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In this study, we applied the conditional nonlinear optimal perturbation (CNOP) method to generate nonlinear fast‐growing initial perturbations for ensemble forecasting, aiming to assess the effectiveness of the CNOP method in improving the forecast skill of climate events. Our findings reveal a significant improvement in the forecast skill of the Indian Ocean Dipole (IOD) within the CNOP ensemble forecast, particularly at long lead times, thereby extending the skilful forecast lead times. Notably, this improvement is more prominent for strong IOD events, with skilful forecast lead times exceeding 12 months, outperforming many current state‐of‐the‐art coupled models. The high forecast skill of the CNOP method is primarily attributed to its ability to capture the uncertainties in the wind anomaly field in the eastern Indian Ocean (EIO) closely associated with IOD evolution. Consequently, CNOP ensemble members exhibit significant deviations from the control forecast, resulting in a large ensemble spread encompassing IOD evolution. Furthermore, a comparison with the climate‐relevant singular vectors (CSV) method in terms of IOD and El Niño–Southern Oscillation (ENSO) predictions reveals the superior performance of the CNOP ensemble forecast. Despite the initial perturbations for ensemble forecasting being generated aimed at improving IOD forecast skill, the CNOP method significantly improves the forecast skill of both IOD and ENSO events, with a greater improvement for ENSO. Additionally, the CNOP ensemble forecast system provides more reliable estimates of forecast uncertainties and exhibits higher reliability with increasing lead times. In conclusion, the CNOP method effectively captures the nonlinear physical processes of climate events and improve the forecast skill. [ABSTRACT FROM AUTHOR]

Traffic flow prediction can support proactive control strategies to alleviate queues and delays at expressway toll stations. The prediction should be enough accurate and advanced that leaves sufficient preparation time to implement control measures for upcoming congestions. The accuracy of prediction and the lead time for accurate prediction are contradictory. To balance both aspects, a control-oriented combined prediction method was proposed in this study. This method automatically tuned prediction horizons based on predicted level of congestion. It used long short-term memory (LSTM) neural networks to learn the spatial–temporal characteristics of historical traffic flow and predict short-term and medium-term traffic flow at toll stations. Then, the level of congestion was determined for graded congestion warning and dynamic tuning of prediction horizons. To validate the proposed method, the Ganggou toll station was selected as the study site. The proposed integrated model can facilitate reliable proactive control measures at expressway toll stations. [ABSTRACT FROM AUTHOR]

The critical role of latent heating (LH) in atmospheric blocking events is well-recognized, yet its influence on the predictability of such events remains underexplored. This study examines the prediction accuracy of winter atmospheric blocking days over the Northern Hemisphere, utilizing 20 years of retrospective forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric blocking events are categorized into three sectors: Atlantic, Continent, and Pacific. Results indicate substantial variations in the LH contribution among different blocking events. During the first 21 days, the Pacific blockings exhibit the most significant impact on prediction skill under strong LH conditions, followed by the Continent blockings, with the Atlantic blockings being least affected. Interestingly, a consistent pattern emerged across sectors: blocking prediction skill is lower under strong LH than under weak LH. During the first 12 days, the Pacific sector typically exhibit lower prediction skill for blocking onset under strong LH, compared to weak LH. For blocking decay, a consistent pattern is observed: lower prediction skill under strong LH than weak LH. Regarding blocking intensity, the forecasting bias escalates with increasing lead time, particularly for strong LH. This study highlights the need to improve moist process simulations in models, aiming to improve atmospheric blocking forecasting accuracy. [ABSTRACT FROM AUTHOR]

We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO. [ABSTRACT FROM AUTHOR]

The operational sea ice forecasts from the Arctic Ice Ocean Prediction System (ArcIOPS) during 2021–2023 are validated against satellite-retrieved sea ice concentration and drift data, in situ and reanalyzed sea ice thickness data. The results indicate that the ArcIOPS has a reliable capacity on the Arctic sea ice forecasts for the future 7 days. Over the validation period, the root mean square error (RMSE) of the ArcIOPS sea ice concentration forecasts at a lead time of up to 168 h ranges between 8% and 20%, and the integrated ice edge error (IIEE) is lower than 1.6 × 106 km2 with respect to the Hai Yang 2B (HY-2B) sea ice concentration data. Compared to the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS), sea ice volume evolution from the ArcIOPS forecasts is closer to that derived from the CS2SMOS sea ice thickness observations, which have been assimilated into the ArcIOPS. Sea ice thickness comparisons at three locations in the Beaufort Sea between the ArcIOPS forecasts and in situ mooring observations also prove that the sea ice thickness forecasts are credible, which sets a solid basis for supporting ice-breaker navigation in the Arctic thick ice zone. The sea ice drift deviations between the ArcIOPS forecasts and the National Snow and Ice Data Center (NSIDC) data are lower than 4 cm/s in most of the months. Future work will emphasize on developing multi-variable data assimilation scheme and fully coupled air‒ice‒ocean forecasting system for the Arctic sea ice forecasts. [ABSTRACT FROM AUTHOR]

To explore tropical cyclone prediction methods that integrate multimodal meteorological data, this study proposes a novel approach. The proposed model employs an LSTM-based temporal branch to extract temporal sequence features from the CMA dataset and a U-Net-based spatial branch to extract three-dimensional spatial features from the ERA5 dataset. These features are then fused through an encoder-decoder structure to integrate high-dimensional spatiotemporal characteristics. Experimental results demonstrate that the spatiotemporal model significantly improves the prediction accuracy for 24-hour lead times. Subsequently, to further optimize the experimental results, the study introduces an ensemble forecasting framework. This framework enhances prediction accuracy by adjusting the outputs of multiple spatiotemporal model prediction members. The optimization is achieved by solving the objective function that reflects the forecast geographical error, thereby optimizing the weighted coefficients. The experimental results indicate that the ensemble forecasting framework can further optimize prediction outcomes. [ABSTRACT FROM AUTHOR]

The Río de la Plata Estuary (RdP) is frequently affected by large storm surges that have historically caused social and economic losses. According to recent research, the number and strength of surge events have been increasing over time as a result of climate change. Although process-based models have been widely used for the storm surge prediction, their high computational demand may be a significant disadvantage in some applications, such as rapid or neartime forecasting. Artificial neural network (ANN) becomes an alternative tool to forecast the water level, taking into account meteorological and astronomical forcing as numerical models also do. In this work, an ANN model performance was evaluated to hindcast and forecast water levels in the RdP. Several combinations of lead times and inputs were assessed in order to find the best configuration. The resulting model provides 4-day forecasts for Buenos Aires and Torre Oyarvide stations (located at the upper and intermediate estuary, respectively), using observed water levels, meteorological inputs and predicted astronomical tides. Results also support the ANN model's ability to simulate even extreme events. For instance, for a 12 h-forecast, the RMSE is about 20 cm. Finally, we conclude that the model developed here can effectively complement the empirical and numerical forecasts executed by Naval Hydrographic Service, reducing computational costs and leveraging available datasets. [ABSTRACT FROM AUTHOR]

Forecast error growth as a function of lead time of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. The question under investigation is whether omitting these atmospheric phenomena will improve the predictability of the resulting value. The topic is studied in the extended Lorenz (2005) system. This system shows that omitting small spatiotemporal scales that significantly affect prediction ability will reduce predictability more than modeling it. In other words, a system with model error (omitting phenomena) will not improve predictability. A hypothesis explaining and describing this behavior is developed, with the difference between systems (model error) produced at each time step seen as the error of the initial conditions. The resulting model error is then defined as the sum of the increments of the time evolution of the initial conditions so defined. The hypothesis is compared to the fit parameters that define the model error in certain approximations of the average forecast error growth. Parameters are interpreted in this context, and the approximations are used to estimate the errors described in the hypothesis. A method is proposed to distinguish increments of prediction error growth from small-spatiotemporal-scale phenomena and model errors. Results are presented for the error growth of the ECMWF system, where a 40 % reduction in model error between 1987 and 2011 is calculated based on the developed hypothesis, while over the same period the instability (error growth rate) of the system with respect to initial condition errors has grown. [ABSTRACT FROM AUTHOR]

