Critical Success Index Values Research Articles

Evaluation of Satellite-Based Rainfall Estimates against Rain Gauge Observations across Agro-Climatic Zones of Nigeria, West Africa

Satellite rainfall estimates (SREs) play a crucial role in weather monitoring, forecasting and modeling, particularly in regions where ground-based observations may be limited. This study presents a comprehensive evaluation of three commonly used SREs—African Rainfall Climatology version 2 (ARC2), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and Tropical Application of Meteorology using SATellite data and ground-based observation (TAMSAT)— with respect to their performance in detecting rainfall patterns in Nigeria at daily scales from 2002 to 2022. Observed data obtained from the Nigeria Meteorological Agency (NiMet) are used as reference data. Evaluation metrics such as correlation coefficient, root mean square error, mean error, bias, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are employed to assess the performance of the SREs. The results show that all the SREs exhibit low bias during the major rainfall season from May to October, and the products significantly overestimate observed rainfall during the dry period from November to March in the Sahel and Savannah Zones. Similarly, over the Guinea Zone, all the products indicate overestimation in the dry season. The underperformance of SREs in dry seasons could be attributed to the rainfall retrieval algorithms, intensity of rainfall occurrence and spatial-temporal resolution. These factors could potentially lead to the accuracy of the rainfall retrieval being reduced due to intense stratiform clouds. However, all the SREs indicated better detection capabilities and less false alarms during the wet season than in dry periods. CHIRPS and TAMSAT exhibited high POD and CSI values with the least FAR across agro-climatic zones during dry periods. Generally, CHIRPS turned out to be the best SRE and, as such, would provide a useful dataset for research and operational use in Nigeria.

Development of a full-scale connected U-Net for reflectivity inpainting in spaceborne radar blind zones

Development of a full-scale connected U-Net for reflectivity inpainting in spaceborne radar blind zones

On the Dependence of the Critical Success Index (CSI) on Prevalence.

The critical success index (CSI) is an established metric used in meteorology to verify the accuracy of weather forecasts. It is defined as the ratio of hits to the sum of hits, false alarms, and misses. Translationally, CSI has gained popularity as a unitary outcome measure in various clinical situations where large numbers of true negatives may influence the interpretation of other, more traditional, outcome measures, such as specificity (Spec) and negative predictive value (NPV), or when unified interpretation of positive predictive value (PPV) and sensitivity (Sens) is needed. The derivation of CSI from measures including PPV has prompted questions as to whether and how CSI values may vary with disease prevalence (P), just as PPV estimates are dependent on P, and hence whether CSI values are generalizable between studies with differing prevalences. As no detailed study of the relation of CSI to prevalence has been undertaken hitherto, the dataset of a previously published test accuracy study of a cognitive screening instrument was interrogated to address this question. Three different methods were used to examine the change in CSI across a range of prevalences, using both the Bayes formula and equations directly relating CSI to Sens, PPV, P, and the test threshold (Q). These approaches showed that, as expected, CSI does vary with prevalence, but the dependence differs according to the method of calculation that is adopted. Bayesian rescaling of both Sens and PPV generates a concave curve, suggesting that CSI will be maximal at a particular prevalence, which may vary according to the particular dataset.

Improving near-real-time satellite precipitation products through multistage modified schemes

