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The article describes the nowcasting systems used by European National Meteorological Services (NMS). These systems use radar data and prediction models to forecast variables such as cloudiness, radiation, temperature, humidity, and wind. Some examples of these systems are PIAF, INCA, MEANDER, SESORA, RAVAKE, SAPHIR, SINFONY, and IMPROVER. These systems are used to predict solar radiation, precipitation probability, and other purposes related to renewable energy and agriculture. European NMS are working on developing seamless systems and applying artificial intelligence to improve prediction accuracy. [Extracted from the article]

The Atlantic Meridional Overturning Circulation (AMOC) is a significant component of the global ocean system, which has so far ensured a relatively warm climate for the North Atlantic and mild conditions in regions, such as Western Europe. The AMOC is also critical for the global climate. The complexity of the dynamical system underlying the AMOC is so vast that a long-term assessment of the potential risk of AMOC collapse is extremely challenging. However, short-term prediction can lead to accurate estimates of the dynamical state of the AMOC and possibly to early warning signals for guiding policy making and control strategies toward preventing AMOC collapse in the long term. We develop a model-free, machine-learning framework to predict the AMOC dynamical state in the short term by employing five datasets: MOVE and RAPID (observational), AMOC fingerprint (proxy records), and AMOC simulated fingerprint and CESM AMOC (synthetic). We demonstrate the power of our framework in predicting the variability of the AMOC within the maximum prediction horizon of 12 or 24 months. A number of issues affecting the prediction performance are investigated. [ABSTRACT FROM AUTHOR]

Rainfall forecasting plays a key role in mitigating environmental risks in urban areas, which are subject to increasing hydrogeological risk due to transformations in the urban landscape. We present a new technique for probabilistic precipitation nowcasting by generating an ensemble of equiprobable forecasts, which is especially useful for weather radars with limited spatial range, and that can be used operationally on devices with low computational capacity. The ensemble members are obtained by a novel stochastic noise generation process, consistent with the spatial scales not resolved by the prediction model, which allows continuous downscaling of the output of a deep generative neural network. Through an in-depth analysis of the results, for precipitation accumulated in the first hour, measured by all the most robust skill indicators, extended to an entire year of data at 5-min time resolution, we demonstrate that the proposed procedure is able to provide calibrated, reliable, and sharp ensemble rainfall forecasts with a quality comparable or superior to the state-of-the-art classical alternative optical flow technique. The ensemble generation procedure we propose is sufficiently general to be applied in principle to other deterministic architectures as well, thus enabling their generalization in probabilistic terms. [ABSTRACT FROM AUTHOR]

The prediction of precipitation patterns at high spatio-temporal resolution up to two hours ahead, also known as precipitation nowcasting, is of great relevance in weather-dependant decision-making and early warning systems. In this study, we are aiming to provide an efficient and easy-to-understand model - CLGAN, to improve the nowcasting skills of heavy precipitation events with deep neural networks for video prediction. The model constitutes a Generative Adversarial Network (GAN) architecture whose generator is built upon an u-shaped encoder-decoder network (U-Net) equipped with recurrent LSTM cells to capture spatio-temporal features. A comprehensive comparison among CLGAN, and baseline models optical flow model DenseRotation as well as the advanced video prediction model PredRNN-v2 is performed. We show that CLGAN outperforms in terms of scores for dichotomous events and object-based diagnostics. The ablation study indicates that the GAN-based architecture helps to capture heavy precipitation events. The results encourage future work based on the proposed CLGAN architecture to improve the precipitation nowcasting and early-warning systems. [ABSTRACT FROM AUTHOR]

This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values. [ABSTRACT FROM AUTHOR]

To support short-range weather forecasts, a high-resolution model (1km) is developed and technically upgraded in the South China Regional Center, including the improvement of the 3D reference scheme and the predictor-corrector method for semi-implicit semi-Lagrangian (SISL) in model dynamical core, as well as the improvement of physical parameterization. Furthermore, the multi-process parallel I/O and parallel nudging techniques are developed and have facilitated rapid updating in the assimilation prediction system and fast-output post processing process. The experimental results show that the improved 3D reference scheme and upgraded physic schemes can effectively improve the prediction accuracy and stability with a longer integration time step. The batch test shows that the precipitation forecast performance of 1-km model is significantly better than that of 3-km model. The 1-km model is in operation with a rapidly updating cycle at 12-minute intervals, which can be applied to short-range forecasts and nowcasting. [ABSTRACT FROM AUTHOR]

This article investigates the accuracy gains that can be made by applying bootstrap aggregation to K nearest-neighbor nowcasting of Swedish gross domestic product. Using both a simulation-based approach and a theoretical approach, the results indicate that substantial nowcasting accuracy gains can be made using bootstrap aggregation when considering one neighbor in the nowcasting algorithm, that is, when K = 1. Furthermore, a comparison of the simulationbased and theoretical approaches to bootstrap aggregation indicates that using as few as 25 bootstrap replications can carry, in expectation, the simulationbased results close to the theoretical results. [ABSTRACT FROM AUTHOR]