Flash floods pose a distinct challenge compared to traditional fluvial flooding, with infrastructure-based solutions proving less effective. Effective responses hinge on advanced early warning systems providing actionable information, emphasising the necessity for computational flood forecasting models. However, hydrodynamic models, renowned for accuracy and completeness, face limitations due to computational intensity. This study explores two 2D flood forecasting models, RIM2D and SERGHEI, both with GPU implementations which allow us to maximise the forecast lead time. While RIM2D is less computationally intensive, suitable for operational use, SERGHEI, with higher computational costs, targets large-scale high-performance computing (HPC) systems. The assessment of applicability and trade-offs is carried out on the 2021 Eifel flood event, particularly in the lower Ahr valley. A set of simulations were performed at various resolutions from 1 to 10 m, which reveal similar accuracy among both models at coarser resolutions, yet discrepancies arise at finer resolutions due to the distinct formulations. Both models exhibit a rapid computational cost escalation, but at resolutions equal to or coarser than 5 m, forecasts are remarkably faster than the real-time ideal for operational use, paving the way for their use in early warning systems. However, higher resolutions necessitate multi-GPU and HPC capabilities, underlining the importance of embracing such technology in addressing broader flood domains. [ABSTRACT FROM AUTHOR]

Atmospheric prediction at 2–6 weeks lead time (so‐called subseasonal‐to‐seasonal timescales) entails large forecast uncertainty. Here we investigate the flow‐dependence of this uncertainty during Boreal winter. We categorize the large‐scale flow using North Atlantic‐European weather regimes. First, we show that forecast uncertainty of near‐surface geopotential height (Z1000) and temperature (T2m) are strongly sensitive to the prevailing regime. Specifically, forecast uncertainty of Z1000 reduces over northern Europe following Greenland Blocking (enhanced predictability) due to a southward shifting eddy‐driven jet. However, due to strong temperature gradients and variable flow patterns, Greenland blocking is linked to increased forecast uncertainty of T2m over Europe (reduced predictability). Second, we show that forecast uncertainty of weather regimes is modulated via the stratospheric polar vortex. Weak polar vortex states tend to reduce regime‐uncertainty, for example, due to more frequent predicted occurrence of Greenland blocking. These regime changes are associated with increased T2m uncertainty over Europe. Plain Language Summary: Weather is chaotic, and forecasts several weeks ahead are quite uncertain. Nevertheless, the degree of uncertainty varies, which can be relevant for long‐term planning in various sectors, including agriculture, energy supply, and public health. Here we show that the degree of uncertainty depends on the weather at the time of forecast start, which we categorize using eight characteristic weather patterns for the North Atlantic and Europe. We analyze a large set of forecasts during winter, with lead times up to 6 weeks. For example, persistent high‐pressure systems over Greenland are known to favor low temperatures over northern Europe. Our results indicate that, in addition, temperatures are highly variable in these cases, leading to unusually high forecast uncertainty. In contrast, uncertainty of near‐surface air pressure tends to decrease due to less frequent storms over the North Atlantic. Furthermore, we show that forecast uncertainty of the weather patterns themselves varies, which is useful when large‐scale flow conditions are more critical than local weather. We analyze forecasts under different circulation conditions in the Arctic stratosphere, as these can have long‐lasting impacts on surface weather. We find forecasts of weather patterns become less uncertain when the circumpolar winds in the Arctic stratosphere are weak. Key Points: Ensemble spread of near‐surface weather in subseasonal forecasts is sensitive to the prevailing North Atlantic‐European weather regimeGreenland blocking is linked to the smallest ensemble spread of geopotential but to the largest spread of temperature over northern EuropeWeak polar vortex states are followed by reduced forecast uncertainty of weather regimes but increased uncertainty of European temperature [ABSTRACT FROM AUTHOR]

Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly. [ABSTRACT FROM AUTHOR]

Accurate prediction of tropical cyclone (TC) intensity is challenging due to the complex physical processes involved. Here, we introduce a new TC intensity prediction scheme for the western North Pacific (WNP) based on a time-dependent theory of TC intensification, termed the energetically based dynamical system (EBDS) model, together with the use of a long short-term memory (LSTM) neural network. In time-dependent theory, TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors, expressed as environmental dynamical efficiency. The LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using best-track TC data and global reanalysis data during 1982–2017. The transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System (GFS) of the National Centers for Environmental Prediction during 2017–21. The predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC intensity. The new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction data. The intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration (CMA) and those by other state-of-art statistical and dynamical forecast systems, except for the 72-h forecast. Particularly at the longer lead times of 96 h and 120 h, the new scheme has smaller forecast errors, with a more than 30% improvement over the official forecasts. [ABSTRACT FROM AUTHOR]

A significant increase in the number of anthropogenic objects in Earth orbit has necessitated the development of satellite conjunction assessment and collision avoidance capabilities for new spacecraft. Neutral mass density variability in the thermosphere, driven by enhanced geomagnetic activity and solar EUV absorption, is a major source of satellite propagation error. This work investigates the impacts of space weather driver forecasting uncertainty on satellite drag and collision avoidance maneuver decision‐making. Since most operational space weather driver forecasts do not offer an uncertainty assessment, the satellite operator community is left to make dangerous assumptions about the trustworthiness of the forecast models they use to perform satellite state propagation. Climatological persistence‐based forecast models are developed for F10.7 and Kp. These models accurately capture the heteroscedastic and, at times, highly non‐Gaussian uncertainty distribution on forecasts of the drivers of interest. A set of realistic satellite conjunction scenarios is simulated to demonstrate the contributions of space weather driver forecast uncertainty on the probability of collision and maneuver decisions. Improved driver forecasts, especially forecasts of F10.7, are demonstrated to be very useful for enabling durable maneuver decisions with additional lead time (up to 24 hr for the period examined), though the improvement depends on the specific conjunction scenario of interest. Plain Language Summary: As low earth orbit has become crowded with new satellites and debris, operators have been forced to maneuver satellites to avoid collisions on a regular basis. The drag force on a satellite, which can significantly affect the orbital path, varies depending on solar and geomagnetic activity. Unfortunately, solar activity and resulting impacts at Earth are difficult to predict even just a few days in advance. This paper traces space weather forecasting ability directly to impacts on satellite maneuver decision‐making and finds that forecast uncertainty plays a critical role in making durable maneuver decisions. Key Points: Poor forecasts of space weather drivers inhibit our ability to perform actionable satellite conjunction assessment with advance noticeClimatology‐based probabilistic forecasts are developed for F10.7 and Kp, which are used to assess satellite conjunction scenariosProviding probabilistic forecasts of the space weather drivers makes a meaningful difference in maneuver decision‐making [ABSTRACT FROM AUTHOR]

