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The article focuses on nowcasting systems in European National Meteorological Services (NMS), defining nowcasting and its unique characteristics, including methods, and applications. Topics include the EUMETNET Nowcasting program's phases, various types of nowcasting systems (object-oriented, field-oriented radar and satellite, NWP models), and seamless systems (deterministic and probabilistic), highlighting their development, methodologies, and applications across European NMSs.

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The article informs on Fourth European Nowcasting Conference, ENC2022 that was held on March 21 and 24, 2022. It included topics such as observation as a basis for nowcasting, seamless prediction, with a special focus on Artificial Intelligence, and nowcasting systems, products and techniques, and verification, impacts on society, as well as applications and user aspects.

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]

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]

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]

A spatially explicit mapping of rainfall is highly required for Southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) spinning enhanced visible and infrared imager (SEVIRI). Rainfall measurements from about 350 weather stations from the years 2010-2014 served as ground truths for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed predicting rainfall area and rainfall quantities during all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (Probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to an Heidke Skill Score (HSS) of 0.18. Predicted hourly rainfall quantities were estimated with an average hourly correlation of rho = 0.33 and a RMSE of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the IMERG product of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection where GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG based retrieval, however, provides rainfall in higher spatial resolution. Though it remains challenging to estimate rainfall from satellite data, especially on a high temporal resolution, this study showed promising results towards improved spatio-temporal estimates of rainfall over Southern Africa. [ABSTRACT FROM AUTHOR]

A novel approach for the nowcasting of clouds and direct normal irradiance (DNI) based on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented for a forecast horizon up to 120 min. The basis of the algorithm is an optical flow method to derive cloud motion vectors for all cloudy pixels. To facilitate forecasts over a relevant time period, a classification of clouds into objects and a weighted triangular interpolation of clear-sky regions are used. Low and high level clouds are forecasted separately because they show different velocities and motion directions. Additionally a distinction in advective and convective clouds together with an intensity correction for quickly thinning convective clouds is integrated. The DNI is calculated from the forecasted optical thickness of the low and high level clouds. In order to quantitatively assess the performance of the algorithm, a forecast validation against MSG/SEVIRI observations is performed for a period of 2 months. Error rates and Hanssen-Kuiper skill scores are derived for forecasted cloud masks. For a forecast of 5 min for most cloud situations more than 95% of all pixels are predicted correctly cloudy or clear. This number decreases to 80-95% for a forecast of 2 h depending on cloud type and vertical cloud level. Hanssen-Kuiper skill scores for cloud mask go down to 0.6-0.7 for a 2 h forecast. Compared to persistence an improvement of forecast horizon by a factor of 2 is reached for all forecasts up to 2 h. A comparison of forecasted optical thickness distributions and DNI against observations yields correlation coefficients larger than 0.9 for 15 min forecasts and around 0.65 for 2 h forecasts. [ABSTRACT FROM AUTHOR]

In addition to turbulence forecasts, which can be used for strategic planning for turbulence avoidance, short-term nowcasts can augment longer-term forecasts by providing much more timely and accurate turbulence locations for real-time tactical avoidance of turbulence hazards, especially those related to short-lived convection. This paper describes a turbulence-nowcasting algorithm that combines recent short-term turbulence forecasts with all currently available direct turbulence observations and inferences of turbulence from other sources. Building upon the need to provide forecasts that are aircraft independent, the nowcasts provide estimates of an atmospheric metric of turbulence, namely, the energy dissipation rate to the one-third power (EDR). Some observations directly provide EDR, such as in situ observations from select commercial aircraft and ground-based radar algorithm output, whereas others must be translated to EDR. A strategy is provided for mapping turbulence observations, such as pilot reports (PIREPs), and inferences from other relevant observational data sources, such as observed surface wind gusts, into EDR. These remapped observation values can then be combined with short-term turbulence forecasts and other convective diagnostics of turbulence to provide a turbulence nowcast of EDR in the national airspace. Case studies are provided to illustrate the algorithm procedure and benefits. The EDR nowcasts are compared with aircraft in situ EDR observations and PIREPs converted to EDR to obtain metrics of statistical performance. It is shown by one common performance metric, the area under the relative operating characteristic curve, that the turbulence nowcasts with assimilated observations considerably outperform the corresponding turbulence forecasts. [ABSTRACT FROM AUTHOR]

