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
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
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
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
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
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.
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.
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.
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.
