Current Issue(Vol.31, No.6, 2020)
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Research Progress of Precipitation Interception by Plants
Guo Jianping
2020, 31(6): 641-652. DOI: 10.11898/1001-7313.20200601
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Abstract:
keep_len="250">Precipitation resource is the main water source for plant growth and development and yield formation. Plants maintain normal growth and development by absorbing water from soil. Precipitation is not only related to species distribution of natural plants, but also closely related to plant productivity. However, the role of precipitation is often overestimated in water resources assessment and farmland water balance research because the interception of precipitation by plant canopy is not considered. Therefore, the study of precipitation interception is of great significance in hydro-ecology and agrometeorology. Indirect and direct methods of estimating precipitation interception are introduced. Indirect measurement method is known as water balance method, and measurement methods of each component and the calculation method of precipitation interception are introduced. Direct measurement method is also known as the weighing method, the detailed operation process of various direct measurement methods, as well as the relevant problems that should be paid attention to in various methods are introduced. There are many researches on the precipitation interception by forest at home and abroad, involving interception storage, interception rate, interception model and other aspects, almost of these research results are obtained based on the indirect measurement method under natural precipitation. Many researches on precipitation interception of the crop focus on corn and wheat, but few on other crops, most of them are based on artificial sprinkling irrigation or simulated precipitation, and few of them are carried out under natural precipitation. The main problems existing in estimating precipitation interception by plants are discussed. First, different understanding of the concept of precipitation interception leads to significant differences in results of interception measurement. Second, there is no perfect method at present, which leads to the insufficient accuracy of results. Third, different planting density of plants leads to different throughfall, which makes significant difference in interception. Fourth, different precipitation intensity can lead to different interception. Finally, other factors, such as wind speed, plant morphology and structure, leaf surface characteristics, can also impact the interception. Although the simulated precipitation experiment can obtain necessary observations in a short period of time, it does not fully represent the canopy interception process and characteristics of the actual precipitation in nature. Therefore, in natural environment, how to perfertly simulate the canopy interception needs extensive exploration and in-depth study. The evaporation of plant leaves, the interception of snowfall, the impact of wind, the scale of research, measurement methods, and the comprehensive simulation model will be the emphases and difficulties in future. Precipitation resource is the main water source for plant growth and development and yield formation. Plants maintain normal growth and development by absorbing water from soil. Precipitation is not only related to species distribution of natural plants, but also closely related to plant productivity. However, the role of precipitation is often overestimated in water resources assessment and farmland water balance research because the interception of precipitation by plant canopy is not considered. Therefore, the study of precipitation interception is of great significance in hydro-ecology and agrometeorology. Indirect and direct methods of estimating precipitation interception are introduced. Indirect measurement method is known as water balance method, and measurement methods of each component and the calculation method of precipitation interception are introduced. Direct measurement method is also known as the weighing method, the detailed operation process of various direct measurement methods, as well as the relevant problems that should be paid attention to in various methods are introduced. There are many researches on the precipitation interception by forest at home and abroad, involving interception storage, interception rate, interception model and other aspects, almost of these research results are obtained based on the indirect measurement method under natural precipitation. Many researches on precipitation interception of the crop focus on corn and wheat, but few on other crops, most of them are based on artificial sprinkling irrigation or simulated precipitation, and few of them are carried out under natural precipitation. The main problems existing in estimating precipitation interception by plants are discussed. First, different understanding of the concept of precipitation interception leads to significant differences in results of interception measurement. Second, there is no perfect method at present, which leads to the insufficient accuracy of results. Third, different planting density of plants leads to different throughfall, which makes significant difference in interception. Fourth, different precipitation intensity can lead to different interception. Finally, other factors, such as wind speed, plant morphology and structure, leaf surface characteristics, can also impact the interception. Although the simulated precipitation experiment can obtain necessary observations in a short period of time, it does not fully represent the canopy interception process and characteristics of the actual precipitation in nature. Therefore, in natural environment, how to perfertly simulate the canopy interception needs extensive exploration and in-depth study. The evaporation of plant leaves, the interception of snowfall, the impact of wind, the scale of research, measurement methods, and the comprehensive simulation model will be the emphases and difficulties in future.