The Sun4Cast solar power forecasting system, designed to predict solar irradiance and power generation at solar farms, is composed of several component models operating on both the nowcasting (0-6 h) and day-ahead forecast horizons. The different nowcasting models include a statistical forecasting model (StatCast), two satellite-based forecasting models [the Cooperative Institute for Research in the Atmosphere Nowcast (CIRACast) and the Multisensor Advection-Diffusion Nowcast (MADCast)], and a numerical weather prediction model (WRFSolar). It is important to better understand and assess the strengths and weaknesses of these short-range models to facilitate further improvements. To that end, each of these models, including four WRF-Solar configurations, was evaluated for four case days in April 2014. For each model, the 15-min average predicted global horizontal irradiance (GHI) was compared with GHI observations from a network of seven pyranometers operated by the Sacramento Municipal Utility District (SMUD) in California. Each case day represents a canonical sky-cover regime for the SMUD region and thus represents different modeling challenges. The analysis found that each of the nowcastingmodels perform better orworse for particular lead times andweather situations. StatCast performs best in clear skies and for 0-1-h forecasts; CIRACast and MADCast perform reasonably well when cloud fields are not rapidly growing or dissipating; and WRF-Solar, when configured with a high-spatial-resolution aerosol climatology and a shallow cumulus parameterization, generally performs well in all situations. Further research is needed to develop an optimal dynamic blending technique that provides a single best forecast to energy utility operators. [ABSTRACT FROM AUTHOR]

Several studies reported sudden increases in the total lightning flash rate (intra-cloud+cloud-to-ground) preceding the occurrence of severe weather (large hail, wind gusts associated to thunderstorms and/or tornadoes). Named “Lightning Jump”, this pattern has demonstrated to be of operational applicability in the forecasting of severe weather phenomena. The present study introduces the application of a lightning jump algorithm, with an identification of cells based solely on total lightning data, revealing that there is no need of radar data to trigger severe weather warnings. The algorithm was validated by means of a dataset severe weather events occurred in Catalonia in the period 2009–2014. Results obtained revealed very promising. [ABSTRACT FROM AUTHOR]

Statistical approaches for quantitative precipitation nowcasts (QPNs) have emerged with recent advances in sensors in geostationary orbits, which provide more frequent observations at higher spatial resolutions. Advanced Meteorological Imager (AMI) onboard South Korea's second geostationary satellite (GEO-KOMPSAT-2A), scheduled for launch in early 2018, is an example of these sensors. This paper introduces operational prototype algorithms that attempt to produce QPN products for GEO-KOMPSAT-2A. The AMI QPN products include the potential accumulated rainfall and the probability of rainfall (PoR) for a 3-h lead time. The potential accumulated rainfall algorithm consists of two major procedures: 1) identification of rainfall features on the outputs from the GEO-KOMPSAT-2A rainfall rate algorithm; and 2) tracking of these rainfall features between two consecutive images. The potential accumulated rainfall algorithm extrapolates precipitation fields every 15 min. Rainfall rates at each time step are accumulated to yield the 3-hourly rainfall. In addition, the extrapolated precipitation fields at 15-min intervals are used as inputs for the PoR algorithm, which produces the probability of precipitation during the same 3-h period. The QPN products can be classified as extrapolated features associated with precipitation. The validation results show that the extrapolated features tend to meet the designated accuracy for the prototype development stage. We also confirm a tendency for decreasing accuracy of the QPN products with increasing forecast lead time. Mitigating the dependence on lead time may remain a challenge that can be incorporated into the next generation of QPN algorithms. [ABSTRACT FROM PUBLISHER]

We propose models that allow us to capture the evolution of objects over time and more importantly, we provide forecasts to describe an object at future unobserved states utilizing information from the current state along with covariate information. We view objects as random sets and proceed to model them in a hierarchical Bayesian framework and estimate the model parameters using a Markov chain Monte Carlo scheme. We illustrate the methodology with an application to nowcasting of severe weather precipitation fields as obtained from weather radar images, where the severe storm cells are treated as random sets and the wind velocity is used to inform the distributions of the model parameters. [ABSTRACT FROM AUTHOR]

