Sudden precipitations may bring troubles or even huge harm to people’s daily lives.Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life.Traditionally,the rai...Sudden precipitations may bring troubles or even huge harm to people’s daily lives.Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life.Traditionally,the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor(Z)and rainfall rate(R),which is typically estimated by location-dependent experiential formula and arguably uncertain.Therefore,in this paper,we propose a deep learning-based method to model the ZR relation.To evaluate,we conducted our experiment with the Shenzhen precipitation dataset.We proposed a combined method of deep learning and the ZR relationship,and compared it with a traditional ZR equation,a ZR equation with its parameters estimated by the least square method,and a pure deep learning model.The experimental results show that our combined model performsmuch better than the equation-based ZRformula and has the similar performance with a pure deep learning nowcasting model,both for all level precipitation and heavy ones only.展开更多
In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high...In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.展开更多
The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP) impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast data.To address ...The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP) impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast data.To address such a challenge,we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM) for Precipitation Nowcasting(MGCPN),which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement(IMERG) data,offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min.The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time.This issue is a common challenge in precipitation forecasting models.Furthermore,we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy.The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data,offering valuable support for precipitation research and forecasting in the TP region.The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model;it outperforms the other considered models in the probability of detection(POD),critical success index,Heidke Skill Score,and mean absolute error,especially showing improvements in POD by approximately 33%,19%,and 8% compared to Convolutional Gated Recurrent Unit(ConvGRU),ConvLSTM,and small Attention-UNet(SmaAt-UNet) models.展开更多
Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution charact...Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution characteristics of meteorological and geological disasters and precipitation were analyzed, and the causes of the occurrence of meteorological geological disasters and the deviation of model precipitation forecast were revealed. Besides, an objective precipitation forecast system and a forecast and early warning system of meteorological and geological disasters were established. The results show that meteorological and geological disasters and precipitation were mainly concentrated from May to October, of which continuous precipitation appeared frequently in June and September, and convective precipitation was mainly distributed in July-August;the occurrence frequency of meteorological and geological disasters was basically consistent with the distribution of accumulated precipitation and short-term heavy precipitation, and they were mainly concentrated in the southern and eastern parts of Qinghai. Meteorological and geological disasters were basically caused by heavy rain and above, and meteorological and geological disasters were divided into three types: continuous precipitation(type I), short-term heavy precipitation(type II) and mixed precipitation(type III). For type I, the early warning conditions of meteorological and geological disasters in Qinghai are as follows: if the soil volumetric water content difference between 0-10 and 10-40 cm is ≤0.03 mm^(3)/mm^(3), or the soil volumetric water content at one of the depths is ≥0.25 mm^(3)/mm^(3), the future effective precipitation reaches 8.4 mm in 1 h, 10.2 mm in 2 h, 11.5 mm in 3 h, 14.2 mm in 6 h, 17.7 mm in 12 h, and 18.2 mm in 24 h, and such warning conditions are mainly used in Yushu, Guoluo, southern Hainan, southern Huangnan and other places. For type II, when the future effective precipitation is up to 11.5 mm in 1 h, 14.9 mm in 2 h, 16.2 mm in 3 h, 19.9 mm in 6 h, 25.3 mm in 12 h, and 26.3 mm in 24 h, such precipitation thresholds are mainly used in Hainan, Huangnan, and eastern Guoluo;as it is up to 13.3 mm in 1 h, 15.5 mm in 2 h, 16.6 mm in 3 h, 19.9 mm in 6 h, 31.1 mm in 12 h, and 34.0 mm in 24 h, such precipitation thresholds are mainly used in Hehuang valley. The precipitation thresholds