ARTICLES
Characteristics of QBWO over the East Asian Monsoon Region Presented by Different Elements
Li Jingyi, Wang Zunya, Wen Min
2020, 31(6): 653-667. DOI: 10.11898/1001-7313.20200602
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Abstract:
keep_len="250">Different variables present discrepancy in characteristics of the quasi-biweekly oscillation (QBWO), which is a dominant sub-seasonal signal in the East Asian monsoon regime. However, there is limited investigation about similarities and differences in features of QBWO presented by varying variables. In order to fill this gap, adopting the empirical orthogonal function (EOF) and composite analysis, such variables as outgoing longwave radiation (OLR), 500 hPa potential vorticity (PV), 850 hPa relative vorticity, 850 hPa zonal wind, 850 hPa meridional wind and 750 hPa specific humidity are compared, regarding of the spatial-temporal distribution, intensity and propagation of QBWO over the East Asian monsoon region. It is found that all these variables show significant QBWO across the region with similar spatial and temporal variation. And the strongest QBWO is observed over the South China Sea (SCS) with all variables. QBWO in OLR propagates north-westward over the East Asian monsoon regime. Centres of active (suppressed) QBWO convection correspond to positive (negative) PV anomalies at 500 hPa level and cyclonic (anticyclonic) vortex at 850 hPa level. These circulations form a northwest-southeast tilted wave train. Two leading modes of QBWO in 500 hPa PV, 850 hPa relative vorticity and 850 hPa zonal wind have greater meridional magnitude than those of OLR. QBWO in three variables also propagate north-westward, but spread faster to north. Oppositely, two leading modes of QBWO in 850 hPa meridional wind are characterized by the zonal dipole pattern and the westward propagation is evident. Actually, its speed of northward propagation is the slowest of all. Different from all others, QBWO in 750 hPa specific humidity propagates south-eastward, and variances explained by QBWO of 750 hPa specific humidity is the smallest. As for the intensity of QBWO, except for 750 hPa specific humidity, other variables have consistent inter-annual variation. Totally, affected by such complex physical processes as transformation of precipitation state, release of heat and so on, characteristics of QBWO is hardly captured by 750 hPa specific humidity. However, OLR, 500 hPa PV, 850 hPa relative vorticity, 850 hPa zonal wind and 850 hPa meridional wind can well characterize QBWO over the East Asian monsoon region. Of all variables compared in this analysis, 500 hPa PV and 850 hPa relative vorticity are highly consistent in describing QBWO over the East Asian monsoon region. Specific causes that lead to different characteristics of QBWO over the East Asian monsoon regime presented by different variables need further discussion, which can provide a new reference for selecting monitoring indices for QBWO over the East Asian monsoon region. Different variables present discrepancy in characteristics of the quasi-biweekly oscillation (QBWO), which is a dominant sub-seasonal signal in the East Asian monsoon regime. However, there is limited investigation about similarities and differences in features of QBWO presented by varying variables. In order to fill this gap, adopting the empirical orthogonal function (EOF) and composite analysis, such variables as outgoing longwave radiation (OLR), 500 hPa potential vorticity (PV), 850 hPa relative vorticity, 850 hPa zonal wind, 850 hPa meridional wind and 750 hPa specific humidity are compared, regarding of the spatial-temporal distribution, intensity and propagation of QBWO over the East Asian monsoon region. It is found that all these variables show significant QBWO across the region with similar spatial and temporal variation. And the strongest QBWO is observed over the South China Sea (SCS) with all variables. QBWO in OLR propagates north-westward over the East Asian monsoon regime. Centres of active (suppressed) QBWO convection correspond to positive (negative) PV anomalies at 500 hPa level and cyclonic (anticyclonic) vortex at 850 hPa level. These circulations form a northwest-southeast tilted wave train. Two leading modes of QBWO in 500 hPa PV, 850 hPa relative vorticity and 850 hPa zonal wind have greater meridional magnitude than those of OLR. QBWO in three variables also propagate north-westward, but spread faster to north. Oppositely, two leading modes of QBWO in 850 hPa meridional wind are characterized by the zonal dipole pattern and the westward propagation is evident. Actually, its speed of northward propagation is the slowest of all. Different from all others, QBWO in 750 hPa specific humidity propagates south-eastward, and variances explained by QBWO of 750 hPa specific humidity is the smallest. As for the intensity of QBWO, except for 750 hPa specific humidity, other variables have consistent inter-annual variation. Totally, affected by such complex physical processes as transformation of precipitation state, release of heat and so on, characteristics of QBWO is hardly captured by 750 hPa specific humidity. However, OLR, 500 hPa PV, 850 hPa relative vorticity, 850 hPa zonal wind and 850 hPa meridional wind can well characterize QBWO over the East Asian monsoon region. Of all variables compared in this analysis, 500 hPa PV and 850 hPa relative vorticity are highly consistent in describing QBWO over the East Asian monsoon region. Specific causes that lead to different characteristics of QBWO over the East Asian monsoon regime presented by different variables need further discussion, which can provide a new reference for selecting monitoring indices for QBWO over the East Asian monsoon region.
Multi-model Consensus Forecasting Technology with Optimal Weight for Precipitation Intensity Levels
Wei Guofei, Liu Huijun, Wu Qishu, et al.