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 cioud-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. [ABSTRACT FROM AUTHOR]

A novel approach for the nowcasting of clouds and Direct Normal Irradiance (DNI) based on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented for a forecast horizon up to 120 min. The basis of the algorithm is an optical flow method to derive cloud motion vectors for all cloudy pixels. To facilitate forecasts over a relevant time period, a classification of clouds into objects and a weighted triangular interpolation of clear sky regions is used. Low and high level clouds are forecasted separately because they show different velocities and motion directions. Additionally a distinction in advective and convective clouds together with an intensity correction for quickly thinning convective clouds is integrated. The DNI is calculated from the forecasted optical thickness of the low and high level clouds. In order to quantitatively assess the performance of the algorithm, a forecast validation against MSG/SEVIRI observations is performed for a period of two months. Error rates and Hanssen-Kuiper skill scores are derived for forecasted cloud masks. For a forecast of 5 min for most cloud situations more than 95 % of all pixels are predicted correctly cloudy or clear. This number decreases towards the 2 h forecast towards 80--95 % depending on cloud type and vertical cloud level. Hanssen-Kuiper skill scores for cloud mask go down to 0.6--0.7 for a two hour forecast. Compared to persistence an improvement of forecast horizon by a factor of two is reached for all forecast up to 2 h. Comparison of forecasted optical thickness distributions and direct normal irradiance at ground against observations yields correlation coefficients larger than 0.9 for 15 min forecasts and around at least 0.65 for 2 h forecasts. [ABSTRACT FROM AUTHOR]

This study examines the univariate error covariances of hourly rainfall accumulations using two different NWP models and a mosaic of radar reflectivity over a continental-scale domain. The study focuses on two main areas. The focus of the first part of the paper is on the ensemble-based and the innovation-based error variance and correlation estimations. An ensemble of forecasts and a set of observations provide the basis for estimating the errors in two different ways. The results indicate that both ensemble- and innovation-based methods lead to comparable variance estimations, while the local error correlation estimates have larger differences due to the sensitivity of calculations to the gradient of the variance field. The second part of the paper uses innovations for identifying the errors. The focus of this part is on a prognostic method for estimating the error statistics from the background based on the Bayesian inference technique. The case study shows that the predictive model produces a similar result regarding the magnitude and the dispersion of variance in comparison with the innovation and ensemble-based variances. This study represents a step toward estimating local error variances and local error correlations to construct a nonhomogeneous and precipitation-dependent error covariance matrix of rainfall. These results will be used in a future paper in the design of a 2D-VAR Assimilation Method for Blending Extrapolated Radars (AMBER) with NWP precipitation forecast to form a precipitation nowcasting model. [ABSTRACT FROM AUTHOR]

This paper introduces the use of Bayesian full Procrustes shape analysis in object-oriented meteorological applications. In particular, the Procrustes methodology is used to generate mean forecast precipitation fields from a set of ensemble forecasts. This approach has advantages over other ensemble averaging techniques in that it can produce a forecast that retains the morphological features of the precipitation structures and present the range of forecast outcomes represented by the ensemble. The production of the ensemble mean avoids the problems of smoothing that result from simple pixel or cell averaging, while producing credible sets that retain information on ensemble spread. Also in this paper, the full Bayesian Procrustes scheme is used as an object verification tool for precipitation forecasts. This is an extension of a previously presented Procrustes shape analysis based verification approach into a full Bayesian format designed to handle the verification of precipitation forecasts that match objects from an ensemble of forecast fields to a single truth image. The methodology is tested on radar reflectivity nowcasts produced in the Warning Decision Support System — Integrated Information (WDSS-II) by varying parameters in the K-means cluster tracking scheme. [ABSTRACT FROM AUTHOR]