In the United Kingdom, geomagnetically induced currents (GICs) are calculated from thin‐sheet electrical conductivity models. In the absence of conductivity models, time derivatives of magnetic fields are sometimes used as proxies for GIC‐related electric fields. An alternative approach, favored in the United States, is to calculate storm time electric fields from time‐independent impedance tensors computed from an array of magnetotelluric (MT) sites and storm time magnetic fields recorded at geomagnetic observatories or assumed from line current models. A paucity of direct measurements of storm time electric fields has restricted validation of these different techniques for nowcasting electric fields and GICs. Here, we present unique storm time electric field data from seven MT sites in Scotland that recorded before, during, and after the September 2017 magnetic storm. By Fourier transforming electric field spectra computed using different techniques back to the time domain, we are able to make direct comparisons with these measured storm time electric field time series. This enables us to test the validity of different approaches to nowcasting electric fields. Our preferred technique involves frequency domain multiplication of magnetic field spectra from a regional site with a local impedance tensor that has been corrected for horizontal magnetic field gradients present between the local site and the regional site using perturbation tensors derived from geomagnetic depth sounding (GDS). Scatter plots of scaling factors between measured and nowcasted electric fields demonstrate the importance of coupling between the polarization of the storm time magnetic source field and Earth's direction‐dependent deep electrical conductivity structure. Plain Language Summary: The Sun emits a constant stream of charged particles that interact with the Earth's magnetic field in complex ways. Sometimes, heightened solar activity in the form of coronal mass ejections results in extreme fluctuations of Earth's magnetic fields called magnetic storms. Time‐varying magnetic fields associated with magnetic storms induce electric fields within the Earth, the magnitudes of which depend on Earth's electrical conductivity structure. These electric fields are potentially hazardous to our power distribution and communication networks, because they drive quasi‐dc currents along their electrically conductive transmission lines. Therefore, several governments around the world are prioritizing research into nowcasting and forecasting the ground‐based impacts of magnetic storms. However, at present, there is little storm time electric field data available against which to compare the electric fields that we nowcast from our models. Here, we use simultaneous recordings of the electric and magnetic fields during the 8 September 2017 magnetic storm to investigate how closely electric fields nowcasted using different techniques match those observed. We use this comparative study to assess the most advantageous approach to nowcasting and forecasting electric fields. Key Points: Unique electromagnetic data from the 8 September 2017 magnetic storm in Scotland is used to validate storm time electric field estimationCoupling between the polarization of the storm time magnetic source field and Earth's direction‐dependent conductivity is demonstratedA method for correcting impedance tensors for horizontal magnetic field gradients to improve electric field nowcasting is presented [ABSTRACT FROM AUTHOR]

Rapid-scan polarimetric data analysis of the dissipation of a likely violent supercell tornado that struck near Sulphur, Oklahoma, on 9 May 2016 is presented. The Rapid X-band Polarimetric Radar was used to obtain data of the tornado at the end of its mature phase and during its entire dissipation phase. The analysis is presented in two parts: dissipation characteristics of the tornadic vortex signature (TVS) associated with the tornado and storm-scale polarimetric features that may be related to processes contributing to tornado dissipation. The TVS exhibited near-surface radial velocities exceeding 100 m s−1 multiple times at the end of its mature phase, and then underwent a two-phased dissipation. Initially, decreases in near-surface intensity occurred rapidly over a ~5-min period followed by a slower decline in intensity that lasted an additional ~12 min. The dissipation of the TVS in time and height in the lowest 2 km above radar level and oscillatory storm-relative motion of the TVS also are discussed. Using polarimetric data, a well-defined low reflectivity ribbon is investigated for its vertical development, evolution, and relationship to the large tornadic debris signature (TDS) collocated with the TVS. The progression of the TDS during dissipation also is discussed with a focus on the presence of several bands of reduced copolar correlation coefficient that extend away from the main TDS and the eventual erosion of the TDS as the tornado dissipated. Finally, TVS and polarimetric data are combined to argue for the importance of a possible internal rear-flank downdraft momentum surge in contributing to the initial rapid dissipation of the tornado. [ABSTRACT FROM AUTHOR]

The overview of papers dealing with the history of development and the current state of thunderstorm nowcasting technologies is presented. A role of information on thunderstorms obtained from different sources (satellites, radars, lightning detectors, ground-based microwave radiometers) and the significance of output data of numerical weather prediction models for thunderstorm nowcasting are shown. The features of thunderstorm nowcasting systems used in the national meteorological services are discussed. [ABSTRACT FROM AUTHOR]

Current algorithms for the real‐time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind‐based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short‐term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer‐term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions. Key Points: Short‐term predictions should be made with solar wind data and long‐term predictions with recurrence forecastThe accuracy of predictions is much lower during disturbed geomagnetic conditionsRebalancing allows us to improve the accuracy of predictions during disturbed geomagnetic conditions [ABSTRACT FROM AUTHOR]

We statistically evaluated the rainfall amount predicted by VIL Nowcast, which is designed using vertically integrated liquid water content (VIL) through experimental data obtained over five months from June to October of 2015. The accuracy of predictions for the start time of heavy rain, which are vital for issuing warnings concerning localized heavy rain, was also reviewed. We revealed that VIL Nowcast could predict the rainfall amount more accurately than conventional methods up to the first 20 min of the evaluation period (30 min in total) with superior accuracy for the start time of severe rain from isolated convective cells in the first 10 min. [ABSTRACT FROM AUTHOR]