2020, 31(6): 668-680. DOI: 10.11898/1001-7313.20200603
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Abstract:
keep_len="250">In the daily weather forecasting business, different model outputs are available for forecasters, but it's difficult to quickly and accurately make quantitative precipitation forecasts based on subjective analysis. Therefore, statistical post-processing techniques are required to scientifically and rationally integrate the multi-model forecast results, so as to obtain a forecast result that take advantages of each model, and the multi-model consensus forecasting technology is introduced. In the past, the research of multi-model consensus precipitation forecast is either based on global model or regional model, but they are rarely integrated. In addition, for a certain forecast, weights are constant, rarely considering variation of forecast ability among different models and precipitation intensity levels. It is found that the unrevised global and regional model present different advantages in forecasting precipitation of different intensities. Multi-model consensus forecasting for precipitation based on both global and regional models, integrating respective advantages of models at different precipitation levels would produce better objective forecasts.To synthesize both forecasting advantages in global and regional models, a consensus forecasting technology combining global and regional models with optimized weights for different precipitation intensity levels is designed. The consensus forecast combined revised ECMWF-IFS's(European Center for Medium-Range Weather Forecasts-Integrated Forecast System) and SMS-WARMS's(Shanghai Meteorological Service WRF ADAS Real-Time Modeling System) precipitation forecasts, which are revised by optimal threat score method(abbreviated as EC-OTS and SMS-OTS) in the Pan-Yangtze River region(23°-39°N, 101°-123°E). Take 2018 as the model training period of consensus weight and 2019 as the independent sample forecast test period. Comparing the consensus forecast with EC-OTS, SMS-OTS and subjective forecast of forecasters, results show that EC-OTS has a greater weight at low precipitation levels, with the increase of precipitation level, the weight of SMS-OTS gradually increases. The average absolute error of the consensus forecast is slightly smaller than EC-OTS and significantly smaller than SMS-OTS with all lead times. The consensus forecast has higher threat scores than EC-OTS and SMS-OTS with almost all lead times at all precipitation levels. The threat score of the 12 h accumulated precipitation of the consensus forecast is -0.009 to 0.041 higher than the subjective forecast of local forecasters, and the threat score of 24 h accumulated precipitation forecast is 0.009 to 0.023 higher than the subjective forecast of China National Meteorological Center forecasters. In the daily weather forecasting business, different model outputs are available for forecasters, but it's difficult to quickly and accurately make quantitative precipitation forecasts based on subjective analysis. Therefore, statistical post-processing techniques are required to scientifically and rationally integrate the multi-model forecast results, so as to obtain a forecast result that take advantages of each model, and the multi-model consensus forecasting technology is introduced. In the past, the research of multi-model consensus precipitation forecast is either based on global model or regional model, but they are rarely integrated. In addition, for a certain forecast, weights are constant, rarely considering variation of forecast ability among different models and precipitation intensity levels. It is found that the unrevised global and regional model present different advantages in forecasting precipitation of different intensities. Multi-model consensus forecasting for precipitation based on both global and regional models, integrating respective advantages of models at different precipitation levels would produce better objective forecasts.To synthesize both forecasting advantages in global and regional models, a consensus forecasting technology combining global and regional models with optimized weights for different precipitation intensity levels is designed. The consensus forecast combined revised ECMWF-IFS's(European Center for Medium-Range Weather Forecasts-Integrated Forecast System) and SMS-WARMS's(Shanghai Meteorological Service WRF ADAS Real-Time Modeling System) precipitation forecasts, which are revised by optimal threat score method(abbreviated as EC-OTS and SMS-OTS) in the Pan-Yangtze River region(23°-39°N, 101°-123°E). Take 2018 as the model training period of consensus weight and 2019 as the independent sample forecast test period. Comparing the consensus forecast with EC-OTS, SMS-OTS and subjective forecast of forecasters, results show that EC-OTS has a greater weight at low precipitation levels, with the increase of precipitation level, the weight of SMS-OTS gradually increases. The average absolute error of the consensus forecast is slightly smaller than EC-OTS and significantly smaller than SMS-OTS with all lead times. The consensus forecast has higher threat scores than EC-OTS and SMS-OTS with almost all lead times at all precipitation levels. The threat score of the 12 h accumulated precipitation of the consensus forecast is -0.009 to 0.041 higher than the subjective forecast of local forecasters, and the threat score of 24 h accumulated precipitation forecast is 0.009 to 0.023 higher than the subjective forecast of China National Meteorological Center forecasters.
Wind Field Verification for Array Weather Radar at Changsha Airport
Li Yu, Ma Shuqing, Yang Ling, et al.
2020, 31(6): 681-693. DOI: 10.11898/1001-7313.20200604
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Abstract:
keep_len="250">Synthesizing or retrieving the radial velocity of weather radar can obtain a three-dimensional wind field, which is an important research direction in radar meteorology. A fine three-dimensional wind field helps to study the structure and motion characteristics of small-scale and meso-scale weather systems. Array weather radar (AWR) consists of three-phased array transmit-receive subarrays (referred as transceiver subarrays), which is used for synchronous detection. AWR data are of high temporal and spatial resolution, thus ensuring the correctness of wind field synthesis and retrieval.According to domestic and aboard research, three-dimensional variational data assimilation (3DVAR) wind field retrieval algorithm is quite mature. Using AWR data of 10 rainfall cases at Changsha airport from April to September in 2019, the wind field is retrieved and evaluated. In the three-dimensional fine detection area of the AWR, detection data of a L-band boundary layer wind profile radar and the AWR synthetic wind field are used as reference value to evaluate the retrieved wind field.Results show that the retrieved wind field, the synthetic wind field, and wind profile radar product are more consistent and reasonable in stable precipitation process. In addition, the result error is larger in convective precipitation. The unevenness of the environmental wind field in convective precipitation can reduce the accuracy of wind measurement, and therefore it is not enough to explain the rationality of the AWR retrieved wind field. The wind profile radar is quite different from the AWR retrieved and the synthetic wind field. For different precipitation types, the wind field structure retrieved by AWR and the wind field obtained by AWR synthetic wind field are consistent with the basic characteristics of various weather systems. The spatial distribution and size direction of the horizontal wind field of two algorithms are very close. Error results show that the relative deviation of horizontal wind speed in the stable and