Volume 52,Issue 3,2024 Table of Contents
2024, 52(3):309-317.
DOI: 10.19517/j.1671-6345.20230123
Abstract:
Aiming at the problems of data quality degradation caused by multi-channel scanning-type loads on geostationary orbit remote sensing satellites in the process of imaging, transmission and storage, i.e., the influence of texture distortion and edge blurring in the meteorological remote sensing feature recognition images on the analysis of meteorological remote sensing images, this study proposes an improved BM3D noise reduction algorithm. The algorithm combines Morlet wavelet decomposition theory (with good symmetry and its decay characteristics follow the exponential law, it is able to match the mutation signals in the meteorological remote sensing images, thus realising signal denoising) and BM3D denoising principle (a non-local filtering algorithm that includes two parts: block matching and 3D collaborative filtering. Block matching involves grouping image blocks similar to a given reference block and composing them into a 3D array). Firstly, the image decomposes using wavelet transform to get four components. Secondly, the meteorological remote sensing image decomposes into three levels with a total of ten components. Finally, each component denoises using a separate BM3D filter, and the output image of the 10 components reconstructs. The output reconstructed image views as an estimate of the desired image, capable of suppressing meteorological remote sensing image noise and preserving edge detail. Compared with the traditional BM3D denoising algorithm, the improved BM3D algorithm is able to reduce the computation by about one-fifth. The eight meteorological remote sensing images process by equalising the grayscale and adding additive Gaussian white noise with mean 0 and standard deviation σ and random impulse noise. The median filter (suitable for removing isolated noise such as pepper noise), mean filter (suitable for removing noise from images), NL-Bayes (suitable for smoothing images and preserving image details), BM3D algorithm and the improved BM3D algorithm also compare to process the images respectively, and based on the results of peak signal-to-noise ratio (according to the definition of peak signal-to-noise ratio, it considers as the main metric to evaluate the quality of an image and utilises to measure the degree of realism of an image, with higher values indicating better denoising effects) of the meteorological remote sensing images, it finds that the average PSNR gain of the algorithms proposed in this study is in the range of 0.39 dB to 4.45 dB. The above experimental results of meteorological remote sensing images indicate that the improved BM3D algorithm works better, especially in the mixed noise denoising of Gaussian white noise and impulse noise.
Aiming at the problems of data quality degradation caused by multi-channel scanning-type loads on geostationary orbit remote sensing satellites in the process of imaging, transmission and storage, i.e., the influence of texture distortion and edge blurring in the meteorological remote sensing feature recognition images on the analysis of meteorological remote sensing images, this study proposes an improved BM3D noise reduction algorithm. The algorithm combines Morlet wavelet decomposition theory (with good symmetry and its decay characteristics follow the exponential law, it is able to match the mutation signals in the meteorological remote sensing images, thus realising signal denoising) and BM3D denoising principle (a non-local filtering algorithm that includes two parts: block matching and 3D collaborative filtering. Block matching involves grouping image blocks similar to a given reference block and composing them into a 3D array). Firstly, the image decomposes using wavelet transform to get four components. Secondly, the meteorological remote sensing image decomposes into three levels with a total of ten components. Finally, each component denoises using a separate BM3D filter, and the output image of the 10 components reconstructs. The output reconstructed image views as an estimate of the desired image, capable of suppressing meteorological remote sensing image noise and preserving edge detail. Compared with the traditional BM3D denoising algorithm, the improved BM3D algorithm is able to reduce the computation by about one-fifth. The eight meteorological remote sensing images process by equalising the grayscale and adding additive Gaussian white noise with mean 0 and standard deviation σ and random impulse noise. The median filter (suitable for removing isolated noise such as pepper noise), mean filter (suitable for removing noise from images), NL-Bayes (suitable for smoothing images and preserving image details), BM3D algorithm and the improved BM3D algorithm also compare to process the images respectively, and based on the results of peak signal-to-noise ratio (according to the definition of peak signal-to-noise ratio, it considers as the main metric to evaluate the quality of an image and utilises to measure the degree of realism of an image, with higher values indicating better denoising effects) of the meteorological remote sensing images, it finds that the average PSNR gain of the algorithms proposed in this study is in the range of 0.39 dB to 4.45 dB. The above experimental results of meteorological remote sensing images indicate that the improved BM3D algorithm works better, especially in the mixed noise denoising of Gaussian white noise and impulse noise.
2024, 52(3):318-329.
DOI: 10.19517/j.1671-6345.20230129
Abstract:
By introducing the relief shading method, which is often used in making the topographic maps, into the visualisation of numerical weather forecast data, this article presents the achievement in three-dimensional drawing of meteorological variables, such as air pressure and geopotential height. Based on the principle and implementation of hill shading, which uses the relationship between the illumination angle, the direction, the slope, and the orientation of the terrain to calculate the brightness value of luminous flux, the relief shading method makes use of the brightness value to display the three-dimensional sense of meteorological model data. At the same height, the steeper the terrain, the darker (brighter) the shaded (sunny) side; under the same slope, the higher the terrain, the darker (brighter) the shaded (sunny) side, which is consistent with the real-life visual effect. The colouring method of the shaded relief map is to use the brightness value (V) in the HSV colour space which is calculated on each grid point, combined with hue (H) and saturation (S) to obtain a complete HSV colour scheme. Through the conversion from the HSV colour space to the RGB colour space, the latter colour space is used for drawing a coloured shaded relief map for meteorological model data. In the shaded relief map, the high-pressure centre in the weather system is often shown as a raised peak, and the low pressure is shown as a depressed valley; a large pressure gradient can be seen as a steep slope, while a small pressure gradient can be seen as a gentle slope. Compared with the traditional isoline and colour filling analysis, it is found that the shaded relief map can help to identify high-low weather systems by concave-convex shapes and reflects the gradient changes of weather systems through the steepness of slope, thus intuitively representing the three-dimensional distribution of atmospheric circulation. In addition, the shaded relief map has the ability to visualise model data in pixel level details, identify early eddy current disturbances in small gradients, and reveal equivalent terrain effects, which helps the meteorologists better interpret the model data and provides the references for the improvements of data process functions in numerical models. Furthermore, the relief shading method is suitable for using the synthetic animations to showcase the fluid characteristics of atmospheric motion, which is conducive to popularising the concept of various weather systems, such as the high, the low, the trough and ridge, and their evolution to the public.
