This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Da...This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.展开更多
Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than...Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.展开更多
Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as...Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.展开更多
A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation （MTREC） was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of r...A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation （MTREC） was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation （TREC） technique with a large ＂box＂ to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small ＂box＂ to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies： the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.展开更多
A thunderstorm tracking algorithm is proposed to nowcast the possibility of lightning activity over an area of concern by using the total lightning data and neighborhood technique.The lightning radiation sources obser...A thunderstorm tracking algorithm is proposed to nowcast the possibility of lightning activity over an area of concern by using the total lightning data and neighborhood technique.The lightning radiation sources observed from the Beijing Lightning Network(BLNET)were used to obtain information about the thunderstorm cells,which are significantly valuable in real-time.The boundaries of thunderstorm cells were obtained through the neighborhood technique.After smoothing,these boundaries were used to track the movement of thunderstorms and then extrapolated to nowcast the lightning approaching in an area of concern.The algorithm can deliver creditable results prior to a thunderstorm arriving at the area of concern,with accuracies of 63%,80%,and 91%for lead times of 30,15,and 5 minutes,respectively.The real-time observations of total lightning appear to be significant for thunderstorm tracking and lightning nowcasting,as total lightning tracking could help to fill the observational gaps in radar reflectivity due to the attenuation by hills or other obstacles.The lightning data used in the algorithm performs well in tracking the active thunderstorm cells associated with lightning activities.展开更多
Using optimal interpolation data assimilation of observed wave spectrum around Northeast coast of Taiwan Island, the typhoon driven wave nowcasting model in Southeast China Sea is setup. The SWAN (simulating waves nea...Using optimal interpolation data assimilation of observed wave spectrum around Northeast coast of Taiwan Island, the typhoon driven wave nowcasting model in Southeast China Sea is setup. The SWAN (simulating waves nearshore) model is used to calculate wave field and the input wind field is the QSCAT/NCEP (Quick Scatterometer/National Centers for Environmental Prediction) data. The two-dimensional wavelet transform is applied to analyze the X-band radar image of nearshore wave field and it reveals that the observed wave spectrum has shoaling characteristics in frequency domain. The reverse calculation approach of wave spectrum in deep water is proposed and validated with experimental tests. The two-dimensional digital low-pass filter is used to obtain the initialization wave field. Wave data during Typhoon Sinlaku is used to calibrate the data assimilation parameters and test the reverse calculation approach. Data assimilation corrects the significant wave height and the low frequency spectra energy evidently at Beishuang Station along Fujian Province coast, where the entire assimilation indexes are positive in verification moments. The nowcasting wave field shows that the present model can obtain more accurate wave predictions for coastal and ocean engineering in Southeast China Sea.展开更多
Precipitation nowcasting is of great significance for severe convective weather warnings.Radar echo extrapolation is a commonly used precipitation nowcasting method.However,the traditional radar echo extrapolation met...Precipitation nowcasting is of great significance for severe convective weather warnings.Radar echo extrapolation is a commonly used precipitation nowcasting method.However,the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation ambiguity.The reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data stream.In order to solve the above problems,we select the spatiotemporal long short-term memory(ST-LSTM)as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model’s ability to capture short-term motion information which plays a vital role in the prediction of radar echo motion trends.For the purpose of enhancing the model’s ability to retain long-term important information,we also introduce the channel attention mechanism to achieve this goal.In the experiment,the training and testing datasets are constructed using radar data of Shanghai,we compare our model with three benchmark models under the reflectance thresholds of 15 and 25.Experimental results demonstrate that the proposed model outperforms the three benchmark models in radar echo extrapolation task,which obtains a higher accuracy rate and improves the clarity of the extrapolated image.展开更多