Inventory prediction is concerned with the forecasting of future demand for products in order to optimize inventory levels and supply chain management. The challenges include demand volatility, data quality, multi-dimensional interactions, lead time variability, seasonal trends, and dynamic pricing. Nevertheless, these models suffer from numerous shortcomings, and in this research, we propose a new model, MMCW-BiLSTM (modified multi-dimensional collaboratively wrapped BiLSTM), for inventory prediction. The MMCW-BiLSTM model reflects a considerable leap in inventory forecasting by combining a number of components in order to consider intricate temporal dependencies and incorporate feature interactions. The MMCW-BiLSTM makes use of BiLSTM layers, collaborative attention mechanisms, and a multi-dimensional attention approach to learn from augmented datasets consisting of the original features and the extracted time series data. Moreover, adding a Taylor series transformation allows for a more precise description of the features in the model, thus improving the prediction precision. The results show that the models make the least mistakes when they use the AV demand forecasting dataset, with MAE values of 1.75, MAPE values of 2.89, MSE values of 6.76, and RMSE values of 2.6. Similarly, when utilizing the product demand dataset, the model also achieves the lowest error values for these metrics at 1.97, 3.91, 8.76, and 2.96. Likewise, when utilizing the dairy goods sales dataset, the model also achieves the lowest error values for these metrics at 2.54, 3.69, 10.39, and 3.22. [ABSTRACT FROM AUTHOR]

Sub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region. Plain Language Summary: Timely and accurate forecasts of weather events before their occurrence is important for decision‐makers in effective planning and actions to cope with weather‐induced disasters. Such forecast plays a crucial role in disaster preparedness over complex terrain regions like Nepal. Since sub‐seasonal to seasonal (S2S) scale prediction can provide forecasts beyond 10 days up to 30 days, is getting popular nowadays. However, their skill needs to be evaluated before any operational use which requires reliable observation data. Since, Nepal lacks the proper ground observing stations: mainly over the high mountains, gridded precipitation datasets produced using multiple sources (like satellites, and numerical models) have been good alternatives. Thus, we evaluated 11 such gridded datasets against the existing ground observing stations. We found that these datasets struggle to estimate extreme precipitation. Additionally, they also fail to capture precipitation characteristics over the complex terrain of the high mountain region compared to flat regions. Overall, gauge interpolated and merged datasets demonstrated better performance in estimating precipitation characteristics over complex terrain; hence, can serve as a benchmark dataset for S2S forecast evaluation in this region. Key Points: Multi‐source precipitation estimates captured the annual to weekly precipitation cycle, but only roughly captured the spatial patternMulti‐source precipitation estimates performed poorly in estimating extreme precipitation and struggled over complex terrainAmong high‐resolution datasets, TPMFD has the potential to be used as a reference dataset for sub‐seasonal to seasonal model evaluation [ABSTRACT FROM AUTHOR]

In this comprehensive study, we have delved into the intricate realm of lightning forecasting, a captivating yet challenging pursuit due to the elusive nature of storm electrification. Our focus rested on evaluating the skill of lightning forecasts of NCMRWF Regional Ensemble Prediction System (NEPS-R), based on the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Based on the lightning hotspots in the observed seasonal mean lightning flash count, study regions were identified over East-Northeast India (ENEI) and Peninsular India (PI) during pre-monsoon season and Central and East-Northeast India (CENEI) for monsoon season during the year 2022. Probabilistic and deterministic skill of NEPS-R in lightning prediction using rank histogram, Relative Operating Characteristic (ROC), Brier Skill Score (BSS), Continuous Ranked Probability Skill Score (CRPSS), reliability diagrams, Probability of Detection (POD), False Alarm ratio (FAR), Equitable Threat Score (ETS), and Fractions Skill Score (FSS) were investigated in the present study. Rank histograms brought out the negative bias, which increases with increasing lead time over all the study regions. While ROC and BSS indicate that forecast is skillful on day-1 over ENEI for all the thresholds (> 1, > 5 and > 10) and over PI on day-2 and day-3, CRPSS has a contrasting trend with most skillful forecast over CENEI region. Reliability diagrams indicate under-forecasting for lower probabilities and over-forecasting for higher probabilities for all the thresholds for all the study areas. The overconfidence in the forecast could be at least partially attributed to the sampling error caused by a small ensemble size of NEPS-R, which also displayed similar trend of skill as BSS. Higher POD values in the CENEI region have surpassed those in ENEI and PI. Lower FAR and higher ETS are found over PI region compared to ENEI and CENEI. Furthermore, FSS (threshold > 1) indicates that CENEI region attains better skill (> 0.5) at a neighborhood size of 36 km in day-1 forecast compared to ENEI (68 km) and PI (84 km). The skill scores do emphasize the model's ability in predicting the dominant large scale features of monsoon. This is reflected in the better skill of CENEI region during monsoon as compared to ENEI and PI regions during the pre-monsoon season, where the model is falling short in predicting the prominent local scale features. Also, the model's inadequacy in proper representation of microphysics and kinematics does affect the lightning prediction capability of the model which could be addressed with improved model initial conditions and parameterization schemes and finer horizontal resolution. [ABSTRACT FROM AUTHOR]

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts. However, the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known. In this study, a series of forecasts with different forecast lead times for January, April, July, and October of 2018 are conducted over the Beijing-Tianjin-Hebei (BTH) region and the impacts of meteorological forecasting uncertainties on surface PM2.5 concentration forecasts with each lead time are investigated. With increased lead time, the forecasted PM2.5 concentrations significantly change and demonstrate obvious seasonal variations. In general, the forecasting uncertainties in monthly mean surface PM2.5 concentrations in the BTH region due to lead time are the largest (80%) in spring, followed by autumn (~50%), summer (~40%), and winter (20%). In winter, the forecasting uncertainties in total surface PM2.5 mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles. In spring, the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds, thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust. In summer, the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates, which are associated with the reduction of near-surface wind speed and precipitation rate. In autumn, the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles, which is associated with changes in the large-scale circulation. [ABSTRACT FROM AUTHOR]

In 2022, a record-breaking monsoon caused flooding throughout Pakistan, particularly in the southern regions, resulting in deaths, property losses, and severe crop damage, affecting the food supply chain that could last for years. This study assesses the accuracy of sub-seasonal calibrated probabilistic rainfall forecasts for Pakistan. The evaluation focuses on forecasts initialized throughout the summer monsoon season (June–September) and utilizes the European Center for Medium Range Forecast (ECMWF) ensemble prediction system. Forecasts are calibrated using a canonical correlation analysis (CCA) and evaluated using cross-validated hindcasts from 2002 to 2021. The calibrated hindcasts exhibit positive ranked probability skill score and are reliable for weeks 1 (days 1–7), 2 (days 8–14), and 3 – 4 (days 15–28), lead times. In the extraordinary monsoon season of 2022, tercile-category probabilistic forecasts provided useful information up to 4 weeks ahead. Furthermore, the occurrences of intense monsoon rainfall in the highly affected southern region of Pakistan were forecasted reasonably well up to 2 weeks in advance. The ECMWF model's ability to predict sub-seasonal monsoon rainfall in Pakistan during 2022 is attributed to the model's successful prediction of monsoonal intra-seasonal oscillations. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) ensembles often exhibit biases and errors in dispersion, so they need some form of postprocessing to yield sharp and well‐calibrated probabilistic predictions. The output of NWP models is usually at a multiplicity of different lead times and, even though information is often required on this range of lead times, many postprocessing methods in the literature are applied either at a fixed lead time or by fitting individual models for each lead time. However, this is (1) computationally expensive because it requires the training of multiple models if users are interested in information at multiple lead times and (2) prohibitive because it restricts the data used for training postprocessing models and the usability of fitted models. This article investigates the lead‐time dependence of postprocessing methods in the idealized Lorenz'96 system as well as temperature and wind‐speed forecast data from the Met Office Global and Regional Ensemble Prediction System (MOGREPS‐G). The results indicate that there is substantial regularity between the models fitted for different lead times and that one can fit models that are lead‐time‐continuous that work for multiple lead times simultaneously by including lead time as a covariate. These models achieve similar and, in small data situations, even improved performance compared with the classical lead‐time‐separated models, whilst saving substantial computation time. [ABSTRACT FROM AUTHOR]

