Particle Probe (when the temperature is below 0 ℃, the liquid water content detected by the CDP is the supercooled water content), which indicates that both instruments are better at detecting the supercooled water content in the cloud. (2) The supercooled water content predominantly ranges from 0.06 to 0.22 g·m-3, with an average value of 0.18 g·m-3. Its probability density function (PDF) exhibits a distinct single-peaked normal skewed distribution, and the cumulative distribution function (CDF) growth rate decreases as the supercooled water content increases. (3) The supercooled water region is predominantly found between 3589 and 4667 metres, approximately 1011 to 2316 metres above the 0 ℃ isotherm. The temperature range is from -8.52 ℃ to -3.52 ℃, humidity levels vary between 86.68% and 100%, and the supercooled water area is predominantly influenced by updrafts. (4) The supercooled water content tends to increase with decreasing altitude from the 0 ℃ isotherm, rising temperature, increasing relative humidity, and stronger updrafts within the clouds. (5) The relationships between the content of supercooled water and various factors such as temperature, relative humidity, altitude above the 0 ℃ isotherm, and vertical wind speed are determined through polynomial fitting. The analysis reveals that the inversion magnitude is substantial when the supercooled water content is low, and conversely, it is minimal when the content is high. However, when considering the overall distribution, there is a distinct positive correlation between the two variables. The retrieval results for six cases outside the statistical sample suggest that some supercooled water is also forecasted during periods when it is actually detected, with the trends of the two being largely consistent and accuracies exceeding 65%. However, the forecasted values are slightly higher than the observed values. This diagnostic method for forecasting supercooled water can, to a certain extent, qualitatively assess the magnitude of supercooled water content under specific conditions, locate areas rich in supercooled water, and offer guidance for the scientific advancement of artificial weather modification operations.
2025, 53(2):259-270.
DOI: 10.19517/j.1671-6345.20240074
Abstract:
In order to enhance the understanding of the spatiotemporal evolution of soil moisture in the subtropical monsoon climate region, the study focuses on Yunnan and its surrounding areas (90°-107°E, 15°-30°N). Utilising ERA5-Land reanalysis soil moisture data and employing various statistical methods such as Theil-Sen Median trend analysis and Mann-Kendall (M-K) non-parametric test, this research analyses the spatiotemporal variations of soil moisture in the Indochina Peninsula and its response to hydrothermal changes, exploring the responses of different soil moisture levels to climate change. The results indicate: (1) During the study period (1950-2020), the study area experiences alternating dry and wet soil moisture conditions, with drier years concentrated in 1955-1961 and 2001-2020, and wetter years mainly distributed in 1951-1954, 1961-1968, and 1971-1978. After entering the 21st century, soil drying becomes more pronounced, with positive anomalies in soil moisture at different depths from January to December, and a stronger drying trend during the dry season for SM1 to SM3. (2) Between 1950 and 2020, there is a clear dry-wet axis in the study area’s soil moisture, with the difference between the dry and wet axes decreasing with depth, and a trend of narrowing dry axis bands and expanding wet axis bands. (3) In the first and last years of the study period, soil moisture remains stable in most regions of the study area, with only 21.4% to 26.2% of the area experiencing drying, primarily transitioning from the (0.4, 0.45］ interval to the (0.35, 0.4］ interval. (4) There is significant spatial heterogeneity in soil moisture changes at different levels, with an overall weak drying trend. Of the area, 37.7% to 61.5% experiences soil moisture changes ranging from -0.001 to 0 m3·m-3·10a-1, and areas with increasing soil moisture are less than 20%. (5) After entering the 21st century, soil moisture in most months of the dry season becomes drier compared to earlier periods, with abrupt changes occurring in 2003 and 2008. (6) The temporal-latitudinal mean high-value centre of soil moisture extends with the increase in soil depth, and the high-value centre of soil moisture expands northward (eastward); the overall variation in soil moisture increases with latitude (longitude), and the seasonal differences in soil moisture decrease with depth and latitude (longitude). (7) The impact of evapotranspiration, precipitation, soil temperature, and Normalised Vegetation Index (NDVI) on soil moisture has a lag of 1 to 5 months. Apart from the negative correlation with NDVI, soil moisture is significantly positively correlated with the other three climate factors. Soil moisture is synergistically influenced by environmental factors, among which precipitation has the strongest impact on soil moisture.
