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.

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.

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.

Application of Multi-Scale Tracking Radar Echoes Scheme in Quantitative Precipitation Nowcasting

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.

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.

Development of typhoon driven wave nowcasting model in Southeast China Sea

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.

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.

基于分层生成对抗网络的短临降水预报方法研究

本文使用深度学习方法中的生成对抗网络(GAN)来提升短临降水预报的准确率,提出了一个基于历史雷达回波图序列预测未来雷达回波图序列的分层生成对抗网络(HGAN)方法。HGAN方法由全局生成器和局部鉴别器两部分组成,全局生成器以多子网的层次结构构建,采用上采样过程训练模型,捕捉雷达回波的演变趋势,有利于生成清晰的未来雷达回波图。局部鉴别器根据局部区域将预测的雷达回波图与观测的雷达回波图区分开,并引入缓冲区机制,保存历史预测序列,使最终预测的结果更加符合时序性。两者以对抗的方式加以训练,得到的模型能够生成足够清晰且接近真实的未来雷达回波序列,对于回波强度极值和范围的刻画更为准确。对HGAN和GAN进行测试集检验及个例分析,分析结果验证了HGAN对雷达回波预测的有效性。同时在检验反射率阈值相同的情况下,HGAN的临界成功指数命中率高于GAN,而虚警率低于GAN,且在相同预测时长下,HGAN结构相似性指数(SSIM)优于GAN。

调频Guassian小波在从NOWCAST图获取SST中的应用

温跃层对于潜艇的水下航行和战斗具有十分重要的意义。由于中国的现状,无法获取大量高精度的SST资料,给研究带来了很大的障碍。该文针对数据获取比较困难和数据精度不高的现状,提出了将图形的颜色表示从RGB空间转换到HIS空间来构造一时间序列,并对此序列用调频Guassian小波来进行分解和重构,其所得的低频信号能很好地还原出颜色变量中所包含地温度信息。经实验证明,该方法比传统的处理方法有了十分明显的提高,所得到的温度数据精度高,能满足温跃层研究的需要。

基于UI-LSTM模型的短时降水预测研究

降水临近预报是为了预测未来短时间的降雨量。现有大多数基于循环神经网络(Recurrent Neural Network,RNN)的降水预报模型,采用单一的卷积核对输入和隐藏状态的特征进行提取存在局部性,不能捕获雷达回波图中复杂的物理变化,且未有效提取时空相关性和对强降雨区域的精准预测。针对现有模型存在的问题,提出了UI-LSTM模型用于降水临近预报,能够有效地提取雷达回波序列的时空相关性,采用了U形结构,同时使用跳过连接进行特征拼接,学习到整个雷达回波图的上下文语义信息,且将浅层和深层信息进行特征融合。加入了Inception结构来代替ConvLSTM细胞结构中输入到输入和状态到状态的卷积,通过不同大小的卷积核,有效提取输入,隐藏状态的特征。在公开数据集(CIKM AnalytiCup 2017)进行实验并与其他模型进行对比实验。实验结果表明,UI-LSTM模型在HSS、CSI、MAE和SSIM指标整体上要远高于其他对比模型,且提高强降水天气预测的准确率。

光流法雷达外推产品在突发强降水预报中的应用

地形条件复杂的山丘区中小河流洪水突发性强、汇流时间短,高准确率和长时效性降水短时临近预报产品对提高突发洪水预报精度尤为关键。以2021年9月河南省鸭河口水库出现的千年一遇特大洪水为例,利用国家气象信息中心提供的三源融合格点实况降水资料,检验基于改进光流法的雷达外推降水预报产品在本次洪水过程中0~1 h和0~3 h降水预报的TS评分和预报偏差。结果表明:(1)改进光流法在0~1 h的逐小时降水预报上较为精准,累计雨量在50 mm以下时,TS评分在0.45~0.85之间;雨量在50~70 mm之间时,TS评分在0.35~0.70之间;雨量在70 mm以上时,TS评分在0.25~0.35之间。50 mm以上雨量有较高TS评分表现出改进光流法在极端强降水预报中的优势性。(2)改进光流法在0~3 h的降水预报上,累计雨量在50 mm以下时,TS评分在0.55~0.85之间;在50 mm以上时,TS评分在0.35~0.75之间。该降水预报产品不仅对极端性降水预报效果较好,且预报时效长达3 h,可为防洪调度提供更长的决策时间。(3)改进光流法在0~3 h的降水预报产品与融合实况格点降水相比,雨量在20 mm以下的预报结果比较接近,平均绝对误差在10 mm以内;雨量在20 mm以上时,随雨量增大,平均误差、平均绝对误差、均方根误差均逐渐增大。(4)改进光流法在0~3 h的降水预报产品对影响范围小、降水强度大、维持时间长、累计雨量大的极端强降水有较好的预报表现。研究成果可为洪水预报模型提供一种较为可靠的降水输入预报。

基于数据同化技术构建传染病现报模型——以新冠疫情为例

考虑政府的管控隔离措施以及疫苗的保护作用,在传统动力学模型SEIR基础上重构背景模型SEIQRDV,同时,融合集合卡尔曼滤波(EnKF)技术,建立疫情现报同化模型SEIQRDV-EnKF,并利用我国湖北省、美国和印度的新冠疫情数据评估模型性能.结果表明,同化模型SEIQRDV-EnKF预测的感染、康复人数与实际情况基本一致,预测均方根误差和平均绝对百分比误差均较模型SEIQRDV低;克服了传统动力学模型SEIR的局限性,能利用较短的历史数据预测较为准确的疫情发展趋势,可在重大疫情发生时为地方政府的决策部署提供技术支撑.