The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale. [ABSTRACT FROM AUTHOR]

This work demonstrates the potential of extreme cool-season precipitation forecasts in the Arabian Peninsula (AP) at sub-seasonal time scales, identifies regions and periods of forecast opportunity, and investigates the predictability of synoptic-scale forcing at sub-seasonal time scales. To this end, we simulate 18 extreme precipitation events using the convective-permitting Weather Research and Forecasting (CP-WRF) model with lateral boundary forcing from the European Centre of Medium-range Weather Forecasts sub-seasonal to seasonal reforecasts (ECMWF S2S reforecasts). The simulations are initiated at one-, two-, and three-week lead times. At all lead times, the CP-WRF improves the mean accumulated precipitation in the extratropical synoptic regimes over the west coastal and central AP and the central Red Sea compared to ECMWF S2S reforecasts as evaluated against the Global Precipitation Measurement Final (GPMF) and King Abdullah University of Science and Technology reanalysis (KAUST-RA) precipitation products. Based on categorical statistics with a threshold of 20 mm accumulated precipitation over 7 days, the CP-WRF skillfully forecasts the precipitation over Jeddah, the west coast of AP, and the central Red Sea up to three-week lead time. The relative operating characteristic curve reconfirmed the high forecast skill of the CP-WRF, with an area under the curve above 0.5 in most of the events at all lead times. Finally, the correlation coefficients between the ECMWF S2S reforecast and ECMWF reanalysis interim 500 hPa geopotential heights are higher in the events associated with an extratropical synoptic regime than in those associated with a tropical synoptic regime, regardless of lead time. Therefore, the convective-permitting model can potentially improve the accuracy of extreme winter precipitation forecasts at two-and three-week lead times over Jeddah, the west coast of AP, and the central Red Sea in the extratropical synoptic regime. [ABSTRACT FROM AUTHOR]

Dynamical prediction of monsoon rainfall has been an important topic and a long-standing issue in both research and operational community. This paper provides a comprehensive evaluation of the subseasonal-to-seasonal (S2S) prediction skill of the East Asian summer monsoon (EASM) rainfall using the hindcast record from the Beijing Climate Center Climate System Model, BCC CSM1.1m, during the period 1983–2019. The model exhibits reasonable skills for predicting the EASM rainfall at all lead times with the skill dropping dramatically from the shortest lead time of about 2 weeks (LM0) to 1-month lead (LM1), and then fluctuating remarkably throughout 2-month to 12-month lead times. Over the EASM domain, the rapid decline of the S2S rainfall prediction skill from LM0 to LM1 is mainly caused by the inferior skills over Central China in July and over Northeast China in August. Composite analysis based on hindcast records suggest that these inferior skills are directly tied to the model's difficulties in capturing above-normal precipitation over eastern Central China and Northeast China in the respective months, which are further shown to be associated with anomalous weakening and meridional movement of the Northwestern Pacific subtropical high and the activity of large-scale teleconnection pattern hard to be predicted over northeastern Asia in summer, respectively. These findings inform the intrinsic limits of the S2S predictability of the EASM rainfall by a dynamical model, and strongly suggest that the level of confidence placed upon S2S forecasts should be stratified by large-scale circulation anomalies known to significantly affect the prediction skill, e.g., the subtropical high and high-latitude teleconnection patterns for summer monsoon rainfall prediction in this region. [ABSTRACT FROM AUTHOR]

Most existing drought forecast systems rely only on observed or forecast rainfall, losing valuable context gained from considering both. The lack of a direct link between observed and forecast rainfall reduces the physical consistency of a system, motivating the development of a methodology that can directly link the two. The methodology developed in this study allows the comparison of the calibrated ensemble forecasts of rainfall totals from a dynamical climate model to observed rainfall deficiencies from a gridded rainfall analysis. The methodology is used to create a probabilistic product that forecasts the chance of entering meteorological drought, with lead times of one month (monthly forecast) and three months (seasonal forecast). Existing deficiency areas are included to facilitate analysis of how these areas are forecast to change. The performance of the developed methodology was verified using Percent Correct (PC), Brier Score (BS), and Relative Operating Characteristic (ROC) statistics. Analysis of the forecast plots was also completed visually. Forecast performance for areas with existing deficiencies as well as for non-deficiency areas was promising (PC rates of >79% and >97%, respectively). Although PC rates for observed deficiencies were low across most months, the mean forecast probability for these areas was 36%, indicating the system had value and outperformed climatology. A calibrated, coupled product like the one scoped in this study has not been explored and we note that it could be an invaluable tool for quantifying meteorological drought onset and persistence in Australia. [ABSTRACT FROM AUTHOR]

Despite a small amplitude, Length of Day (LOD) change, which varies from one year to another due to changes in Atmospheric Angular Momentum (AAM), determines the accuracy of Global Positioning System (GPS) time calculation. In this study, we examine the prediction skill of LOD and AAM in nine decadal prediction systems archived for the Decadal Climate Prediction Project. A persistence and rebound in LOD prediction skill at one year or longer lead time is found in most models. A poleward propagation of AAM anomaly via wave-mean flow interaction is also qualitatively well reproduced. This long-lead prediction of LOD and AAM is attributed to reliable predictions of the El Niño–Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (QBO), the former being more systematically related than the latter. This result indicates that the improved ENSO prediction and atmospheric wave-mean flow interaction may lead to better prediction of LOD, AAM and related extratropical climate in the decadal prediction systems. [ABSTRACT FROM AUTHOR]

Skill scores of the reproducibility of oceanographic fields in applying the INM-CM5 climate model to long-term forecasting for several months are presented. It is shown that disagreements between the model forecasts and the control data in terms of their absolute values are mainly due to the model climate bias. A degree of consistency with the control data significantly increases when comparing anomalies counted from the individual normals of each of the compared data types. In particular, the forecasts of SST anomalies for the Niño-3.4 region with a lead time up to six months appear to be quite successful. [ABSTRACT FROM AUTHOR]

During the 2021 Spring Forecasting Experiment (SFE), the usefulness of the experimental Warn-on-Forecast System (WoFS) ensemble guidance was tested with the issuance of short-term probabilistic hazard forecasts. One group of participants used the WoFS guidance, while another group did not. Individual forecasts issued by two NWS participants in each group were evaluated alongside a consensus forecast from the remaining participants. Participant forecasts of tornadoes, hail, and wind at lead times of ∼2–3 h and valid at 2200–2300, 2300–0000, and 0000–0100 UTC were evaluated subjectively during the SFE by participants the day after issuance, and objectively after the SFE concluded. These forecasts exist between the watch and the warning time frame, where WoFS is anticipated to be particularly impactful. The hourly probabilistic forecasts were skillful according to objective metrics like the fractions skill score. While the tornado forecasts were more reliable than the other hazards, there was no clear indication of any one hazard scoring highest across all metrics. WoFS availability improved the hourly probabilistic forecasts as measured by the subjective ratings and several objective metrics, including increased POD and decreased FAR at high probability thresholds. Generally, expert forecasts performed better than consensus forecasts, though expert forecasts overforecasted. Finally, this work explored the appropriate construction of practically perfect fields used during subjective verification, which participants frequently found to be too small and precise. Using a Gaussian smoother with σ = 70 km is recommended to create hourly practically perfect fields in future experiments. Significance Statement: This work explores the impact of cutting-edge numerical weather prediction ensemble guidance (the Warn-on-Forecast System) on severe thunderstorm hazard outlooks at watch-to-warning time scales, typically between 1 and 6 h of lead time. Real-time forecast products in this time frame are currently provided on an as-needed basis, and the transition to continuous probabilistic forecast products across scales requires targeted research. Results showed that hourly probabilistic participant forecasts were skillful subjectively and statistically, and that the experimental guidance improved the forecasts. These results are promising for the implementation and value of the Warn-on-Forecast System to provide improved hazard timing and location guidance within severe weather watches. Suggestions are made to aid future subjective evaluations of watch-to-warning-scale probabilistic forecasts. [ABSTRACT FROM AUTHOR]