Abstract The study presents a new method nowcasting precipitation called the Ensemble Tree Method (ETM), which gives probability forecast of accumulated precipitation based on the extrapolation of radar reflectivity. ETM combines a tree model with a Bootstrap technique. It forecasts the probability that the hourly accumulated precipitation exceeds a given threshold for cells of 3 by 3 km size. ETM was tested using radar reflectivity data from July 2012 in a domain of 489 km by 291 km covering the Czech Republic (Central Europe). While forecasting, we considered a lead time of up to 180 min having a time step of 30 min and four precipitation thresholds (0.1, 1.0, 5.0, and 10.0 mm). ETM provided us forecasts of the probability of exceeding an hourly precipitation threshold from 0 to 60 min, 30 to 90 min, ..., and 120 to 180 min. The performance of ETM was assessed using a skill score derived from the mean-square-error, and was compared with the performance of forecasts based on a logistic regression that was used as reference forecast. We demonstrated that the prediction of ETM is better than that of the reference forecast. The main advantage of ETM is that the ETM reflects the uncertainty of forecast better as compared to the overconfident reference forecasts, which is particularly true for the higher precipitation thresholds. Thus, despite low predicted probabilities, the forecasts given by ETM seem more realistic. Highlights • A method of probabilistic precipitation nowcasting is proposed. • The probability nowcasting is based on ensemble approach to forecast precipitation. • The method uses an extrapolation technique and a decision tree model. [ABSTRACT FROM AUTHOR]

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-2E, 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. [ABSTRACT FROM AUTHOR]

Highlights • Strengths and weaknesses of spatio-temporal VAR and CMV models are analyzed. • CMV forecasts present consistently lower rMAEs excepted for few sites. • STVAR is more accurate than CMV in regions with strong micro-climatic features. • A hybrid spatio-temporal models with a smart blend is argued. Abstract This paper examines two spatio-temporal approaches for short-term forecasting of global horizontal irradiance using gridded satellite-derived irradiances as experimental support. The first approach is a spatio-temporal vector autoregressive (STVAR) model combined with a statistical process for optimum selection of input variables. The second is an existing operational cloud motion vector (CMV) model. An evaluation of the predictive performance of these models is presented for a case study area in the Caribbean Islands. This region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting context. Using scaled persistence as a reference, we benchmark the performance of the two spatio-temporal models over an extended 220 × 220 km domain, and for three specific, climatically distinct locations within this domain. We also assess the influence of intra-day solar resource variability on model performance. Finally, we present preliminary evidence that a blend of CMV and STVAR forecasts leads to improved accuracy under all conditions. [ABSTRACT FROM AUTHOR]

Highlights • ASI based nowcasting system with individual 3-D cloud objects. • Comparison of three cloud height detection approaches against reference ceilometer. • Comparison of three cloud tracking approaches against reference shadow camera system. • Overall mean absolute error of 648 m (height), 1.3 m/s (speed) and 16.2° (direction) Abstract Solar irradiance nowcasts can be derived with sky images from all sky imagers (ASI) by detecting and analyzing transient clouds, which are the main contributor of intra-hour solar irradiance variability. The accuracy of ASI based solar irradiance nowcasting systems depends on various processing steps. Two vital steps are the cloud height detection and cloud tracking. This task is challenging, due to the atmospheric conditions that are often complex, including various cloud layers moving in different directions simultaneously. This challenge is addressed by detecting and tracking individual clouds. For this, we developed two distinct ASI nowcasting approaches with four or two cameras and a third hybridized approach. These three systems create individual 3-D cloud models with unique attributes including height, position, size, optical properties and motion. This enables us to describe complex multi-layer conditions. In this paper, derived cloud height and motion vectors are compared with a reference ceilometer (height) and shadow camera system (motion) over a 30 day validation period. The validation data set includes a wide range of cloud heights, cloud motion patterns and atmospheric conditions. Furthermore, limitations of ASI based nowcasting systems due to image resolution and image perspective constrains are discussed. The most promising system is found to be the hybridized approach. This approach uses four ASIs and a voxel carving based cloud modeling combined with a cloud segmentation independent stereoscopic cloud height and tracking detection. We observed for this approach an overall mean absolute error of 648 m for the height, 1.3 m/s for the cloud speed and 16.2° for the motion direction. [ABSTRACT FROM AUTHOR]