Near-real-time (NRT) satellite precipitation products (SPPs) exhibit great application potential in global precipitation estimation for their convenient acquisition and access. A universal framework was developed to modify precipitation occurrence and intensity in stages to correct and integrate multiple NRT SPPs with open-access gridded observational data. Based on this framework, both machine learning- and statistical-based correction methods were utilized to construct multi-stage modified schemes and three schemes, namely the double machine learning (DML), statistical error correction (SEC) and hybrid (ML-SEC) schemes, were stepwise established. The improvements of NRT SPPs by these three schemes were comparatively analyzed based on a dense network of rain gauges located in the Yiluo River Basin, China, which does not depend on reference data for modification and has derived a reliable evaluation result at grid-point scale. The results indicated that the DML scheme attains the highest accuracy with a grid-averaged Kling-Gupta efficiency (KGE) value of 0.70 and a critical success index (CSI) value of 0.60, followed by the ML-SEC and SEC schemes. The KGE and CSI values of NRT SPPs have been improved by 22.20% ∼ 535.48% and 30.30% ∼ 100.24%, respectively, through the DML scheme. The DML-modified result also outperformed the mainstream post-processed SPPs with a 1.95% ∼ 222.25% and 9.22% ∼ 102.00% higher KGE and CSI values, respectively. The main error sources of the SPPs due to false and hit events were reduced by the multistage schemes, and the DML scheme achieved a balanced efficiency at both occurrence and intensity modification stages. Moreover, the DML scheme was more advantageous than spatial interpolation under the scenario with fewer gauges, particularly in estimating precipitation occurrence. The multistage modified schemes in this study, especially the DML scheme, exhibit potential in near-real time and sub-daily scale applications. Multistage schemes for NRT SPP modification are helpful for obtaining high-quality precipitation estimates.

Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library

AbstractFlash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation‐based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short‐term rainfall forecasts, which should increase decision‐making times. We implemented an adaptive scale‐dependent ensemble blending method in the open‐source pysteps library, based on the Short‐Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill‐dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48‐member blended forecasts against the Belgian NWP forecast, a 48‐member nowcast, and a simple 48‐member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event‐averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment‐average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless prediction systems.

Evaluating Magnitude Agreement and Occurrence Consistency of CHIRPS Product with Ground-Based Observations over Medium-Sized River Basins in Nepal

High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (<0.4 on average), while the false alarm ratio was significant (>0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations.

Development of a wind power ramp forecasting system via meteorological pattern analysis

AbstractRamp phenomena caused by abrupt changes in wind speed may confound the stable operation of correlated electrical power supply systems, yet accurate numerical predictions are challenging, as the wind is affected by complex interactions between large‐scale weather patterns and local geographical conditions. Further, optimal numerical weather prediction (NWP) methods and physics schemes vary as a function of weather patterns. The present study proposed a new real‐time wind power ramp forecast framework based on the flexible selection of optimal NWP models, which were derived via principal component analysis (PCA). The novelty of this analysis lies in that statistical methods were employed for NWP optimization, compared with their more conventional use during an NWP postprocessing. Here, a weather pattern was classified by PCA using outcomes from the global‐scale prediction models, and the optimum regional NWP system settings were acquired according to the weather patterns for further wind field dynamical downscaling. The performance of the developed prediction system was verified with wind power at wind turbine hub‐heights for three areas in eastern Japan, and the Critical Success Index (CSI) indicated an improvement of prediction accuracy over benchmark predictions by ≤0.184 for ramp‐up events and ≤0.127 for ramp‐down events (both observed in Tohoku area). Higher CSI values were consistently seen in three wind farm areas, indicative of the improvement in detection probability for actual ramp events compared with benchmark.

Assessment of three gridded satellite-based precipitation products and their performance variabilities during typhoons over Zhejiang, southeastern China