The Taiwan Strait Nowcast/Forecast System (TFOR), which is based on the Regional Ocean Modeling System, may be the first operational ocean model to include both tide and circulation processes in the Taiwan Strait. In this study, we assessed the performance of TFOR by investigating the differences between observational data and the results obtained by TFOR, thereby illustrating the ability of TFOR to reproduce significant physical processes. We also evaluated the utility and reliability of TFOR products for successful applications in maritime search and rescue. The mean bias, root mean-squared difference, correlation coefficient (CC), and Willmott skill for the differences in temperature between the cruise observations and the TFOR results were –0.01°C, 1.2°C, 0.87, and 0.92, respectively, and those for the corresponding salinity results were 0.06 PSU, 0.4 PSU, 0.74, and 0.83, respectively. The distributions of the TFOR M 2 harmonic constants indicated that TFOR simulates the tidal characteristics well in the Taiwan Strait. The CC between the Taiwan Strait volume transport based on the TFOR results and the along-strait wind stress was 0.77, thereby indicating that monsoon winds exert an important influence on the variability in volume transport through the Taiwan Strait. Statistical analyses of the TFOR results showed that the annual, spring, summer, autumn, and winter mean transport volumes were 1.16, 1.28, 2.52, 0.80, and 0.04 Sv, respectively. In conclusion, the TFOR is robust and it can simulate the temperature, salinity, and velocity in a reasonable and stable manner. [ABSTRACT FROM AUTHOR]

The hourly values of the F-layer critical frequency from the ionospheric sounder in Dourbes (50.1°N, 4.6°E) during the time interval from 1957 to 2010, comprising five solar cycles, were analyzed for the effects of the solar activity. The hourly time series were reduced to hourly monthly medians which in turn were used for fitting a single station foF2 monthly median model. Two functional approaches have been investigated: a statistical approach and a spectral approach. The solar flux F10.7 is used to model the dependence of foF2 on the solar activity and is incorporated into both models by a polynomial expression. The statistical model employs polynomial functions to fit the F-layer critical frequency while the spectral model is based on spectral decomposition of the measured data and offers a better physical interpretation of the fitting parameters. The daytime and nighttime foF2 values calculated by both approaches are compared during high and low solar activity. In general, the statistical model has a slightly lower uncertainty at the expense of the larger number of fitting parameters. However, the spectral approach is superior for modeling the periodic effects and performs better when comparing the results for high and low solar activity. Comparison with the International Reference Ionosphere () shows that both local models are better at describing the local values of the F-layer critical frequency. [ABSTRACT FROM AUTHOR]

This study deals with the evaluation of the possibility of using the current numerical models for nowcasting of large hail (diameter ≥ 2.5 cm). Evaluation is focused on deterministic forecast with the lead times up to 2 h. As in our previous work (Sokol et al., 2014 ) the COSMO NWP model with a two moment cloud microphysics explicitly considering hail was applied. The model is run at a horizontal resolution of 1.1 km and with the assimilation of radar reflectivity. The model forecasts are evaluated for five events with large hail determined by direct observations and derived using a recognition algorithm based on radar and sounding data. The accuracy of model forecasts was evaluated subjectively by eye, by Fractions Skill Score and by bias. In addition distances between locations of forecasted and observed hail were analyzed and evaluated. Basic results of this study are that (i) forecasts longer than 90 min are problematic because of large errors and significant underestimation of forecasted hail, (ii) when the model forecasts large hail then there is a 50% chance that large hail will be observed within approximately 30 km. [ABSTRACT FROM AUTHOR]

The development and efficiency assessment of a lightning activity nowcasting tool is presented. The tool employs MSG IR imagery and real-time lightning data provided by ZEUS network to nowcast the manifestation of lightning activity over the Greek mainland for a time span of 1 h. The efficiency of the tool is assessed for 20 days with widespread lightning activity observed during the warm period of the year through a verification procedure that computes a collection of appropriate statistics for selected areas. The analysis of these statistics shows that the tool estimates successfully almost 80% of the upcoming activity. The false alarm rate is close to 40%, while a small overestimation is evident. Since the adverse effects of a case of missed activity are much more than that of a false alarm, the tool is considered successful and fit for operational use. [ABSTRACT FROM AUTHOR]