By introducing the relief shading method, which is often used in making the topographic maps, into the visualisation of numerical weather forecast data, this article presents the achievement in three-dimensional drawing of meteorological variables, such as air pressure and geopotential height. Based on the principle and implementation of hill shading, which uses the relationship between the illumination angle, the direction, the slope, and the orientation of the terrain to calculate the brightness value of luminous flux, the relief shading method makes use of the brightness value to display the three-dimensional sense of meteorological model data. At the same height, the steeper the terrain, the darker (brighter) the shaded (sunny) side; under the same slope, the higher the terrain, the darker (brighter) the shaded (sunny) side, which is consistent with the real-life visual effect. The colouring method of the shaded relief map is to use the brightness value (V) in the HSV colour space which is calculated on each grid point, combined with hue (H) and saturation (S) to obtain a complete HSV colour scheme. Through the conversion from the HSV colour space to the RGB colour space, the latter colour space is used for drawing a coloured shaded relief map for meteorological model data. In the shaded relief map, the high-pressure centre in the weather system is often shown as a raised peak, and the low pressure is shown as a depressed valley; a large pressure gradient can be seen as a steep slope, while a small pressure gradient can be seen as a gentle slope. Compared with the traditional isoline and colour filling analysis, it is found that the shaded relief map can help to identify high-low weather systems by concave-convex shapes and reflects the gradient changes of weather systems through the steepness of slope, thus intuitively representing the three-dimensional distribution of atmospheric circulation. In addition, the shaded relief map has the ability to visualise model data in pixel level details, identify early eddy current disturbances in small gradients, and reveal equivalent terrain effects, which helps the meteorologists better interpret the model data and provides the references for the improvements of data process functions in numerical models. Furthermore, the relief shading method is suitable for using the synthetic animations to showcase the fluid characteristics of atmospheric motion, which is conducive to popularising the concept of various weather systems, such as the high, the low, the trough and ridge, and their evolution to the public.
2024, 52(3):330-339.
DOI: 10.19517/j.1671-6345.20230166
Abstract:
In order to apply the Hail Detection Algorithm (HDA) related products more extensively and correctly, for the 22 hail cases monitored in Pu’er area from 2015 to 2020, the new Radar Operational Software Engineering (ROSE2.0) is used to replay radar-based data and analyse the relevant products. The recognition effect of the HDA algorithm in the Pu’er area is evaluated with the probability of detection (POD), false alarm rate (FAR), and critical success index (CSI), and a localised parameter configuration scheme is provided after that, which is useful to improve the local hail warning ability. The results show that although the POD of the HDA algorithm in Pu’er area is close to 100%, there are also many ordinary storm cells that are identified as hail cells mistakenly. The number of false alarms is very huge, and the low CSI cannot meet the requirement of the weather forecasting operation. The warning effect of using Probability of Severe Hail (POSH) is better than that of Probability of Hail (POH) for any size of hail, and the larger the size of hail, the lower the probability of false alarm of POSH. Further analysis of the adaptation parameters of the POSH algorithm by a simulation test method shows that the height of the 0 ℃ and -20 ℃ layers has a significant impact on the recognition ability of POSH, the original default value is significantly lower in Pu’er area, correctly inputting the height of 0 ℃ and -20 ℃ layers on the day of hail can effectively reduce the FAR and improve the CSI of POSH; at the same time, it can control the situation that the maximum hail diameter predicted by the algorithm is generally too large, and the maximum expected hail size (MEHS) is closer to the observation value; the deviation percentage of small and medium-sized hail diameter decreases by 76.07%, with a significantly higher improvement effect than large hail, but the diameter prediction error of MEHS for large hail is smaller. In addition, increasing the reflectivity factor and POSH threshold can effectively control FAR, but it also leads to a rapid increase in the number of missed alarms. When the threshold is too large, the POD significantly decreases. In order to achieve a higher POD and CSI, selecting Z=50 dBz or POSH=70% as the threshold can improve the recognition effect of the HDA algorithm. Setting the optimal threshold of multiple parameters at the same time can effectively improve the recognition ability of the HDA algorithm in Pu’er.
In order to apply the Hail Detection Algorithm (HDA) related products more extensively and correctly, for the 22 hail cases monitored in Pu’er area from 2015 to 2020, the new Radar Operational Software Engineering (ROSE2.0) is used to replay radar-based data and analyse the relevant products. The recognition effect of the HDA algorithm in the Pu’er area is evaluated with the probability of detection (POD), false alarm rate (FAR), and critical success index (CSI), and a localised parameter configuration scheme is provided after that, which is useful to improve the local hail warning ability. The results show that although the POD of the HDA algorithm in Pu’er area is close to 100%, there are also many ordinary storm cells that are identified as hail cells mistakenly. The number of false alarms is very huge, and the low CSI cannot meet the requirement of the weather forecasting operation. The warning effect of using Probability of Severe Hail (POSH) is better than that of Probability of Hail (POH) for any size of hail, and the larger the size of hail, the lower the probability of false alarm of POSH. Further analysis of the adaptation parameters of the POSH algorithm by a simulation test method shows that the height of the 0 ℃ and -20 ℃ layers has a significant impact on the recognition ability of POSH, the original default value is significantly lower in Pu’er area, correctly inputting the height of 0 ℃ and -20 ℃ layers on the day of hail can effectively reduce the FAR and improve the CSI of POSH; at the same time, it can control the situation that the maximum hail diameter predicted by the algorithm is generally too large, and the maximum expected hail size (MEHS) is closer to the observation value; the deviation percentage of small and medium-sized hail diameter decreases by 76.07%, with a significantly higher improvement effect than large hail, but the diameter prediction error of MEHS for large hail is smaller. In addition, increasing the reflectivity factor and POSH threshold can effectively control FAR, but it also leads to a rapid increase in the number of missed alarms. When the threshold is too large, the POD significantly decreases. In order to achieve a higher POD and CSI, selecting Z=50 dBz or POSH=70% as the threshold can improve the recognition effect of the HDA algorithm. Setting the optimal threshold of multiple parameters at the same time can effectively improve the recognition ability of the HDA algorithm in Pu’er.
2024, 52(3):340-346.
DOI: 10.19517/j.1671-6345.20230182
Abstract:
In order to achieve the goal of independent and controllable key core technologies for Meteo by 2025, the Meteo Big Data Cloud Platform (referred to as Tianqing) establishes a simulation environment based on Hygon X86 CPU and Kylin OS. However, in the operation of simulation platforms, it finds that the docker scheduling performance of data processing and assembly line subsystems based on Kubernetes is poor, which cannot meet the timeliness requirements of user integration algorithms. In response to this issue, this article adopts a comparative analysis method, selecting servers based on three types of CPU and three types of operating systems from the simulation environment and business environment for Tianqing as the research objects. A series of combined comparative test cases are designed. It finds that the kernel is the key factor affecting docker scheduling performance. Further analysis is conducted on the impact of operating system kernel settings on real-time and throughput, as well as the suitable business scenarios. Finally, a method for adjusting the Kylin OS kernel is provided. By adjusting the kernel settings, the docker scheduling performance significantly improves, meeting the timeliness requirements of the data processing system and laying the foundation for achieving self-supporting of the key core technology of Tianqing.
In order to achieve the goal of independent and controllable key core technologies for Meteo by 2025, the Meteo Big Data Cloud Platform (referred to as Tianqing) establishes a simulation environment based on Hygon X86 CPU and Kylin OS. However, in the operation of simulation platforms, it finds that the docker scheduling performance of data processing and assembly line subsystems based on Kubernetes is poor, which cannot meet the timeliness requirements of user integration algorithms. In response to this issue, this article adopts a comparative analysis method, selecting servers based on three types of CPU and three types of operating systems from the simulation environment and business environment for Tianqing as the research objects. A series of combined comparative test cases are designed. It finds that the kernel is the key factor affecting docker scheduling performance. Further analysis is conducted on the impact of operating system kernel settings on real-time and throughput, as well as the suitable business scenarios. Finally, a method for adjusting the Kylin OS kernel is provided. By adjusting the kernel settings, the docker scheduling performance significantly improves, meeting the timeliness requirements of the data processing system and laying the foundation for achieving self-supporting of the key core technology of Tianqing.