convective precipitation is less than 19% and 29%, and the difference of horizontal wind direction is lower than 14.92° and 26.35°, respectively. The error is within the acceptable range. Compared with the AWR synthetic wind field, the retrieved wind field result during stable precipitation process is better than that during convective precipitation process. Synthesizing or retrieving the radial velocity of weather radar can obtain a three-dimensional wind field, which is an important research direction in radar meteorology. A fine three-dimensional wind field helps to study the structure and motion characteristics of small-scale and meso-scale weather systems. Array weather radar (AWR) consists of three-phased array transmit-receive subarrays (referred as transceiver subarrays), which is used for synchronous detection. AWR data are of high temporal and spatial resolution, thus ensuring the correctness of wind field synthesis and retrieval.According to domestic and aboard research, three-dimensional variational data assimilation (3DVAR) wind field retrieval algorithm is quite mature. Using AWR data of 10 rainfall cases at Changsha airport from April to September in 2019, the wind field is retrieved and evaluated. In the three-dimensional fine detection area of the AWR, detection data of a L-band boundary layer wind profile radar and the AWR synthetic wind field are used as reference value to evaluate the retrieved wind field.Results show that the retrieved wind field, the synthetic wind field, and wind profile radar product are more consistent and reasonable in stable precipitation process. In addition, the result error is larger in convective precipitation. The unevenness of the environmental wind field in convective precipitation can reduce the accuracy of wind measurement, and therefore it is not enough to explain the rationality of the AWR retrieved wind field. The wind profile radar is quite different from the AWR retrieved and the synthetic wind field. For different precipitation types, the wind field structure retrieved by AWR and the wind field obtained by AWR synthetic wind field are consistent with the basic characteristics of various weather systems. The spatial distribution and size direction of the horizontal wind field of two algorithms are very close. Error results show that the relative deviation of horizontal wind speed in the stable and convective precipitation is less than 19% and 29%, and the difference of horizontal wind direction is lower than 14.92° and 26.35°, respectively. The error is within the acceptable range. Compared with the AWR synthetic wind field, the retrieved wind field result during stable precipitation process is better than that during convective precipitation process.
Improving the Processing Algorithm of Beijing MST Radar Power Spectral Density Data
Chen Ze, Tian Yufang, Lü Daren
2020, 31(6): 694-705. DOI: 10.11898/1001-7313.20200605
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Abstract:
keep_len="250">Beijing MST radar is a unique large instrument for atmospheric dynamic structure detection in Chinese Meridian Project. It plays an essential role in the in-depth understanding of the vertical structure of atmospheric wind, waves and turbulence in the troposphere, lower stratosphere, mesosphere, and lower thermosphere in North China. Since the completion of Beijing MST radar in 2011, wind data have been well acquired. However, there is still a need for improving the extraction of some elements. To achieve this goal, the power spectral density data processing algorithms is improved mainly from two aspects including accurate noise level estimation and target signal recognition. The improved algorithm derived data, radar products, radiosonde data and ERA5 reanalysis data from 1 January to 31 December in 2012 are statistically analyzed and compared. A log-linear fitting scheme is put forward and applied to realize rapid implementation of objective determination of the noise level. The root mean square error(RMSE) of noise values between the log-linear fitting and the conventional scheme is about 0.43 dB and mean values are 168.6 dB and 168.5 dB, respectively. Results show that the noise level estimation can be fast and accurate using the log-linear fitting scheme. Based on the property that atmospheric signals have spatio-temporal consistency and diffident signals show different spectral characteristics, the target signal can be accurately identified and extracted by the improved algorithms. The RMSE of zonal wind speed between the improved algorithm derived data and radiosonde data at different height are in the range of 2-3 m·s-1 while the RMSE of zonal wind speed between radar products and radiosonde data at different height are 3-4 m·s-1. Moreover, the mean value of the spectral width derived by the improved algorithm is 2.5 m·s-1, which is less than the mean value of radar products. Under precipitation weather condition, the mean bias and RMSE of horizontal wind speed between the improved algorithm derived data and radiosonde data at different height are both less than values between radar products and radiosonde data. Results show that the improved algorithm can reduce non-atmospheric signals such as noise and intermittent clutter and effectively suppress signals caused by precipitation. Thus the effectiveness and reliability of the improved algorithm are verified, and it is relatively easy to implement. Beijing MST radar is a unique large instrument for atmospheric dynamic structure detection in Chinese Meridian Project. It plays an essential role in the in-depth understanding of the vertical structure of atmospheric wind, waves and turbulence in the troposphere, lower stratosphere, mesosphere, and lower thermosphere in North China. Since the completion of Beijing MST radar in 2011, wind data have been well acquired. However, there is still a need for improving the extraction of some elements. To achieve this goal, the power spectral density data processing algorithms is improved mainly from two aspects including accurate noise level estimation and target signal recognition. The improved algorithm derived data, radar products, radiosonde data and ERA5 reanalysis data from 1 January to 31 December in 2012 are statistically analyzed and compared. A log-linear fitting scheme is put forward and applied to realize rapid implementation of objective determination of the noise level. The root mean square error(RMSE) of noise values between the log-linear fitting and the conventional scheme is about 0.43 dB and mean values are 168.6 dB and 168.5 dB, respectively. Results show that the noise level estimation can be fast and accurate using the log-linear fitting scheme. Based on the property that atmospheric signals have spatio-temporal consistency and diffident signals show different spectral characteristics, the target signal can be accurately identified and extracted by the improved algorithms. The RMSE of zonal wind speed between the improved algorithm derived data and radiosonde data at different height are in the range of 2-3 m·s-1 while the RMSE of zonal wind speed between radar products and radiosonde data at different height are 3-4 m·s-1. Moreover, the mean value of the spectral width derived by the improved algorithm is 2.5 m·s-1, which is less than the mean value of radar products. Under precipitation weather condition, the mean bias and RMSE of horizontal wind speed between the improved algorithm derived data and radiosonde data at different height are both less than values between radar products and radiosonde data. Results show that the improved algorithm can reduce non-atmospheric signals such as noise and intermittent clutter and effectively suppress signals caused by precipitation. Thus the effectiveness and reliability of the improved algorithm are verified, and it is relatively easy to implement.