Seismic nowcasting uses counts of small earthquakes as proxy data to estimate the current dynamical state of an earthquake fault system. The result is an earthquake potential score that characterizes the current state of progress of a defined geographic region through its nominal earthquake "cycle." The count of small earthquakes since the last large earthquake is the natural time that has elapsed since the last large earthquake (Varotsos et al., 2006, https://doi.org/10.1103/PhysRevE.74.021123). In addition to natural time, earthquake sequences can also be analyzed using Shannon information entropy ("information"), an idea that was pioneered by Shannon (1948, https://doi.org/10.1002/j.1538‐7305.1948.tb01338.x). As a first step to add seismic information entropy into the nowcasting method, we incorporate magnitude information into the natural time counts by using event self‐information. We find in this first application of seismic information entropy that the earthquake potential score values are similar to the values using only natural time. However, other characteristics of earthquake sequences, including the interevent time intervals, or the departure of higher magnitude events from the magnitude‐frequency scaling line, may contain additional information. Plain Language Summary: Earthquake nowcasting has been developed to determine the current level of earthquake risk for seismically active areas. Historically, statistical communication theory has been developed to analyze the information content of signals involved in telecommunications and computer systems. We combine these techniques to analyze the information contained in earthquake magnitudes for great earthquakes and mega‐tsunamis. Key Points: Nowcasting was previously used to analyze earthquake data to determine current seismic risk to global megacities for magnitudes M ≥ 6We extend the method to computing hazard from great earthquake and mega‐tsunami sources arising from earthquakes with magnitudes M > 7.9We also develop nowcasting methods using the information entropy of earthquake sequences [ABSTRACT FROM AUTHOR]

In this study, multiple linear regression (MLR) and the generalized additive model (GAM) approaches were used to build statistical models for 6 hr nowcasts of road surface temperature (RST) in the northeast of Vienna, Austria. GAMs were more suitable for historical analysis, particularly for decomposing the terms to identify the different influences of the meteorological covariates on RST. By contrast, for RST nowcasting, the simpler and more robust MLR models are recommended, with better applicability for real‐time operational runs. In MLR models, the forecasted air temperature was the most prominent predictor, followed by the measured RST. In independent testing, the MLR models showed better prediction skill, with daily root‐mean‐square error (RMSE) around 1°C. In accordance with the linear correlativity, the MLR models were built with more predictors for daytime than at night but still generated a larger RMSE at midday. Furthermore, the MLR models could reproduce the correct diurnal variation and could forecast RST below freezing point better than above 0°C. Four case studies, i.e. snowy, cold front, cloudy and sunny, were diagnosed in detail. The predicted RSTs were close to the measurements and depicted the trend well, including the persistent and rapid cooling (warming) and correct diurnal variation. In independent testing, the models showed better prediction skill, with daily root‐mean‐square error around 1°C. The multiple linear regression (MLR) models could reproduce the correct diurnal variation and could forecast road surface temperature (RST) below freezing point better than that above 0°C. Four case studies, i.e. snowy, cold front, cloudy and sunny, were diagnosed in detail. The predicted RSTs were close to the measurements and depicted the trend well, including persistent and rapid cooling (warming) and correct diurnal variation. The percentage of predictive errors from MLR UP prediction in winter 2015, averaged over three sites for (a) 01 hr, (b) 02 hr, (c) 03 hr, (d) 04 hr, (e) 05 hr and (f) 06 hr, were calculated and shown. The mean correlation co‐efficient (mcc) is shown at the top of each plot. The percentages for the interval ± 1 and ± 2°C are plotted as lines with circles and squares, respectively. [ABSTRACT FROM AUTHOR]

With the rapid increase and availability of seismic data, an automatic, transparent and regular way of earthquake hazard estimation strategy is highly desirable in many seismically active large geographical regions. In this paper, we implement a novel method of nowcasting (Rundle et al., 2016) that can indirectly assess the current progression of a region through its earthquake cycle of large events. Nowcasting differs from the method of forecasting in which future earthquake probabilities are calculated. Using statistics of natural times, counts of small earthquakes between large earthquakes in a defined region, nowcasting provides an earthquake potential score (EPS) to enable scientists and city planners a snapshot of the current level of earthquake hazard in the region. Applied to a number of selected major cities in the northwest Himalaya and surrounding regions, we found that the EPS values corresponding to M ≥ 6 events in New Delhi, Chandigarh, Dehradun and Shimla reach about 0.56, 0.87, 0.85 and 0.88, respectively. These estimated scores thus indicate that New Delhi is about half-way through its cycle for magnitude 6.0 or higher earthquakes, while Dehradun is about 85 percent of the way through its cycle. Towards the end, we discuss some implications and applications of these nowcast values to improve the present earthquake hazard assessment practice in the study region. [ABSTRACT FROM AUTHOR]