2024, 52(3):347-355.
DOI: 10.19517/j.1671-6345.20230189
Abstract:
In order to expand the space of meteorological business, integrate multiple fields of monitoring, and promote the development of the meteorological industry towards efficiency, convenience, and intensification, the Guizhou Province Meteorological Comprehensive Monitoring System APP is developed using mainstream mobile apps as carriers, based on the Springboot+Vue+Mybatis Plus development framework, and using multi-platform compatible development (uni-app), real-time capture of change data (FlinkCDC), and an efficient packaging framework (Mybatis-Plus) among other technical means. The article provides a detailed introduction to the framework structure and functional design adopted by the APP as an independent monitoring system, as well as the big data development technology and its business advantages involved. At the technical level, the system utilises uni-app development technology to make the APP client more compatible and can simultaneously adapt to various application platforms such as iOS, Android, Web, and various mini-programs; using Mybatis-Plus as the database driver framework to improve code reusability and reduce database performance overhead; by using FlinkCDC as a data processing and incremental synchronisation tool, resource waste caused by full data synchronisation can be avoided, simultaneously serving as a one-way synchronisation tool to enhance the security of meteorological data. At the framework level, in order to avoid security risks caused by network mixing, the system introduces a Demilitarised Zone (DMZ) to isolate the internal and external network data environments. The internal network department is responsible for collecting and storing meteorological data from various formats such as databases, static files, API interfaces, logs, etc. Then, it will be synchronised unidirectionally with the external network environment through FlinkCDC. The external network interacts with the mobile APP by receiving data pushed by FlinkCDC. The software is aimed at meteorological users at all levels of province, city, county, and station. Through preliminary research and analysis, four functional modules have been developed for different users, including regional automatic stations, weather radar stations, network connectivity, and interface service status. This provides convenience for meteorological data monitoring and equipment maintenance, and improves the timeliness of response. The system has been put into use throughout the province since 2022. The application results show that the APP adapts to multiple mobile system platforms such as Android and iOS, and has a friendly interface, simple operation, and stable operation. Since its application, the timeliness of meteorological data has improved, enriching the monitoring business methods of Guizhou Province, meeting the user needs at all levels, and playing a positive role in the development of the meteorological industry.
In order to expand the space of meteorological business, integrate multiple fields of monitoring, and promote the development of the meteorological industry towards efficiency, convenience, and intensification, the Guizhou Province Meteorological Comprehensive Monitoring System APP is developed using mainstream mobile apps as carriers, based on the Springboot+Vue+Mybatis Plus development framework, and using multi-platform compatible development (uni-app), real-time capture of change data (FlinkCDC), and an efficient packaging framework (Mybatis-Plus) among other technical means. The article provides a detailed introduction to the framework structure and functional design adopted by the APP as an independent monitoring system, as well as the big data development technology and its business advantages involved. At the technical level, the system utilises uni-app development technology to make the APP client more compatible and can simultaneously adapt to various application platforms such as iOS, Android, Web, and various mini-programs; using Mybatis-Plus as the database driver framework to improve code reusability and reduce database performance overhead; by using FlinkCDC as a data processing and incremental synchronisation tool, resource waste caused by full data synchronisation can be avoided, simultaneously serving as a one-way synchronisation tool to enhance the security of meteorological data. At the framework level, in order to avoid security risks caused by network mixing, the system introduces a Demilitarised Zone (DMZ) to isolate the internal and external network data environments. The internal network department is responsible for collecting and storing meteorological data from various formats such as databases, static files, API interfaces, logs, etc. Then, it will be synchronised unidirectionally with the external network environment through FlinkCDC. The external network interacts with the mobile APP by receiving data pushed by FlinkCDC. The software is aimed at meteorological users at all levels of province, city, county, and station. Through preliminary research and analysis, four functional modules have been developed for different users, including regional automatic stations, weather radar stations, network connectivity, and interface service status. This provides convenience for meteorological data monitoring and equipment maintenance, and improves the timeliness of response. The system has been put into use throughout the province since 2022. The application results show that the APP adapts to multiple mobile system platforms such as Android and iOS, and has a friendly interface, simple operation, and stable operation. Since its application, the timeliness of meteorological data has improved, enriching the monitoring business methods of Guizhou Province, meeting the user needs at all levels, and playing a positive role in the development of the meteorological industry.
2024, 52(3):356-366.
DOI: 10.19517/j.1671-6345.20230120
Abstract:
This study aims to improve the forecast capability of mid-to-long term visibility by analysing the impact of pollution levels, circulation systems, and spatiotemporal distribution characteristics on low visibility weather. A neural network approach is utilised to model over 2500 stations nationwide, incorporating multi-year meteorological observations, pollution data, and reanalysis data. The selection of model structure and parameterisation schemes takes into account performance evaluations based on empirical formulas and varying parameter values across different datasets. Cross-validation is employed to split the neural network datasets into training and validation sets during the parameter training phase. Different parameterisation schemes are applied to train the models on the training set, and their performance is assessed on the validation set. By comparing the models’ performance under different parameterisation schemes, an optimal balance between fitting accuracy and generalisation capability is achieved. Using the previously established forecasting models, a visibility ensemble forecast product is created based on 15-day PM2.5 CAMx-NCEP model, observed data, and ECMWF ensemble forecast. The ensemble forecast product includes control forecast values, ensemble means, and 50th percentile values. In the winter of 2022, the TS score evaluation test in all forecast durations, including medium-to-long term, shows that the ensemble forecast’s control forecast values and ensemble means outperform the 50th percentile forecast values and ECMWF’s visibility products in the visibility ranges of 1 km, 1-3 km, and 3-5 km. For the visibility ranges of 5-10 km and greater than 10 km, the TS scores of the control forecast values, ensemble means, 50th percentile forecast values, and ECMWF’s visibility products are relatively close. Based on the visibility ensemble forecast product, three post-processing methods (probability matching, optimal percentiles, and neural networks) are developed to improve forecast TS scores compared to the ensemble forecast product. The average TS scores for visibility below 1 km are 0.126, 0.126, and 0.130 for the optimal percentiles, probability matching, and neural network methods, respectively. For visibility in the range of 1-3 km, the average TS scores are 0.168, 0.168, and 0.170, respectively. These post-processing methods provide an improvement of around 10% and 7% for visibility below 1 km and in the 1-3 km range, respectively, compared to the ensemble forecast. Analysis of the forecast model reveals errors primarily originating from discrepancies between the ECMWF model’s input factors and observed values, such as 2 m humidity and wind fields. Each post-processing method exhibits advantages in different forecast lead times and visibility ranges, which are integrated using statistical methods for optimal ensemble forecasting. The TS score evaluation of the visibility post-processing optimal ensemble shows overall similarity or slight superiority compared to individual methods in the low visibility range. The minimum ensemble method slightly outperforms mean and weighted ensemble products in TS scores between 0-3 km but performs worse above 3 km. To emphasise the forecast focus on low visibility, the minimum ensemble method is selected to generate the optimal ensemble forecast product, enhancing the forecast service capability for low visibility weather during the extended period.
