2022 Vol. 28, No. 2
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2022, 28(2): .
Abstract:
2022, 28(2): 121-138.
doi: 10.46267/j.1006-8775.2022.010
Abstract:
In this paper, we set out to study the ensemble forecast for tropical cyclones. The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter (CNOP-P) method and the WRF model to improve the prediction accuracy for track and intensity, and two different typhoons are selected as cases for analysis. We first select perturbed parameters in the YSU and WSM6 schemes, and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters. Finally, perturbations are imposed on default parameter values to generate the ensemble members. The whole proposed procedures are referred to as the PerturbedParameter Ensemble (PPE). We also conduct two experiments, which are control forecast and ensemble forecast, termed Ctrl and perturbed-physics ensemble (PPhyE) respectively, to demonstrate the performance for contrast. In the article, we compare the effects of three experiments on tropical cyclones in aspects of track and intensity, respectively. For track, the prediction errors of PPE are smaller. The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members. As for intensity, ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time. The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall. The PPE also shows uncertainty in the forecast. Moreover, we also analyze the track and intensity from physical variable fields of PPE. Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.
In this paper, we set out to study the ensemble forecast for tropical cyclones. The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter (CNOP-P) method and the WRF model to improve the prediction accuracy for track and intensity, and two different typhoons are selected as cases for analysis. We first select perturbed parameters in the YSU and WSM6 schemes, and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters. Finally, perturbations are imposed on default parameter values to generate the ensemble members. The whole proposed procedures are referred to as the PerturbedParameter Ensemble (PPE). We also conduct two experiments, which are control forecast and ensemble forecast, termed Ctrl and perturbed-physics ensemble (PPhyE) respectively, to demonstrate the performance for contrast. In the article, we compare the effects of three experiments on tropical cyclones in aspects of track and intensity, respectively. For track, the prediction errors of PPE are smaller. The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members. As for intensity, ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time. The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall. The PPE also shows uncertainty in the forecast. Moreover, we also analyze the track and intensity from physical variable fields of PPE. Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.
2022, 28(2): 139-153.
doi: 10.46267/j.1006-8775.2022.011
Abstract:
In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.
In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.
2022, 28(2): 154-168.
doi: 10.46267/j.1006-8775.2022.012
Abstract:
Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast. The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model. To improve the accuracy of rainfall data, the successive correction method is introduced to correct the bias of rainfall, and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China. The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System (CMPAS), and the simulated streamflow from the model is further studied. It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall. By contrast, the statistical metrics of the corrected WRF rainfall are better. When the corrected WRF rainfall is used to drive the WRF-Hydro model, the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall. Among the studied events, the largest improvement of the NSE is from -0.68 to 0.67. It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast. In general, the WRF / WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.
Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast. The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model. To improve the accuracy of rainfall data, the successive correction method is introduced to correct the bias of rainfall, and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China. The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System (CMPAS), and the simulated streamflow from the model is further studied. It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall. By contrast, the statistical metrics of the corrected WRF rainfall are better. When the corrected WRF rainfall is used to drive the WRF-Hydro model, the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall. Among the studied events, the largest improvement of the NSE is from -0.68 to 0.67. It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast. In general, the WRF / WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.
2022, 28(2): 169-182.
doi: 10.46267/j.1006-8775.2022.013
Abstract:
The polarimetric radar network in Jiangsu Province has just been operationalized since 2020. The first intense precipitation event observed by this polarimetric radar network and disdrometer occurred during August 28-29, 2020 and caused severe flooding and serious damage in eastern Jiangsu Province. The microphysics and kinetics for this heavy precipitation convective storm is diagnosed in this study, in order to promote the application of this polarimetric radar network. Drop size distribution (DSD) of this event is estimated from measurements of a ground disdrometer, and the corresponding three-dimensional atmospheric microphysical features are obtained from the multiple polarimetric radars. According to features of updraft and lighting, the evolution of the convective storm is divided into four stages: developing, mature with lightning, mature without lightning and dissipating. The DSD of this event is featured by a large number of raindrops and a considerable number of large raindrops. The microphysical characteristics are similar to those of warm-rain process, and ice-phase microphysical processes are active in the mature stages. The composite vertical structure of the convective storm indicates that deep ZDR and KDP columns coincide with strong updrafts during both mature stages. The hierarchical microphysical structure retrieved by the Hydrometeor Identification Algorithm (HID) shows that depositional growth has occurred above the melting level, and aggregation is the most widespread ice-phase process at the -10℃ level or higher. During negative lightning activity, the presence of strongest updrafts and a large amount of ice-phase graupel by riming between the 0℃ and -35℃ layers generate strong negative electric fields within the cloud. These convective storms are typical warm clouds with very high precipitation efficiency, which cause high concentration of raindrops, especially the presence of large raindrops within a short period of time. The ice-phase microphysical processes above the melting layer also play an important role in the triggering and enhancing of precipitation.