Abstract: Because of the cloud‐induced variability of the solar resource, the growing contributions of photovoltaic plants to the overall power generation challenges the stability of electricity grids. To avoid blackouts, administrations started to define maximum negative ramp rates. Storages can be used to reduce the occurring ramps. Their required capacity, durability, and costs can be optimized by nowcasting systems. Nowcasting systems use the input of upward‐facing cameras to predict future irradiances. Previously, many nowcasting systems were developed and validated. However, these validations did not consider aggregation effects, which are present in industrial‐sized power plants. In this paper, we present the validation of nowcasted global horizontal irradiance (GHI) and direct normal irradiance maps derived from an example system consisting of 4 all‐sky cameras (“WobaS‐4cam”). The WobaS‐4cam system is operational at 2 solar energy research centers and at a commercial 50‐MW solar power plant. Besides its validation on 30 days, the working principle is briefly explained. The forecasting deviations are investigated with a focus on temporal and spatial aggregation effects. The validation found that spatial and temporal aggregations significantly improve forecast accuracies: Spatial aggregation reduces the relative root mean square error (GHI) from 30.9% (considering field sizes of 25 m2) to 23.5% (considering a field size of 4 km2) on a day with variable conditions for 1 minute averages and a lead time of 15 minutes. Over 30 days of validation, a relative root mean square error (GHI) of 20.4% for the next 15 minutes is observed at pixel basis (25 m2). Although the deviations of nowcasting systems strongly depend on the validation period and the specific weather conditions, the WobaS‐4cam system is considered to be at least state of the art. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study proposes an algorithm that combines ensemble numerical weather‐prediction model data and aircraft flight data in a wind nowcasting system for safe and efficient aircraft operation. It uses an ensemble‐weighted average method based on sequential importance sampling (SIS), which is a particle filter method for forecasting the wind field in real time. SIS is applied to the ensemble forecast data and control run data of the European Centre for Medium‐Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), Korea Meteorological Administration (KMA), National Centers for Environmental Prediction (NCEP) and United Kingdom Met Office (UKMO) for the two case studies that use flight data from 72 commercial aircraft flights. The results show that SIS can forecast better than the other four methods: direct ensemble average (DEA), elite strategy (ES), and selective ensemble average (SEAV) and weighted average (SEWE), with average improvements in forecast performance of about 10–15%, even at 300 min ahead. In addition, the overall forecast performance between the forecast wind and observation of the radiosonde of SIS was slightly better than DEA. In both cases, the forecast performance was significantly improved on points along the flight path of the aircraft used for this study. Case analyses and the impact of differences in the hyper‐parameters of SIS on forecast performance are also presented in this study. [ABSTRACT FROM AUTHOR]

Nowcasting short-duration (i.e., <6 h) rainfall (SDR) events is examined using total [i.e., cloud-to-ground (CG) and intra-cloud (IC)] lightning observations over the Beijing Metropolitan Region (BMR) during the warm seasons of 2006–2007. A total of 928 moderate and 554 intense SDR events, i.e., with the respective hourly rainfall rates (HRR) of 10–20 and ≥20 mm h −1 , are utilized to estimate sharp-increasing rates in rainfall and lightning flash, termed as rainfall and lightning jumps, respectively. By optimizing the parameters in a lightning jump and a rainfall jump algorithm, their different jump intensity grades are verified for the above two categories of SDR events. Then, their corresponding graded nowcast-warning models are developed for the moderate and intense SDR events, respectively, with a low-grade warning for hitting more SDR events and a high-grade warning for reducing false alarms. Any issued warning in the nowcast-warning models is designed to last for 2 h after the occurrence of a lightning jump. It is demonstrated that the low-grade warnings can have the probability of detection (POD) of 67.8% (87.0%) and the high-grade warnings have the false alarms ratio (FAR) of 27.0% (22.2%) for the moderate (intense) SDR events, with an averaged lead time of 36.7 (52.0) min. The nowcast-warning models are further validated using three typical heavy-rain-producing storms that are independent from those used to develop the models. Results show that the nowcast-warning models can provide encouraging early warnings for the associated SDR events from the regional to meso-γ scales, indicating that they have a great potential in being applied to the other regions where high-resolution total lightning observations are available. [ABSTRACT FROM AUTHOR]

This paper proposes a new object-based storm tracking algorithm, based upon TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting). TITAN is a widely-used convective storm tracking algorithm but has limitations in handling small-scale yet high-intensity storm entities due to its single-threshold identification approach. It also has difficulties to effectively track fast-moving storms because of the employed matching approach that largely relies on the overlapping areas between successive storm entities. To address these deficiencies, a number of modifications are proposed and tested in this paper. These include a two-stage multi-threshold storm identification, a new formulation for characterizing storm's physical features, and an enhanced matching technique in synergy with an optical-flow storm field tracker, as well as, according to these modifications, a more complex merging and splitting scheme. High-resolution (5-min and 529-m) radar reflectivity data for 18 storm events over Belgium are used to calibrate and evaluate the algorithm. The performance of the proposed algorithm is compared with that of the original TITAN. The results suggest that the proposed algorithm can better isolate and match convective rainfall entities, as well as to provide more reliable and detailed motion estimates. Furthermore, the improvement is found to be more significant for higher rainfall intensities. The new algorithm has the potential to serve as a basis for further applications, such as storm nowcasting and long-term stochastic spatial and temporal rainfall generation. [ABSTRACT FROM AUTHOR]