• IMERG-Early, GSMaP-NRT and FY4A QPE were evaluated for overall performance and five typhoons during 2018–2019. • IMERG-Early and GSMaP-NRT performed much better than FY4A QPE. • Satellite-based products tended to underestimate heavy precipitation but overestimate light precipitation. • Satellite-based products showed unstable performance and were more likely to present low estimations during typhoons. • Satellite-based products were difficult to detect heavy precipitation centers during typhoons in some parts of southwestern Zhejiang with complex topography. Effective and accurate estimation of heavy precipitation from typhoons is critical for the early warning of hydrological extremes and disaster mitigation. Satellite-based precipitation products are widely used for various purposes, including supporting climate, hydrology, and severe weather event monitoring and research activities. In this study, the performance of Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Chinese Fengyun-4A quantitative precipitation estimates (FY4A QPE) were compared to daily observations from 2018 to 2019. Further comparison of these products at an hourly scale was implemented for five typhoons that made landfall in the coastal areas of southeastern China. The results demonstrate that IMERG-Early and GSMaP-NRT performed much better than FY4A QPE, in both overall assessment and specific typhoons. All the satellite-based products showed the best correlations with the gauge observations for heavy precipitation events, and their correlations became worse with lower precipitation intensity. They tended to underestimate heavy precipitation but overestimate light precipitation. Furthermore, their random errors increased with increasing precipitation, according to an analysis of unbiased root mean squared error (ubRMSE). Based upon analyses of the probability of detection (POD) and critical success index (CSI), satellite-based products performed better during light precipitation events than moderate and heavy precipitation events. During typhoons, IMERG-Early and GSMaP-NRT had better accuracy in detecting typhoon precipitation with higher POD and CSI values. FY4A QPE showed instability in different typhoons and a low correlation with gauge observations. In addition, satellite-based products produced precipitation estimates more reliably in northern and eastern Zhejiang, but were difficult to detect heavy precipitation centers in some parts of southwestern Zhejiang with complex topography. These findings not only provide valuable references for the development of satellite-based retrieval algorithms to enhance the ability to monitor typhoon heavy precipitation but also support diverse hydrological models and applications.

WRF Model Sensitivity to Spatial Resolution in Singapore: Analysis for a Heavy Rain Event and General Suitability

Granularity of the grid (both horizontally and vertically) is a key consideration when conducting localised Numerical Weather Prediction (NWP) modelling. Generally speaking, an NWP model with a finer grid can explicitly resolve more processes and require less parameterisation. However, a finer grid also requires more computation and it is not always clear that a finer grid will produce a more accurate forecast. In this study, we explore the sensitivity of rainfall prediction over Singapore to grid resolution. We use the Weather and Research Forecasting model (WRF) to forecast rainfall over Singapore and explore the performance of horizontal resolutions ranging from 1 km to 12 km. We test the performance on a set of dates from across the years 2020–2021 against both ground observations and radar-derived rain rates. When compared to ground observations, we show that, overall, the higher resolution produces the highest Critical Success Index (CSI) for rain rates in excess of 0.5 mm/h. When compared against radar-derived rain rates, the 1 km domain produces superior CSI values for all rain rates. The daily-average hourly Fractional Skill Score (FSS) was then calculated for some dates and showed agreement with the CSI results with the exception of a north-east monsoon day where, for heavier rain rates, the 3 km domain has superior FSS. We also investigate a particularly heavy rain event on 10 January 2021 and show that the 3 km grid has highest CSI for rain rates of 4 mm/h and 16 mm/h (based on both ground-based and radar-derived rain rates), however, the 1 km has superior FSS for this event.

Development and Preliminary Testing of a System for the Multimodal Analysis of Gait Training in a Virtual Reality Environment

Gait training in a virtual reality (VR) environment is promising for children affected by different disorders. However, the efficacy of VR therapy is still under debate, and more research is needed to clarify its effects on clinical conditions. The combination of VR with neuroimaging methods, such as the electroencephalography (EEG), might help in answering this need. The aim of the present work was to set up and test a system for the multimodal analysis of the gait pattern during VR gait training of pediatric populations by analyzing the EEG correlates as well as the kinematic and kinetic parameters of the gait. An EEG system was integrated with the Gait Real-time Analysis Interactive Lab (GRAIL). We developed and validated, with healthy adults (n = 5) and children (n = 4, healthy or affected by cerebral palsy (CP)), the hardware and software integration of the two systems, which allowed the synchronization of the acquired signals and a reliable identification of the initial contact (IC) of each gait cycle, showing good sensitivity and critical success index values. Moreover, we tested the multimodal acquisition by successfully analyzing EEG data and kinematic and kinetic parameters of one healthy child and one child with CP. This system gives the possibility of monitoring the effect of the VR therapy and studying the neural correlates of gait.

Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beet-growing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84%, ≥78%, and ≤15%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH > 90% during the first four hours and RH > 60% during the following 9 h, daytime T > 16 °C and nighttime T > 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season.

Performance of Multiple Satellite Precipitation Estimates over a Typical Arid Mountainous Area of China: Spatiotemporal Patterns and Extremes

AbstractPrecipitation in arid mountainous areas is characterized by low rainfall intensity and large spatial heterogeneity, which challenges satellite-based monitoring by the spaceborne sensors. This study aims to comparatively evaluate the detection ability of spatiotemporal patterns and extremes of rainfall by a range of mainstream satellite precipitation products [TMPA, Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and PERSIANN–Climate Data Record (PERSIANN-CDR)] over a typical arid mountainous basin of China, benchmarking against rain gauge data from 2000 to 2015. Results showed that satellite precipitation estimates had relatively low accuracy at the daily scale, while a significant improvement of correlation coefficient (CC; >0.6) and a significant reduction of relative root-mean-square error (RRMSE; <1.0) were found as time scale increases beyond the monthly scale. CHIRPS tended to overestimate the gauge precipitation with positive relative bias (RB), while the negative RB values for TMPA and PERSIANN-CDR indicated there was an underestimation. CHIRPS had the most similar spatial pattern and slope trends of the seasonal precipitation and interannual variations of annual precipitation with gauge observations. With the increase in rainfall rates, the probability of detection (POD) and critical success index (CSI) were reduced and the false alarm ratio (FAR) was increased significantly, demonstrating the limited capability for all the three satellite products for detecting heavy rainfall events. CHIRPS showed the best performance in detecting rainfall extremes compared to TMPA and PERSIANN-CDR, evidenced by the larger CSI values and similar extreme rainfall indices obtained from gauge records. This study provides valuable guidance for choosing satellite precipitation products instead of gauge observations for rainfall monitoring (especially rainfall extremes) and agricultural production management over arid mountainous area.

Assessment of Real Time, Multi-Satellite Precipitation Products under Diverse Climatic and Topographic Conditions

A preliminary assessment of real-time multi-satellite precipitation products, Global Precipitation Measurement (GPM) IMERG real time (IMRT), TMPA 3B42 real time (TMPA), PERSIANN (PERS) and CMORPH (CMOR) was carried out in Pakistan on entire study area basis and by dividing the study area in to five regions. It is extended from 61o to 77o East and 23.5 o to 37 o North, with varied climatic and topographic conditions. In south coastline along the Arabian Sea, plateaus, deserts and plain areas in the center and in north snow caped mountains. A set of evaluating (continuous) statistical indices, correlation coefficient (CC), bias (Bias), root mean square error (RMSE) and detective (categorical) statistical indices, probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) were applied to evaluate the satellite products against rain gauge data at daily time scales. However CC values were computed at monthly time scale and comparison between average annual values of satellite data and gauge values was also conducted. At annual basis significant resemblance was observed between spatial distribution of satellite and gauge data. At monthly time scale satellite products showed correlation in the range of 0.7 to 0.9 against reference data at major portion of the study area. But at daily time scale a weak correlation from 0.2 to 0.45, higher values of bias and RMSE in hilly terrains with highest average annual rainfall and the adjacent foot hill areas as compared to the plain areas, lower values of POD, CSI and higher values of FAR were observed. An inter comparison among the used satellite products revealed that overall performance of IMRT was better in the study area.

An evaluative study of TRMM precipitation estimates over multi-day scales in a semi-arid region, Iran