Articles
Observation of A Tornado Event in Outside-region of Typhoon Mangkhut by X-band Polarimetric Phased Array Radar in 2018
Fu Peiling, Hu Dongming, Huang Hao, et al.
2020, 31(6): 706-718. DOI: 10.11898/1001-7313.20200606
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Abstract:
keep_len="250">It is well realized that the phased array radar provides fine information for meso-scale weather system, e.g., tornados. The detecting capability of Guangzhou X-band polarimetric phased array radar for severe storms is investigated, focusing on a violent tornado induced by a miniature supercell in the outer rain band of typhoon Mangkhut near Foshan on 17 September 2018 after Mangkhut's landfall. The rare complete typical tornado is captured, which is of category 2 on the enhanced Fujita scale (EF2), and it lasts for 23 minutes and causes great national economy loss.The structure, evolution and environmental conditions of the tornadic miniature supercell are discussed based on coastal Doppler S-band radar measurements. Environment conditions in the outer rain band are consistent with those of typhoon tornadoes in previous studies, with moderate convection effective potential energy and large shear below 3 km. S-band radar analysis indicate that this tornadic, miniature supercell exhibits characteristics similar to those found in landfalling hurricanes, including a hook echo, a small and shallow mesocyclone, and a relative long lifespan (~3 h).However, limited by beam blockage and resolution, further tornadic features are only observed by Guangzhou X-band polarimetric phased array radar. With the strengthening of inflow from right rear of the miniature supercell, hook echo is formed when the tornado occurs in the shallow and strong mesocyclone with the depth below the height of 2-3 km. It touches down when its parent circulation reaches its peak intensity of about 21 m·s-1. Along with intensifying of strength and contraction of couplet diameter, the height of the rotation declines below 1 km and characteristics of tornado vortex signature (TVS) are detected. The echo eye of weak echo region indicating the tornado eye is first observed. The X-band phased array radar shows great advantage in tornado observation, capturing some key characteristics of tornado evolution: Continually declining strong meso-cyclone and the appearance of TVS. The strengthening and deepening of TVS and the appearance of weak echo eye is highly likely to indicate the increase of tornado intensity. Data observed in the experiment and the preliminary results will be used in studies of tornado mechanism. It is well realized that the phased array radar provides fine information for meso-scale weather system, e.g., tornados. The detecting capability of Guangzhou X-band polarimetric phased array radar for severe storms is investigated, focusing on a violent tornado induced by a miniature supercell in the outer rain band of typhoon Mangkhut near Foshan on 17 September 2018 after Mangkhut's landfall. The rare complete typical tornado is captured, which is of category 2 on the enhanced Fujita scale (EF2), and it lasts for 23 minutes and causes great national economy loss.The structure, evolution and environmental conditions of the tornadic miniature supercell are discussed based on coastal Doppler S-band radar measurements. Environment conditions in the outer rain band are consistent with those of typhoon tornadoes in previous studies, with moderate convection effective potential energy and large shear below 3 km. S-band radar analysis indicate that this tornadic, miniature supercell exhibits characteristics similar to those found in landfalling hurricanes, including a hook echo, a small and shallow mesocyclone, and a relative long lifespan (~3 h).However, limited by beam blockage and resolution, further tornadic features are only observed by Guangzhou X-band polarimetric phased array radar. With the strengthening of inflow from right rear of the miniature supercell, hook echo is formed when the tornado occurs in the shallow and strong mesocyclone with the depth below the height of 2-3 km. It touches down when its parent circulation reaches its peak intensity of about 21 m·s-1. Along with intensifying of strength and contraction of couplet diameter, the height of the rotation declines below 1 km and characteristics of tornado vortex signature (TVS) are detected. The echo eye of weak echo region indicating the tornado eye is first observed. The X-band phased array radar shows great advantage in tornado observation, capturing some key characteristics of tornado evolution: Continually declining strong meso-cyclone and the appearance of TVS. The strengthening and deepening of TVS and the appearance of weak echo eye is highly likely to indicate the increase of tornado intensity. Data observed in the experiment and the preliminary results will be used in studies of tornado mechanism.
Variation and Vertical Structure of Clear-air Echo by Ka-band Cloud Radar
Tao Fa, Guan Li, Zhang Xuefen, et al.