Nowcasting is a new method of statistically classifying seismicity and seismic risk (Rundle et al. 2016). In this paper, the method is applied to the induced seismicity at the Geysers geothermal region in California and the induced seismicity due to fluid injection in Oklahoma. Nowcasting utilizes the catalogs of seismicity in these regions. Two earthquake magnitudes are selected, one large say $$M_{\lambda } \ge 4$$ , and one small say $$M_{\sigma } \ge 2$$ . The method utilizes the number of small earthquakes that occurs between pairs of large earthquakes. The cumulative probability distribution of these values is obtained. The earthquake potential score (EPS) is defined by the number of small earthquakes that has occurred since the last large earthquake, the point where this number falls on the cumulative probability distribution of interevent counts defines the EPS. A major advantage of nowcasting is that it utilizes 'natural time', earthquake counts, between events rather than clock time. Thus, it is not necessary to decluster aftershocks and the results are applicable if the level of induced seismicity varies in time. The application of natural time to the accumulation of the seismic hazard depends on the applicability of Gutenberg-Richter (GR) scaling. The increasing number of small earthquakes that occur after a large earthquake can be scaled to give the risk of a large earthquake occurring. To illustrate our approach, we utilize the number of $$M_{\sigma } \ge 2.75$$ earthquakes in Oklahoma to nowcast the number of $$M_{\lambda } \ge 4.0$$ earthquakes in Oklahoma. The applicability of the scaling is illustrated during the rapid build-up of injection-induced seismicity between 2012 and 2016, and the subsequent reduction in seismicity associated with a reduction in fluid injections. The same method is applied to the geothermal-induced seismicity at the Geysers, California, for comparison. [ABSTRACT FROM AUTHOR]

The forecast of flash floods is sometimes impossible. In the last two decades, Numerical Weather Prediction Systems have become increasingly reliable with significant improvements in terms of quantitative precipitation forecasts. However, despite the application of modern probabilistic hydrometeorological chains, some significant flood events remain unpredicted. This was also the case with an event that occurred on the 8th and 9th of June, 2011 in the eastern part of the Liguria Region, Italy. This event affected in particular the Entella basin, a small watershed that flows into the Mediterranean Sea. The application of a hydrological nowcasting chain as a tool for predicting flash floods in small basins with an anticipation time of a few hours (2-5) is presented here. This work investigated the 'behaviour' of the chain in the cited event as well as in other verification cases and showed how the chain could be exploited for operational purposes. The results were encouraging. However, the analysis provided evidence that the difficulties in using operational hydrometeorological tools are not always and only dependent on the performance of such systems, but also on the way the results are made available to forecasters and on the efficiency of communication with the civil protection officials. [ABSTRACT FROM AUTHOR]

Thunderstorms are the most hazardous meteorological phenomena in Latvia in the summer season, and the assessment of their characteristics is essential for the development of an effective national climate and weather prediction service. However, the complex nature of convective processes sets specific limitations to their observation, analysis and forecasting. Therefore, the aim of this study is to analyse thunderstorm features associated with severe thunderstorms observed in weather radar and satellite data in Latvia over the period 2006-2015. The obtained results confirm the applicability of the selected thunderstorm features for thunderstorm nowcasting and analysis in Latvia. The most frequent features observed on days with thunderstorm were maximum radar reflectivities exceeding 50 dBZ and the occurrence of overshooting tops and tilted updrafts, while the occurrence of gravity waves, V-shaped storm structures and small ice particles have been found to be useful indicators of increased thunderstorm severity potential. [ABSTRACT FROM AUTHOR]

The aim of this study is to present a statistics-based Lagrangian nowcasting model to predict intense rainfall convective events based on dual polarization radar parameters. The data employed in this study are from X-band radar collected during the CHUVA-Vale campaign from November 2011 to March 2012 in southeast Brazil. The model was designed to catch the important physical characteristics of storms, such as the presence of supercooled water above 0 °C isotherm, vertical ice crystals in high levels, graupel development in the mixed-phase layer and storm vertical growth, using polarimetric radar in the mixed-phase layer. These parameters are based on different polarimetric radar quantities in the mixed phase, such as negative differential reflectivity ( Z ) and specific differential phase ( K ), low correlation coefficient ( ρ ) and high reflectivity Z values. Storms were tracked to allow the Lagrangian temporal derivation. The model is based on the estimation of the proportion of radar echo volume in the mixed phase that is likely to be associated with intense storm hydrometeors. Thirteen parameters are used in this probabilistic nowcasting model, which is able to predict the potential for future storm development. The model distinguishes two different categories of storms, intense and non-intense rain cell events by determining how many parameters reach the 'intense' storm threshold. [ABSTRACT FROM AUTHOR]

Many public safety officials (e.g., emergency managers and first responders) use weather-radar data to inform many life-saving decisions, such as sounding outdoor warning sirens and directing storm spotters. Therefore, to include this important user group in ongoing radar applications research, a knowledge coproduction framework is used to interact with, learn from, and provide information to public safety officials. From these interactions, it became clear that radar-based products that estimate a tornado's location, intensity, or both could be valuable to public safety officials. Therefore, a survey was conducted and a focus group formed to 1) collect feedback on several of these products currently under development, 2) identify potential decisions that could be made with these products, and 3) examine the impact of radar update time on product usefulness. An analysis of the survey and focus group responses revealed that public safety officials preferred simple interactive products provided to them using multiple communication methods. Once received, any product that could clearly communicate where a tornado may have occurred would likely help public safety officials focus search and rescue efforts in the immediate aftermath of a tornado. Additionally, public safety officials preferred products created using rapid-update data (1-2-min volumetric updates) over conventional-update data (4-5-min volumetric updates) because it provided them with more complete information. [ABSTRACT FROM AUTHOR]

