Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models

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.

Evaluation of the Added Value of Probabilistic Nowcasting Ensemble Forecasts on Regional Ensemble Forecasts

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.

Segmentation and Classification of Individual Clouds in Images Captured with Horizon-Aimed Cameras for Nowcasting of Solar Irradiance Absorption

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.

Methods of Lightning Nowcasting Based on Radar Echo Extrapolation Technology

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.

Lightning Nowcasting with an Algorithm of Thunderstorm Tracking Based on Lightning Location Data over the Beijing Area

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.

THE INFLUENCE OF CLOUD PARAMETERIZATION ADJUSTMENT USING REFLECTIVITY OF DOPPLER ON NOWCASTING WITH GRAPES MODEL

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.

Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

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.

A Novel Method for Precipitation Nowcasting Based on ST-LSTM

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.

FDNet:A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting

With the goal of predicting the future rainfall intensity in a local region over a relatively short period time,precipitation nowcasting has been a long-time scientific challenge with great social and economic impact.The radar echo extrapolation approaches for precipitation nowcasting take radar echo images as input,aiming to generate future radar echo images by learning from the historical images.To effectively handle complex and high non-stationary evolution of radar echoes,we propose to decompose the movement into optical flow field motion and morphologic deformation.Following this idea,we introduce Flow-Deformation Network(FDNet),a neural network that models flow and deformation in two parallel cross pathways.The flow encoder captures the optical flow field motion between consecutive images and the deformation encoder distinguishes the change of shape from the translational motion of radar echoes.We evaluate the proposed network architecture on two real-world radar echo datasets.Our model achieves state-of-the-art prediction results compared with recent approaches.To the best of our knowledge,this is the first network architecture with flow and deformation separation to model the evolution of radar echoes for precipitation nowcasting.We believe that the general idea of this work could not only inspire much more effective approaches but also be applied to other similar spatio-temporal prediction tasks.

Nowcasting:原理、应用及启示

即时预测宏观经济具有重要的现实意义,通过有效利用可获得的信息对宏观经济进行预测,能够为制定公共政策和企业决策提供参考。本文研究Nowcasting模型的原理,该模型从大型、高维、混频、异构的信息集中提取出能够捕获大部分宏观经济动态的"潜在因子"。Nowcasting模型,不仅可以利用实时数据集对季度GDP增长率进行预测,还可以提取指标中的"新闻"对GDP增长率的影响权重,获得每个实时数据对GDP增长率的边际影响值。此外,我们分析纽约联储Nowcasting模型的变量设定、预测和更新等过程,为中国未来开展即时预测工作提供参考。最后,我们结合Nowcasting模型的扩展和中国国情,验证建立中国版Nowcasting模型的可行性。

A Nowcasting Technique Based on Application of the Particle Filter Blending Algorithm

To improve the accuracy of nowcasting, a new extrapolation technique called particle filter blending was configured in this study and applied to experimental nowcasting. Radar echo extrapolation was performed by using the radar mosaic at an altitude of 2.5 km obtained from the radar images of 12 S-band radars in Guangdong Province,China. The first bilateral filter was applied in the quality control of the radar data; an optical flow method based on the Lucas–Kanade algorithm and the Harris corner detection algorithm were used to track radar echoes and retrieve the echo motion vectors; then, the motion vectors were blended with the particle filter blending algorithm to estimate the optimal motion vector of the true echo motions; finally, semi-Lagrangian extrapolation was used for radar echo extrapolation based on the obtained motion vector field. A comparative study of the extrapolated forecasts of four precipitation events in 2016 in Guangdong was conducted. The results indicate that the particle filter blending algorithm could realistically reproduce the spatial pattern, echo intensity, and echo location at 30-and 60-min forecast lead times. The forecasts agreed well with observations, and the results were of operational significance. Quantitative evaluation of the forecasts indicates that the particle filter blending algorithm performed better than the cross-correlation method and the optical flow method. Therefore, the particle filter blending method is proved to be superior to the traditional forecasting methods and it can be used to enhance the ability of nowcasting in operational weather forecasts.