Limitations are existed in current ensemble forecasting initial perturbation methods for describing the interactions among various spheres of the Earth system. In this study, a new method is proposed, namely, the coupled conditional nonlinear optimal perturbation (C-CNOP) method, which incorporates multisphere interactions much appropriately. The El Niño-Southern Oscillation (ENSO) is a typical ocean-atmosphere "coupling" (or "interaction") phenomenon. The C-CNOP method is applied to ensemble forecasting of ENSO. It is demonstrated that the C-CNOP method can generate coupled initial perturbations (CPs) that appropriately consider initial ocean-atmosphere coupling uncertainty for ENSO ensemble forecasts. Results reveal that the CPs effectively improve the ability of ENSO ensemble-mean forecasts in both temporal variability of Niño3.4 sea surface temperature anomalies (SSTAs) and spatial variability of ENSO mature-phase SSTAs. Notably, despite the weakest ocean-atmosphere coupling strength in the tropical Pacific occurring during the boreal spring and summer, CPs still capture the uncertainties of this weak coupling when ENSO predictions are initialized at these seasons. This performance of CPs significantly suppresses the rapid increase of ENSO prediction errors due to the high ocean-atmosphere coupling instability during these seasons, and thus effectively extends the lead time of skillful ENSO forecasting. Hence, the C-CNOP method is a suitable initial perturbation approach for ENSO ensemble forecast that can describe initial ocean-atmosphere coupling uncertainty. It is expected that the C-CNOP method plays a significant role in predictions of other high-impact climate phenomena, and even future Earth system predictions. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) genesis forecasting is essential for daily operational practices during the typhoon season. The updated version of the Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS) offers forecasters reliable numerical weather prediction (NWP) products with improved configurations and fine resolution. While traditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has been proposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, predicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 h in advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors of operational models according to the annual evaluation. The study also briefly investigated the forecast of Noul (2011). The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, consistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized threedimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis, fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters. [ABSTRACT FROM AUTHOR]

Accurate flood runoff and water level predictions are crucial research topics due to their significance for early warning systems, particularly in improving peak flood level forecasts and reducing time lags. This study proposes a novel method, Trend Forecasting Method (TFM), to improve model accuracy and overcome the time lag problem due to data scarcity. The proposed method includes the following steps: (1) select appropriate input factors causing flood events, (2) determine the most suitable AI method as the basis for forecasting models, (3) a forecasting model using a multi-step-ahead approach and a forecasting model with variation in flood depth as input are developed as compared to the selected model in Step 2, and (4) according to the rising limb and falling limb of a flood hydrograph, the maximum and minimum values predicted by the models above are respectively selected as the final outputs. The application to demonstrate the advantages of the proposed method was conducted in the Annan District of Tainan City, Taiwan. Of all the models tested, the Gated Recurrent Unit (GRU) demonstrated superior accuracy in forecasting flood depths, followed by Long Short-Term Memory (LSTM) and Bidirectional LSTM, with the Back Propagation Neural Network falling behind. With a Nash–Sutcliffe efficiency coefficient (NSE) of 0.56 for the next hour's forecast, the GRU model's structure appears particularly fitting for flood depth forecast. However, all four models showed time lag issues. TFM substantially enhanced the GRU model's forecast accuracy, mitigating the time lag. TFM achieved an NSE of 0.82 for forecasting 10-, 20-, 30-, 40-, 50-, and 60-min lead time. The observed flood depths had a 68% probability of consistent rise or fall, validating TFM's underlying hypothesis. Furthermore, including an autoregressive model in TFM reduced the time lag problem. [ABSTRACT FROM AUTHOR]

Demand forecasts are the basis of most decisions in supply chain management. The granularity of these decisions lead to different forecast requirements. For example, inventory replenishment decisions require forecasts at the individual SKU level over lead time, whereas forecasts at higher levels, over longer horizons, are required for supply chain strategic decisions. The most accurate forecasts are not always obtained from data at the 'natural' level of aggregation. In some cases, forecast accuracy may be improved by aggregating data or forecasts at lower levels, or disaggregating data or forecasts at higher levels, or by combining forecasts at multiple levels of aggregation. Temporal and cross-sectional aggregation approaches are well established in the literature. More recently, it has been argued that these two approaches do not make the fullest use of data available at the different hierarchical levels of the supply chain. Therefore, consideration of forecasting hierarchies (over time and other dimensions), and combinations of forecasts across hierarchical levels, have been recommended. This paper provides a comprehensive review of research dealing with aggregation and hierarchical forecasting in supply chains, based on a systematic search. The review enables the identification of major research gaps and the presentation of an agenda for further research. [ABSTRACT FROM AUTHOR]

Most tropical cyclone (TC) forecasting centers have implemented a probabilistic circle to represent track uncertainty at a specified lead time. Recent studies suggest that probability ellipses constructed from ensemble prediction systems can convey the anisotropy of track predictability. In this study, a new probability ellipse model is developed to interpret the extent of forward speed and heading uncertainties in ensemble forecasts by selecting an equal proportion of members in the along‐ and cross‐track directions. This method is validated using the 2019–2021 western North Pacific (WNP) TC track forecasts from the ensemble predictions of the European Centre for Medium‐Range Weather Forecasts, the United States National Centers for Environmental Prediction, and the Korea Meteorological Administration. When the proportion of ensemble members in the ellipse is set to 70%, more than one‐half (50.0%–73.6%) of the forecasts, depending on the lead time, indicate reduced area compared with that of the circle. The mean areas of the probability ellipses are 4.9%, 7.0%, 10.0%, and 11.5% smaller than those of the circle in 48‐, 72‐, 96‐, and 120‐hr forecasts, respectively. The forward speed shows greater uncertainty than the heading, as evidenced by the along‐track radii being larger than the cross‐track counterpart in ∼60% of the samples, regardless of the lead time. In addition, the regional distribution of the along‐track/cross‐track ratio in the probability ellipses can explain the dominant direction of the track error in a particular location. The proposed probability ellipse shows potential for application in operational TC track predictions. Plain Language Summary: Operational tropical cyclone (TC) forecasting centers usually represent the uncertainty of a TC track forecast with a circle, namely the probabilistic circle. In this paper, the circle is generalized to a probability ellipse directly using the output of ensemble prediction systems. The key to the new ellipse is that an equal proportion of ensemble members are selected in the along‐ and cross‐track directions to determine the two elliptical radii. This probability ellipse is smaller in size than the circle when representing the same level of uncertainty. The eccentric property of the probability ellipse allows for easy interpretation of the direction with larger ensemble‐forecast uncertainty. Such results can aid in operational TC track predictions and subsequent implementation of preventive measures. Key Points: The proposed probability ellipse explicitly shows the uncertainty of ensemble forecast tracks in the along‐ and cross‐track directionsThe probability ellipse is capable of explaining the dominant direction of error in track forecastsRegional distributions of uncertainty and error resemble each other in terms of magnitude and direction [ABSTRACT FROM AUTHOR]