2020, 31(6): 719-728. DOI: 10.11898/1001-7313.20200607
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Abstract:
keep_len="250">Ka-band cloud radar data from 2017 to 2019 in Beijing Atmosphere Observation test-bed of CMA, combining with observations of automatic weather station and ceilometer are used to analyze the variation and vertical structure of low-level clear-air echo from aspects of intensity, velocity, space scale, depolarization ratio and height of clear-air echo.Based on ceilometer and cloud radar detection sensitivity difference of particle radius and density, clouds and clear-air echo are identified, using image processing technology to distinguish the layered turbulence echo and the dot-like insect echo.Based on different scattering mechanisms, scattering characteristics of the layered turbulence echo and the dot-like insect echo are analyzed. Generally, the range of equivalent reflectivity factors in millimeter wavebands of the clear-air echo caused by atmospheric turbulence is -70 dBZ to -30 dBZ, while the dot-like insect echo reflectivity factor is greater than -30 dBZ.Results show that radar clear-air echoes mainly contain the layered turbulence echo and the dot-like insect echo in the boundary layer and the echo height is within 3000 m. The intensity and height of clear-air echoes show obvious seasonal and diurnal variation characteristics. The echo height is lower in winter and higher in summer, which is well correlated with the surface temperature. There is almost no clear-air echo when the surface temperature is below 5℃, so there is almost no clear-air echo in January, February, November and December, while the average echo height is the highest in July and August. The radar reflectivity factor of clear-air echo is within the range of -40 to -15 dBZ with mean value of -28 dBZ. As the height increases, the reflectivity factor intensity decreases gradually, the peak value of the probability density distribution function of the radar reflectivity factor for clear-air layered turbulence echo is -35 dBZ, and that of the dot-like insect echo is -30 dBZ. The vertical movement speed of clear-air echo is mainly within -1.5 to +0.5 m·s-1, and downward movement is dominant. The linear depolarization ratio value of layered turbulent echo is larger than that of dot-like insect echo, which is generally within the range of -10 to -5 dB. Within 1000 meters at the lower level, the range of linear depolarization ratio is wide and gradually narrows with the increase of height. The linear depolarization ratio of dot-like insect echoes is generally within the range of -15 to -8 dB and increases gradually with the height. Ka-band cloud radar data from 2017 to 2019 in Beijing Atmosphere Observation test-bed of CMA, combining with observations of automatic weather station and ceilometer are used to analyze the variation and vertical structure of low-level clear-air echo from aspects of intensity, velocity, space scale, depolarization ratio and height of clear-air echo.Based on ceilometer and cloud radar detection sensitivity difference of particle radius and density, clouds and clear-air echo are identified, using image processing technology to distinguish the layered turbulence echo and the dot-like insect echo.Based on different scattering mechanisms, scattering characteristics of the layered turbulence echo and the dot-like insect echo are analyzed. Generally, the range of equivalent reflectivity factors in millimeter wavebands of the clear-air echo caused by atmospheric turbulence is -70 dBZ to -30 dBZ, while the dot-like insect echo reflectivity factor is greater than -30 dBZ.Results show that radar clear-air echoes mainly contain the layered turbulence echo and the dot-like insect echo in the boundary layer and the echo height is within 3000 m. The intensity and height of clear-air echoes show obvious seasonal and diurnal variation characteristics. The echo height is lower in winter and higher in summer, which is well correlated with the surface temperature. There is almost no clear-air echo when the surface temperature is below 5℃, so there is almost no clear-air echo in January, February, November and December, while the average echo height is the highest in July and August. The radar reflectivity factor of clear-air echo is within the range of -40 to -15 dBZ with mean value of -28 dBZ. As the height increases, the reflectivity factor intensity decreases gradually, the peak value of the probability density distribution function of the radar reflectivity factor for clear-air layered turbulence echo is -35 dBZ, and that of the dot-like insect echo is -30 dBZ. The vertical movement speed of clear-air echo is mainly within -1.5 to +0.5 m·s-1, and downward movement is dominant. The linear depolarization ratio value of layered turbulent echo is larger than that of dot-like insect echo, which is generally within the range of -10 to -5 dB. Within 1000 meters at the lower level, the range of linear depolarization ratio is wide and gradually narrows with the increase of height. The linear depolarization ratio of dot-like insect echoes is generally within the range of -15 to -8 dB and increases gradually with the height.
FY-3C/VIRR Sea Surface Temperature Products and Quality Validation
Wang Sujuan, Cui Peng, Zhang Peng, et al.