An improved echo extrapolation technology( MOD-COTREC) was introduced firstly,and then two plans for lightning nowcasting based on MOD-COTREC and both isothermal radar reflectivity and MOD-COTREC were proposed based o...An improved echo extrapolation technology( MOD-COTREC) was introduced firstly,and then two plans for lightning nowcasting based on MOD-COTREC and both isothermal radar reflectivity and MOD-COTREC were proposed based on the technology. Afterwards,the two plans for lightning nowcasting were tested by a case respectively. It is concluded that during the process of lightning nowcasting singly based on MOD-COTREC,the appearance and disappearance of lightning are not considered,and only lightning position is predicted when lightning density is constant,so the prediction error is big. The plan for lightning nowcasting based on both isothermal radar reflectivity and MOD-COTREC is still at an experimental stage,and the nowcasting products of cloud-to-ground lightning based on the plan are very different from the actual density and position of cloud-to-ground lightning,so it needs to be improved further.展开更多
Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forec...Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells.展开更多
Ensemble forecasting systems have become an important tool for estimating the uncertainties in initial conditions and model formulations and they are receiving increased attention from various applications.The Regiona...Ensemble forecasting systems have become an important tool for estimating the uncertainties in initial conditions and model formulations and they are receiving increased attention from various applications.The Regional Ensemble Prediction System(REPS),which has operated at the Beijing Meteorological Service(BMS)since 2017,allows for probabilistic forecasts.However,it still suffers from systematic deficiencies during the first couple of forecast hours.This paper presents an integrated probabilistic nowcasting ensemble prediction system(NEPS)that is constructed by applying a mixed dynamicintegrated method.It essentially combines the uncertainty information(i.e.,ensemble variance)provided by the REPS with the nowcasting method provided by the rapid-refresh deterministic nowcasting prediction system(NPS)that has operated at the Beijing Meteorological Service(BMS)since 2019.The NEPS provides hourly updated analyses and probabilistic forecasts in the nowcasting and short range(0-6 h)with a spatial grid spacing of 500 m.It covers the three meteorological parameters:temperature,wind,and precipitation.The outcome of an evaluation experiment over the deterministic and probabilistic forecasts indicates that the NEPS outperforms the REPS and NPS in terms of surface weather variables.Analysis of two cases demonstrates the superior reliability of the NEPS and suggests that the NEPS gives more details about the spatial intensity and distribution of the meteorological parameters.展开更多
It was difficult to probe the clear air echo by the general traditional radar for echo's weak intensity.Therefore,its investigation was less because of the restrictions of probe technique and data.In recent years,...It was difficult to probe the clear air echo by the general traditional radar for echo's weak intensity.Therefore,its investigation was less because of the restrictions of probe technique and data.In recent years,with the probe tools improving,more clear air echoes were probed,and the relative investigations were more and more.However,most investigations stayed in the theory at present,and the relative literatures about its application in the practical forecast work were few.For a new generation of Doppler radars' powers and sensitivities were all high,they were put into service successively in China.People could observe more and more the clear air atmospheric echoes in the daily business.Its Doppler radar velocity provided the important basis for daily short-term predication and had very important indication meaning for the nowcasting of seasons which were spring,summer and fall.It was important to forecast the precipitation,especially the abrupt rainstorm by using the symptom of clear air echo which was probed by the new generation of Doppler radar products.Therefore,the advances on clear air echo research at home and abroad were summarized simply.展开更多
Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predic...Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predict by standard meteorological numerical models.This work proposes the M5Images method for performing the very short-term prediction(nowcasting)of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural network.The recurrent part of it is a Long Short-Term Memory(LSTM)neural network.Prediction tests were performed for the city and surroundings of Campinas,located in the Southeastern Brazil.The convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall events.The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.展开更多