The polarimetric radar network in Jiangsu Province has just been operationalized since 2020. The first intense precipitation event observed by this polarimetric radar network and disdrometer occurred during August 28-29, 2020 and caused severe flooding and serious damage in eastern Jiangsu Province. The microphysics and kinetics for this heavy precipitation convective storm is diagnosed in this study, in order to promote the application of this polarimetric radar network. Drop size distribution (DSD) of this event is estimated from measurements of a ground disdrometer, and the corresponding three-dimensional atmospheric microphysical features are obtained from the multiple polarimetric radars. According to features of updraft and lighting, the evolution of the convective storm is divided into four stages: developing, mature with lightning, mature without lightning and dissipating. The DSD of this event is featured by a large number of raindrops and a considerable number of large raindrops. The microphysical characteristics are similar to those of warm-rain process, and ice-phase microphysical processes are active in the mature stages. The composite vertical structure of the convective storm indicates that deep ZDR and KDP columns coincide with strong updrafts during both mature stages. The hierarchical microphysical structure retrieved by the Hydrometeor Identification Algorithm (HID) shows that depositional growth has occurred above the melting level, and aggregation is the most widespread ice-phase process at the -10℃ level or higher. During negative lightning activity, the presence of strongest updrafts and a large amount of ice-phase graupel by riming between the 0℃ and -35℃ layers generate strong negative electric fields within the cloud. These convective storms are typical warm clouds with very high precipitation efficiency, which cause high concentration of raindrops, especially the presence of large raindrops within a short period of time. The ice-phase microphysical processes above the melting layer also play an important role in the triggering and enhancing of precipitation.
2022, 28(2): 183-193.
doi: 10.46267/j.1006-8775.2022.014
Abstract:
Multi-scale contributions are involved in the South China Sea (SCS) summer monsoon (SCSSM) onset process. The relative roles of intraseasonal oscillation and above-seasonal component in the year-to-year variation of the SCSSM onset are evaluated in this study. The 30-90-day and above-90-day components are major contributors to the year-to-year variation of the SCSSM onset, and the former contributes greater portion, while the 8-30-day component has little contribution to the onset. In the early onset cases, the 30-90-day westerly winds move and extend eastward from the tropical Indian Ocean (TIO) to the SCS monsoon region relatively earlier, and replace the easterly winds over the SCS with the cooperation of the 30-90-day cyclone moving southward from northern East Asia. The westerly anomalies of the above-90-day component in spring jointly contribute to the early SCSSM onset. In the late onset cases, the late eastward expansion of 30-90-day westerly wind over the TIO, accompanied by the late occurrence and weakening of the 30-90-day anticyclone over the SCS, and its late withdraw from the SCS, as well as the persistent easterly anomalies of above-90-day component, suppress the SCSSM onset. However, the SCSSM outbreaks in the obvious weakening stage of 30-90-day easterly anomalies. The easterlies-to-westerlies transition of the 30-90-day 850-hPa zonal wind over the SCS in spring is closely associated with sea surface temperature in the tropical western Pacific in preceding winter and spring, while the interannual variation of the above-90-day zonal wind in April-May is closely related to the decaying stage of the El Ni?o-Southern Oscillation events.