2020, 31(6): 729-739. DOI: 10.11898/1001-7313.20200608
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Abstract:
keep_len="250">Sea surface temperature (SST) products are generated at National Satellite Meteorological Center (NSMC) of China Meteorological Administration (CMA) from the visible and infrared radiometer (VIRR) on board FY-3C polar orbiting satellite. The production chain is based on FY-3C/VIRR cloud mask products and a classical multichannel SST (MCSST) algorithm is applied to derive SST in cloud-free zones. Operational MCSST procedures and products are described in detail. FY-3C/VIRR SST products are generated in satellite projection at full resolution in 5-minute granule, and in synthetic fields remapped onto a regular world grid at 0.05 degree resolution (5 km).The quality index (QI) information is delivered with each pixel to provide information about the conditions of the processing. They include in particular a quality level in the last two bits of QI (saved in a 8-bit CHAR) for each pixel defined as follows: Excellent, good, bad and unprocessed (cloud, land, no satellite data etc.). Users can select the SST data with certain quality level according to their application purposes (e.g., for climate-related studies, only the SST data with excellent quality level in the time series are used, and for identifying and tracking specific ocean features, users may be more tolerant of lower-quality SST data). The matchup database (MDB) combining FY-3C/VIRR measurements and buoy measurements is built on a routine basis. Validation methods and results are described in detail. The performance of SST retrievals is characterized with bias and root mean square error (RMSE) with respect to Reynolds L4 daily analysis (OISST). The validation bias and RMSE for FY-3C/VIRR operational granule SST with excellent quality level between January 2015 and December 2019 is found to be -0.18℃ and 0.85℃ in day-time, -0.06℃ and 0.8℃ in night-time, respectively. For day-time, the RMSE fluctuates seasonally. Some monthly RMSE is greater than 1℃ in summer. The bias at night is found fluctuating seasonally highly correlated to the black body temperature on board FY-3C since January 2016, and the SST regression coefficient (SST_COEF_V3) is used ever since then. Causes of FY-3C/VIRR SST products anomaly is analyzed, such as L1 data abnormal (e.g., single event upset), navigation error and operational running environmental error. Above all, some important reference information are provided to users for using FY-3C/VIRR SST products and FY-3C/VIRR data re-geolocation, re-calibration and products reprocessing. Sea surface temperature (SST) products are generated at National Satellite Meteorological Center (NSMC) of China Meteorological Administration (CMA) from the visible and infrared radiometer (VIRR) on board FY-3C polar orbiting satellite. The production chain is based on FY-3C/VIRR cloud mask products and a classical multichannel SST (MCSST) algorithm is applied to derive SST in cloud-free zones. Operational MCSST procedures and products are described in detail. FY-3C/VIRR SST products are generated in satellite projection at full resolution in 5-minute granule, and in synthetic fields remapped onto a regular world grid at 0.05 degree resolution (5 km).The quality index (QI) information is delivered with each pixel to provide information about the conditions of the processing. They include in particular a quality level in the last two bits of QI (saved in a 8-bit CHAR) for each pixel defined as follows: Excellent, good, bad and unprocessed (cloud, land, no satellite data etc.). Users can select the SST data with certain quality level according to their application purposes (e.g., for climate-related studies, only the SST data with excellent quality level in the time series are used, and for identifying and tracking specific ocean features, users may be more tolerant of lower-quality SST data). The matchup database (MDB) combining FY-3C/VIRR measurements and buoy measurements is built on a routine basis. Validation methods and results are described in detail. The performance of SST retrievals is characterized with bias and root mean square error (RMSE) with respect to Reynolds L4 daily analysis (OISST). The validation bias and RMSE for FY-3C/VIRR operational granule SST with excellent quality level between January 2015 and December 2019 is found to be -0.18℃ and 0.85℃ in day-time, -0.06℃ and 0.8℃ in night-time, respectively. For day-time, the RMSE fluctuates seasonally. Some monthly RMSE is greater than 1℃ in summer. The bias at night is found fluctuating seasonally highly correlated to the black body temperature on board FY-3C since January 2016, and the SST regression coefficient (SST_COEF_V3) is used ever since then. Causes of FY-3C/VIRR SST products anomaly is analyzed, such as L1 data abnormal (e.g., single event upset), navigation error and operational running environmental error. Above all, some important reference information are provided to users for using FY-3C/VIRR SST products and FY-3C/VIRR data re-geolocation, re-calibration and products reprocessing.
A Three-dimensional Model Establishment of Multiple Connecting Leaders Initiated from Tall Structures
Yu Junhao, Tan Yongbo, Zheng Tianxue, et al.
2020, 31(6): 740-748. DOI: 10.11898/1001-7313.20200609
[FullText HTML](63) [PDF](24)
Abstract:
keep_len="250">A new model for simulating multiple upward leaders initiated from tall structures in cloud-to-ground (CG) lightning flash is established, in which the initiation and development module of are implanted in the existing 3D stochastic parameterization of leader attachment process, using electric field parallel computing technology to improve the simulation efficiency. The new model is applied to simulate real CG lightning and is compared with observation results of statistical data and leader morphological characteristics. Several model output parameters include the length of upward unconnected leaders (UULs), the inception height of UULs, horizontal distance between the strike point and the UUL's inception point, 3D distance between the nearest tip of the downward leader branches and the UUL's inception point when the UUL is initiated. Values range from 12 m to 709 m, 360 m to 600 m, 255 m to 1026 m, 326 m to 589 m, which are in high agreement with the observation. The new model can represent characteristics that UUL starts earlier than upward connected leaders (UCL) and channels of UUL are straight in a real CG lightning case F1215. It can also simulate 4 typical connecting behaviors which are observed in natural CG lightning flash, including the tip of downward leader (DL) to the tip of upward connecting leader (UCL) and the DL's tip to the lateral surface of UCL in cases where one or more upward leaders starts. The comparison with the observation proves that the simulation is reasonable to some extent and provides a basic model. By analyzing the simulated CG lightning data and morphological characteristics, it shows that the highest tower can protect a certain area of buildings nearby and attract more distant downward leader branches. The inception of multiple upward leaders and the strike point of last jump are influenced by the distribution, height of high structures and the initial position of the DL, which are of great significance to the lightning protection. A new model for simulating multiple upward leaders initiated from tall structures in cloud-to-ground (CG) lightning flash is established, in which the initiation and development module of are implanted in the existing 3D stochastic parameterization of leader attachment process, using electric field parallel computing technology to improve the simulation efficiency. The new model is applied to simulate real CG lightning and is compared with observation results of statistical data and leader morphological characteristics. Several model output parameters include the length of upward unconnected leaders (UULs), the inception height of UULs, horizontal distance between the strike point and the UUL's inception point, 3D distance between the nearest tip of the downward leader branches and the UUL's inception point when the UUL is initiated. Values range from 12 m to 709 m, 360 m to 600 m, 255 m to 1026 m, 326 m to 589 m, which are in high agreement with the observation. The new model can represent characteristics that UUL starts earlier than upward connected leaders (UCL) and channels of UUL are straight in a real CG lightning case F1215. It can also simulate 4 typical connecting behaviors which are observed in natural CG lightning flash, including the tip of downward leader (DL) to the tip of upward connecting leader (UCL) and the DL's tip to the lateral surface of UCL in cases where one or more upward leaders starts. The comparison with the observation proves that the simulation is reasonable to some extent and provides a basic model. By analyzing the simulated CG lightning data and morphological characteristics, it shows that the highest tower can protect a certain area of buildings nearby and attract more distant downward leader branches. The inception of multiple upward leaders and the strike point of last jump are influenced by the distribution, height of high structures and the initial position of the DL, which are of great significance to the lightning protection.
