An Automated, Multiparameter Dryline Identification Algorithm

ABSTRACTIn this paper, we have proposed a method of sea fog prediction using model sea surface wind (SSW) data and hourly optical satellite data with 500 m spatial resolution. Three sea fog cases in the Yellow Sea were selected from Geostationary Ocean Color Imager (GOCI) images, and their shifts were determined from the fog cluster centroid displacements at successive hours. The 4 km resolution Weather Research and Forecasting (WRF) model SSW were used to predict sea fog shifts. The sea fog prediction results were verified using validation indices like probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The predictions started with high initial skill scores (POD = 0.76, FAR = 0.18, and CSI = 0.66), and showed values 0.59, 0.30, and 0.46 for POD, FAR, and CSI, respectively for predictions after two hours. After three hours their values still remained close to the medians though decreased, but afterwards decreased considerably for POD and CSI. Thus the method is found suitable for short-term prediction of sea fog. Additionally, trajectory sensitivities were compared between 20 and 4 km resolution WRF which, in general, resulted in less errors for 4 km WRF model SSW.

The evaluation and usefulness of lightning prediction for the Indian subcontinent are demonstrated. Implementation of the lightning parameterizations based on storm parameters, in the Weather Research and Forecasting (WRF) Model, with different microphysics schemes are carried out. With the availability of observed lightning measurements over Maharashtra from the lightning detection network (LDN), lightning cases have been identified during the pre-monsoon season of 2016–18. Lightning parameterization based on cloud top height defined by a reflectivity threshold factor of 20 dBZ is chosen. Initial analysis is carried out for 16 lightning events with four microphysical schemes for the usefulness in lightning prediction. Objective analysis is carried out and quantitative model performance (skill scores) is assessed based on observed data. The skills are evaluated for 10- and 50-km2 boxes from the 1-km domain. There is good POD of 0.86, 0.82, 0.85, and 0.84, and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from WSM6, Thompson, Morrison, and WDM6, respectively. There is an overestimation in lightning flash with a spatial and temporal shift. The fractional skill score is evaluated as a function of spatial scale with neighborhoods from 25 to 250 km. These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast over India. The skill for 2019 and 2020 pre-monsoon are calculated to address the predictability of operational lightning prediction over India. Significance Statement A high-resolution model, namely, the Weather Research and Forecasting (WRF) Model, with multiple microphysics parameterization schemes and lightning parameterization is used here. The objective analysis is carried out for the lightning cases over India and the quantitative performance is assessed. The results highlight that there is fairly good probability of detection (POD) of 0.86, 0.82, 0.85, and 0.84 and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from four different microphysical schemes (WSM6, Thompson, Morrison, and WDM6, respectively). These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast. The validation of lightning forecast system deployed over India for five pre-monsoon months in real time is carried out, which gives POD of 0.90, FAR of 0.64, hit rate of 0.57, and POFD of 0.50 for the whole Indian region.

This study investigates the short-range (0–12 h) predictability of convection initiation (CI) using the Advanced Research Weather Research and Forecasting (WRF) Model (ARW) with a horizontal grid spacing of 429 m. A unique object-based method is used to evaluate model performance for 25 cases of CI across the west-central high plains of the United States from the 2010 convective season. In the aggregate, there exists a high probability of detection but, due to the significant overproduction of CI events by the model, high false alarm and bias ratios that lead to modestly skillful forecasts. Model CI objects that are matched with observed CI objects show, on average, an early bias of about 3 min and distance errors of around 38 km. The operational utility and inherent biases of such high-resolution simulations are discussed.

In this study, the potential of two numerical weather prediction (NWP) models in quantitative precipitation forecasting over a tropical region is examined. The precipitation forecasts produced from the Fifth Generation Penn State/NCAR Mesoscale (MM5) and Weather Research and Forecasting (WRF) models are statistically verified with the observed rain in Kelantan River Basin, Malaysia. The statistical verification indicates that the root mean squared error (RMSE) increases for higher rainfall rate, though the models have performed quite satisfactorily in certain cases during heavy rainfalls that cause flood. It is also shown that the longer the rainfall forecast duration, the higher the probability of detection (POD) and the lesser probability to be the false alarm ratio (FAR)

Within the HESPERIA Horizon 2020 project, two novel real-time tools to predict Solar Energetic Particle (SEP) events were developed. The HESPERIA UMASEP-500 tool makes real-time predictions using a lag-correlation between the soft X-ray (SXR) flux and high-energy differential proton fluxes of the GOES satellite network. We found that the use of proton data alone allowed this tool to make predictions before any Neutron Monitor (NM) station’s alert. The performance of this tool for predicting Ground Level Enhancement (GLE) events for the period 2000–2016 may be summarized as follows: the probability of detection (POD) was 53.8%, the false alarm ratio (FAR) was 30%, and the average warning time (AWT) to the first NM station’s alert was 8 min. The developed HESPERIA REleASE tool makes real-time predictions of the proton flux-time profiles of 30–50 MeV protons at L1 and is based on electron intensity measurements of energies from 0.25 to 1 MeV and their intensity changes. The performance was tested by using all historic ACE/EPAM and SOHO/EPHIN data from 2009 until 2016 and has shown that the forecast tools have a low FAR (∼30%) and a high POD (63%). Furthermore, two methods using historical data were explored for predicting SEP events and compared. The UMASEP-10mw tool was developed for predicting >10 MeV SEP events using microwave data. The time derivative of the soft X-rays (SXR) was replaced by the microwave flux density. It was found that the use of SXRs and microwave data produced the same POD (∼78%) with the most notable difference being that the use of microwave data does not yield any false alarm. Furthermore, a study was carried out on the possibility for the microwave emissions to be used to predict the spectral hardness of the SEP event and important results were deduced.