This study aims to improve the forecast capability of mid-to-long term visibility by analysing the impact of pollution levels, circulation systems, and spatiotemporal distribution characteristics on low visibility weather. A neural network approach is utilised to model over 2500 stations nationwide, incorporating multi-year meteorological observations, pollution data, and reanalysis data. The selection of model structure and parameterisation schemes takes into account performance evaluations based on empirical formulas and varying parameter values across different datasets. Cross-validation is employed to split the neural network datasets into training and validation sets during the parameter training phase. Different parameterisation schemes are applied to train the models on the training set, and their performance is assessed on the validation set. By comparing the models’ performance under different parameterisation schemes, an optimal balance between fitting accuracy and generalisation capability is achieved. Using the previously established forecasting models, a visibility ensemble forecast product is created based on 15-day PM2.5 CAMx-NCEP model, observed data, and ECMWF ensemble forecast. The ensemble forecast product includes control forecast values, ensemble means, and 50th percentile values. In the winter of 2022, the TS score evaluation test in all forecast durations, including medium-to-long term, shows that the ensemble forecast’s control forecast values and ensemble means outperform the 50th percentile forecast values and ECMWF’s visibility products in the visibility ranges of 1 km, 1-3 km, and 3-5 km. For the visibility ranges of 5-10 km and greater than 10 km, the TS scores of the control forecast values, ensemble means, 50th percentile forecast values, and ECMWF’s visibility products are relatively close. Based on the visibility ensemble forecast product, three post-processing methods (probability matching, optimal percentiles, and neural networks) are developed to improve forecast TS scores compared to the ensemble forecast product. The average TS scores for visibility below 1 km are 0.126, 0.126, and 0.130 for the optimal percentiles, probability matching, and neural network methods, respectively. For visibility in the range of 1-3 km, the average TS scores are 0.168, 0.168, and 0.170, respectively. These post-processing methods provide an improvement of around 10% and 7% for visibility below 1 km and in the 1-3 km range, respectively, compared to the ensemble forecast. Analysis of the forecast model reveals errors primarily originating from discrepancies between the ECMWF model’s input factors and observed values, such as 2 m humidity and wind fields. Each post-processing method exhibits advantages in different forecast lead times and visibility ranges, which are integrated using statistical methods for optimal ensemble forecasting. The TS score evaluation of the visibility post-processing optimal ensemble shows overall similarity or slight superiority compared to individual methods in the low visibility range. The minimum ensemble method slightly outperforms mean and weighted ensemble products in TS scores between 0-3 km but performs worse above 3 km. To emphasise the forecast focus on low visibility, the minimum ensemble method is selected to generate the optimal ensemble forecast product, enhancing the forecast service capability for low visibility weather during the extended period.
2024, 52(3):367-379.
DOI: 10.19517/j.1671-6345.20230172
Abstract:
Based on the ensemble forecast data derived from European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast system and observation data derived from automatic observation stations in Zhejiang region, the Bayesian Model Averaging (BMA) method is used to calibrate the probabilistic forecasts of precipitation during the super long Meiyu season in 2020. In this paper, we verify the raw ensemble probabilistic forecast and BMA calibrated probabilistic forecast from 1 June to 15 July, 2020, by Mean Absolute Error (MAE), Continuous Ranked Probability Score (CRPS), Brier Score (BS), Talagrand, Probability Integral Transform (PIT) histogram, and attribute diagram. The verification results before and after calibration are compared. The analysis results are listed as follows. (1) In 8 different training periods (10 days to 80 days), 50 days correspond to smaller MAE and CRPS score values. So we set 50 days as the optimal BMA training period for ECMWF ensemble forecast calibration in the Meiyu season in Zhejiang Province. After BMA calibration in the optimal training period, the spread of ensemble forecast increases and the forecast error decreases. Analysing from the quantitative verification indicators, BMA can effectively calibrate the overall precipitation in the test stage, but it cannot calibrate the daily precipitation in the test stage. (2) For forecasting of different threshold precipitation, BMA has different calibration performance. For the thresholds of 0.1 mm, 10.0 mm, and 25.0 mm, BMA has a significant calibration effect. After BMA calibration, the CRPS of precipitation probabilistic forecast for these three thresholds (0.1 mm, 10.0 mm, and 25.0 mm) decreases by 25.92%, 19.29%, and 4.76%, respectively. However, the calibration effect of BMA weakens with the increase of precipitation threshold. For the events with total precipitation exceeding 50.0 mm, the BMA calibration effect is not as significant as that of the smaller threshold. In addition, BMA can effectively improve the forecast skills of 0.1 mm, 10.0 mm and 25.0 mm threshold precipitation and make the forecast probability more closely match the observation. (3) In the case of heavy rain, the high probability range of the raw ensemble probabilistic forecast is always wider than that of the observation. BMA has the ability to slightly calibrate the raw ensemble forecast probability. After BMA calibration, the high probability range of precipitation forecast at each threshold effectively reduces the deviation. The empty message information and the probability of empty message events also reduce after calibration. So BMA can make the calibrated high probability range of precipitation forecast more consistent with the observed range. But unfortunately, BMA cannot adjust the spatial distribution of precipitation forecast probability.
Based on the ensemble forecast data derived from European Centre for Medium-range Weather Forecasts (ECMWF) ensemble forecast system and observation data derived from automatic observation stations in Zhejiang region, the Bayesian Model Averaging (BMA) method is used to calibrate the probabilistic forecasts of precipitation during the super long Meiyu season in 2020. In this paper, we verify the raw ensemble probabilistic forecast and BMA calibrated probabilistic forecast from 1 June to 15 July, 2020, by Mean Absolute Error (MAE), Continuous Ranked Probability Score (CRPS), Brier Score (BS), Talagrand, Probability Integral Transform (PIT) histogram, and attribute diagram. The verification results before and after calibration are compared. The analysis results are listed as follows. (1) In 8 different training periods (10 days to 80 days), 50 days correspond to smaller MAE and CRPS score values. So we set 50 days as the optimal BMA training period for ECMWF ensemble forecast calibration in the Meiyu season in Zhejiang Province. After BMA calibration in the optimal training period, the spread of ensemble forecast increases and the forecast error decreases. Analysing from the quantitative verification indicators, BMA can effectively calibrate the overall precipitation in the test stage, but it cannot calibrate the daily precipitation in the test stage. (2) For forecasting of different threshold precipitation, BMA has different calibration performance. For the thresholds of 0.1 mm, 10.0 mm, and 25.0 mm, BMA has a significant calibration effect. After BMA calibration, the CRPS of precipitation probabilistic forecast for these three thresholds (0.1 mm, 10.0 mm, and 25.0 mm) decreases by 25.92%, 19.29%, and 4.76%, respectively. However, the calibration effect of BMA weakens with the increase of precipitation threshold. For the events with total precipitation exceeding 50.0 mm, the BMA calibration effect is not as significant as that of the smaller threshold. In addition, BMA can effectively improve the forecast skills of 0.1 mm, 10.0 mm and 25.0 mm threshold precipitation and make the forecast probability more closely match the observation. (3) In the case of heavy rain, the high probability range of the raw ensemble probabilistic forecast is always wider than that of the observation. BMA has the ability to slightly calibrate the raw ensemble forecast probability. After BMA calibration, the high probability range of precipitation forecast at each threshold effectively reduces the deviation. The empty message information and the probability of empty message events also reduce after calibration. So BMA can make the calibrated high probability range of precipitation forecast more consistent with the observed range. But unfortunately, BMA cannot adjust the spatial distribution of precipitation forecast probability.