In order to enhance the understanding of the spatiotemporal evolution of soil moisture in the subtropical monsoon climate region, the study focuses on Yunnan and its surrounding areas (90°-107°E, 15°-30°N). Utilising ERA5-Land reanalysis soil moisture data and employing various statistical methods such as Theil-Sen Median trend analysis and Mann-Kendall (M-K) non-parametric test, this research analyses the spatiotemporal variations of soil moisture in the Indochina Peninsula and its response to hydrothermal changes, exploring the responses of different soil moisture levels to climate change. The results indicate: (1) During the study period (1950-2020), the study area experiences alternating dry and wet soil moisture conditions, with drier years concentrated in 1955-1961 and 2001-2020, and wetter years mainly distributed in 1951-1954, 1961-1968, and 1971-1978. After entering the 21st century, soil drying becomes more pronounced, with positive anomalies in soil moisture at different depths from January to December, and a stronger drying trend during the dry season for SM1 to SM3. (2) Between 1950 and 2020, there is a clear dry-wet axis in the study area’s soil moisture, with the difference between the dry and wet axes decreasing with depth, and a trend of narrowing dry axis bands and expanding wet axis bands. (3) In the first and last years of the study period, soil moisture remains stable in most regions of the study area, with only 21.4% to 26.2% of the area experiencing drying, primarily transitioning from the (0.4, 0.45］ interval to the (0.35, 0.4］ interval. (4) There is significant spatial heterogeneity in soil moisture changes at different levels, with an overall weak drying trend. Of the area, 37.7% to 61.5% experiences soil moisture changes ranging from -0.001 to 0 m3·m-3·10a-1, and areas with increasing soil moisture are less than 20%. (5) After entering the 21st century, soil moisture in most months of the dry season becomes drier compared to earlier periods, with abrupt changes occurring in 2003 and 2008. (6) The temporal-latitudinal mean high-value centre of soil moisture extends with the increase in soil depth, and the high-value centre of soil moisture expands northward (eastward); the overall variation in soil moisture increases with latitude (longitude), and the seasonal differences in soil moisture decrease with depth and latitude (longitude). (7) The impact of evapotranspiration, precipitation, soil temperature, and Normalised Vegetation Index (NDVI) on soil moisture has a lag of 1 to 5 months. Apart from the negative correlation with NDVI, soil moisture is significantly positively correlated with the other three climate factors. Soil moisture is synergistically influenced by environmental factors, among which precipitation has the strongest impact on soil moisture.
2025, 53(2):271-284.
DOI: 10.19517/j.1671-6345.20240195
Abstract:
Based on the data of brown planthopper (Nilaparvata lugens (Stal)) and meteorological factors including temperature and precipitation in the Quanzhou region of Guangxi Province from 1979 to 2023, this study utilises Sen's slope, wavelet analysis, M-K trend, and mutation testing to analyse variation characteristics of the Nilaparvata lugens's phenological periods and its response to climate change in this region over the past 45 years. The results show that the initial appearance date shows an early trend at a rate of 0.43 days per year (p<0.05), and the final disappearance date and residence time show a delayed or prolonged trend at rates of 0.42 days per year and 0.85 days per year (p<0.01) respectively. No variation occurs in the phenological periods, and they remain relatively stable over time. The initial appearance date, final disappearance date, and duration of stay undergo mutations in 2009, 2003, and 2007, respectively. There are also multiple periodic variation characteristics. The first main period of the initial appearance date is 26 years, while the first main period of the final disappearance date and duration of stay is 28 years. The average temperatures in spring, summer, and autumn have increased extremely significantly and undergo mutations in 2001, 2021, and 2012, respectively. Seasonal precipitation has large inter-annual fluctuation and the change trend is not obvious. The significant increase in temperature in different seasons has a profound impact on the ecological environment. The large fluctuations in seasonal precipitation make the water environment more variable, which in turn affects the living conditions of the brown planthopper. Although the trend of precipitation change is not clear, its inter-annual variability still poses challenges to the adaptation of the brown planthopper. The initial appearance date has a highly significant response to spring temperature, and a warm spring can lead to an earlier initial appearance date. The final disappearance date is positively correlated with the autumn temperature element, and a warm autumn will make the final disappearance date of brown planthoppers later. Warm spring and warm autumn both prolong the staying time of brown planthoppers. The initial appearance date is negatively correlated with the precipitation in March, and the final disappearance date is positively correlated with the precipitation and the number of days in November. This shows that temperature and precipitation in specific seasons have specific regulatory effects on the phenological period of brown planthoppers. The relationship between temperature and phenological period reflects the direct impact of thermal conditions on the growth and development rhythm of brown planthoppers. The correlation between precipitation and phenological period indicates that water availability also plays an important role in determining the key life cycle nodes of brown planthoppers.