PPNet:基于预先预测的降雨短时预测模型

降雨短时预测一直以来都是气象预测问题中的热点问题。传统的预测方法基于数值天气预测模型展开预报,但近些年利用深度学习展开基于雷达回波图的降雨短时预测方法受到了广大研究者的关注。其中,时序预测网络存在不能并行计算导致耗时过长的问题且存在梯度爆炸问题。全卷积网络可以解决上述两个问题,但是却不具备时序信息提取的能力。因此,该文以泰勒冻结假设为理论依据,提出一个基于预先预测辅助推断结构的2维全卷积网络(PPNet)。网络先行提取粗粒度时序信息与空间信息,然后利用全卷积结构细化特征粒度,有效缓解2维卷积网络不能提取时序信息的缺陷。此外,该文还提供一种时序特征约束器对预先预测特征进行时间维度的特征约束,使预测特征更倾向于真实特征。消融实验证明所提预先预测辅助推断结构和时序特征约束器具有优秀的时序特征能力,可以提升网络对时序信息的敏感度。与目前最好的降雨预测算法或视频预测算法相比,该文网络均取得较好结果,特别在暴雨指标上达到最优。

基于Halo注意力机制的双阶段临近降水预报网络

先前基于深度学习进行临近降水预报的方法试图在统一架构中建模雷达回波的时空演变,然而,这些方法可能难以完全捕捉到这种复杂的时空关系.本文提出了一种基于Halo注意力机制的双阶段临近降水预报网络,该网络将降水预测的时空演变过程分为运动趋势预测和空间外观重建两个阶段.首先,可学习光流模块对雷达回波的运动趋势进行建模并生成粗略的预测结果.其次,特征重建模块对历史雷达回波序列的空间外观变化建模并对粗粒度预测结果的空间外观进行特征细化重建,生成精细的雷达回波图.通过在CIKM数据集上的实验表明,本文所提出的方法与主流方法相比,平均的海德克技能得分和关键成功指数分别提高了4.60%和3.63%,达到了0.48和0.45;结构相似性提高了4.84%,达0.52;均方误差降低了6.13%,达70.23.

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.

台湾地区强震活动特征分析

基于定性分析和Morlet小波分析方法,研究了我国台湾地区的主要构造带和强震分布特征。1900年以来台湾地区M_(S)≥7.0地震存在三个活跃时段:第一个活跃时段为1902—1925年,长达近23年;第二个活跃时段为1935—1978年,约43年;第三个活跃时段为1986—2006年,时长20年。台湾地区自2006年12月26日恒春海域发生M_(S)7.2地震之后,M_(S)≥7.0地震平静已近16年,为历史最长平静时段,存在开始新的活跃时段的可能。从区域分布看,台东地震带M_(S)≥6.9地震具有六个活动周期,大部分活动周期平均约为16年,每个活动周期均包含活跃和平静时段,所有M_(S)≥6.9地震均发生在活跃时段,统计显示台东地震带的活动强度自2002年进入第六个活动周期后逐渐减弱,直到2022年9月份台湾东带才再次发生M_(S)6.9地震,可能进入了新一轮活跃时段。台湾西带M_(S)≥6.0地震存在92年左右和14年左右的周期,1901—1993年为一个活跃-平静大周期(92年左右),1994年开始新一轮的大周期活动,同时,大周期又包含平均周期为14年左右的小周期。临近预报(nowcasting)方法计算的小震积累水平显示,台湾东带M_(S)≥7.0地震和台湾西带MS≥6.0地震具有较高的发震背景概率,台湾地区强震在年尺度上与华南地区中强地震具有一定的对应关系。

An Assessment of a Nowcast/Forecast System for the Straits of Florida/Florida Current Regime

The Florida Current (FC) largely fills the Straits of Florida and is variable on a broad spectrum of time and space scales. Some portions of the variability are due to variable forcing by tides, winds, heating/cooling, and throughflow; other portions are due to intrinsic instabilities of the FC. To predict, as well as to better understand this complex regime, a nowcast/forecast system (East Florida Shelf Information System (EFSIS)) has been implemented and assessed (http://efsis. rsmas. miami. edu). EFSIS is based on an implementation of the Princeton Ocean Model (POM) with mesoscale - admitting resolution on a curvilinear grid. It is forced by a mesoscale numerical weather prediction system (called Eta) run operationally by the National Centers for Environmental Prediction (NCEP), eight tidal constituents from a global tidal model, and lateral boundary conditions from an operational global ocean prediction model, i.e., the Navy Coastal Ocean Model (NCOM).Real-time observations of coastal sea level, coastal sea surface temperature, coastal HF radar-derived surface current maps,and FC volume transport are used to verify and validate EFSIS. EFSIS is part of an evolving strategy for real-time predictive coastal ocean modeling methodology, and for fostering the understanding of the variability of the regime on several time and space scales. Here, some of the verification and validation results are provided, as well as diagnostic analyses of dynamical servations and numerical circulation models to yield a credible sequence of synoptic views of coastal ocean circulation for the first time.