2024, 52(3):380-391.
DOI: 10.19517/j.1671-6345.20230246
Abstract:
Flash heavy rain and the resulting low visibility make it difficult for pilots to visually assess the runway clearly, severely impacting the take-off and landing of aircraft, thereby posing a threat to aviation operational safety. Moreover, the flight delays and diversions caused by this also result in significant losses for airlines and negatively affect socioeconomic benefits. Therefore, conducting comprehensive studies on flash heavy rain is crucial for ensuring aviation safety and enhancing flight punctuality. A thorough analysis of sufficiently detailed observational data is beneficial for clarifying the dynamic mechanisms of convective organisation and enhancement. On July 15, 2022, Xiamen Airport experienced a rare flash heavy rain event triggered by a weak background gust front. During this period, the precipitation intensity peaked at 2.5 mm per minute, and runway visibility rapidly decreased to 600 m, which is relatively uncommon at Xiamen Airport. To analyse this flash heavy rain event, this study utilises minute rainfall data from both ends of the runway, conventional observational data, densified automatic weather station data, ERA5 reanalysis, and S-band dual-polarisation and X-band dual-polarisation phased array radar data of Xiamen. The results of the study indicate that this event occurred under weak weather-scale forcing, where the gust front triggered uplift by intersecting and merging with the surface convergence line during propagation. In an environment characterised by negative large values of pseudo-equivalent potential temperature (θse500-850 hPa) and a warm and humid lower atmosphere, new convection was stimulated, resulting in the rare flash heavy rain at Xiamen Airport. During heavy rain, strong water vapour convergence appeared in the boundary layer at 1000 hPa. Minute rainfall on the runway showed an inverse correlation with visibility, but this correlation weakened when the minute rainfall exceeded 1.6 mm, and the visibility minimum lagged behind the rainfall peak by 7 minutes. Observational analysis reveals that the cyclonic shear of radial velocity was consistent with the trend of minute rainfall change. The peak minute rainfall at both ends of the runway corresponded to the peak cyclonic shear at a certain height layer, indicating a good correspondence between the two. When there was cyclonic shear in the radial velocity at heights of 2-5 km, rainfall significantly intensified. When the shear intensity at two height layers exceeded 2×10-3s-1, minute rainfall could reach approximately 2 mm (equivalent to an hourly rainfall of 120 mm), which emerged as a characteristic feature of this flash heavy rain event.
Flash heavy rain and the resulting low visibility make it difficult for pilots to visually assess the runway clearly, severely impacting the take-off and landing of aircraft, thereby posing a threat to aviation operational safety. Moreover, the flight delays and diversions caused by this also result in significant losses for airlines and negatively affect socioeconomic benefits. Therefore, conducting comprehensive studies on flash heavy rain is crucial for ensuring aviation safety and enhancing flight punctuality. A thorough analysis of sufficiently detailed observational data is beneficial for clarifying the dynamic mechanisms of convective organisation and enhancement. On July 15, 2022, Xiamen Airport experienced a rare flash heavy rain event triggered by a weak background gust front. During this period, the precipitation intensity peaked at 2.5 mm per minute, and runway visibility rapidly decreased to 600 m, which is relatively uncommon at Xiamen Airport. To analyse this flash heavy rain event, this study utilises minute rainfall data from both ends of the runway, conventional observational data, densified automatic weather station data, ERA5 reanalysis, and S-band dual-polarisation and X-band dual-polarisation phased array radar data of Xiamen. The results of the study indicate that this event occurred under weak weather-scale forcing, where the gust front triggered uplift by intersecting and merging with the surface convergence line during propagation. In an environment characterised by negative large values of pseudo-equivalent potential temperature (θse500-850 hPa) and a warm and humid lower atmosphere, new convection was stimulated, resulting in the rare flash heavy rain at Xiamen Airport. During heavy rain, strong water vapour convergence appeared in the boundary layer at 1000 hPa. Minute rainfall on the runway showed an inverse correlation with visibility, but this correlation weakened when the minute rainfall exceeded 1.6 mm, and the visibility minimum lagged behind the rainfall peak by 7 minutes. Observational analysis reveals that the cyclonic shear of radial velocity was consistent with the trend of minute rainfall change. The peak minute rainfall at both ends of the runway corresponded to the peak cyclonic shear at a certain height layer, indicating a good correspondence between the two. When there was cyclonic shear in the radial velocity at heights of 2-5 km, rainfall significantly intensified. When the shear intensity at two height layers exceeded 2×10-3s-1, minute rainfall could reach approximately 2 mm (equivalent to an hourly rainfall of 120 mm), which emerged as a characteristic feature of this flash heavy rain event.
2024, 52(3):392-402.
DOI: 10.19517/j.1671-6345.20230211
Abstract:
Snow disaster is one of the meteorological disasters with a wide range of impact in winter. The technical parameters for engineering snow disaster prevention include the calculation of snow density, snow pressure, and other snow accumulation parameters. Due to the scarcity of snow observation stations in southern provinces and the lack of data, the calculation of important parameters for snow disaster prevention often uses data from other meteorological stations with snow accumulation observations as a substitute. How to choose representative stations with scientific significance is an urgent problem to be solved. The snowfall climate zoning based on similar climate backgrounds can provide a scientific basis for the selection of representative stations for snow cover parameters in areas without data. In the study of snowfall climate zoning in Hubei Province, due to the significant interannual fluctuations of climate indicators such as the first and last days of snowfall, snowfall amount, we use 12 climate indicators such as the dates of the first and last days of snowfall, the number of snow days, the number of snow cover days, the amount of snowfall and the maximum snow depth, to reflect the fluctuation characteristics of climate indicators in the snowfall area of Hubei Province. At the same time, we draw inspiration from common research methods on snowfall climate in the northern region and adopt the clustering analysis method based on Hilbert Schmidt Independence Criterion (HSIC) kernel function to calculate the overall similarity of multidimensional time series indicators to carry out the classification and zoning of snowfall climate in Hubei Province. The results show that the snowfall climate in Hubei Province can be divided into four climatic zones: southeast, central, northwest, and southwest. The zonal distribution characteristics of the zones are consistent with the climatic background of heavy snowfall in Hubei Province caused by the cold air from the north. The first snow day is delayed from the northwest to the central, southwest, and southeast, and the last snow day is just the opposite. The number of snow days and the number of days with snow cover in the northwest are the greatest. The southeast representative station is Huangshi station; the central representative station is Macheng, Wuhan, and Zhongxiang; the southwest representative station is Xianfeng and Badong; and the northwest representative station is Yunxi and Laohekou. The HSIC kernel function can handle the similarity between sets of indicator sequences with significant interannual fluctuations well, and its clustering method is more reasonable for the climate zoning of Hubei snowfall. The zoning results provide a technical basis for the refined snow disaster prevention in Hubei Province.