of type III are between type I and type II, and closer to that of type II;such precipitation thresholds are mainly used in Hainan, Huangnan, and northern Guoluo. The forecasting ability of global models for heavy rain and above was not as good as that of mesoscale numerical prediction model, and global models had a wet bias for small-scale precipitation and a dry bias for large-scale precipitation;meso-scale models had a significantly larger precipitation bias. The forecast ability of precipitation objective forecast system constructed by frequency matching and multi-model integration has improved. At the same time, the constructed grid forecast and early warning system of meteorological and geological disasters is more precise and accurate, and is of instructive significance for the forecast and early warning of meteorological and geological disasters.展开更多
运用WRF模式(Weather Research and Forecasting Model,天气研究和预报模式)和WRFDA同化(WRF Data Assimilation,WRF资料同化)系统,探究采用物理滤波初始化四维变分同化方法提高数值预报在临近预报时效的预报能力的可能性。通过采用12 ...运用WRF模式(Weather Research and Forecasting Model,天气研究和预报模式)和WRFDA同化(WRF Data Assimilation,WRF资料同化)系统,探究采用物理滤波初始化四维变分同化方法提高数值预报在临近预报时效的预报能力的可能性。通过采用12 min同化窗,在不显著增加计算量的情况下,得到更协调的模式初始场,从而提高模式预报能力。选取2018年8月华北地区17个降水个例进行研究,结果表明:采用物理滤波初始化四维变分同化技术能够明显改进模式短时临近降水预报能力,明显提高对大量级降水预报的ETS评分,6 h累积降水大于25.0 mm量级的ETS评分由0.125提高到0.190,且6 h累积降水大于60.0 mm量级的ETS评分由0.016提高到0.081。研究还表明:同化雷达风场通过改进初始动力场使次网格尺度降水过程(积云参数化)快速响应,可提高短时临近时段的降水预报能力。展开更多
The Hong Kong Observatory operates an in-house developed nowcasting system, namely “Short-range Warning of Intense Rainstorms in Localized Systems(SWIRLS)”, to support the operation of rainstorm and severe weather w...The Hong Kong Observatory operates an in-house developed nowcasting system, namely “Short-range Warning of Intense Rainstorms in Localized Systems(SWIRLS)”, to support the operation of rainstorm and severe weather warnings as well as to provide rainfall nowcast services for the public and for special users in Hong Kong. Aiming to enhancing its performance in nowcast of rainfall brought by tropical cyclones, a new radar echo tracking scheme that separates the motion of the spiraling rain bands from the overall movement of tropical cyclone has been developed. Back-testing with historical cases in the past ten years reveals that the new scheme is more capable of preserving tropical cyclone rain band structures and can enhance forecast skills.展开更多
基金supported by Sichuan Provincial Key Research and Development Program(No.2021YFG0345,to J.Ma)the National Key Research and Development Program of China(No.2020YFA0608001,to J.Ma).
文摘Sudden precipitations may bring troubles or even huge harm to people’s daily lives.Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life.Traditionally,the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor(Z)and rainfall rate(R),which is typically estimated by location-dependent experiential formula and arguably uncertain.Therefore,in this paper,we propose a deep learning-based method to model the ZR relation.To evaluate,we conducted our experiment with the Shenzhen precipitation dataset.We proposed a combined method of deep learning and the ZR relationship,and compared it with a traditional ZR equation,a ZR equation with its parameters estimated by the least square method,and a pure deep learning model.The experimental results show that our combined model performsmuch better than the equation-based ZRformula and has the similar performance with a pure deep learning nowcasting model,both for all level precipitation and heavy ones only.
文摘In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.
基金Supported by the National Natural Science Foundation of China (41871285 and 52104158)。
文摘The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP) impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast data.To address such a challenge,we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM) for Precipitation Nowcasting(MGCPN),which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement(IMERG) data,offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min.The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time.This issue is a common challenge in precipitation forecasting models.Furthermore,we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy.The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data,offering valuable support for precipitation research and forecasting in the TP region.The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model;it outperforms the other considered models in the probability of detection(POD),critical success index,Heidke Skill Score,and mean absolute error,especially showing improvements in POD by approximately 33%,19%,and 8% compared to Convolutional Gated Recurrent Unit(ConvGRU),ConvLSTM,and small Attention-UNet(SmaAt-UNet) models.