As the need for regional reanalyses emerged around the world, a short period of the East Asia Regional Reanalysis (EARR) system was recently developed based on the Unified Model (UM). In this study, the quality of the EARR is evaluated by comparing the short-range precipitation reforecasts against reforecasts of ERA-Interim (ERA-I) reanalysis and operational forecasts of the Korea Meteorological Administration (OPER). For the verification, two different periods are selected for 14 days in the summer (July 2013, denoted as 201307) and winter (February 2014, denoted as 201402). The equitable threat score (ETS) of EARR and OPER is generally greater than that of ERA-I. The frequency bias index (FBI) of EARR and OPER is overall closer to 1 than that of ERA-I for all thresholds, which indicates that EARR and OPER are much closer to the observation compared to ERA-I. For the period 201307, the ERA-I FBI is greater than 1 for lower thresholds and the probability of detection (POD) and false alarm ratio (FAR) of ERA-I are high, implying that ERA-I tends to overforecast light precipitation. In addition, using the same Weather Research and Forecasting (WRF) Model, the 6-h precipitation forecasts are integrated every 12 h (initialized from 0000/1200 UTC) for 4 months for the summer and winter season. Although the differences of ETS and FBI between EARR and ERA-I are not distinct for the summer season, overall EARR ETS is higher than ERA-I ETS, and EARR FBI is closer to 1 than ERA-I FBI. Based on several evaluations, the precipitation reforecasts of EARR are confirmed to be more accurate than those of OPER and ERA-I in East Asia.

Warm-sector thunderstorms (WSTs), characterized by weak synoptic forcing and extreme precipitation rates, pose a major global forecasting challenge. This study investigates the mesoscale processes initiating WSTs over the complex terrain of the Democratic People's Republic of Korea (DPRK), a region where triggering mechanisms remain poorly understood. We analyze three extreme rainfall events (Hoichang 2016, Unpa 2017, Pyongyang 2018), each producing rainfall rates exceeding 60 mm h⁻1 under the weak forcing typical of the northwestern periphery of the West Pacific Subtropical High. While operational global models failed to predict these events, the convection-permitting WRF model skillfully replicated the initiating mechanisms and subsequent convection. Model performance was quantitatively assessed using multiple verification metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), Bias, and Critical Success Index (CSI). Integrated analysis of observations and high-resolution (3 km) Weather Research and Forecasting (WRF) model simulations reveals a consistent trigger: mesoscale boundary-layer convergence lines. These zones formed through the interaction of synoptic southwesterlies with localized, terrain-modulated flows and were collocated with horizontal moisture gradients. Crucially, the three-dimensional structure of Convective Available Potential Energy (CAPE) manifested as narrow, vertical towers of high instability, delineating regions of deep convection initiation 2–4 h in advance. A pre-convective, deep moist layer (relative humidity >80% in the 850–700 hPa layer) was identified as a necessary precondition. This study establishes terrain-forced boundary-layer convergence as a primary trigger for WSTs over the DPRK, providing a valuable framework for improving prediction in other monsoonal regions with complex topography.

In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor.

The prediction of solar energetic particle (SEP) events may help to improve the mitigation of adverse effects on humans and technology in space. UMASEP (University of Málaga Solar particle Event Predictor) is an empirical model scheme that predicts SEP events. This scheme is based on a dual-model approach. The first model predicts well-connected events by using an improved lag-correlation algorithm for analyzing soft X-ray (SXR) and differential proton fluxes to estimate empirically the Sun–Earth magnetic connectivity. The second model predicts poorly connected events by analyzing the evolution of differential proton fluxes. This study presents the evaluation of UMASEP-10 version 2, a tool based on the aforementioned scheme for predicting all >10 MeV SEP events, including those without associated flare. The evaluation of this tool is presented in terms of the probability of detection (POD), false alarm ratio (FAR) and average warning time (AWT). The best performance was achieved for the solar cycle 24 (i.e., 2008–2019), obtaining a POD of 91.1% (41/45), a FAR of 12.8% (6/47) and an AWT of 2 h 46 min. These results show that UMASEP-10 version 2 obtains a high POD and low FAR mainly because it is able to detect true Sun–Earth magnetic connections.