Snow disaster is one of the meteorological disasters with a wide range of impact in winter. The technical parameters for engineering snow disaster prevention include the calculation of snow density, snow pressure, and other snow accumulation parameters. Due to the scarcity of snow observation stations in southern provinces and the lack of data, the calculation of important parameters for snow disaster prevention often uses data from other meteorological stations with snow accumulation observations as a substitute. How to choose representative stations with scientific significance is an urgent problem to be solved. The snowfall climate zoning based on similar climate backgrounds can provide a scientific basis for the selection of representative stations for snow cover parameters in areas without data. In the study of snowfall climate zoning in Hubei Province, due to the significant interannual fluctuations of climate indicators such as the first and last days of snowfall, snowfall amount, we use 12 climate indicators such as the dates of the first and last days of snowfall, the number of snow days, the number of snow cover days, the amount of snowfall and the maximum snow depth, to reflect the fluctuation characteristics of climate indicators in the snowfall area of Hubei Province. At the same time, we draw inspiration from common research methods on snowfall climate in the northern region and adopt the clustering analysis method based on Hilbert Schmidt Independence Criterion (HSIC) kernel function to calculate the overall similarity of multidimensional time series indicators to carry out the classification and zoning of snowfall climate in Hubei Province. The results show that the snowfall climate in Hubei Province can be divided into four climatic zones: southeast, central, northwest, and southwest. The zonal distribution characteristics of the zones are consistent with the climatic background of heavy snowfall in Hubei Province caused by the cold air from the north. The first snow day is delayed from the northwest to the central, southwest, and southeast, and the last snow day is just the opposite. The number of snow days and the number of days with snow cover in the northwest are the greatest. The southeast representative station is Huangshi station; the central representative station is Macheng, Wuhan, and Zhongxiang; the southwest representative station is Xianfeng and Badong; and the northwest representative station is Yunxi and Laohekou. The HSIC kernel function can handle the similarity between sets of indicator sequences with significant interannual fluctuations well, and its clustering method is more reasonable for the climate zoning of Hubei snowfall. The zoning results provide a technical basis for the refined snow disaster prevention in Hubei Province.
2024, 52(3):403-414.
DOI: 10.19517/j.1671-6345.20230186
Abstract:
Based on the hourly and daily precipitation data of 61 national meteorological stations from 1961 to 2020 and 998 regional automatic meteorological observation stations from the beginning of the establishment to 2020 in Liaoning Province, we analyse the main causing factors of rainstorm and flood disaster, calculate the environmental indicators of rainstorm and flood disaster, and complete the hazard assessment of rainstorm and flood disaster in Liaoning Province. The results show that the high-risk area of rainstorms and floods is mainly located in Dandong. The high population risk areas of rainstorm and flood disaster are mainly located in Shenyang and Dalian urban areas. The high economic risk areas of rainstorm and flood disaster are mainly located in Dalian and Panjin urban areas. The high-risk areas of rice and maize are mainly located in Jinzhou, Panjin, and Dandong. The disaster risk of the rainstorm process on 28-29 July 2022 is pre-assessed using the intelligent grid forecast data of Liaoning Province. It is found that the high hazardous areas are mainly distributed in Chaoyang, Huludao, and the central part of Liaoning. The population and economic high-risk areas caused by the rainstorm disaster are mainly located in the western and central areas. The high-risk areas of rice and maize caused by rainstorm disaster are mainly located in Shenyang, Tieling, and the north of Chaoyang. It is estimated that the population affected in the high-risk area is about 4.49 million, the economic loss is about 14.32 million yuan. The affected rice area is about 10,280 hectares, the maize area is about 17,798 hectares. Through the post-disaster effect test, it is found that the pre-assessment model is effective and can be used in the actual rainstorm and flood disaster risk assessment business.
Based on the hourly and daily precipitation data of 61 national meteorological stations from 1961 to 2020 and 998 regional automatic meteorological observation stations from the beginning of the establishment to 2020 in Liaoning Province, we analyse the main causing factors of rainstorm and flood disaster, calculate the environmental indicators of rainstorm and flood disaster, and complete the hazard assessment of rainstorm and flood disaster in Liaoning Province. The results show that the high-risk area of rainstorms and floods is mainly located in Dandong. The high population risk areas of rainstorm and flood disaster are mainly located in Shenyang and Dalian urban areas. The high economic risk areas of rainstorm and flood disaster are mainly located in Dalian and Panjin urban areas. The high-risk areas of rice and maize are mainly located in Jinzhou, Panjin, and Dandong. The disaster risk of the rainstorm process on 28-29 July 2022 is pre-assessed using the intelligent grid forecast data of Liaoning Province. It is found that the high hazardous areas are mainly distributed in Chaoyang, Huludao, and the central part of Liaoning. The population and economic high-risk areas caused by the rainstorm disaster are mainly located in the western and central areas. The high-risk areas of rice and maize caused by rainstorm disaster are mainly located in Shenyang, Tieling, and the north of Chaoyang. It is estimated that the population affected in the high-risk area is about 4.49 million, the economic loss is about 14.32 million yuan. The affected rice area is about 10,280 hectares, the maize area is about 17,798 hectares. Through the post-disaster effect test, it is found that the pre-assessment model is effective and can be used in the actual rainstorm and flood disaster risk assessment business.
2024, 52(3):415-423.
DOI: 10.19517/j.1671-6345.20230176
Abstract:
Due to the need for pre-disaster prediction and rapid post-disaster estimation of disaster situations during the “Dragon Boat Water” period (from late May to mid-June) in Guangdong, the integrated disaster index (IDI) of “Dragon Boat Water” is calculated in this study, by using rainfall, heavy rainfall flood disaster data during the “Dragon Boat Water” period in Guangdong spanning from 1995 to 2021. Then, IDI is classified into light, medium, and heavy levels, employing the quantile method. The study focuses on analysing disaster features and the relationship between rainfall and disaster situations. The key results are as follows: To begin with, the years 2005 to 2010 are the peak of rainfall intensity, rainfall range, heavy rainfall frequency, and duration of the Guangdong “Dragon Boat Water” from 1995 to 2021. The peak of disaster intensity is in 2005-2008. The two peak intervals are relatively consistent. Following 2008, the heaviest disaster situation year, there is a downward tendency in integrated disaster intensity. Over the past decade (from 2012 to 2021), all five kinds of disasters also show a downward tendency. The number of collapsed buildings, the number of affected people, and affected crop areas show the most pronounced reductions, while direct economic losses display a more moderate decrease. Furthermore, the correlation between affected crop areas and rainfall factors is the highest, followed by the number of affected people and direct economic losses. The levels of integrated disaster and direct economic losses are primarily affected by the intensity of rainfall and the frequency of heavy rainfall. Meanwhile, the number of affected people and affected crop areas are primarily influenced by both the intensity and range of rainfall. The number of collapsed buildings and the number of deaths are mainly influenced by the frequency of heavy rainfall. Lastly, the established fitting equation between the average total rainfall and disaster of “Dragon Boat Water” shows reliability, by estimating the integrated disaster level and disaster situation data of “Dragon Boat Water” approximate to the actual disaster situations. The hit rate of estimates for integrated disaster level is 59%, and the hit rates for estimates of heavy, medium, and light levels are 20%, 50% and 78.5% respectively. The estimates of a heavy integrated disaster level are slightly lighter than the actual situation, the estimates of the medium level are consistent or slightly lighter, and the estimates of light level are basically consistent. Applying the meteorological disaster risk assessment method in this study, the disaster level and various disaster data can be quantitatively estimated in advance based on rainfall prediction of the “Dragon Boat Water” period. thereby providing a reference for emergency management departments in disaster prevention and reduction.
