Abstract An accurate track forecast for tropical cyclones (TCs) is critical for disaster prevention and mitigation. Although various models, including statistical, numerical, and artificial intelligence (AI) based, have demonstrated good skill in TC track forecasting, they have not ever been (and will not ever be) perfect, resulting in large uncertainties among them. To reduce the uncertainties, the Spatiotemporal Integrated Forecast Network (ST-IFNet), an integrated model, is developed using deep learning techniques and the track forecasts from seven operational agencies. ST-IFNet utilizes agency-time dual-branch attentions to dynamically adjust the weights of each agency at each time step and concurrently mines the physical trends of the changes in TC movement by the incorporation of environmental data. Experimental results show that ST-IFNet outperforms the operational forecast from any of the agencies as well as a simple average of them, especially in the medium-term (36–48 h) and long-term (60–120 h) TC track forecasts, achieving a track position error (TPE) as low as 62.89, 78.93, 123.96, 139.91, and 177.69 km for 24, 48, 72, 96, and 120 h, respectively. Further analysis reveals that large-scale potential height fields (≥1000 km) and mesoscale flow fields (200–1000 km) obtained from scale separation provide the most valuable guidance for ST-IFNet. This work builds a bridge between numerical forecasts and artificial intelligence techniques and provides a useful and efficient tool for operational TC track forecasts in the future. Significance Statement This study develops Spatiotemporal Integrated Forecast Network (ST-IFNet), an integrated forecast model, based on deep learning techniques to forecast tropical cyclone (TC) tracks. It effectively utilizes multiagency forecasts of TC tracks as well as the corresponding environmental data to achieve higher accuracy of track forecast with minimal computational costs, significantly enhancing the TC track forecast in the South China Sea. In particular, the large-scale potential height fields (≥1000 km) and flow fields that span 200–1000 km are found to provide the most valuable guidance for ST-IFNet.

Abstract High-shear, low-CAPE (HSLC) quasi-linear convective systems (QLCSs) in the Southeastern United States pose a challenge to operational forecasters. Prior studies have investigated bulk environmental ingredients associated with the severe weather produced by these systems; however, there is spatial and temporal variability in HSLC severe weather from QLCSs that has yet to be explained. This study sought to improve the conceptual model for HSLC QLCSs by characterizing heterogeneities in their environments. In pursuit of this goal, QLCS-relative analyses that incorporate hourly data spanning the full duration of 79 HSLC QLCS events occurring across the Southeastern U.S. were created using archived operational High-Resolution Rapid Refresh (HRRR) model data. ‘All case’ composites reveal that HSLC environments are not uniformly “low-CAPE”, and HSLC QLCSs may be partially fed by elevated “most unstable” parcels. Additionally, comparisons of different QLCS regimes show greater bulk shear magnitudes along the poleward portions of the QLCS convective lines during tornadic hours, which may explain the poleward bias of QLCS tornado reports noted by prior studies. A strong correlation was also identified between low-level nocturnal stabilization and the enhancement of both the low-level jet and low-level vertical wind shear. These enhancements could account for the prevalence of nocturnal tornadoes produced by these systems. Finally, the potential response to diabatic heating (i.e., low-level flow enhancements) may contribute to increased bulk shear magnitudes during the mature phase of these systems, which could help address the noted time lag between system formation and the occurrence of the first tornado reports.

Abstract Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting.