In this study, we attempted to improve the nowcasting of GRAPES model by adjusting the model initial field through modifying the cloud water, rain water and vapor as well as revising vapor-following rain water. The re...In this study, we attempted to improve the nowcasting of GRAPES model by adjusting the model initial field through modifying the cloud water, rain water and vapor as well as revising vapor-following rain water. The results show that the model nowcasting is improved when only the cloud water and rain water are adjusted or all of the cloud water, rain water and vapor are adjusted in the initial field. The forecasting of the former(latter) approach during 0-3(0-6) hours is significantly improved. Furthermore, for the forecast for 0-3 hours, the latter approach is better than the former. Compared with the forecasting results for which the vapor of the model initial field is adjusted by the background vapor with those by the revised vapor, the nowcasting of the revised vapor is much better than that of background vapor. Analysis of the reasons indicated that when the vapor is adjusted in the model initial field, especially when the saturated vapor is considered, the forecasting of the vapor field is significantly affected. The changed vapor field influences the circulation, which in turn improves the model forecasting of radar reflectivity and rainfall.展开更多
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
One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, th...One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, this monitoring is made by local cameras. However, cloud detection and monitoring are not trivial due to cloud shape dynamics, the camera is a linear and self-adjusting device, with fish-eye lenses generating a flat image that distorts images near the horizon. The present work focuses on cloud identification to predict its effects on solar plants that are distinct for every site’s climatology and geography. We used RASPBERY-PI-based cameras pointed at the horizon to allow observation of clouds’ vertical distribution, not possible with a unique fish-eye lens. A large number of cloud image identification analyses led the researchers to use deep learning methods such as U-net, HRnet, and Detectron. We use transfer learning with weights trained over the “2012 ILSVRC ImageNet” data set and architecture configurations like Resnet, Efficient, and Detectron2. While cloud identification proved a difficult task, we achieved the best results by using Jaccard Coefficient as a validation metric, with the best model being a U-net with Resnet18 using 486 × 648 resolution. This model had an average IoU of 0.6, indicating a satisfactory performance in cloud segmentation. We also observed that the data imbalance affected the overall performance of all models, with the tree class creating a favorable bias. The HRNet model, which works with different resolutions, showed promising results with a more refined segmentation at the pixel level, but it was not necessary to detect the most predominant clouds in the sky. We are currently working on balancing the dataset and mapping out data augmentation transformations for our next experiments. Our ultimate goal is to use such models to predict cloud motion and forecast the impact it will have on solar power generation. The present work has contributed to a better understanding of what techniques work best for cloud identification and paves the way for future studies on the development of a better overall cloud classification model.展开更多
This article reviews advances in monitoring and nowcasting of severe convective weather(SCW),along with developments in operational nowcasting systems.It focuses on deep learning(DL)-based techniques using multisource...This article reviews advances in monitoring and nowcasting of severe convective weather(SCW),along with developments in operational nowcasting systems.It focuses on deep learning(DL)-based techniques using multisource data,highlighting associated challenges and opportunities.Based on multisource observations including those from dual-polarization weather radars and geostationary satellites,the monitoring capabilities of SCW types and intensities,convective initiation,and identification and tracking of convective storm cells have been significantly improved using advanced technologies,including storm structural feature recognition,fuzzy logic,and DL.Among these approaches,deep generative models have proven particularly effective,substantially improving the accuracy and extending the lead time of SCW nowcasting.The performance of the China Meteorological Administration's Severe Weather Analysis and Forecasting(SWAN)3.0 system continues to advance,with widespread operational adoption across China.Future efforts will leverage higher-resolution observations and numerical weather prediction products at the hundred-meter resolution to enhance the understanding of the underlying mechanisms of SCW development at meso-γ-and microscales.Current purely data-driven AI models are transitioning toward physics-informed frameworks for SCW nowcasting.Integrating forecasters'operational expertise with state-of-the-art AI technology will further enhance operational capabilities in monitoring and nowcasting extreme SCW events.展开更多
基金Scientific Research and Development Project of Hebei Meteorological Bureau(23ky08).
文摘This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.
基金National Natural Science Foundation of China(42075075)National Key R&D Program of China(2023YFC3007700)Pre-Research Fund of USTC(YZ2082300006)。
文摘Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.
文摘Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.