Monitoring and Evaluation of High Temperature and Heat Damage of Summer Maize Based on Remote Sensing Data
Yang Lei, Han Lijuan, Song Jinling, et al.
2020, 31(6): 749-758. DOI: 10.11898/1001-7313.20200610
[FullText HTML](42) [PDF](51)
Abstract:
keep_len="250">In the context of global warming, the high temperature heat damage of summer maize occurs frequently in recent years, which seriously affects the yield and quality of corn. As an important food crop, output of summer maize has a crucial impact on national food security. Most studies on the high temperature heat damage of summer maize are based on data of discrete weather stations, which are less representative for large areas; and there are few published works or studies using remote sensing data to monitor and evaluate the high temperature heat damage of summer maize.MOD09A1 land surface reflectance products are used to extract the Huang-Huai-Hai summer maize planting area. Combined with MOD/MYD11A1 land surface temperature products and ground measured temperature data, based on linear correlation between land surface temperature and air temperature, a method of combining multiple stepwise regression and principal component analysis is used to construct a high-temperature damage evaluation model for the Huang-Huai-Hai summer maize production area. Results show that the determination coefficient of daily average air temperature in the plain area is above 0.8, and the determination coefficient of daily maximum temperature is above 0.7, and passing the test of 0.001 level. The accuracy in mountain area is slightly lower, the determination coefficient of daily average temperature is above 0.7, and the determination coefficient of daily maximum temperature is above 0.6, passing the test of 0.001 level. The root mean square error of simulation results of daily average temperature and daily maximum temperature fluctuates within a small range of about 2℃, and the inversion accuracy of daily average temperature is higher than that of daily maximum temperature. Using this model to evaluate the high-temperature heat damage in the main summer maize production area of the Huang-Huai-Hai from 2008 to 2018, it is found that the high-temperature heat damage area increases, and the spatial distribution are similar. Affected areas are mainly in the southern part of Beijing-Tianjin-Hebei region, the northern part of Henan Province, and the western part of Shandong Province. The main summer maize production areas in 2017 and 2018 are severely affected by high temperature heat damage. The affected areas are mainly distributed in the southeast of Hebei Province, most of Henan Province and western Shandong Province. This study has an important reference role for the development of a large-scale summer corn high temperature monitoring and evaluation work. In the context of global warming, the high temperature heat damage of summer maize occurs frequently in recent years, which seriously affects the yield and quality of corn. As an important food crop, output of summer maize has a crucial impact on national food security. Most studies on the high temperature heat damage of summer maize are based on data of discrete weather stations, which are less representative for large areas; and there are few published works or studies using remote sensing data to monitor and evaluate the high temperature heat damage of summer maize.MOD09A1 land surface reflectance products are used to extract the Huang-Huai-Hai summer maize planting area. Combined with MOD/MYD11A1 land surface temperature products and ground measured temperature data, based on linear correlation between land surface temperature and air temperature, a method of combining multiple stepwise regression and principal component analysis is used to construct a high-temperature damage evaluation model for the Huang-Huai-Hai summer maize production area. Results show that the determination coefficient of daily average air temperature in the plain area is above 0.8, and the determination coefficient of daily maximum temperature is above 0.7, and passing the test of 0.001 level. The accuracy in mountain area is slightly lower, the determination coefficient of daily average temperature is above 0.7, and the determination coefficient of daily maximum temperature is above 0.6, passing the test of 0.001 level. The root mean square error of simulation results of daily average temperature and daily maximum temperature fluctuates within a small range of about 2℃, and the inversion accuracy of daily average temperature is higher than that of daily maximum temperature. Using this model to evaluate the high-temperature heat damage in the main summer maize production area of the Huang-Huai-Hai from 2008 to 2018, it is found that the high-temperature heat damage area increases, and the spatial distribution are similar. Affected areas are mainly in the southern part of Beijing-Tianjin-Hebei region, the northern part of Henan Province, and the western part of Shandong Province. The main summer maize production areas in 2017 and 2018 are severely affected by high temperature heat damage. The affected areas are mainly distributed in the southeast of Hebei Province, most of Henan Province and western Shandong Province. This study has an important reference role for the development of a large-scale summer corn high temperature monitoring and evaluation work.