Abstract Coastal breeze circulations [bay breeze (BB) and sea breeze (SB)] modulate weather conditions by advecting a maritime air mass onshore, altering air quality, and often initiating deep convection. Coastal breeze interactions with the planetary boundary layer (PBL) are not well simulated due to small-scale interactions being parameterized by numerical weather models. As part of the Tracking Aerosol Convection Interactions Experiment (TRACER) in Houston, Texas, PBL observations from remote sensors and uncrewed aerial systems provide a unique benchmark for evaluating model performance and investigating underlying processes. The Warn-on-Forecast System (WoFS) is a convection-allowing, 18-member ensemble designed to predict high-impact weather by combining rapidly updating data assimilation cycles with varying PBL and radiation parameterizations. The TRACER datasets are used to evaluate WoFS’s simulation of the PBL, coastal breezes, and their interactions with high vertical and temporal resolution. There is broad variability across members in the depth, arrival time, and intensity of the simulated SB case. Moreover, only a subset of members simulate the preceding BB, but none do so accurately. The radiation scheme impacts the ability to simulate a BB and the onset time of the SB. The mixing scheme for PBL parameterization lends differences in depth, intensity, and evolution. Nonlocal mixing schemes allow baroclinic circulations to develop more readily, thus simulating coastal flows better, but overestimate PBL depth and temperature. The influence parameterization schemes have on meteorological biases offers potential solutions to improve simulations of coastal PBL processes.

Abstract A probabilistic thunderstorm forecast contest has been hosted by the Department of Atmospheric and Environmental Sciences at the University at Albany since 1979. The contest involves issuing forecasts of the 24-h probability of thunder at 10 locations in the contiguous United States during the summer. A previous study by Bosart and Landin in 1994 used consensus forecasts from this contest to assess probabilistic thunder forecast skill during the 1983–93 period. Given the research advances and improvements in observation and modeling systems that have occurred since 1993, a renewed analysis of thunderstorm forecast skill in the contest is warranted. This study revisits the conclusions of the previous study and provides an updated assessment of the state of human and model thunderstorm forecast skill for the period from 2009 to 2022. Through the lens of this contest, probabilistic thunderstorm forecast skill has increased substantially in the past two decades. The 14-yr average skill from 2009 to 2022 was 34.2% (standard deviation of 5.7%) relative to climatology, an increase from 21.8% during the 1983–93 period. This increase in forecast skill is found to largely be a result of improved forecast confidence with continued forecast reliability. In the 2009–22 period, contest participants tended to forecast at the extreme ends of the probability distribution more often than in 1983–93, evidence of greater forecast confidence due to improved nowcasting and short-term forecasting tools. Significance Statement The purpose of this study is to examine how the ability of forecasters to predict the probability of thunderstorms has improved over time. To examine thunderstorm forecast skill, we analyze entries in a thunderstorm forecast contest. Our results demonstrate that probabilistic thunderstorm forecast skill has improved greatly in the past few decades. This skill increase occurred as forecasters have become more confident in issuing forecasts of either very high or very low thunder probabilities, although participants continue to overforecast the probability of thunder at low probabilities and underforecast the probability of thunder at high probabilities. These results demonstrate that research advances and improvements in technology are resulting in more skillful forecasts of hazardous weather such as thunderstorms.

Abstract This study presents a comprehensive evaluation of the recently developed Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) microphysics parameterization scheme with prognostic small ice crystal number concentration (WDM6_NI), using three wintertime air-sea interaction cases over the northeastern Korean Peninsula. Compared to the default WDM6, WDM6_NI produces reduced surface precipitation, which is inconsistent with Automatic Weather Station (AWS) observations. Sensitivity experiments (hereafter EFF), in which the accretion efficiency is set to 1 without the reduction factor, confirm that reduced surface precipitation in WDM6_NI results from its decreased accretion efficiency, causing reduction in mixing ratios of solid-phase hydrometeors in the atmosphere. EFF increases mixing ratios of solid-phase hydrometeors and results in greater surface snow and graupel along the coast, improving agreement with precipitation-type classifications from radar/AWS observations. Additionally, EFF enhances the probability of detection and pattern correlation scores for surface precipitation, demonstrating improved predictive performance for wintertime air-sea interaction events over the Korean Peninsula. These results support improving the operational configuration of WDM6 in NWP. This study also compares implicit and explicit approaches for classifying surface precipitation types using EFF simulations. Two implicit methods—the revised Matsuo (RM) and the 1000–700 hPa thickness (TK700)—are assessed against precipitation types from radar/AWS observations. The explicit method, which classifies precipitation types based on simulated surface hydrometeors from the microphysics scheme, outperforms TK700 and yields performance comparable to RM. These findings suggest that, when used with refined microphysics schemes, the explicit method may provide more accurate precipitation-type classifications than commonly used implicit methods.