基金This study was supported by the Special Fund for Basic Research and Operation of Chinese Academy of Meteorological Science:Development on quantitative precipitation forecasts for 0-6 h lead times by blending radar-based extrapolation and GRAPES-meso,Observation and retrieval methods of micro-physics，the National Natural Science Foundation of China
文摘A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation （MTREC） was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation （TREC） technique with a large ＂box＂ to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small ＂box＂ to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies： the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.
基金The National Natural Science Foundation of China(Grant Nos.41630425,41761144074 and 41875007)supported the researchthe Chinese Academy of Sciences for the CAS-PIFI fellowship grant。
文摘A thunderstorm tracking algorithm is proposed to nowcast the possibility of lightning activity over an area of concern by using the total lightning data and neighborhood technique.The lightning radiation sources observed from the Beijing Lightning Network(BLNET)were used to obtain information about the thunderstorm cells,which are significantly valuable in real-time.The boundaries of thunderstorm cells were obtained through the neighborhood technique.After smoothing,these boundaries were used to track the movement of thunderstorms and then extrapolated to nowcast the lightning approaching in an area of concern.The algorithm can deliver creditable results prior to a thunderstorm arriving at the area of concern,with accuracies of 63%,80%,and 91%for lead times of 30,15,and 5 minutes,respectively.The real-time observations of total lightning appear to be significant for thunderstorm tracking and lightning nowcasting,as total lightning tracking could help to fill the observational gaps in radar reflectivity due to the attenuation by hills or other obstacles.The lightning data used in the algorithm performs well in tracking the active thunderstorm cells associated with lightning activities.
基金supported by the Commonweal Program of Chinese Ministry of Water Resources( No.200901062)the National Natural Science Foundation of China ( No.50979033)the Research Fund of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering ( No. 2009585812 and No. 2008491011)
文摘Using optimal interpolation data assimilation of observed wave spectrum around Northeast coast of Taiwan Island, the typhoon driven wave nowcasting model in Southeast China Sea is setup. The SWAN (simulating waves nearshore) model is used to calculate wave field and the input wind field is the QSCAT/NCEP (Quick Scatterometer/National Centers for Environmental Prediction) data. The two-dimensional wavelet transform is applied to analyze the X-band radar image of nearshore wave field and it reveals that the observed wave spectrum has shoaling characteristics in frequency domain. The reverse calculation approach of wave spectrum in deep water is proposed and validated with experimental tests. The two-dimensional digital low-pass filter is used to obtain the initialization wave field. Wave data during Typhoon Sinlaku is used to calibrate the data assimilation parameters and test the reverse calculation approach. Data assimilation corrects the significant wave height and the low frequency spectra energy evidently at Beishuang Station along Fujian Province coast, where the entire assimilation indexes are positive in verification moments. The nowcasting wave field shows that the present model can obtain more accurate wave predictions for coastal and ocean engineering in Southeast China Sea.
基金This work was supported by the National Natural Science Foundation of China(Grant No.42075007)the Open Grants of the State Key Laboratory of Severe Weather(No.2021LASW-B19).
文摘Precipitation nowcasting is of great significance for severe convective weather warnings.Radar echo extrapolation is a commonly used precipitation nowcasting method.However,the traditional radar echo extrapolation methods are encountered with the dilemma of low prediction accuracy and extrapolation ambiguity.The reason is that those methods cannot retain important long-term information and fail to capture short-term motion information from the long-range data stream.In order to solve the above problems,we select the spatiotemporal long short-term memory(ST-LSTM)as the recurrent unit of the model and integrate the 3D convolution operation in it to strengthen the model’s ability to capture short-term motion information which plays a vital role in the prediction of radar echo motion trends.For the purpose of enhancing the model’s ability to retain long-term important information,we also introduce the channel attention mechanism to achieve this goal.In the experiment,the training and testing datasets are constructed using radar data of Shanghai,we compare our model with three benchmark models under the reflectance thresholds of 15 and 25.Experimental results demonstrate that the proposed model outperforms the three benchmark models in radar echo extrapolation task,which obtains a higher accuracy rate and improves the clarity of the extrapolated image.