A six-year climatology of radiation fog has been compiled at two sites: the Research Centre for the Lower Atmosphere (CIBA, Spain) and the Cabauw Experimental Site for Atmospheric Research (CESAR, The Netherlands). These sites are contrasted in terms of geographical situation, climate zone, altitude, humidity and soil water availability. Therefore, several climatological differences in fog abundance, onset, dissipation and duration have been quantified between the two sites. The more humid site (CESAR) is characterised by relatively short radiation fog events distributed throughout the year. However, radiation fog at the drier site (CIBA) is more persistent and appears during late autumn/winter months. In general, its formation requires more time after sunset (~2h more), since further cooling is required to reach saturation. The forecast of these fog events has been evaluated through two different approaches. First, we extend the statistical method presented by Menut et al. (2014) (M14). This method uses statistics to define threshold values on key variables for fog formation (pre-fog) and verifies its predictability using observations and numerical model output.We present some of the most appropriate threshold values for the forecasting of pre-fog periods at both sites, which differ from those presented in M14 and depend on the optimisation of the hit rate or the false-alarm rate. Additionally, we also extend M14 by suggesting other variables as potential predictors for fog formation (friction velocity and visibility tendency). Finally, we focus on fog simulation by the Weather Research and Forecasting (WRF) model in terms of liquid water content. The WRF model was able to simulate radiation fog when configured with sophisticated physical options and high resolution. However it failed in simulating the onset, dissipation and the vertical extent of fog (which was overestimated). The model results were extremely sensitive to the spin-up time. [ABSTRACT FROM AUTHOR]

A precipitation nowcasting system (SATIN) is presented which relies entirely on satellite based precipitation products and rain gauge measurements. Thus, the proposed system is most suitable for areas where ground based radar observations are not available, or potentially suffer from low quality. SATIN delivers analyses on a 1 km grid every 15 min and nowcasts (obtained through motion vectors) in 15 min time steps. Nowcasts are gradually merged with NWP precipitation forecasts. An extensive validation including comparisons to different NWP models yields superior performance for SATIN analyses as well as nowcasts for lead times up to 1 h. Reducing the station density still yields better performance than operationally available NWP's. [ABSTRACT FROM AUTHOR]

Operational nowcasting techniques for sea fog over the Yellow Sea rely on data from weather satellites because ground-based observations are hardly available. While there are several algorithms for detecting low stratus (LST) that are applicable to geostationary weather satellite data, sea fog retrieval is more complicated. These schemes mostly need ancillary data such as Cloud Optical Thickness (COT) and Droplet Effective Radius (DER). To retrieve the necessary parameters for sea fog detection over the Yellow Sea, the Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM) scheme developed by Kawamoto et al. (2001) was adapted to the Japanese Multifunctional Transport Satellites (MTSAT) system-Japanese Advanced Meteorological Imager (JAMI). COT and DER values were then retrieved for 64 cases over the Yellow Sea (=85,000 LST pixels) and compared with the COT and DER products from the MYD06/MOD06, CAPCOM-MODIS (Moderate Resolution Imaging Spectroradiometer) and CloudSat (cloud radar). Results showed that the COT and DER values retrieved from JAMI were satisfactory. The MTSAT-2 JAMI data delivered better COT values than the MTSAT-1R JAMI data, due to the re-calibration of MTSAT-2 JAMI's visible (VIS) band in 2011. Similarly, improvements were seen in DER retrieval, even though the VIS re-calibration primarily affects COT retrieval. By comparing the difference in stratus thickness calculated byMTSAT-1R andMTSAT-2, the COT and DER retrieved fromMTSAT-2 JAMI can be used in ground fog retrieval schemes. These values exhibit less bias, especially in cases involving high cloud top and thin cloud thickness. Both the COT and DER retrievals from MTSAT-2 JAMI offer potential as reliable parameters for Yellow Sea fog detection. [ABSTRACT FROM AUTHOR]

Multiple wind retrieval algorithms are performed to retrieve wind fields, based on which radar reflectivity is extrapolated to implement nowcasting. The frequently used nowcasting algorithm COTREC (continuity of tracking radar echo by correlation), based on the reflectivity and wind retrieval algorithm GVAD (gradient velocity azimuth display), based on radial velocity are used. The wind fields retrieved by the two methods are taken as motion vectors to give a 60-min forecast of radar reflectivity. Comparison analysis on precipitation events shows that GVAD can be used in operational nowcasting as COTREC performs and for stable precipitation duration, COTREC gives better nowcasting results than GVAD, while, for evolving precipitation duration, GVAD gives better nowcasting results than COTREC. [ABSTRACT FROM AUTHOR]