ABSTRACTData from the Tropical Rainfall Measuring Mission (TRMM) rainfall estimations have been evaluated at different time scales in the previous research, in particular, sub-daily, monthly, seasonally and annually. However, in arid and semi-arid regions water balance may be reached several days after a rainfall event. Hence, it becomes of crucial importance to investigate sub-monthly time periods (i.e. multi-day periods). For this reason, TRMM precipitation data version 3B42 (3B42) were evaluated and calibrated for 1, 2, 3, 5, 7, 10, 15, 20 days and monthly time scales using rain gauges data in Fars province, Islamic Republic of Iran, 1 January 2000 to 31 December 2014. Pearson’s correlation coefficient (r), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Bias (MB), Prediction of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) were used for the purpose of evaluation. The results showed that with a logarithmic trend, r, NRMSE, and FAR values decreased while RMSE, CSI, and POD values increased with increasing time scales. Moreover, the spatial average MB was almost constant for various time scales, although the percentage of grid cells with over-estimated rainfall increased from 1 day to 1 month. By fitting logarithmic functions over the values of r, RMSE, NRMSE, POD, FAR, and CSI at 1, 10 days, and monthly time scales, the corresponding values of these measures were predicted for other time scales with the relative error (NRMSE) of less than 0.1, which indicates the accurate performance of these functions. Through linear regression analysis, the slope (M) and interception (B) of the equations for calibrating 3B42 precipitation estimates at various time scales were obtained. Furthermore, the results showed that the obtained values of M and B in 1, 10 days, and monthly time scales can be estimated with a high accuracy at 2, 3, 5, 7, 15, and 20 days.

Observational test of empirical magnetopause location models using geosynchronous satellite data

AbstractIn this study, we identify 123 geosynchronous magnetopause crossings using geosynchronous satellite observation data from 1996 to 2010 as well as make an observational test of magnetopause location models using the identified events. For this, we consider three models: Petrinec and Russell (1996), Shue et al. (1998), and Lin et al. (2010). To evaluate the models, we estimate a probability of detection (PoD) and a critical success index (CSI) as a function of year. To examine the effect of solar cycle phase, we consider three different time periods: (1) ascending phase (1996–1999), (2) maximum phase (2000–2002), and (3) descending phase (2003–2008). Major results from this study are as follows. First, the PoD values of all models range from 0.4 to 0.8 for the most of years. Second, the PoD values of Lin et al. (2010) are noticeably higher than those of the other models. Third, the CSI values of all models range from 0.1 to 0.3, and those of Shue et al. (1998) are slightly higher than those of the other models. Fourth, the predicted magnetopause radii based on Lin et al.(2010) well match the observed ones within 1 Earth radius, while those on Shue et al. (1998) overestimate the observed ones by about 2 Earth radii. Fifth, the PoD and critical success index (CSI) values of all the models are better for the solar maximum phase than those for the other phases, implying that the models are more optimized for the phase.

기상청 현업 모형(UM)과 1차원 난류모형(PAFOG)의 접합시스템 개발 및 검증

As an attempt to improve fog predictability at Incheon International Airport (IIA) we couple the 3D weather forecasting model currently operational in Korea Meteorological Administration (regional Unified Model, UM_RE) with a 1D turbulence model (PAFOG). The coupling is done by extracting the meteorological data from the 3D model and properly insert- ing them in the PAFOG model as initial conditions and external forcing. The initial conditions include surface temperature, 2 m temperature and dew point temperature, geostrophic wind at 850 hPa and vertical profiles of temperature and dew point temperature. Moisture and tempera- ture advections are included as external forcing and updated every hr. To validate the perfor- mance of the coupled system, simulation results of the coupled system are compared to those of the 3D model alone for the 22 sea fog cases observed over the Yellow Sea. Three statistical indices, i.e., Root Mean Square Error (RMSE), linear correlation coefficient (R) and Critical Success Index (CSI), are examined, and they all indicate that the coupled system performs bet- ter than the 3D model alone. These are certainly promising results but more improvement is required before the coupled system can actually be used as an operational fog forecasting model. For the RMSE, R, and CSI values for the coupled system are still not good enough for operational fog forecast.