The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing (SYNOP) stations of the German Weather Service and the results of the ground fog detection schemes reveals that the lookup table approach shows the best performance for both a valley fog situation and an extended layer of low stratus with complex local visibility structures. Even if the results are very encouraging [probability of detection (POD) = 0.76], relatively high percentage errors and false alarm ratios still occur. Uncertainties in the retrieval scheme are mostly due to possible collocation errors and known problems caused by comparing point and pixel data (time lag between satellite overpass and ground observation, etc.). A careful inspection of the pixels that mainly contribute to the false alarm ratio reveals problems with thin cirrus layers and the fog-edge position of the SYNOP stations. Validation results can be improved by removing these suspicious pixels (e.g., percentage error decreases from 28% to 22%).

Rainfall is the most important factor in the Earth’s water and energy cycles. The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the correlation coefficient (CC) provides better validation in the rainy season while root mean square error (RMSE) is more accurate in the dry season. The highest proportion correct (PC) value is obtained for Bali-NTT, while the highest probability of detection (POD) and false alarm ratio (FAR) values are obtained for Kalimantan. GSMaP-MVK data is over-estimated compared to observations in Indonesia, with the mean accuracy for daily rainfall estimation being 85.47% in 2014, 85.74% in 2015, 82.73 in 2016, and 82.59% in 2017.

<p>Radar rainfall nowcasting promises to result in more accurate and timely rainfall forecasts up to several hours in advance, compared to numerical weather prediction model forecasts. As flood early warning systems can benefit from this, we assessed the potential of radar rainfall nowcasting for (flash) flood forecasting with a large sample of 659 individual rainfall events for 12 catchments in the Netherlands. In this assessment, we tested four open-source nowcasting algorithms: Rainymotion Sparse (RM-S), Rainymotion DenseRotation (RM-DR), Pysteps deterministic (PS-D) and Pysteps probabilistic (PS-P) with 20 ensemble members. Eulerian Persistence (EP) forecasts and zero precipitation input (ZP) were the benchmark. The discharge forecasts had a 12-h forecast horizon and were issued for every 5-min step in the available nowcasts for the set of events. We regarded the simulations using the observed radar rainfall as reference.</p><p>We found that rainfall and discharge forecast errors increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship also depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity, when the initial conditions are wet and groundwater tables are shallow. Overall, discharge forecasts using RM-DR, PS-D and PS-P outperform the other nowcasting methods.</p><p>Furthermore, we tested the potential for forecasting threshold exceedances by setting the highest discharge, per event, as threshold. Compared to benchmark ZP, a threshold exceedance is, on average, forecast 223 (EP), 196 (RM-S), 213 (RM-DR), 119 (PS-D) and 143 min (PS-P) earlier. The relatively high time profits with EP are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS-D and PS-P show both lower FAR and inconsistency index values, which can lead to more trust in the simulations.</p><p>Based on this large-sample analysis, we conclude that all nowcasting methods have shown a benefit for short-term discharge forecasting compared to issuing no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in (flash) flood forecasting, we recommend a future focus on improving this aspect in nowcasting.</p>

Extreme precipitation events have increasingly happened at global and regional scales as the global climate has changed in recent decades. Accurate quantitative precipitation estimation (QPE) plays an important role in the warning of extreme precipitation events. With hourly rain gauge observations as a reference, this study compares the performance of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) quantitative precipitation estimation (QPE) products over Northern China in 2021. The Probability of Detection (POD), Relative Bias (RB), Root-Mean-Squared Error (RMSE), and Fractional Standard Error (FSE) are among the assessment metrics, as are the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). We examined the spatial distribution of cumulative precipitation and the temporal distribution of hourly average precipitation for three severe precipitation occurrences using these assessment metrics. The IMERG products capture strong precipitation centers that are compatible with the gauge observations, especially in extreme precipitation events in areas with relatively flat terrain and low-altitude (≤1000 m). Both IMERG (National Aeronautics and Space Administration, NASA) and GSMaP (Japan Aerospace Exploration Agency, JAXA) satellite-based QPE products have precipitation peaks in advance (2–4 h) and generally underestimate (overestimate) precipitation when the actual precipitation is heavy (light). The satellite-based QPE products generally overestimate the heavy rainfall caused by non-typhoons and underestimate the heavy rainfall caused by typhoons. The GSMaP products may have the capacity to detect short-term rainstorm events. The accuracy of satellite-based QPE products may be influenced by precipitation intensity, sensors, terrain, and other variables. Therefore, in accordance with our recommendations, more ground rainfall stations should be used to collect actual precipitation data in regions with high levels of spatial heterogeneity and complex topography. The data programmers should strengthen the weights computation retrieval technique and fully utilize infrared (IR)-based data. Furthermore, this study is expected to give helpful feedback to the algorithm developers of IMERG and GSMaP products, as well as those researchers into the use of IMERG and GSMaP satellite-based QPE products in applications.

Assimilating GNSS PWV and radiosonde meteorological profiles to improve the PWV and rainfall forecasting performance from the Weather Research and Forecasting (WRF) model over the South China

Evaluations on gridded precipitation products spanning more than half a century over the Tibetan Plateau and its surroundings