文摘An improved echo extrapolation technology( MOD-COTREC) was introduced firstly,and then two plans for lightning nowcasting based on MOD-COTREC and both isothermal radar reflectivity and MOD-COTREC were proposed based on the technology. Afterwards,the two plans for lightning nowcasting were tested by a case respectively. It is concluded that during the process of lightning nowcasting singly based on MOD-COTREC,the appearance and disappearance of lightning are not considered,and only lightning position is predicted when lightning density is constant,so the prediction error is big. The plan for lightning nowcasting based on both isothermal radar reflectivity and MOD-COTREC is still at an experimental stage,and the nowcasting products of cloud-to-ground lightning based on the plan are very different from the actual density and position of cloud-to-ground lightning,so it needs to be improved further.
基金supported by the Science and Technology Grant No.520120210003,Jibei Electric Power Company of the State Grid Corporation of China。
文摘Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells.
基金supported by National Key Research and Development Program of China(Grant No.2018YFC1506804)the Beijing Natural Science Foundation(Grant No.8222051)the Key Innovation Team of China Meteorological Administration(CMA2022ZD04)。
文摘Ensemble forecasting systems have become an important tool for estimating the uncertainties in initial conditions and model formulations and they are receiving increased attention from various applications.The Regional Ensemble Prediction System(REPS),which has operated at the Beijing Meteorological Service(BMS)since 2017,allows for probabilistic forecasts.However,it still suffers from systematic deficiencies during the first couple of forecast hours.This paper presents an integrated probabilistic nowcasting ensemble prediction system(NEPS)that is constructed by applying a mixed dynamicintegrated method.It essentially combines the uncertainty information(i.e.,ensemble variance)provided by the REPS with the nowcasting method provided by the rapid-refresh deterministic nowcasting prediction system(NPS)that has operated at the Beijing Meteorological Service(BMS)since 2019.The NEPS provides hourly updated analyses and probabilistic forecasts in the nowcasting and short range(0-6 h)with a spatial grid spacing of 500 m.It covers the three meteorological parameters:temperature,wind,and precipitation.The outcome of an evaluation experiment over the deterministic and probabilistic forecasts indicates that the NEPS outperforms the REPS and NPS in terms of surface weather variables.Analysis of two cases demonstrates the superior reliability of the NEPS and suggests that the NEPS gives more details about the spatial intensity and distribution of the meteorological parameters.
文摘It was difficult to probe the clear air echo by the general traditional radar for echo's weak intensity.Therefore,its investigation was less because of the restrictions of probe technique and data.In recent years,with the probe tools improving,more clear air echoes were probed,and the relative investigations were more and more.However,most investigations stayed in the theory at present,and the relative literatures about its application in the practical forecast work were few.For a new generation of Doppler radars' powers and sensitivities were all high,they were put into service successively in China.People could observe more and more the clear air atmospheric echoes in the daily business.Its Doppler radar velocity provided the important basis for daily short-term predication and had very important indication meaning for the nowcasting of seasons which were spring,summer and fall.It was important to forecast the precipitation,especially the abrupt rainstorm by using the symptom of clear air echo which was probed by the new generation of Doppler radar products.Therefore,the advances on clear air echo research at home and abroad were summarized simply.
文摘Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predict by standard meteorological numerical models.This work proposes the M5Images method for performing the very short-term prediction(nowcasting)of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural network.The recurrent part of it is a Long Short-Term Memory(LSTM)neural network.Prediction tests were performed for the city and surroundings of Campinas,located in the Southeastern Brazil.The convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall events.The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.