Impacts of Forecaster Involvement on Convective Storm Initiation and Evolution Nowcasting

Abstract A forecaster-interactive capability was added to an automated convective storm nowcasting system [Auto-Nowcaster (ANC)] to allow forecasters to enhance the performance of 1-h nowcasts of convective storm initiation and evolution produced every 6 min. This Forecaster-Over-The-Loop (FOTL-ANC) system was tested at the National Weather Service Fort Worth–Dallas, Texas, Weather Forecast Office during daily operations from 2005 to 2010. The forecaster’s role was to enter the locations of surface convergence boundaries into the ANC prior to dissemination of nowcasts to the Center Weather Service Unit. Verification of the FOTL-ANC versus ANC (no human) nowcasts was conducted on the convective scale. Categorical verification scores were computed for 30 subdomains within the forecast domain. Special focus was placed on subdomains that included convergence boundaries for evaluation of forecaster involvement and impact on the FOTL-ANC nowcasts. The probability of detection of convective storms increased by 20%–60% with little to no change observed in the false-alarm ratios. Bias values increased from 0.8–1.0 to 1.0–3.0 with human involvement. The accuracy of storm nowcasts notably improved with forecaster involvement; critical success index (CSI) values increased from 0.15–0.25 (ANC) to 0.2–0.4 (FOTL-ANC). Over short time periods, CSI values as large as 0.6 were also observed. This study demonstrated definitively that forecaster involvement led to positive improvement in the nowcasts in most cases while causing no degradation in other cases; a few exceptions are noted. Results show that forecasters can play an important role in the production of rapidly updated, convective storm nowcasts for end users.

Radar Nowcasting of Cloud-to-Ground Lightning over Houston, Texas

Abstract Ten years (1997–2006) of summer (June–August) daytime (1400–0000 UTC) Weather Surveillance Radar-1988 Doppler data for Houston, Texas, were examined to determine the best radar-derived predictors of the first cloud-to-ground lightning flash from a convective cell. Convective cells were tracked using a modified version of the Storm Cell Identification and Tracking (SCIT) algorithm and then correlated to cloud-to-ground lightning data from the National Lightning Detection Network (NLDN). Combinations of three radar reflectivity values (30, 35, and 40 dBZ) at four isothermal levels (−10°, −15°, −20°, and updraft −10°C) and a new radar-derived product, vertically integrated ice (VII), were used to optimize a radar-based lightning forecast algorithm. Forecasts were also delineated by range and the number of times a cell was identified and tracked by the modified SCIT algorithm. This study objectively analyzed 67 384 unique cells and 1 028 510 lightning flashes to find the best lightning forecast criteria. Results show that using 30 dBZ at the −15° or −20°C isotherm on cells within 75 km of the radar that have been tracked for at least two consecutive scans produces the best lightning forecasts with a critical success index (CSI) of 0.68. The best VII predictor values were 0.42 or 0.58 kg m−2 on cells within 75 km of the radar that have been tracked for at least two consecutive scans, producing a CSI of 0.67. Lead times for these predictors were 10.0 and 13.4 min, respectively. Lead times greater than 10 min occurred with less stringent predictors (e.g., 30 dBZ at −10°C or VII greater than 0.25 kg m−2 on cells within 125 km with a minimum track count of 2), but lower CSI values result. In general, cells tracked for multiple scans provide higher CSIs and lead times than decreasing the range from the radar or changing the reflectivity threshold and height.

Categorical forecasts of convective events using upper air predictors

AbstractAerological data from the European area and Czech ground data were used to derive predictor vectors that would be efficient for making categorical forecasts of convective events over the Czech Republic. A series of simulated forecasts were performed with sets of predictor vectors. The Critical Success Index (CSI) was chosen to be the measure of the forecast accuracy. The convective precipitation and thunderstorms that occurred during the periods <0000,0600>, <0600,1200> and <0000,1200> UTC were forecast with the use of the data from 0000 UTC. Likewise, the data from 1200 UTC were used to predict event occurrences during periods <1200,1800>, <1800,2400> and <1200,2400> UTC. The resultant maximum CSI values corresponding to the different predictands are discussed, and their dependence on the event frequency is described. The Heidke Score and the Equitable Score determined for the predictor vectors with maximum CSI are indicated. The diagnostic predictor vectors can be viewed as the representation of the prognostic information under the Perfect Prog assumption.