Based on the data of brown planthopper (Nilaparvata lugens (Stal)) and meteorological factors including temperature and precipitation in the Quanzhou region of Guangxi Province from 1979 to 2023, this study utilises Sen's slope, wavelet analysis, M-K trend, and mutation testing to analyse variation characteristics of the Nilaparvata lugens's phenological periods and its response to climate change in this region over the past 45 years. The results show that the initial appearance date shows an early trend at a rate of 0.43 days per year (p<0.05), and the final disappearance date and residence time show a delayed or prolonged trend at rates of 0.42 days per year and 0.85 days per year (p<0.01) respectively. No variation occurs in the phenological periods, and they remain relatively stable over time. The initial appearance date, final disappearance date, and duration of stay undergo mutations in 2009, 2003, and 2007, respectively. There are also multiple periodic variation characteristics. The first main period of the initial appearance date is 26 years, while the first main period of the final disappearance date and duration of stay is 28 years. The average temperatures in spring, summer, and autumn have increased extremely significantly and undergo mutations in 2001, 2021, and 2012, respectively. Seasonal precipitation has large inter-annual fluctuation and the change trend is not obvious. The significant increase in temperature in different seasons has a profound impact on the ecological environment. The large fluctuations in seasonal precipitation make the water environment more variable, which in turn affects the living conditions of the brown planthopper. Although the trend of precipitation change is not clear, its inter-annual variability still poses challenges to the adaptation of the brown planthopper. The initial appearance date has a highly significant response to spring temperature, and a warm spring can lead to an earlier initial appearance date. The final disappearance date is positively correlated with the autumn temperature element, and a warm autumn will make the final disappearance date of brown planthoppers later. Warm spring and warm autumn both prolong the staying time of brown planthoppers. The initial appearance date is negatively correlated with the precipitation in March, and the final disappearance date is positively correlated with the precipitation and the number of days in November. This shows that temperature and precipitation in specific seasons have specific regulatory effects on the phenological period of brown planthoppers. The relationship between temperature and phenological period reflects the direct impact of thermal conditions on the growth and development rhythm of brown planthoppers. The correlation between precipitation and phenological period indicates that water availability also plays an important role in determining the key life cycle nodes of brown planthoppers.
2025, 53(2):285-292.