基金Supported by the Project of Key Laboratory for Disaster Prevention and Mitigation of Qinghai Province (QFZ-2021-Z04)Project of Qinghai Science and Technology Department (2020-ZJ-739)Key Project of Qinghai Provincial Meteorological Bureau (QXZ2020-03)。
文摘Based on the meteorological and geological disaster data, ground observation data set, CLDAS grid point data set, and EC, BJ and other model product data during 2008-2020, the temporal and spatial distribution characteristics of meteorological and geological disasters and precipitation were analyzed, and the causes of the occurrence of meteorological geological disasters and the deviation of model precipitation forecast were revealed. Besides, an objective precipitation forecast system and a forecast and early warning system of meteorological and geological disasters were established. The results show that meteorological and geological disasters and precipitation were mainly concentrated from May to October, of which continuous precipitation appeared frequently in June and September, and convective precipitation was mainly distributed in July-August;the occurrence frequency of meteorological and geological disasters was basically consistent with the distribution of accumulated precipitation and short-term heavy precipitation, and they were mainly concentrated in the southern and eastern parts of Qinghai. Meteorological and geological disasters were basically caused by heavy rain and above, and meteorological and geological disasters were divided into three types: continuous precipitation(type I), short-term heavy precipitation(type II) and mixed precipitation(type III). For type I, the early warning conditions of meteorological and geological disasters in Qinghai are as follows: if the soil volumetric water content difference between 0-10 and 10-40 cm is ≤0.03 mm^(3)/mm^(3), or the soil volumetric water content at one of the depths is ≥0.25 mm^(3)/mm^(3), the future effective precipitation reaches 8.4 mm in 1 h, 10.2 mm in 2 h, 11.5 mm in 3 h, 14.2 mm in 6 h, 17.7 mm in 12 h, and 18.2 mm in 24 h, and such warning conditions are mainly used in Yushu, Guoluo, southern Hainan, southern Huangnan and other places. For type II, when the future effective precipitation is up to 11.5 mm in 1 h, 14.9 mm in 2 h, 16.2 mm in 3 h, 19.9 mm in 6 h, 25.3 mm in 12 h, and 26.3 mm in 24 h, such precipitation thresholds are mainly used in Hainan, Huangnan, and eastern Guoluo;as it is up to 13.3 mm in 1 h, 15.5 mm in 2 h, 16.6 mm in 3 h, 19.9 mm in 6 h, 31.1 mm in 12 h, and 34.0 mm in 24 h, such precipitation thresholds are mainly used in Hehuang valley. The precipitation thresholds of type III are between type I and type II, and closer to that of type II;such precipitation thresholds are mainly used in Hainan, Huangnan, and northern Guoluo. The forecasting ability of global models for heavy rain and above was not as good as that of mesoscale numerical prediction model, and global models had a wet bias for small-scale precipitation and a dry bias for large-scale precipitation;meso-scale models had a significantly larger precipitation bias. The forecast ability of precipitation objective forecast system constructed by frequency matching and multi-model integration has improved. At the same time, the constructed grid forecast and early warning system of meteorological and geological disasters is more precise and accurate, and is of instructive significance for the forecast and early warning of meteorological and geological disasters.
文摘运用WRF模式(Weather Research and Forecasting Model,天气研究和预报模式)和WRFDA同化(WRF Data Assimilation,WRF资料同化)系统,探究采用物理滤波初始化四维变分同化方法提高数值预报在临近预报时效的预报能力的可能性。通过采用12 min同化窗,在不显著增加计算量的情况下,得到更协调的模式初始场,从而提高模式预报能力。选取2018年8月华北地区17个降水个例进行研究,结果表明:采用物理滤波初始化四维变分同化技术能够明显改进模式短时临近降水预报能力,明显提高对大量级降水预报的ETS评分,6 h累积降水大于25.0 mm量级的ETS评分由0.125提高到0.190,且6 h累积降水大于60.0 mm量级的ETS评分由0.016提高到0.081。研究还表明:同化雷达风场通过改进初始动力场使次网格尺度降水过程(积云参数化)快速响应,可提高短时临近时段的降水预报能力。
文摘The Hong Kong Observatory operates an in-house developed nowcasting system, namely “Short-range Warning of Intense Rainstorms in Localized Systems(SWIRLS)”, to support the operation of rainstorm and severe weather warnings as well as to provide rainfall nowcast services for the public and for special users in Hong Kong. Aiming to enhancing its performance in nowcast of rainfall brought by tropical cyclones, a new radar echo tracking scheme that separates the motion of the spiraling rain bands from the overall movement of tropical cyclone has been developed. Back-testing with historical cases in the past ten years reveals that the new scheme is more capable of preserving tropical cyclone rain band structures and can enhance forecast skills.