An experiment was performed on using advanced macroscopic nonlocal correlations to forecast slow random oscillations of the Dst index of geomagnetic activity. The global maximum of the correlation of Dst with the electrode detector signal reaches 0.97, which is sufficient for forecasting, and its time shift corresponds to advance of the detector signal relative to Dst for 329 days. The large magnitude of the time shift is due to the slow diffusion mechanism of entanglement swapping between the detector and the source. In this case, the position of the global maximum of the correlation function coincides with the position of the global minimum of the entropy independence function, which confirms that it is not distorted by possible nonlinearity of the relationship and determines the optimal lead time of the forecast. Long series of test Dst forecasts have been calculated based on data from a nonlocal correlation detector with a fixed lead time using three methods: current regression, current impulse transient response, and current neural network. The forecast accuracy is sufficient for all practical purposes. [ABSTRACT FROM AUTHOR]

We evaluate the soil moisture hindcasts and the reconstruction runs giving the hindcasts initial conditions in version 2.1 of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2.1). Different strategies are used to initialize the hindcasts for the two CanSIPSv2.1 models, CanCM4i and the coupled Global Environmental Multiscale, version 5.1, (GEM5)–NEMO model (GEM5-NEMO), with contrasting impacts on the soil moisture initial conditions and forecast performance. Forecast correlation skill is decomposed into contributions from persistence of the initial anomalies and contributions not linked to persistence, with performance largely driven by the accuracy of the initial conditions in regions of strong persistence. Seasonal soil moisture correlation skill is significant for several months into the hindcasts depending on initial and target months, with contributions not linked to persistence becoming more notable at longer lead times. For the first 2–4 months, the quality of CanSIPSv2.1 ensemble mean forecasts tends to be higher on average during summer and fall and is comparable to that of the best performing model, whereas CanSIPSv2.1 outperforms the single models during spring and winter. For longer lead times, remote climate influences from the Pacific Ocean are notable and contribute to predictable soil moisture variability in teleconnected regions. [ABSTRACT FROM AUTHOR]

On-site earthquake early warning (EEW) systems represent an important way to reduce seismic hazard. Since these systems are fast in providing an alert and reliable in the prediction of the ground motion intensity at targets, they are particularly suitable in the areas where the seismogenic zones are close to cities and infrastructures, such as Central Italy. In this work, we use Gradient Boosting Regressor (GBR) to predict peak ground acceleration (PGA), and hypocentral distance (D) starting from P -wave features. We use two data sets of waveforms from two seismic sequences in Central Italy: L'Aquila sequence (2009) and the Amatrice–Norcia–Visso sequence (2016–2017), for a total of about 80   000 three-component waveforms. We compute 60 different features related to the physics of the earthquake using three different time windows (1 s, 2 s and 3 s). We validate and train our models using the 2016–2017 data sets (the bigger one) and we test it on the 2009 data set. We study the performances of GBR predicting D and PGA in terms of prediction scores, finding that the models can well predict both targets even using 1 s window, and that, as expected, the results improve using longer time windows. Moreover, we perform a residual analysis on the test set finding that the PGA can be predicted without any bias, while the D prediction presents a correlation with the moment magnitude. In the end, we propose a prototype for a probabilistic on-site EEW system based on the prediction of D and PGA. The proposed system is a threshold-based approach and it releases an alert on four possible levels, from 0 (far and small event) to 3 (close and strong event). The system computes the probability related to each alert level. We test two different set of thresholds: the Felt Alert and the Damage Alert. Furthermore, we consider the lead time (LT) of the PGA to distinguish between useful alerts (positive LT) and Missed Alerts (MA). In the end, we analyse the performance of such a system considering four possible scenarios: Successful Alert (SA), Missed Alert (MA), Overestimated Alert (OA) and Underestimated Alert (UA). We find that the system obtains SA rate about 80 per cent at 1 s, and that it decreases to about 65 per cent due to the increase in MA. This result shows how the proposed system is already reliable at 1 s, which would be a huge advantage for seismic prone regions as Central Italy, an area characterized by moderate-to-large earthquakes (M w < 7). [ABSTRACT FROM AUTHOR]

Accurate sea surface temperature (SST) prediction is vital for disaster prevention, ocean circulation, and climate change. Traditional SST prediction methods, predominantly reliant on time-intensive numerical models, face challenges in terms of speed and efficiency. In this study, we developed a novel deep learning approach using a 3D U-Net structure with multi-source data to forecast SST in the South China Sea (SCS). SST, sea surface height anomaly (SSHA), and sea surface wind (SSW) were used as input variables. Compared with the convolutional long short-term memory (ConvLSTM) model, the 3D U-Net model achieved more accurate predictions at all lead times (from 1 to 30 days) and performed better in different seasons. Spatially, the 3D U-Net model's SST predictions exhibited low errors (RMSE < 0.5 °C) and high correlation (R > 0.9) across most of the SCS. The spatially averaged time series of SST, both predicted by the 3D U-Net and observed in 2021, showed remarkable consistency. A noteworthy application of the 3D U-Net model in this research was the successful detection of marine heat wave (MHW) events in the SCS in 2021. The model accurately captured the occurrence frequency, total duration, average duration, and average cumulative intensity of MHW events, aligning closely with the observed data. Sensitive experiments showed that SSHA and SSW have significant impacts on the prediction of the 3D U-Net model, which can improve the accuracy and play different roles in different forecast periods. The combination of the 3D U-Net model with multi-source sea surface variables, not only rapidly predicted SST in the SCS but also presented a novel method for forecasting MHW events, highlighting its significant potential and advantages. [ABSTRACT FROM AUTHOR]

Volcanic ash hazards present life‐threatening dangers to populations near volcanoes during large explosive eruptions. Vulnerable infrastructures demand a comprehensive disaster risk reduction strategy to protect residents from enormous ashfall accumulations. To prepare for the next large eruption of Sakurajima volcano, authorities in Kagoshima City are developing a countermeasure plan utilizing ash dispersal prediction 24 hr before an eruption. However, the absence of large eruptions in the last century makes it challenging to confirm the accuracy of predictions for hazard area designation. To ensure established protocols are effective, uncertainties in ashfall predictions must be addressed, enabling authorities to make appropriate responses. We simulated the previous large eruption of Sakurajima volcano using multiple wind forecast lead times as historical predictions from July 2018 to December 2022. The uncertainty in prediction results was evaluated by comparing simulation outputs with validation data from the ash dispersal data set. We examined uncertainty in hazard area assignment and its variation depending on risk items, wind field states, and the influences of seasonal patterns and events. Updating ashfall predictions with improved wind forecasts reduces uncertainty for all risk items and increases the precision of identified hazard zones. Furthermore, uncertainty levels are influenced by seasonality and can shift significantly with varying wind strengths controlling ash dispersal process. Providing uncertainty information is vital for decision‐making during ash fallout events, and it is recommended to update response decisions 12 hr after the initial prediction. This study's outcomes will aid in developing better disaster risk management strategies, focusing on volcanic ash hazards. Plain Language Summary: Massive ash fallout during a large eruption requires an effective disaster response to protect people living near the volcano. Currently, the authorities are working on a response plan to reduce damage and losses from ash fallout during a large eruption at Sakurajima Volcano. This plan uses ashfall prediction to select high‐risk areas for warning and evacuation one day before an eruption. However, large eruptions are rare and it is difficult to verify that the prediction system can correctly predict the entire affected area. Therefore, it is important to evaluate how accurate the ashfall prediction is when a large eruption occurs. We carried out the simulation of a large eruption of Sakurajima volcano to evaluate the performance of ashfall prediction. We calculated the uncertainty in the prediction result by matching it with validation data, and found the general uncertainty in the prediction results and how it can change based on various conditions. The uncertainty information provided in this study can increase the utility of volcanic disaster risk management by providing effective early warning and evacuation plans. Key Points: We clarify the spatio‐temporal uncertainties in the ashfall prediction system at Sakurajima volcano under various weather conditionsWe analyzed how the uncertainty can change given the risk items considered and the prediction interval from the start of the eruptionThe timing of an eruption also influences the uncertainty in ashfall prediction due to seasonal patterns and high precipitation events [ABSTRACT FROM AUTHOR]

Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2's phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2's modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model's accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. [ABSTRACT FROM AUTHOR]

Forecasting convection initiation (CI) has advanced greatly during the past decade through the use of high-resolution satellite observations and model output. One of the primary CI products used in forecast operations is based on GOES-16 visible and infrared imagery along with GLM lightning flash detections to determine the location of growing ice-containing cumulus clouds that are the precursor to developing thunderstorms. Another approach to CI forecasting that has recently become available is high frequency output from numerical weather prediction (NWP) models such as the Warn-on-Forecast System (WoFS). NWP model simulated composite reflectivity forecasts are one method used to determine when and where severe thunderstorms might develop. However, waiting for high reflectivity (> 40 dBZ) to be created within the NWP model limits the potential lead time available to forecasters when using WoFS output to anticipate areas where convection might form. Also, forecast reflectivity alone does not always give an indication of whether or not the precipitation developed by the NWP model is convective in nature. To address these limitations, this work applies a CI forecasting methodology developed for GOES satellite data on synthetic satellite imagery produced from WoFS output. Forecast cloud objects are tracked over a 10-min interval and CI forecasting parameters are applied to determine whether or not these cloud objects will continue to develop into organized thunderstorms. [ABSTRACT FROM AUTHOR]

(1) Background: Transvenous lead extraction (TLE) can become far more complex when unanticipated difficulties arise. The aim was to develop a simple scoring system that allows for the prediction of the difficulty and complexity of this significant procedure. (2) Methods: Based on analysis of 3741 TLE procedures with and without complicating factors (extended fluoroscopy time, need for second-line instruments, and advanced techniques and instruments), a five-point Complex Indicator of Difficulty of (TLE) Procedure (CID-TLEP) scale was developed. Two or more points on the CID-TLEP scale indicate a higher level of procedure complexity. (3) Results: Patient age below 51 years at first CIED implantation, number of abandoned leads, number of previous procedures, passive fixation and multiple leads to be extracted, and a ratio of dwell time of oldest lead to patient age during TLE of >0.13 are significant predictors of higher levels of lead extraction complexity. The ROC analysis demonstrates that a point total (being the sum of the odds ratios of the above variables) of >9.697 indicates a 21.83% higher probability of complex TLE (sensitivity 74.08%, specificity 74.46%). Finally, a logistic function was calculated, and we constructed a simple equation for lead extraction complexity that can predict the probability of a difficult procedure. The risk of complex extraction (as a percentage) is calculated as [1/(1 + 55.34 · 0.754X)] · 100 (p < 0.001). (4) Conclusion: The LECOM score can effectively predict the risk of a difficult transvenous lead extraction procedure, and predicting the probability of a more complex procedure may help clinicians in planning lead removal and improving patient management. [ABSTRACT FROM AUTHOR]

Studying solar-wind conditions is central to forecasting the impact of space weather on Earth. Under the assumption that the structure of this wind is constant in time and co-rotates with the Sun, solar-wind and thereby space-weather forecasts have been made quite effectively. Such co-rotation forecasts are well studied with decades of observations from STEREO and near-Earth spacecraft. Forecast accuracy is primarily determined by three factors: i) the longitudinal separation of spacecraft from Earth determines the corotation time (and hence forecast lead time) [ δ t] over which the solar wind must be assumed to be constant, ii) the latitudinal separation (or offset) between Earth and spacecraft [ δ θ ]] determines the degree to which the same solar wind is being encountered at both locations, and iii) the solar cycle, via the sunspot number (SSN), acts as a proxy for both how fast the solar-wind structure is evolving and how much it varies in latitude. However, the precise dependencies factoring in uncertainties are a mixture of influences from each of these factors. Furthermore, for high-precision forecasts, it is important to understand what drives the forecast accuracy and its uncertainty. Here we present a causal inference approach based on information-theoretic measures to do this. Our framework can compute not only the direct (linear and nonlinear) dependencies of the forecast mean absolute error (MAE) on SSN, Δ θ , and Δ t , but also how these individual variables combine to enhance or diminish the MAE. We provide an initial assessment of this with the potential of aiding data assimilation in the future. [ABSTRACT FROM AUTHOR]

Precipitation data are known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellitE Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA's Goddard Earth Observing System Forward Processing (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance let alone explored the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1–5-day forecasting with the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity (VIC) Environment (DRIVE) model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, the GEOS-FP-driven model (DRIVE-FP) performance is close to the IMERG-E-driven model (DRIVE-E) performance on day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead-time forecasts, the bias tends to diminish in most regions, likely because the under- or overestimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most regions, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts. [ABSTRACT FROM AUTHOR]

Additive logistic regression models for lightning (AR lig) and large hail (AR hail) were developed using convective parameters from the ERA5 reanalysis, hail reports from the European Severe Weather Database (ESWD), and lightning observations from the Met Office Arrival Time Difference network (ATDnet). The models yield the probability of lightning and large hail in a given timeframe over a particular grid point. To explore the value of this approach to medium-range forecasting, the models were applied to the European Centre for Medium Range Weather Forecasts (ECMWF) reforecasts to reconstruct probabilistic lightning and large hail forecasts for 11 ensemble members, from 2008 to 2019 and for lead times up to 228 h. The lightning and large hail models were based on different predictor parameters: most unstable convective available potential energy (CAPE), 925–500 hPa bulk shear, mixed layer mixing ratio, wet bulb zero height (for large hail), most unstable lifted index, mean relative humidity between 850 and 500 hPa, 1 hourly accumulated convective precipitation and specific humidity at 925 hPa (for lightning). First, we compared the lightning and hail ensemble forecasts for different lead times with observed lightning and hail focusing on a recent hail outbreak. Second, we evaluated the predictive skill of the model as a function of forecast lead time using the area under the ROC curve (AUC) as a validation score. This analysis showed that AR hail has a very high predictive skill (AUC > 0.95) for a lead time up to 60 h. AR hail retains a high predictive skill even for extended forecasts (AUC = 0.86 at 180 h lead time). Although AR lig exhibits a lower predictive skill than AR hail , lightning forecasts are also skilful both in the short term (AUC = 0.92 at 60 h) and in the medium range (AUC = 0.82 at 180 h). Finally, we compared the performance of the 4-dimensional hail model with that of composite parameters such as the significant hail parameter (SHP) or the product of CAPE and the 925–500 hPa bulk shear (CAPESHEAR). Results show that AR hail outperforms CAPESHEAR at all lead times and SHP at short-to-medium lead times. These findings suggests that the combination of additive logistic regression models and ECMWF ensemble forecasts can create highly skilful medium-range hail and lightning forecasts for Europe. [ABSTRACT FROM AUTHOR]

The article focuses on the significance of forecasting accuracy in business decision-making and its actual economic impact. Topics include the potential overemphasis on forecast accuracy, the relationship between forecast accuracy and economic performance, and the need for a more value-centric approach to forecasting.