Focusing on afternoon thunderstorms in Taiwan during the warm season (May-October) under weak synoptic forcing, this study applied the Taiwan Auto-NowCaster (TANC) to produce 1-h likelihood nowcasts of afternoon convection initiation (ACI) using a fuzzy logic approach. The primary objective is to design more useful forecast products with uncertainty regions of predicted thunderstorms to provide nowcast guidance of ACI for forecasters. Four sensitivity tests on forecast performance were conducted to improve the usefulness of nowcasts for forecasters. The optimal likelihood threshold (Lt) for ACIs, which is the likelihood value that best corresponds to the observed ACIs, was determined to be 0.6. Because of the high uncertainty on the exact location or timing of ACIs in nowcasts, location displacement and temporal shifting of ACIs should be considered in operational applications. When a spatial window of 5 km and a temporal window of 18 min are applied, the TANC displays moderate accuracy and satisfactory discrimination with an acceptable degree of overforecasting. The nonparametric Mann-Whitney test indicated that the performance of the TANC substantially surpasses the competing Space and Time Multiscale Analysis System-Weather Research and Forecasting Model, which serves as a pertinent reference for short-range (0-6 h) forecasts at the Central Weather Bureau in Taiwan. [ABSTRACT FROM AUTHOR]

The Met Office developed a prototype high-resolution, hourly-cycling NWP forecasting system using four-dimensional variational data assimilation, covering the southern half of England. This system was known as the Met Office Nowcasting Demonstration Project ( NDP), and ran in real time from June 2012 to March 2013 (covering the period of the London 2012 Olympics and Paralympic Games). It was principally developed to assess the abilities of a frequently updated NWP-based nowcasting system to forecast precipitation at short range such as convective storms for flood forecasting. This article provides a comprehensive assessment of the precipitation forecast skill of the NDP and its comparison against the Met Office operational nowcasting system. It was found that the NWP-based nowcasting system becomes more skilful than an advanced extrapolation-based nowcasting system from T + 1.5 to T + 2 h depending on weather type. [ABSTRACT FROM AUTHOR]

The Fourth European Nowcasting Conference, ENC2022, took place from March 21st to 24th, 2022 and was sponsored by the EUMETNET nowcasting program. Over 110 attendees participated in the event, which was divided into four sessions addressing topics such as observation, seamless prediction with a focus on Artificial Intelligence, nowcasting systems and techniques, and verification and impacts on society. The importance of AI and deep learning in nowcasting was highlighted, as well as the crucial role of radar and satellite observations. Improvements in seamless nowcasting systems and techniques used by different meteorological services were also presented. The article also mentions improvements in convective object-oriented systems, such as the KONRAD3D radar and the TITAN system, as well as artificial intelligence methods applied to the analysis of convective cells and precipitation nowcasting. The usefulness of automatic alerts based on lightning and extrapolations of convective cell centroids is highlighted, and the results of two WMO projects and surveys conducted with different institutions and conference participants are presented. [Extracted from the article]

Automatic nowcasting of convective initiation and thunderstorms has potential applications in several sectors including aviation planning and disaster management. In this paper, random forest based machine learning algorithm is tested for nowcasting of convective rain with a ground based radiometer. Brightness temperatures measured at 14 frequencies (7 frequencies in 22–31 GHz band and 7 frequencies in 51–58 GHz bands) are utilized as the inputs of the model. The lower frequency band is associated to the water vapor absorption whereas the upper frequency band relates to the oxygen absorption and hence, provide information on the temperature and humidity of the atmosphere. Synthetic minority over-sampling technique is used to balance the data set and 10-fold cross validation is used to assess the performance of the model. Results indicate that random forest algorithm with fixed alarm generation time of 30 min and 60 min performs quite well (probability of detection of all types of weather condition ∼90%) with low false alarms. It is, however, also observed that reducing the alarm generation time improves the threat score significantly and also decreases false alarms. The proposed model is found to be very sensitive to the boundary layer instability as indicated by the variable importance measure. The study shows the suitability of a random forest algorithm for nowcasting application utilizing a large number of input parameters from diverse sources and can be utilized in other forecasting problems. [ABSTRACT FROM AUTHOR]

NCAR's AutoNowCaster (ANC) was modified to run over a large domain that encompasses the air traffic management hubs of Chicago, Illinois; New York City, New York; and Atlanta, Georgia. ANC produces nowcasts of convective likelihood (CL), with higher values delineating areas where storms are likely to form and be sustained, and vice versa. This paper presents the results of verifying ANC's 60-min nowcasts of CL over the study area using data collected from 11 June to 30 September 2012. To reduce the high sensitivity of statistical scores to small errors in location and timing, spatial and temporal relaxation techniques were explored. The results show that, at a spatial scale of roughly 50 km and with no temporal relaxation, a CL value of 0.6 is an optimum threshold for nowcasting the general areas both where new storms may initiate and where existing storms will be sustained. Moreover, at that same spatial scale and with temporal relaxation (45-90 min from the nowcast issuance time), a CL value of 0.7 is a good threshold for nowcasting convective initiation alone. [ABSTRACT FROM AUTHOR]