基金National Natural Science Foundation of China(41075083)On the Techniques of 0-6h Quantitative Forecast of Rain(Snow)(GYHY201006001)Science and Technology Planning Project for Guangdong Province(2011A032100006,2012A061400012)
文摘In this study, we attempted to improve the nowcasting of GRAPES model by adjusting the model initial field through modifying the cloud water, rain water and vapor as well as revising vapor-following rain water. The results show that the model nowcasting is improved when only the cloud water and rain water are adjusted or all of the cloud water, rain water and vapor are adjusted in the initial field. The forecasting of the former(latter) approach during 0-3(0-6) hours is significantly improved. Furthermore, for the forecast for 0-3 hours, the latter approach is better than the former. Compared with the forecasting results for which the vapor of the model initial field is adjusted by the background vapor with those by the revised vapor, the nowcasting of the revised vapor is much better than that of background vapor. Analysis of the reasons indicated that when the vapor is adjusted in the model initial field, especially when the saturated vapor is considered, the forecasting of the vapor field is significantly affected. The changed vapor field influences the circulation, which in turn improves the model forecasting of radar reflectivity and rainfall.
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One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, this monitoring is made by local cameras. However, cloud detection and monitoring are not trivial due to cloud shape dynamics, the camera is a linear and self-adjusting device, with fish-eye lenses generating a flat image that distorts images near the horizon. The present work focuses on cloud identification to predict its effects on solar plants that are distinct for every site’s climatology and geography. We used RASPBERY-PI-based cameras pointed at the horizon to allow observation of clouds’ vertical distribution, not possible with a unique fish-eye lens. A large number of cloud image identification analyses led the researchers to use deep learning methods such as U-net, HRnet, and Detectron. We use transfer learning with weights trained over the “2012 ILSVRC ImageNet” data set and architecture configurations like Resnet, Efficient, and Detectron2. While cloud identification proved a difficult task, we achieved the best results by using Jaccard Coefficient as a validation metric, with the best model being a U-net with Resnet18 using 486 × 648 resolution. This model had an average IoU of 0.6, indicating a satisfactory performance in cloud segmentation. We also observed that the data imbalance affected the overall performance of all models, with the tree class creating a favorable bias. The HRNet model, which works with different resolutions, showed promising results with a more refined segmentation at the pixel level, but it was not necessary to detect the most predominant clouds in the sky. We are currently working on balancing the dataset and mapping out data augmentation transformations for our next experiments. Our ultimate goal is to use such models to predict cloud motion and forecast the impact it will have on solar power generation. The present work has contributed to a better understanding of what techniques work best for cloud identification and paves the way for future studies on the development of a better overall cloud classification model.
基金the National Natural Science Foundation of China(U2342204 and 42175017)Joint Research Special Project for Meteorological Capacity Enhancement of CMA(24NLTSZ002)+1 种基金National Key Research and Development Program of China(2022YFC3004104 and 2018YFC1507504)Key Innovation Team for Severe Convection Weather of the CMA(CMA2022ZD07)。
文摘This article reviews advances in monitoring and nowcasting of severe convective weather(SCW),along with developments in operational nowcasting systems.It focuses on deep learning(DL)-based techniques using multisource data,highlighting associated challenges and opportunities.Based on multisource observations including those from dual-polarization weather radars and geostationary satellites,the monitoring capabilities of SCW types and intensities,convective initiation,and identification and tracking of convective storm cells have been significantly improved using advanced technologies,including storm structural feature recognition,fuzzy logic,and DL.Among these approaches,deep generative models have proven particularly effective,substantially improving the accuracy and extending the lead time of SCW nowcasting.The performance of the China Meteorological Administration's Severe Weather Analysis and Forecasting(SWAN)3.0 system continues to advance,with widespread operational adoption across China.Future efforts will leverage higher-resolution observations and numerical weather prediction products at the hundred-meter resolution to enhance the understanding of the underlying mechanisms of SCW development at meso-γ-and microscales.Current purely data-driven AI models are transitioning toward physics-informed frameworks for SCW nowcasting.Integrating forecasters'operational expertise with state-of-the-art AI technology will further enhance operational capabilities in monitoring and nowcasting extreme SCW events.