Due to the need for pre-disaster prediction and rapid post-disaster estimation of disaster situations during the “Dragon Boat Water” period (from late May to mid-June) in Guangdong, the integrated disaster index (IDI) of “Dragon Boat Water” is calculated in this study, by using rainfall, heavy rainfall flood disaster data during the “Dragon Boat Water” period in Guangdong spanning from 1995 to 2021. Then, IDI is classified into light, medium, and heavy levels, employing the quantile method. The study focuses on analysing disaster features and the relationship between rainfall and disaster situations. The key results are as follows: To begin with, the years 2005 to 2010 are the peak of rainfall intensity, rainfall range, heavy rainfall frequency, and duration of the Guangdong “Dragon Boat Water” from 1995 to 2021. The peak of disaster intensity is in 2005-2008. The two peak intervals are relatively consistent. Following 2008, the heaviest disaster situation year, there is a downward tendency in integrated disaster intensity. Over the past decade (from 2012 to 2021), all five kinds of disasters also show a downward tendency. The number of collapsed buildings, the number of affected people, and affected crop areas show the most pronounced reductions, while direct economic losses display a more moderate decrease. Furthermore, the correlation between affected crop areas and rainfall factors is the highest, followed by the number of affected people and direct economic losses. The levels of integrated disaster and direct economic losses are primarily affected by the intensity of rainfall and the frequency of heavy rainfall. Meanwhile, the number of affected people and affected crop areas are primarily influenced by both the intensity and range of rainfall. The number of collapsed buildings and the number of deaths are mainly influenced by the frequency of heavy rainfall. Lastly, the established fitting equation between the average total rainfall and disaster of “Dragon Boat Water” shows reliability, by estimating the integrated disaster level and disaster situation data of “Dragon Boat Water” approximate to the actual disaster situations. The hit rate of estimates for integrated disaster level is 59%, and the hit rates for estimates of heavy, medium, and light levels are 20%, 50% and 78.5% respectively. The estimates of a heavy integrated disaster level are slightly lighter than the actual situation, the estimates of the medium level are consistent or slightly lighter, and the estimates of light level are basically consistent. Applying the meteorological disaster risk assessment method in this study, the disaster level and various disaster data can be quantitatively estimated in advance based on rainfall prediction of the “Dragon Boat Water” period. thereby providing a reference for emergency management departments in disaster prevention and reduction.
2024, 52(3):424-433.
DOI: 10.19517/j.1671-6345.20230152
Abstract:
In order to further strengthen the application of satellite-to-ground lightning, the spatial-temporal distribution characteristics and spatial-temporal matching features are comparatively analysed in Zhejiang Province based on lightning data from FengYun (FY)-4A Lightning Mapping Imager (LMI) and Advanced Direction and Time-of-arrival Detecting (ADTD)-2C three-dimensional lightning location system from June to August in 2020. In addition, by combining reflectivity of Doppler radar mosaics and cloud top brightness temperature from FY-4A Advanced Geosynchronous Radiation Imager (AGRI), the spatial and temporal evolution patterns of lightning data from two observation systems are analysed during a thunderstorm process in Zhejiang Province on 15 July 2020. The results show that from June to August in 2020, the number of LMIG detected by LMI was 8483, while the number of lightning detected by the ADTD-2C three-dimensional lightning location system was 376932. The ratio of the two sets of data was approximately 1∶44.43. The monthly and spatial distributions of lightning detected by these two systems were generally consistent, while diurnal variation of which were different. Specifically, diurnal variation of LMIG presented two peaks, and diurnal variation of three-dimensional lightning showed only one peak. Besides, when the time matching window was larger than 1.8 seconds, and the latitude and longitude matching window was larger than 0.5°, the matching rate gradually tended to be stable. Furthermore, the height of three-dimensional lightning matched with LMIG was mainly concentrated below 16 km, and the lightning intensity of which was mainly concentrated below 50 kA. During the thunderstorm weather in Zhejiang Province in the afternoon on 15 July 2020, the ratio of LMIG to three-dimensional lightning was approximately 1∶25.44. The time of the first LMIG and its peak time were both later than the time of the first three-dimensional lightning and its peak time. What’s more, the lightning data observed by the two systems corresponded well with the development process of the thunderstorm. When the thunderstorm was at the developing stage, the number of lightning data detected by the two systems was both gradually increasing, and when the thunderstorm was at the mature stage, the number of lightning data detected by the two systems was both maintaining a relatively high value, and when the thunderstorms were at the dissipation stage, the number of lightning data detected by the two systems was both decreasing rapidly. When it came to the spatial distribution of the lightning, both of the two datasets corresponded well with the spatial distribution of low cloud top brightness temperature.
In order to further strengthen the application of satellite-to-ground lightning, the spatial-temporal distribution characteristics and spatial-temporal matching features are comparatively analysed in Zhejiang Province based on lightning data from FengYun (FY)-4A Lightning Mapping Imager (LMI) and Advanced Direction and Time-of-arrival Detecting (ADTD)-2C three-dimensional lightning location system from June to August in 2020. In addition, by combining reflectivity of Doppler radar mosaics and cloud top brightness temperature from FY-4A Advanced Geosynchronous Radiation Imager (AGRI), the spatial and temporal evolution patterns of lightning data from two observation systems are analysed during a thunderstorm process in Zhejiang Province on 15 July 2020. The results show that from June to August in 2020, the number of LMIG detected by LMI was 8483, while the number of lightning detected by the ADTD-2C three-dimensional lightning location system was 376932. The ratio of the two sets of data was approximately 1∶44.43. The monthly and spatial distributions of lightning detected by these two systems were generally consistent, while diurnal variation of which were different. Specifically, diurnal variation of LMIG presented two peaks, and diurnal variation of three-dimensional lightning showed only one peak. Besides, when the time matching window was larger than 1.8 seconds, and the latitude and longitude matching window was larger than 0.5°, the matching rate gradually tended to be stable. Furthermore, the height of three-dimensional lightning matched with LMIG was mainly concentrated below 16 km, and the lightning intensity of which was mainly concentrated below 50 kA. During the thunderstorm weather in Zhejiang Province in the afternoon on 15 July 2020, the ratio of LMIG to three-dimensional lightning was approximately 1∶25.44. The time of the first LMIG and its peak time were both later than the time of the first three-dimensional lightning and its peak time. What’s more, the lightning data observed by the two systems corresponded well with the development process of the thunderstorm. When the thunderstorm was at the developing stage, the number of lightning data detected by the two systems was both gradually increasing, and when the thunderstorm was at the mature stage, the number of lightning data detected by the two systems was both maintaining a relatively high value, and when the thunderstorms were at the dissipation stage, the number of lightning data detected by the two systems was both decreasing rapidly. When it came to the spatial distribution of the lightning, both of the two datasets corresponded well with the spatial distribution of low cloud top brightness temperature.
2024, 52(3):434-445.