Multi-scale contributions are involved in the South China Sea (SCS) summer monsoon (SCSSM) onset process. The relative roles of intraseasonal oscillation and above-seasonal component in the year-to-year variation of the SCSSM onset are evaluated in this study. The 30-90-day and above-90-day components are major contributors to the year-to-year variation of the SCSSM onset, and the former contributes greater portion, while the 8-30-day component has little contribution to the onset. In the early onset cases, the 30-90-day westerly winds move and extend eastward from the tropical Indian Ocean (TIO) to the SCS monsoon region relatively earlier, and replace the easterly winds over the SCS with the cooperation of the 30-90-day cyclone moving southward from northern East Asia. The westerly anomalies of the above-90-day component in spring jointly contribute to the early SCSSM onset. In the late onset cases, the late eastward expansion of 30-90-day westerly wind over the TIO, accompanied by the late occurrence and weakening of the 30-90-day anticyclone over the SCS, and its late withdraw from the SCS, as well as the persistent easterly anomalies of above-90-day component, suppress the SCSSM onset. However, the SCSSM outbreaks in the obvious weakening stage of 30-90-day easterly anomalies. The easterlies-to-westerlies transition of the 30-90-day 850-hPa zonal wind over the SCS in spring is closely associated with sea surface temperature in the tropical western Pacific in preceding winter and spring, while the interannual variation of the above-90-day zonal wind in April-May is closely related to the decaying stage of the El Ni?o-Southern Oscillation events.
2022, 28(2): 194-206.
doi: 10.46267/j.1006-8775.2022.015
Abstract:
The spatiotemporal variations of water vapor budget (Bt) and their relationships with local precipitation over the Tibetan Plateau (TP) are critical for understanding the characteristics of spatial distributions and evolutions of water resources over the TP. Based on a boundary of the TP, this paper explored the spatiotemporal characteristics of Bt over the TP using the European Centre for Medium-Range Weather Forecasts interim (ERA-Interim) reanalysis datasets. On the climatological mean, the TP is a water vapor sink throughout four seasons and the seasonal variation of Bt is closely associated with the water vapor budget at the southern boundary of the TP. The transient water vapor transport is quasimeridional in the mid- and high-latitude areas and plays a leading role in winter Bt but contributes little in other seasons. At the interannual timescale, the variation of Bt is mainly determined by anomalous water vapor transports at the western and southern boundaries. The Bay of Bengal, the North Arabian Sea, and mid-latitude West Asia are the main sources of excessive water vapor for a wetter TP. At the southern and western boundaries, the transient water vapor budget regulates one-third to four-fifths of Bt anomalies. Moreover, the variability of the TP Bt is closely associated with precipitation over the central-southern and southeastern parts of the TP in summer and winter, which is attributed to the combined effect of the stationary and transient water vapor budgets. Given the role of the transient water vapor transport, the linkage between the TP Bt and local precipitation is tighter.
The spatiotemporal variations of water vapor budget (Bt) and their relationships with local precipitation over the Tibetan Plateau (TP) are critical for understanding the characteristics of spatial distributions and evolutions of water resources over the TP. Based on a boundary of the TP, this paper explored the spatiotemporal characteristics of Bt over the TP using the European Centre for Medium-Range Weather Forecasts interim (ERA-Interim) reanalysis datasets. On the climatological mean, the TP is a water vapor sink throughout four seasons and the seasonal variation of Bt is closely associated with the water vapor budget at the southern boundary of the TP. The transient water vapor transport is quasimeridional in the mid- and high-latitude areas and plays a leading role in winter Bt but contributes little in other seasons. At the interannual timescale, the variation of Bt is mainly determined by anomalous water vapor transports at the western and southern boundaries. The Bay of Bengal, the North Arabian Sea, and mid-latitude West Asia are the main sources of excessive water vapor for a wetter TP. At the southern and western boundaries, the transient water vapor budget regulates one-third to four-fifths of Bt anomalies. Moreover, the variability of the TP Bt is closely associated with precipitation over the central-southern and southeastern parts of the TP in summer and winter, which is attributed to the combined effect of the stationary and transient water vapor budgets. Given the role of the transient water vapor transport, the linkage between the TP Bt and local precipitation is tighter.