Abstract When extreme hydrometeorological events threaten the United States, quantitative precipitation forecasts (QPFs) from the National Centers for Environmental Prediction’s Weather Prediction Center (WPC) provide critical decision support to help mitigate risks to life and property. This study builds on previous work by providing an updated benchmark of WPC QPF skill for extreme precipitation events from 2012 to 2024, through evaluating the 6- and 24-h accumulations for the day 1–3 lead times. Extreme precipitation is defined using three thresholds: the 99th and 99.9th percentile precipitation values of all wet-site days from 2012 to 2024 for each National Oceanic and Atmospheric Administration (NOAA) River Forecast Center (RFC) region and the 2-yr average recurrence interval from the NOAA Atlas 14. WPC forecasts are verified against Stage IV quantitative precipitation estimates. Forecast skill is assessed seasonally, regionally, and temporally for the 1200 UTC forecast cycle. Results show that WPC forecasts of extreme precipitation have improved over time. The highest skill is observed along the West Coast, where events are dominated by atmospheric rivers from late fall through early spring. Forecasts in these regions often retain skill through day 3. The East Coast shows the second-highest skill, particularly in fall and winter, when extreme precipitation occurs from tropical and synoptic systems. Skill is lowest in inland areas, especially in the summer months when precipitation systems are weakly forced, smaller in scale, and often underforecasted by WPC. These events rarely show skill after a day 1 lead time. Significance Statement Extreme precipitation remains a primary driver of billion-dollar disasters in the United States, underscoring the growing need for accurate quantitative precipitation forecasts. By benchmarking the skill of National Oceanic and Atmospheric Administration’s (NOAA) Weather Prediction Center forecasts of extreme precipitation, we can understand where regional and seasonal weaknesses exist and evaluate the current boundaries of precipitation predictability. These insights are essential for guiding future advancements in operational forecasting and model development.

Abstract This study investigates the factors contributing to the delay in the rapid intensification (RI) of Hurricane Ida (2021). Using data assimilation to modify the steering flow and environmental moisture, several mechansims responsible for the RI onset delay during Ida’s development can be identified. Specifically, we show that the presence of dry air in the Gulf of Mexico is the dominant inhibiting factor for Ida’s RI delay, rather than the land effects associated with Ida’s passage over Cuba or other large-scale conditions. This dry-air intrusion could extend deeply into the storm’s inner core, suppressing deep convection and preventing Ida’s further intensification even after the storm has developed a sufficiently organized structure. Comparing two data assimilation approaches based on the local ensemble transform Kalman filter (LETKF) and three-dimensional variational data assimilation (3DVAR) shows further that LETKF more effectively isolates environmental conditions responsible for Ida’s RI delay. This is largely due to LETKF’s ability to generate ensemble-based analyses and incorporate model uncertainty in the background covariance matrix through the use of multiple physics. These findings are robust across sensitivity experiments and underscore the strong dependence of RI onset predictability on tropical cyclone (TC) track such that evaluating environmental conditions at one particular time or location is generally insufficient for real-time RI forecasting. In this regard, our results have significant implications for future observing strategies such as the design or deployment of reconnaissance missions targeting RI onset and its predictability. Significance Statement This study makes use of data assimilation techniques to examine different environmental factors causing the delay of rapid intensification (RI) for Hurricane Ida (2021). Our results show the key role of the large-scale moisture environment during Ida’s RI delay, which was strongly influenced by a persistent dry-air intrusion to the west of its track. Analysis of different data assimilation experiments reveals a critical window within the next 24–30 h after a potential RI onset moment, during which any inimical environmental conditions can disrupt tropical cyclone (TC) development and restrain RI. In contrast, land effects associated with Ida’s passage over Cuba play a minor role for the case of Ida, given its short duration and limited ability to inhibit RI. These findings highlight the importance of observing environmental conditions for more accurate RI forecasting.