A rain-type adaptive optical flow method and its application in tropical cyclone rainfall nowcasting

A rain-type adaptive pyramid Kanade-Lucas-Tomasi(A-PKLT)optical flow method for radar echo extrapolation is proposed.This method introduces a rain-type classification algorithm that can classify radar echoes into six types:convective,stratiform,surrounding convective,isolated convective core,isolated convective fringe,and weak echoes.Then,new schemes are designed to optimize specific parameters of the PKLT optical flow based on the rain type of the echo.At the same time,the gradients of radar reflectivity in the fringe positions corresponding to all types of rain echoes are increased.As a result,corner points that are characteristic points used for PKLT optical flow tracking in the surrounding area will be increased.Therefore,more motion vectors are purposefully obtained in the whole radar echo area.This helps to describe the motion characteristics of the precipitation more precisely.Then,the motion vectors corresponding to each type of rain echo are merged,and a denser motion vector field is generated by an interpolation algorithm on the basis of merged motion vectors.Finally,the dense motion vectors are used to extrapolate rain echoes into 0-60-min nowcasts by a semi-Lagrangian scheme.Compared with other nowcasting methods for four landfalling typhoons in or near Shanghai,the new optical flow method is found to be more accurate than the traditional cross-correlation and optical flow methods,particularly showing a clear improvement in the nowcasting of convective echoes on the spiral rainbands of typhoons.

The Possibility of Numerical Nowcasting of Storms

Since L. F. Richardson introduced the concept of numerical weather prediction in 1922, it has become an important part of meteorological services. The operational nurnerical weather prediction of large-scale atmospheric circulation systems has a 30-year history, but precipitation forecasting and nowcasting of meso-scale severe weather remain in its experimental stage, and the advance of their numerical predictions is slow. Theoretical studies indicate that the predictability time lhnit of the convective storm-scale system has

Nowcasting China's GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated.Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises.In this study,we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data.We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors.We demonstrate the validity of our approach through simulation studies,and explore the applicability of our approach through an empirical study in nowcasting the China's GDP using 117 monthly data series of several categories in the Chinese market.The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China's GDP.

Is the September 5,2022,Luding MS6.8 earthquake an‘unexpected’event?

After the September 5,2022(Beijing time).Luding Ms6.8 earthquake(29.59°N.102.08°E.depth 16 km.according to the initial determination by the China Earthquake Networks Center(CENC)).field investigation was carried out by the China Earthquake Administration(CEA).which associated the earthquake to the Moxi segment on the south part of the Xianshuihe fault system.This segment,with horizontal slip rate 5-10 mm/a.locates in the convergent part among the Xianshuihe fault.

Trade space for time for inspecting an earthquake cycle by modern seismological observation: The central-southern part of the Sichuan-Yunnan rhombic block

The central-southern part of the eastern border of the Sichuan-Yunnan rhombic block provides the research strategy of ‘trade space for time' with an interesting fault system, where the segments have similar focal mechanisms and cover almost continuous spectra of elapse rates. We experiment to study the seismological characteristics of different segments with different elapse rates. We employed the de-clustered earthquake catalog for the calculation of b values for each segment. The analysis revealed that different segments have similar b values,which implies that, although different segments have different periods of earthquake recurrence, the 'natural time' for the whole fault system elapses with a homogeneous pace. We extended the earthquake potential score(EPS)for nowcasting earthquakes to a quasi-EPS(q EPS). It is found that q EPS increases with the increase of elapse rates,albeit for those fault segments whose elapse rates have exceeded 1, q EPS may better reflect the seismic hazard.