DOI: 10.19517/j.1671-6345.20230303
Abstract:
Rice-wheat rotation farmland is a typical type of farmland understory in China, and its simulation effect has important reference value for climate modelling of agricultural fields in our country. The results of mesoscale climate simulation are highly sensitive to the calculation schemes of surface fluxes, and selecting appropriate flux calculation schemes is crucial for simulation accuracy. Therefore, it is of great significance to compare and analyse different flux calculation schemes under the rice-wheat crop rotation system. In this paper, eight representative near-surface turbulent flux calculation schemes are selected, and the measured data from the Shouxian National Climatological Observatory are used to compare and analyse the calculation characteristics and differences of the schemes in the underlay of rice-wheat rotation farmland using the method of normalised standard deviation. The results show that the error characteristics of the schemes are different under different stabilised stratification conditions and wind speed conditions. In general, the overall average normalised mean difference of momentum flux of all schemes is 0.536, of which the maximum is 0.575 for the Sharan and Srivastava, 2014 scheme (SS14 scheme) and the minimum is 0.517 for the Sharan and Srivastava et al., 2020 scheme; and the overall average normalised standard deviation of sensible heat flux of all schemes is 0.638, of which the maximum is 0.871 for the Gryanik et al., 2020 scheme and the minimum is 0.476 for the SS14 scheme. In addition, this study is also giving the error characteristics of each flux calculation scheme under different stratification conditions and wind speed conditions under the rice-wheat rotation farmland, and finally, this paper is giving recommendations for the selection of turbulent flux calculation schemes for the rice-wheat rotation farmland, for the momentum fluxes, under the unstable stratification conditions, it is recommended to use the SS14 or Li et al., 2014 & 2015 schemes (Li1415 scheme), the Businger, 1971 scheme is recommended for weakly stabilised stratification, and Wang et al., 2002 scheme (Wa02 scheme) is recommended for strongly stabilised stratification; for heat flux calculation, Li et al., 2010 scheme (Li10 scheme) is recommended for unstable and weakly stabilised stratification and Li1415 scheme is recommended for strongly stabilised stratification. Under low wind conditions, it is recommended to use SS14 or Li1415 scheme to calculate the momentum flux and Li10 scheme to calculate the sensible heat flux; under strong wind conditions, it is recommended to use Li1415 scheme to calculate the momentum flux and Gryanik and Lüpkes, 2018 scheme or Wa02 scheme to calculate the sensible heat flux. These findings of this paper support the accurate calculation of near-surface layer turbulent flux.
Rice-wheat rotation farmland is a typical type of farmland understory in China, and its simulation effect has important reference value for climate modelling of agricultural fields in our country. The results of mesoscale climate simulation are highly sensitive to the calculation schemes of surface fluxes, and selecting appropriate flux calculation schemes is crucial for simulation accuracy. Therefore, it is of great significance to compare and analyse different flux calculation schemes under the rice-wheat crop rotation system. In this paper, eight representative near-surface turbulent flux calculation schemes are selected, and the measured data from the Shouxian National Climatological Observatory are used to compare and analyse the calculation characteristics and differences of the schemes in the underlay of rice-wheat rotation farmland using the method of normalised standard deviation. The results show that the error characteristics of the schemes are different under different stabilised stratification conditions and wind speed conditions. In general, the overall average normalised mean difference of momentum flux of all schemes is 0.536, of which the maximum is 0.575 for the Sharan and Srivastava, 2014 scheme (SS14 scheme) and the minimum is 0.517 for the Sharan and Srivastava et al., 2020 scheme; and the overall average normalised standard deviation of sensible heat flux of all schemes is 0.638, of which the maximum is 0.871 for the Gryanik et al., 2020 scheme and the minimum is 0.476 for the SS14 scheme. In addition, this study is also giving the error characteristics of each flux calculation scheme under different stratification conditions and wind speed conditions under the rice-wheat rotation farmland, and finally, this paper is giving recommendations for the selection of turbulent flux calculation schemes for the rice-wheat rotation farmland, for the momentum fluxes, under the unstable stratification conditions, it is recommended to use the SS14 or Li et al., 2014 & 2015 schemes (Li1415 scheme), the Businger, 1971 scheme is recommended for weakly stabilised stratification, and Wang et al., 2002 scheme (Wa02 scheme) is recommended for strongly stabilised stratification; for heat flux calculation, Li et al., 2010 scheme (Li10 scheme) is recommended for unstable and weakly stabilised stratification and Li1415 scheme is recommended for strongly stabilised stratification. Under low wind conditions, it is recommended to use SS14 or Li1415 scheme to calculate the momentum flux and Li10 scheme to calculate the sensible heat flux; under strong wind conditions, it is recommended to use Li1415 scheme to calculate the momentum flux and Gryanik and Lüpkes, 2018 scheme or Wa02 scheme to calculate the sensible heat flux. These findings of this paper support the accurate calculation of near-surface layer turbulent flux.
2024, 52(3):446-455.
DOI: 10.19517/j.1671-6345.20230140
Abstract:
Under the common influence of factors including complex terrain, subtropical high pressure, and monsoon weather, the wind field in the alpine canyon areas of is complex and changeable, and it is easy to form the “narrow pipe effect”, which leads to disastrous gales that have a great impact on the construction and operation of large-scale projects. In this paper, based on Fluent, a fluid dynamics computing software, a standard turbulence model and PISO algorithm are used to study the variation of wind velocity field near the dam during dam construction and the influence of dam construction on the wind velocity field, taking the level 7 north wind in Baihetan Hydropower Station as a typical calculation condition. The research results show that the blocking effect of the dam body makes the wind velocity field at the top of the dam generate flow separation and wind field uplift, and a low wind velocity zone forms below the dam elevation. When the dam elevation is 650 m and 750 m, the wind speed within the cable platform is about 15 m/s to 16 m/s, and the channel length of the significant influence area by the wind speed vertical distribution downstream of the dam is 4.4 Ht and 4.5 Ht (Ht being the dam height). The significant influence heights of the wind velocity field at the top of the dam are 2.0 Ht and 3.0 Ht respectively. When the dam is filled to the normal water level of 825 m, the channel length of the significant influence area by the wind field downstream of the dam is 8.0 times the dam height (2.3 km), and the maximum influence channel length is 30.4 times the dam height (8.8 km). The influence height of the dam top reaches about 1500 m height, which is 3.5 times the dam height.
Under the common influence of factors including complex terrain, subtropical high pressure, and monsoon weather, the wind field in the alpine canyon areas of is complex and changeable, and it is easy to form the “narrow pipe effect”, which leads to disastrous gales that have a great impact on the construction and operation of large-scale projects. In this paper, based on Fluent, a fluid dynamics computing software, a standard turbulence model and PISO algorithm are used to study the variation of wind velocity field near the dam during dam construction and the influence of dam construction on the wind velocity field, taking the level 7 north wind in Baihetan Hydropower Station as a typical calculation condition. The research results show that the blocking effect of the dam body makes the wind velocity field at the top of the dam generate flow separation and wind field uplift, and a low wind velocity zone forms below the dam elevation. When the dam elevation is 650 m and 750 m, the wind speed within the cable platform is about 15 m/s to 16 m/s, and the channel length of the significant influence area by the wind speed vertical distribution downstream of the dam is 4.4 Ht and 4.5 Ht (Ht being the dam height). The significant influence heights of the wind velocity field at the top of the dam are 2.0 Ht and 3.0 Ht respectively. When the dam is filled to the normal water level of 825 m, the channel length of the significant influence area by the wind field downstream of the dam is 8.0 times the dam height (2.3 km), and the maximum influence channel length is 30.4 times the dam height (8.8 km). The influence height of the dam top reaches about 1500 m height, which is 3.5 times the dam height.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.