DOI: 10.19517/j.1671-6345.20240178
Abstract:
With the development of artificial weather modification efforts, the importance of aircraft for cloud seeding becomes increasingly significant. The effectiveness of aircraft for cloud seeding heavily relies on the stability of onboard detection equipment. To effectively simulate the dynamic operational environment of airborne detection equipment used in artificial weather modification and to enhance the detection capabilities of such equipment, this study presents a design for a recirculating closed-circuit wind tunnel. The primary structure comprises several sections: the test section, diffuser section, corner section, power section, heat exchange section, stabilisation section, contraction section, and reserved section. The interior contains built-in components such as corner guide deflectors, cellular devices, damping screens, heat exchangers, etc. The wind tunnel is arranged horizontally, with a centreline length of 28.09 m and a short axis distance of 5.71 m. The maximum dimensions of the wind tunnel are 31.33 m×10.18 m×5.436 m (length×width×height), while the cross-sectional dimensions of the test segment measure 1.2 m×1.2 m with a stable wind speed range from 30 to 150 m/s. The actual sizes of the airborne detection instruments are taken into account in the design of the test section. Based on a contraction ratio of 12.25, the stable section is designed with a square cross-section of 4.2 m×4.2 m. The diameter of the power section is set at 2.4 m. To prevent damage to the fan blade from test pieces or other objects, a protective net is installed in front of the power section. To verify the rationality of the wind tunnel design, the flow field quality of the wind tunnel is tested according to the “Low-Speed and High-Speed Wind Tunnel Flow Field Quality Requirements” (GJB1179A-2012) and the “Wind Tunnel Control System Design and Checking Guidelines” (GJB 5221-2004). Following flow field calibration tests, various quality indicators-including wind speed range, dynamic pressure stability, drop coefficient, turbulence intensity, airflow temperature variations, axial static pressure gradient as well as directional and dynamic pressure fields are confirmed to meet GJB1179A-2012 standards and adequately fulfil the testing requirements for airborne equipment. Specifically, the dynamic pressure stability is less than 0.5%, and the turbulence intensity is less than 0.2%. When the wind speed is 100 m/s, the temperature rise is 1.31 ℃/h. The aerodynamic and structural design principles applied in this wind tunnel are reasonable and may serve as valuable references for future designs and constructions of similar facilities.
With the development of artificial weather modification efforts, the importance of aircraft for cloud seeding becomes increasingly significant. The effectiveness of aircraft for cloud seeding heavily relies on the stability of onboard detection equipment. To effectively simulate the dynamic operational environment of airborne detection equipment used in artificial weather modification and to enhance the detection capabilities of such equipment, this study presents a design for a recirculating closed-circuit wind tunnel. The primary structure comprises several sections: the test section, diffuser section, corner section, power section, heat exchange section, stabilisation section, contraction section, and reserved section. The interior contains built-in components such as corner guide deflectors, cellular devices, damping screens, heat exchangers, etc. The wind tunnel is arranged horizontally, with a centreline length of 28.09 m and a short axis distance of 5.71 m. The maximum dimensions of the wind tunnel are 31.33 m×10.18 m×5.436 m (length×width×height), while the cross-sectional dimensions of the test segment measure 1.2 m×1.2 m with a stable wind speed range from 30 to 150 m/s. The actual sizes of the airborne detection instruments are taken into account in the design of the test section. Based on a contraction ratio of 12.25, the stable section is designed with a square cross-section of 4.2 m×4.2 m. The diameter of the power section is set at 2.4 m. To prevent damage to the fan blade from test pieces or other objects, a protective net is installed in front of the power section. To verify the rationality of the wind tunnel design, the flow field quality of the wind tunnel is tested according to the “Low-Speed and High-Speed Wind Tunnel Flow Field Quality Requirements” (GJB1179A-2012) and the “Wind Tunnel Control System Design and Checking Guidelines” (GJB 5221-2004). Following flow field calibration tests, various quality indicators-including wind speed range, dynamic pressure stability, drop coefficient, turbulence intensity, airflow temperature variations, axial static pressure gradient as well as directional and dynamic pressure fields are confirmed to meet GJB1179A-2012 standards and adequately fulfil the testing requirements for airborne equipment. Specifically, the dynamic pressure stability is less than 0.5%, and the turbulence intensity is less than 0.2%. When the wind speed is 100 m/s, the temperature rise is 1.31 ℃/h. The aerodynamic and structural design principles applied in this wind tunnel are reasonable and may serve as valuable references for future designs and constructions of similar facilities.
2025, 53(2):293-300.