An algorithm for automatic storm identification, tracking, and nowcasting using tree structure representation of radar reflectivity images is proposed. The algorithm aims to track and nowcast different kinds of storm objects (stratiform regions, convective storms, and storm cells) simultaneously and to preserve their spatial relationships in the tracking and nowcasting processes. The algorithm applies a region tree structure to represent intensity regions and their spatial relationships in radar reflectivity images. Storm objects are identified by clustering regions within the region tree structure. Storm tracking is accomplished using an iterative region tree matching algorithm. Storm nowcasting applies the tree structure to the nowcasting of the internal structures of storm objects. Using eight cases with different storm types, a comparative evaluation with the enhanced Thunderstorm Identification, Tracking, Analysis, and Nowcasting (ETITAN) method and the Storm Cell Identification and Tracking (SCIT) method has shown that the proposed tree-based storm-tracking algorithm achieves better performance in storm tracking and nowcasting. The critical success index (CSI) value of storm association is 78.16% for the tree-based method, as compared with 74.88% for SCIT and 74.71% for ETITAN. The CSI value of an 18-min nowcast is 29.02% for the tree-based method, as compared with 24.98% for SCIT and 24.44% for ETITAN. The evaluation also shows that the tree-based method is able to nowcast the internal structure of storms and therefore produces small mean absolute errors (MAE). [ABSTRACT FROM AUTHOR]

The development and verification of a forecast method for localized, meso-γ-scale (2-20 km), extreme heavy rainfall ( MγExHR) is important, because it can cause urban flash flooding and inundation with accompanying damage and potential loss of life. Although previous studies have examined the predictability of precipitation at a very short range (≤1 h) using extrapolation-based nowcasts, they did not specifically focus on MγExHR. In this study, we examine the predictability of 23 selected events of MγExHR (1 h rainfall accumulation ≥50 mm) that occurred during the warm season of 2014 in Japan using High-Resolution Precipitation Nowcasts ( HRPNs) provided by the Japan Meteorological Agency, which are extrapolation-based nowcasts. Traditional grid-scale verification using the equitable threat score shows that the HRPNs usefully predict the heavy rainfall areas of ≥20 mm h−1 for at least 12 min at the grid scale of 1 km. Neighbourhood verification using fractions skill scores shows that HRPNs usefully predict the areas of ≥20 mm h−1 up to 29 min by tolerating 11 km displacement errors. After 30 min, a useful forecast cannot be obtained, even if the 11 km displacement errors are tolerated for the ≥20 mm h−1 areas. This result suggests that a numerical weather prediction ( NWP) model, whose accuracy is useful after ∼30 min, is necessary to seamlessly provide useful forecasts for heavy rainfall areas of ≥20 mm h−1 for MγExHR with ∼10 km displacement errors, by blending the extrapolation-based nowcast with NWP. [ABSTRACT FROM AUTHOR]

Radar systems provide the most useful information about the intensity, movement, and characteristics of severe thunderstorms, but are expensive to maintain and require extensive maintenance. In South Africa, some areas are not covered by radar systems, while very few operational radar systems exist in other southern African countries. Despite these shortcomings, all meteorological centers still have to warn the public of pending severe weather events. The Nowcasting Satellite Application Facility (NWC SAF) in Europe developed software that utilizes satellite data to identify and track rapidly developing thunderstorms (RDT). The NWC software was installed at the South African Weather Service in 2014. Initially, the RDT product was validated against lightning data and the results showed that the RDT product could provide very useful information on possible severe or intense convective storms. This study focusses on the effects of including lightning as an ancillary dataset into the algorithms and then validating the RDT product against radar data. Twenty-five summer cases were considered to determine whether the inclusion of lightning data had a positive effect on the accuracy of the RDT product, when compared to radar data. The results of this study show that in the majority of the cases, the inclusion of lightning data was beneficial to the RDT product. On average the Probability of Detection (POD) improved by 6.6%, the Heidke Skill Score (HSS) by 4.6%, and the False alarm ratio (FAR) by 0.1%. To our knowledge, South Africa is the only African country which is running the NWC SAF software operationally and which has performed an evaluation of the product over Africa against observations from radar systems and lightning sensors. The outcomes of this study are very encouraging for other countries in Africa where convection and severe convection often occur and sophisticated data sources are absent. Initial studies over East Africa indicate that the RDT product can benefit operational practices for the nowcasting of severe convection events. [ABSTRACT FROM AUTHOR]

Hong Kong Observatory has been operating an in-house developed rainfall nowcasting system called "Short-range Warning of Intense Rainstorms in Localized Systems (SWIRLS)" to support rainstorm warning and rainfall nowcasting services. A crucial step in rainfall nowcasting is the tracking of radar echoes to generate motion fields for extrapolation of rainfall areas in the following few hours. SWIRLS adopted a correlation-based method in its first operational version in 1999, which was subsequently replaced by optical flow algorithm in 2010 and further enhanced in 2013. The latest optical flow algorithm employs a transformation function to enhance a selected range of reflectivity for feature tracking. It also adopts variational optical flow computation that takes advantage of the Horn-Schunck approach and the Lucas-Kanade method. This paper details the three radar echo tracking algorithms, examines their performances in several significant rainstorm cases and summaries verification results of multi-year performances. The limitations of the current approach are discussed. Developments underway along with future research areas are also presented. [ABSTRACT FROM AUTHOR]