The precise prediction of polar motion parameters is needed for the astrogeodynamics, navigation and positioning of the deep space probe. However, the current prediction methods are limited to predicting polar motion for specific periods, either short- or long-term. In this study, a sliding multilayer perceptron (MLP) method combined singular spectrum analysis (SSA) and autoregressive moving average (ARMA) for short- and long-term polar motion prediction was proposed. MLP was introduced into PM prediction due to its automatic learning characteristics and its ability to effectively process nonlinear and multi-dimensional data. The SSA was used to extract and predict the principal components of polar motion, while the remaining components were predicted using ARMA. In the meantime, SSA and ARMA were used to provide training data and target learning data for the MLP model. MLP input data were constructed by sliding processing with a window of 7 days, composed of n series of the same length (18 years). Finally, MLP was employed to predict the residuals generated during SSA and ARMA prediction. To evaluate the accuracy of the proposed method, the polar motion prediction was applied for a 364-day lead time based on the IERS EOP 14C04 product. The method outperformed the IERS Bulletin A, as demonstrated by the mean-absolute errors of the x and y components of polar motion on the 30th day, which were lower (5.14 mas and 3.37 mas, respectively) than those predicted by IERS Bulletin A (6.66 mas and 3.94 mas). Similarly, the mean-absolute errors on the 364th day were 17.79 mas and 16.29 mas, respectively, compared to the 19.24 mas and 18.81 mas predicted by IERS Bulletin A. [ABSTRACT FROM AUTHOR]

El Niño events represent anomalous episodic warmings, which can peak in the equatorial Central Pacific (CP events) or Eastern Pacific (EP events). The type of an El Niño (CP or EP) has a major influence on its impact and can even lead to either dry or wet conditions in the same areas on the globe. Here we show that the difference of the sea surface temperature anomalies between the equatorial western and central Pacific in December enables an early forecast of the type of an upcoming El Niño (p-value < 10−3). Combined with a previously introduced climate network-based approach that allows to forecast the onset of an El Niño event, both the onset and type of an upcoming El Niño can be efficiently forecasted. The lead time is about 1 year and should allow early mitigation measures. In December 2022, the combined approach forecasted the onset of an EP event in 2023. [ABSTRACT FROM AUTHOR]

As a challenge in the construction of a "seamless forecast" system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and deep-learning models for subseasonal forecasts, as forecast times increase, the prediction skill for high-frequency components will decrease, as the lead time is already far beyond the predictability. Meanwhile, intraseasonal low-frequency components are essential to the change in general circulation on subseasonal timescales. In this paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the intraseasonal oscillation (ISO) components of atmospheric signals and used an improved deep-learning model (SE-ResNet) to train and predict the ISO components of geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results show that the 10–30 day prediction performance of the SE-ResNet model is better than that of the model trained directly with original data. Compared with other models/methods, this model has a good ability to depict the subseasonal evolution of the ISO components of Z500 and T850. In particular, although the prediction results from the Climate Forecast System Version 2 have better performance through 10 days, the SE-ResNet model is substantially superior to CFSv2 through 10–30 days, especially in the middle and high latitudes. The SE-ResNet model also has a better effect in predicting planetary waves with wavenumbers of 3–8. Thus, the application of data-driven subseasonal forecasts of atmospheric ISO components may shed light on improving the skill of seasonal forecasts. [ABSTRACT FROM AUTHOR]

Due to climate change, extreme temperature events are receiving increased attention. Based on the climate state deviation and threat score (TS), the ability of the Global/Regional Assimilation and Prediction System (GRAPES) ensemble model to forecast extreme temperature events was examined. The "optimal" Extreme Forecast Index (EFI) was derived for plateau forecasting, and its predictability was examined based on the receiver operating characteristic (ROC) curve method. Meanwhile, the applicability of the Shift of Tails (SOT) index to extreme temperature forecasting was analyzed using extreme temperature cases. Results showed that the GRAPES model has a warm bias for both summer extreme high temperature and winter extreme low temperature, and the warm bias decreases slightly with an increase in the forecasting lead time. The ensemble mean and median forecasts are less effective, and the maximum value is more predictable. However, for the ensemble forecast model, the extreme information in its forecast is more unstable, and the limitation of the extreme temperature forecast in the plateau region is higher. With different forecast lead times, the TS tends to increase and then decrease with an increase in the EFI threshold, which means that there is an optimal EFI. The optimal EFI thresholds for summer extreme high-temperature forecasts are all less than −0.5, while for winter extreme low-temperature forecasts, they are almost all less than 0. From the ROC curves, the EFI has a certain level of predictability for summer extreme high temperatures but poorer forecasting effects. Furthermore, the EFI has some predictability for extreme summer high temperatures, but the prediction effect is poor. For the extremely low temperatures in winter, which are poorly predicted by the model itself, post-processing of the extreme information predicted by the model with the EFI can improve the forecasting effect of the model. Through analysis of individual cases, it was found that the extreme intensity reflected by the SOT_+ (0.9) index of the model was closer to reality for the prediction of extremely high temperatures, whereas for the prediction of extremely low temperatures, the extreme intensity indicated by the SOT_− (0.1) index of the model was weaker. Therefore, the SOT index can play an important auxiliary role in the prediction of the intensity of extreme events based on the EFI. [ABSTRACT FROM AUTHOR]

Subseasonal to seasonal (S2S) predictions, which bridge the gap between weather forecasts and climate outlooks, have the great societal benefits of improving water resource management and food security. However, there are tremendous disparities in the forecasting skills of subseasonal precipitation prediction products. This study investigates the spatiotemporal variations in the precipitation forecasting skill of three subseasonal prediction products from the CMA, ECMWF, and NCEP over China. Daily precipitation predictions with lead times ranging from 1 to 30 days and cumulative precipitation predictions over 1-30 days were evaluated in nine major river basins. The daily prediction skill rapidly declines with lead time. In contrast, the correlation coefficient between the cumulative precipitation predictions and corresponding observations increases at first and peaks at 0.7-0.8 after 3-5 days, then gradually decreases and settles at approximately 0.2-0.6. Among the three evaluated models, the ECMWF model demonstrates the best skill, maintaining a correlation coefficient of approximately 0.5 for 2-week cumulative precipitation. Moreover, the correlation coefficient of the model's prediction is 0.2-0.5 higher than that of the climatological prediction over a large domain for the 30-day cumulative precipitation during the rainy summer. Similarly, the equitable threat score for forecasting below- and above-normal precipitation events presents good results in eastern China but is affected by biases of raw predictions. The variations in the subseasonal prediction skill at different time scales reveal the potential values of cumulative precipitation predictions. The findings of this study can provide practical information for applications that prioritize the long-term aggregation of hydrometeorological variables. [ABSTRACT FROM AUTHOR]

The skill of subseasonal (multiweek) forecasts of tropical cyclone (TC) occurrence over the Southern Hemisphere is examined in the Australian Bureau of Meteorology's (BoM) multiweek to seasonal prediction system, ACCESS-S2. Relative to its predecessor, ACCESS-S1, ACCESS-S2 shows improved biases in spatial TC frequency in the South Pacific and Southwest Indian Ocean. However, there is no improvement to the known negative bias in TC frequency off the coast of NW Australia. The ability of ACCESS-S2 to provide probabilistic forecasts of TC occurrence for the Southern Hemisphere on multiweek timescales is examined using reliability measures and Brier skill scores. For the period November-February 1990-2012, both ACCESS-S1 and ACCESS-S2 show positive skill relative to climatology for calibrated forecasts out to week 5. However, the skill of ACCESS-S2 is slightly reduced compared to ACCESS-S1 at all lead times, which may be due to the fewer number of ensemble members available. For the full ACCESS-S2 hindcast period, November-April 1981-2018, ACCESS-S2 again shows positive skill of calibrated forecasts over climatology out to week 5. For weeks 1-2, skill is reduced compared to the shorter 1990-2012 period; whereas it is marginally improved for longer lead times (weeks 3-5). Use of lagged ensembles, an alternative linear regression calibration, as well as removing weaker model TCs were examined to potentially improve the skill of ACCESS-S2 forecasts; however, none of these methods were able to significantly increase skill at all lead times. Continued use of the original calibration method is therefore recommended in order to retain skill and continuity of service of the BoM operational and public multiweek TC forecasts. [ABSTRACT FROM AUTHOR]

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan'an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE. [ABSTRACT FROM AUTHOR]