2022, 28(2): 207-217.
doi: 10.46267/j.1006-8775.2022.016
Abstract:
Inhalable particles (PM10), with aerodynamic equivalent diameters that are generally 10 micrometers or smaller, are basic pollutants in many areas, especially in northern China, and thus the pollution from PM10 inhalable particulate matter is a growing concern for public health. Independent long-term observations are necessary to evaluate the efficacy of PM10 reduction actions. Variations in the PM10 concentration from 2006 to 2017 at an observation station (NJ) in Beijing were recorded and analyzed. The average value ±1 standard deviation of daily mean PM10 concentrations was 138.8 ±96.1 μg m-3 for 1307 days (accounting for 34.7% of the total days), showing PM10 concentration exceeding the National Ambient Air Quality Standard (NAAQS) 24-h average of 150 μg m-3. Particulate concentration depended upon various meteorological conditions as also observed in this work: at low wind speed (< 4 m s-1), the concentrations of PM10 revealed a downward trend with -19 μg m-3 per unit of wind speed, but when wind speed rose (> 4 m s-1), the values increased by 49 μg m-3 per unit of wind speed. In Beijing, air masses from northwest China, especially from the Gobi Desert and other desert areas, had net contributions to long-range transport of natural dust, enhancing the PM10 concentrations by up to 29%. Overall, PM10 mass concentration showed a significant downward trend with -8.0 μg/m3/yr from 2006 to 2017. Although with higher fluctuations in recorded data, similar downward trends derived from the government released data were also found at the nearby districts. The result delivered a proof of efficacy for the reduction actions recently adopted to limit PM10 concentrations in Beijing. Very significant difference of diurnal changes in PM10 concentrations was also found in two periods of 2006-2011 and 2012-2017, which might be due to the different contributions of fugitive dust. Nevertheless, further efforts, especially on controlling fugitive dust, should be planned as the PM10 concentration annual mean value (94 μg m-3) in 2017 still exceeded the NAAQS standard. The results showed that there is still a long way to go to reduce PM10 in Beijing.
Inhalable particles (PM10), with aerodynamic equivalent diameters that are generally 10 micrometers or smaller, are basic pollutants in many areas, especially in northern China, and thus the pollution from PM10 inhalable particulate matter is a growing concern for public health. Independent long-term observations are necessary to evaluate the efficacy of PM10 reduction actions. Variations in the PM10 concentration from 2006 to 2017 at an observation station (NJ) in Beijing were recorded and analyzed. The average value ±1 standard deviation of daily mean PM10 concentrations was 138.8 ±96.1 μg m-3 for 1307 days (accounting for 34.7% of the total days), showing PM10 concentration exceeding the National Ambient Air Quality Standard (NAAQS) 24-h average of 150 μg m-3. Particulate concentration depended upon various meteorological conditions as also observed in this work: at low wind speed (< 4 m s-1), the concentrations of PM10 revealed a downward trend with -19 μg m-3 per unit of wind speed, but when wind speed rose (> 4 m s-1), the values increased by 49 μg m-3 per unit of wind speed. In Beijing, air masses from northwest China, especially from the Gobi Desert and other desert areas, had net contributions to long-range transport of natural dust, enhancing the PM10 concentrations by up to 29%. Overall, PM10 mass concentration showed a significant downward trend with -8.0 μg/m3/yr from 2006 to 2017. Although with higher fluctuations in recorded data, similar downward trends derived from the government released data were also found at the nearby districts. The result delivered a proof of efficacy for the reduction actions recently adopted to limit PM10 concentrations in Beijing. Very significant difference of diurnal changes in PM10 concentrations was also found in two periods of 2006-2011 and 2012-2017, which might be due to the different contributions of fugitive dust. Nevertheless, further efforts, especially on controlling fugitive dust, should be planned as the PM10 concentration annual mean value (94 μg m-3) in 2017 still exceeded the NAAQS standard. The results showed that there is still a long way to go to reduce PM10 in Beijing.