DOI: 10.19517/j.1671-6345.20240112
Abstract:
The National Early Warning Release System (NEWRS) is an essential part of the National Emergency Command and Dispatch Platform System. It serves as a key transmission hub connecting various departments and plays a very important role in disseminating warning information. With the increase in the variety and volume of warning data, the NEWRS faces challenges. The capacity and throughput of the NEWRS are under intense restrictions. Problems like low transmission efficiency, limited data processing capability, and high system failure are serious. These problems affect the timely availability of disseminating warning information. The phenomenon that a large amount of data can only exist in the original system is significant. These data are not shared, which does not help to integrate data. It also limits data mining and advanced warning decision-making services. The paper presents a distributed transmission plan for warning data. It promotes a warning transmission model to solve these problems. The transmission model can serve four levels of application: national, provincial, municipal, and county levels. However, it is implemented at national and provincial levels to relieve the instability of the NEWRS caused by multi-level deployment. Meanwhile, it enhances data transmission with caching services using message-oriented middleware technologies. Provincial nodes can serve as a caching function. Once the fault is fixed, provincial nodes can resume transmission automatically. Besides, the transmission model employs distributed clustering technologies to help increase the scalability of the system and reduce the possibility of large-scale failures. It can continue to work when there are some node errors. The warning transmission model improves message-oriented middleware by designing a message data structure, so the transmission model can support dynamic expansion for data types. At the same time, security authentication and data consistency verification for data exchange have been strengthened. All these functions are provided as interface services to facilitate integration with other business systems. To verify these capabilities of the warning transmission model, we conduct experiments in data carrying capacity. Writing a Common Alert Protocol message data requires around 6.7 milliseconds, and reading it takes about 11.4 milliseconds. In timely recovery response, the experiments also show the model is effective. Notably, the transmission model achieves remarkable results in the pilot application in Guizhou Province. The results show that the transmission efficiency of the model is confirmed to be about 94.5 times higher compared to the NEWRS. It has been shown as a practical solution with the potential for nationwide implementation. This will improve the efficiency of communication between national and provincial levels.
The National Early Warning Release System (NEWRS) is an essential part of the National Emergency Command and Dispatch Platform System. It serves as a key transmission hub connecting various departments and plays a very important role in disseminating warning information. With the increase in the variety and volume of warning data, the NEWRS faces challenges. The capacity and throughput of the NEWRS are under intense restrictions. Problems like low transmission efficiency, limited data processing capability, and high system failure are serious. These problems affect the timely availability of disseminating warning information. The phenomenon that a large amount of data can only exist in the original system is significant. These data are not shared, which does not help to integrate data. It also limits data mining and advanced warning decision-making services. The paper presents a distributed transmission plan for warning data. It promotes a warning transmission model to solve these problems. The transmission model can serve four levels of application: national, provincial, municipal, and county levels. However, it is implemented at national and provincial levels to relieve the instability of the NEWRS caused by multi-level deployment. Meanwhile, it enhances data transmission with caching services using message-oriented middleware technologies. Provincial nodes can serve as a caching function. Once the fault is fixed, provincial nodes can resume transmission automatically. Besides, the transmission model employs distributed clustering technologies to help increase the scalability of the system and reduce the possibility of large-scale failures. It can continue to work when there are some node errors. The warning transmission model improves message-oriented middleware by designing a message data structure, so the transmission model can support dynamic expansion for data types. At the same time, security authentication and data consistency verification for data exchange have been strengthened. All these functions are provided as interface services to facilitate integration with other business systems. To verify these capabilities of the warning transmission model, we conduct experiments in data carrying capacity. Writing a Common Alert Protocol message data requires around 6.7 milliseconds, and reading it takes about 11.4 milliseconds. In timely recovery response, the experiments also show the model is effective. Notably, the transmission model achieves remarkable results in the pilot application in Guizhou Province. The results show that the transmission efficiency of the model is confirmed to be about 94.5 times higher compared to the NEWRS. It has been shown as a practical solution with the potential for nationwide implementation. This will improve the efficiency of communication between national and provincial levels.
External LinksChina Meteorological AdministrationCMA Meteorological Observation CentreChinese Academy of Meteorological SciencesBeijing Meteorological ServiceNational Satellite Meteorological Center
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.