Abstract As the demand and availability of seasonal forecasts grow steadily over the past two decades, this study investigates the skill of operational 2-m temperature seasonal forecasts in Europe. The assessment is based on multimodel ensemble hindcasts consisting of 182 members from seven weather and climate prediction centers across the world. In particular, monthly mean 2-m temperature forecasts at lead times of +1, +2, and +3 months issued each month in the 1993–2016 period are verified based on the respective ERA5 reanalysis. The skill of the probabilistic forecasts in providing reliable, accurate, and sharp indications of the monthly mean 2-m temperature anomaly category (i.e., below normal, normal, or above normal) in the upcoming months is characterized by regional and intra-annual variations. Maps of the ranked probability skill score indicate that the skill is generally higher in the MAM and JJA seasons, with southeastern Europe exhibiting better scores than other regions. Predictions in December–February (DJF) and SON only marginally exceed the skill of climatological forecasts over most land areas in Europe. Focusing on southeastern Europe, it is found that pronounced skill variations also emerge between adjacent calendar months of the same season. The annual cycles of three different skill measures [ranked probability skill score (RPSS), hit score, and probability of observed outcomes] consistently show that the skill in this region is highest in March and June–September and lowest in January, May, October, and December valid months. Finally, the ensemble spread of southeastern Europe seasonal 2-m temperature forecasts is found to provide some indication of the expected ensemble-mean error in year-round analyses, owing to the similar seasonal variations of these properties.

Abstract Hawai‘i sees a significant number of aviation accidents each year, many of which are caused by weather. In an effort to mitigate such incidents in the future, weather-related aviation accidents in Hawai‘i between 2003 and 2022 are investigated. A data catalog is created using the National Transportation Safety Board (NTSB) reports, National Oceanic and Atmospheric Administration (NOAA) weather charts, satellite imagery, atmospheric soundings, Automated Surface Observing System/Automated Weather Observing System (ASOS/AWOS) surface winds, and Next Generation Weather Radar (NEXRAD) data. It serves as the basis for the analysis of the days on which accidents occurred. A machine learning algorithm is implemented to compare accident days to nonaccident days. Results show that most weather-related accidents take place during trade wind conditions and occur on O‘ahu, Moloka‘i, and Maui. When looking at the monthly distribution, there is a higher incidence of accidents during the winter months. This is due to the fact that in addition to trade wind accidents, over 75% of cases that are linked to a synoptic-scale disturbance occur during this time. Atmospheric stability and wind speeds below the trade wind inversion are deemed most crucial when assessing atmospheric conditions for aviation safety. For fatal accidents, surface winds are observed to be a factor. Shearlines and troughs are associated with the majority of fatal weather-related accidents. In particular, trade wind days during which a shearline is present account for the largest number of fatal cases. This suggests that the most hazardous days for aviation in Hawai‘i are not necessarily those with the most severe weather but those that subtly deviate from the familiar trade wind regime—conditions that can be easily overlooked. Significance Statement Weather is believed to play a significant role in aviation accidents in Hawai‘i. This study analyzes weather-related helicopter and airplane accidents in Hawai‘i from 2003 to 2022 with the objective to better understand the conditions that caused them and help minimize future accidents. We found that weather was indeed the leading cause or contributing factor for aviation accidents in Hawai‘i during this period and identified atmospheric stability and wind speeds below the trade wind inversion as the atmospheric variables most critical for aviation safety among those considered. Additionally, we were able to show that machine learning models have the potential to assist in determining the factors responsible for these accidents and recommend future research to further explore this path.