2022, 28(2): 218-236.
doi: 10.46267/j.1006-8775.2022.017
Abstract:
Based on the observations of a squall line on 11 May 2020 and stratiform precipitation on 6 June 2020 from two X-band dual-polarization phased array weather radars (DP-PAWRs) and an S-band dual-polarization Doppler weather radar (CINRAD/SA-D), the data reliability of DP-PAWR and its ability to detect the fine structures of mesoscale weather systems were assessed. After location matching, the observations of DP-PAWR and CINRAD/SA-D were compared in terms of reflectivity (ZH), radial velocity (V), differential reflectivity (ZDR), and specific differential phase (KDP). The results showed that: (1) DP-PAWR has better ability to detect mesoscale weather systems than CINRAD/SA-D; the multi-elevation-angles scanning of the RHI mode enables DP-PAWR to obtain a wider detection range in the vertical direction. (2) DP-PAWR's ZH and V structures are acceptable, while its sensitivity is worse than that of CINRAD/SA-D. The ZH suffers from attenuation and the ZH area distribution is distorted around strong rainfall regions. (3) DP-PAWR's ZDR is close to a normal distribution but slightly smaller than that of CINRAD/SA-D. The KDP products of DP-PAWR have much higher sensitivity, showing a better indication of precipitation. (4) DP-PAWR is capable of revealing a detailed and complete structure of the evolution of the whole storm and the characteristics of particle phase variations during the process of triggering and enhancement of a small cell in the front of a squall line, as well as the merging of the cell with the squall line, which cannot be observed by CINRAD/SA-D. With its fast volume scan feature and dual-polarization detection capability, DP-PAWR shows great potential in further understanding the development and evolution mechanisms of meso-γ-scale and microscale weather systems.
Based on the observations of a squall line on 11 May 2020 and stratiform precipitation on 6 June 2020 from two X-band dual-polarization phased array weather radars (DP-PAWRs) and an S-band dual-polarization Doppler weather radar (CINRAD/SA-D), the data reliability of DP-PAWR and its ability to detect the fine structures of mesoscale weather systems were assessed. After location matching, the observations of DP-PAWR and CINRAD/SA-D were compared in terms of reflectivity (ZH), radial velocity (V), differential reflectivity (ZDR), and specific differential phase (KDP). The results showed that: (1) DP-PAWR has better ability to detect mesoscale weather systems than CINRAD/SA-D; the multi-elevation-angles scanning of the RHI mode enables DP-PAWR to obtain a wider detection range in the vertical direction. (2) DP-PAWR's ZH and V structures are acceptable, while its sensitivity is worse than that of CINRAD/SA-D. The ZH suffers from attenuation and the ZH area distribution is distorted around strong rainfall regions. (3) DP-PAWR's ZDR is close to a normal distribution but slightly smaller than that of CINRAD/SA-D. The KDP products of DP-PAWR have much higher sensitivity, showing a better indication of precipitation. (4) DP-PAWR is capable of revealing a detailed and complete structure of the evolution of the whole storm and the characteristics of particle phase variations during the process of triggering and enhancement of a small cell in the front of a squall line, as well as the merging of the cell with the squall line, which cannot be observed by CINRAD/SA-D. With its fast volume scan feature and dual-polarization detection capability, DP-PAWR shows great potential in further understanding the development and evolution mechanisms of meso-γ-scale and microscale weather systems.
2022, 28(2): 237-251.
doi: 10.46267/j.1006-8775.2022.018
Abstract:
To analyze the effects of gas cannons on clouds and precipitation, multisource observational data, including those from National Centers for Environmental Prediction (NCEP) reanalysis, Hangzhou and Huzhou new-generation weather radars, laser disdrometer, ground-based automatic weather station, wind profiler radar, and Lin'an C-band dual-polarization radar, were adopted in this study. Based on the variational dual-Doppler wind retrieval method and the polarimetric variables obtained by the dual-polarization radar, we analyzed the microphysical processes and the variations in the macro- and microphysical quantities in clouds from the perspective of the synoptic background before precipitation enhancement, the polarization echo characteristics before, during and after enhancement, and the evolution of the fine three-dimensional kinematic structure and the microphysical structure. The results show that the precipitation enhancement operation promoted the development of radar echoes and prolonged their duration, and both the horizontal and vertical wind speeds increased. The dual-polarization radar echo showed that the diameter of the precipitation particles increased, and the concentration of raindrops increased after precipitation enhancement. The raindrops were lifted to a height corresponding to 0 to -20 ℃ due to vertical updrafts. Based on the disdrometer data during precipitation enhancement, the concentration of small raindrops (lgNw) showed a significant increase, and the mass-weighted diameter Dm value decreased, indicating that the precipitation enhancement operation played a certain "lubricating" effect. After the precipitation enhancement, the concentration of raindrops did not change much compared with that during the enhancement process, while the Dm increased, corresponding to an increase in rain intensity. The results suggest the positive effect of gas cannons on precipitation enhancement.