全球及主要构造带大震活动状态研究

在定性分析全球、环太平洋地震带、低纬度环球剪切带和大三角地区(我国大陆西部及邻区)地震活动特征的基础上,使用Morlet小波变换对其周期成分进行了定量计算,并应用Nowcasting方法对各研究区域当前大震风险进行定量评估.结果显示,①全球大震活动显著周期为48.6年,8.5级以上地震的潜在危险性评分为0.91.2004年开始的大震活跃时段可能仍将持续,发生8.5级以上地震危险性较大.②环太平洋地震带地震活动显著周期为48.6年,其大震活动起伏特征与全球大体一致,8.5级以上地震的潜在危险性评分为0.91.2010年开始的8.5级以上地震活跃时段持续时间尚短,发生8.5级以上地震的危险性较大.③低纬度环球剪切带地震活动显著周期为33.1年,反映了该区8.0级以上地震的起伏活动,8.0级以上地震的潜在危险性评分为0.46.2004年开始的活跃时段可能趋于结束,未来10年左右该区发生8.0级以上地震的可能性较小.④大三角地区地震活动显著周期为23.4年,发生7.8级以上地震的潜在危险性评分为0.72.2001年以来该区7.8级以上地震活跃,其活动状态与20世纪前50年类似.23.4年的周期成分反映了20世纪前50年7.8级以上地震活动韵律特征.2015年兴都库什7.8级地震后,该区可能进入了7.8级以上地震相对平静时段,该时段可能持续7~12年,未来几年存在发生7.8级以上地震的可能,但危险性较小.

奥运青岛帆船赛场气象服务关键技术分析

帆船比赛是与气象关系最密切的比赛项目。以青岛 8,9月份的天气为分析、预报的对象 ,在深入分析及参照悉尼 2 0 0 0年奥运气象服务的预报技术组成、实施方法与技术要求的基础上 ,分析讨论了青岛奥运帆船比赛气象服务体系中关键性环节的技术要求与技术构成。主要研究问题包括 :( 1 ) GIS青岛气象资料预报服务系统 ;( 2 )青岛地区站点的布设及资料观测系统 ;( 3 )

美国两大区域联储GDP实时预测经验做法及其启示

针对经济不确定性增加,美国亚特兰大联储和纽约联储分别开发了GDPNow和Nowcasting工具,对美国GDP开展实时预测,为美联储货币政策决策提供支持。本文对两种GDP实时预测方法进行了深入研究,详细阐述了其预测方法、模型特点和采用的主要指标,并对我国开展GDP实时预测提出了相应的建议。

Satellite-based Observational Study of the Tibetan Plateau Vortex:Features of Deep Convective Cloud Tops

In this study, an east-moving Tibetan Plateau vortex(TPV) is analyzed by using the ERA-5 reanalysis and multi-source satellite data, including FengYun-2 E, Aqua/MODIS and CALIPSO. The objective is to demonstrate:(i) the usefulness of multi-spectral satellite observations in understanding the evolution of a TPV and the associated rainfall, and(ii) the potential significance of cloud-top quantitative information in improving Southwest China weather forecasts. Results in this study show that the heavy rainfall is caused by the coupling of an east-moving TPV and some low-level weather systems [a Plateau shear line and a Southwest Vortex(SWV)], wherein the TPV is a key component. During the TPV's life cycle, the rainfall and vortex intensity maintain a significant positive correlation with the convective cloud-top fraction and height within a 2.5?radius away from its center. Moreover, its growth is found to be quite sensitive to the cloud phases and particle sizes. In the mature stage when the TPV is coupled with an SWV, an increase of small ice crystal particles and appearance of ring-and U/V-shaped cold cloud-top structures can be seen as the signature of a stronger convection and rainfall enhancement within the TPV. A tropopause folding caused by ageostrophic flows at the upper level may be a key factor in the formation of ring-shaped and U/V-shaped cloud-top structures. Based on these results, we believe that the supplementary quantitative information of an east-moving TPV cloud top collected by multi-spectral satellite observations could help to improve Southwest China short-range/nowcasting weather forecasts.