To analyze the effects of gas cannons on clouds and precipitation, multisource observational data, including those from National Centers for Environmental Prediction (NCEP) reanalysis, Hangzhou and Huzhou new-generation weather radars, laser disdrometer, ground-based automatic weather station, wind profiler radar, and Lin'an C-band dual-polarization radar, were adopted in this study. Based on the variational dual-Doppler wind retrieval method and the polarimetric variables obtained by the dual-polarization radar, we analyzed the microphysical processes and the variations in the macro- and microphysical quantities in clouds from the perspective of the synoptic background before precipitation enhancement, the polarization echo characteristics before, during and after enhancement, and the evolution of the fine three-dimensional kinematic structure and the microphysical structure. The results show that the precipitation enhancement operation promoted the development of radar echoes and prolonged their duration, and both the horizontal and vertical wind speeds increased. The dual-polarization radar echo showed that the diameter of the precipitation particles increased, and the concentration of raindrops increased after precipitation enhancement. The raindrops were lifted to a height corresponding to 0 to -20 ℃ due to vertical updrafts. Based on the disdrometer data during precipitation enhancement, the concentration of small raindrops (lgNw) showed a significant increase, and the mass-weighted diameter Dm value decreased, indicating that the precipitation enhancement operation played a certain "lubricating" effect. After the precipitation enhancement, the concentration of raindrops did not change much compared with that during the enhancement process, while the Dm increased, corresponding to an increase in rain intensity. The results suggest the positive effect of gas cannons on precipitation enhancement.
2022, 28(2): 252-260.
doi: 10.46267/j.1006-8775.2022.019
Abstract:
An outbreak of powerful tornadoes tore through multiple states in the central and southern United States from 10 to 11 December 2021. It is claimed the deadliest tornado outbreak that has taken place on December days. The National Oceanic and Atmospheric Administration had confirmed 66 tornadoes as of 21 December, producing at least 90 fatalities. Most tornadoes occurred at night and thus they were difficult to be visually located, which directly increases the risk for local residents. Two violent nighttime tornadoes were rated category 4 on the enhanced Fujita scale (EF4). Although a high death toll was caused during this event, the operational service actually presented an excellent performance. This tornado outbreak has aroused extensive discussion from both the public and the research community in China. This paper presents a brief discussion on the formation environment and warning services of the tornado outbreak. Recall the deadliest violent tornado in the past 45 years in China, the radar-based tornadic vortex signatures at the locations with EF4 damages show a comparable strength with those in the current cases. Some views on the tornado warning issuance and receiving and damage surveys in China are also presented.
An outbreak of powerful tornadoes tore through multiple states in the central and southern United States from 10 to 11 December 2021. It is claimed the deadliest tornado outbreak that has taken place on December days. The National Oceanic and Atmospheric Administration had confirmed 66 tornadoes as of 21 December, producing at least 90 fatalities. Most tornadoes occurred at night and thus they were difficult to be visually located, which directly increases the risk for local residents. Two violent nighttime tornadoes were rated category 4 on the enhanced Fujita scale (EF4). Although a high death toll was caused during this event, the operational service actually presented an excellent performance. This tornado outbreak has aroused extensive discussion from both the public and the research community in China. This paper presents a brief discussion on the formation environment and warning services of the tornado outbreak. Recall the deadliest violent tornado in the past 45 years in China, the radar-based tornadic vortex signatures at the locations with EF4 damages show a comparable strength with those in the current cases. Some views on the tornado warning issuance and receiving and damage surveys in China are also presented.
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