Weather and Forecasting最新文献

{"title":"Impacts of sampling and storm-motion estimates on RUC/RAP-based discriminations of nontornadic and tornadic supercell environments","authors":"M. Coniglio, Richard L. Thompson","doi":"10.1175/waf-d-24-0027.1","DOIUrl":"https://doi.org/10.1175/waf-d-24-0027.1","url":null,"abstract":"\u0000This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0-1-km SRH becomes more skillful than 0–500 m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer CAPE (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increases for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE are unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating ensemble predictions of South Asian monsoon low pressure system genesis","authors":"D. Suhas, William R. Boos","doi":"10.1175/waf-d-24-0044.1","DOIUrl":"https://doi.org/10.1175/waf-d-24-0044.1","url":null,"abstract":"\u0000Synoptic-scale vortices known as monsoon low pressure systems (LPS) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–2022. The GEFS successfully predicted about half the observed LPS genesis events one to two days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a False Alarm Ratio (FAR) around 50% for one- to two-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing that of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPS form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias-correction.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating machine learning-based probabilistic convective hazard forecasts using the HRRR: Quantifying hazard predictability and sensitivity to training choices","authors":"R. Sobash, David A. Ahijevych","doi":"10.1175/waf-d-23-0221.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0221.1","url":null,"abstract":"\u0000The High Resolution Rapid Refresh (HRRR) model provides hourly-updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–2020 HRRR version 4 (HRRRv4) forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts (NNPFs) for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day, but were all superior to the UH forecast. NNPFs valid at hours between 18 UTC – 00 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 06–12 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–2020 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Forecasting for Morning Fog Expansion (Sea of Clouds)","authors":"Yukitaka Ohashi, Kazuki Hara","doi":"10.1175/waf-d-23-0237.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0237.1","url":null,"abstract":"\u0000This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine-learning technique, the gradient boosting method, was adopted as the AI algorithm. The Miyoshi Basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October, November, and December 2018–2021. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high Area Under the Curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dew-point temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141827698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Meteorology of the August 2023 Maui Wildfire","authors":"Clifford F. Mass, David Ovens","doi":"10.1175/waf-d-23-0210.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0210.1","url":null,"abstract":"\u0000On 8 August 2023, a wind-driven wildfire pushed across the city of Lahaina, located in West Maui, Hawaii, resulting in at least 100 deaths and an estimated economic loss of 4-6 billion dollars. The Lahaina wildfire was associated with strong, dry downslope winds gusting to 31-41 ms−1 (60-80 kt) that initiated the fire by damaging power infrastructure. The fire spread rapidly in invasive grasses growing in abandoned agricultural land upslope from Lahaina. This paper describes the synoptic and mesoscale meteorology associated with this event, as well as its predictability. Stronger than normal northeast trade winds, accompanied by a stable layer near the crest level of the West Maui Mountains, resulted in a high-amplitude mountain wave response and a strong downslope windstorm. Mesoscale model predictions were highly accurate regarding the location, strength, and timing of the strong winds. Hurricane Dora, which passed approximately 1300 km to the south of Maui, does not appear to have had a significant impact on the occurrence and intensity of the winds associated with the wildfire event. The Maui wildfire was preceded by a wetter-than-normal winter and near-normal summer conditions.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Usefulness of Machine Learning Severe Weather Guidance in the Warn-on-Forecast System: Results from the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment","authors":"Montgomery Flora, Burkely T. Gallo, Corey K. Potvin, Adam J. Clark, Katie A. Wilson","doi":"10.1175/waf-d-24-0038.1","DOIUrl":"https://doi.org/10.1175/waf-d-24-0038.1","url":null,"abstract":"\u0000Artificial intelligence (AI) is gaining popularity for severe weather forecasting. Recently, the authors developed an AI system using machine learning (ML) to produce probabilistic guidance for severe weather hazards, including tornadoes, large hail, and severe winds, using the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast System as input (WoFS). Known as WoFS-ML-Severe, it performed well in retrospective cases, but its operational usefulness had yet to be determined. To examine the potential usefulness of the ML guidance, we conducted a control and treatment (experimental) group experiment during the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (HWT-SFE). The control group had full access to WoFS, while the experimental group had access to WoFS and ML products. Explainability graphics were also integrated into the WoFS web viewer. Both groups issued 1-hr convective outlooks for each hazard. After issuing their forecasts, we surveyed participants on their confidence, the number of products viewed, and the usefulness of the ML guidance. We found the ML-based outlooks outperformed non-ML-based outlooks for multiple verification metrics for all three hazards and were rated subjectively higher by the participants. However, the difference in confidence between the two groups was not significant, and the experimental group self-reported viewing more products than the control group. Participants had mixed sentiments towards explainability products as it improved their understanding of the input/output relationships, but viewing them added to their workload. Although the experiment demonstrated the usefulness of ML guidance for severe weather forecasting, there are avenues to improve upon the ML guidance, and more training and exposure are needed to exploit its benefits fully.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140983325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing NOAA Rip-Current Hazard Likelihood Predictions: Comparison with Lifeguard Observations and Parameterizations of Bathymetric and Transient Rip-Current Types","authors":"Audrey Casper, E. S. Nuss, C. M. Baker, M. Moulton, Gregory Dusek","doi":"10.1175/waf-d-23-0181.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0181.1","url":null,"abstract":"\u0000Rip currents, fast offshore-directed flows, are the leading cause of death and rescues on surf beaches worldwide. The National Oceanographic and Atmospheric Administration (NOAA) seeks to minimize this threat by providing rip current hazard likelihood forecasts based on environmental conditions from the Nearshore Wave Prediction System. Rip currents come in several types, including bathymetric rip currents that form when waves break on sandbars interspersed with channels, and transient rip currents that form when there are breaking waves coming from multiple directions. The NOAA model was developed and tested in an area where bathymetric rip currents may be the most prevalent type of rip current. Therefore, model performance in regions where other types of rip currents (e.g., transient rip currents) may be more ubiquitous remains unknown. To investigate the efficacy of the NOAA model guidance in the context of different rip-current types, we compared modeled rip-current probabilities with physical-based parameterizations of bathymetric and transient rip-current speeds. We also compared these probabilities to lifeguard observations of bathymetric and transient rip currents from Salt Creek Beach, CA in Summer-Fall 2021. We found that the NOAA model skillfully predicts a wide range of hazardous parameterized bathymetric speeds but generally underpredicts hazardous transient rip-current speeds and the hazardous rip currents observed at Salt Creek Beach. Our results demonstrate how wave parameters, including directional spread, may serve as environmental indicators of rip-current hazard. By evaluating factors that influence the skill of modeled rip-current predictions, we strive towards improved rip-current hazard forecasting.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140982764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Near-Surface Wind Fields of Downbursts using a Series of High-Resolution Idealized Simulations","authors":"Andrew Moore","doi":"10.1175/waf-d-23-0164.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0164.1","url":null,"abstract":"\u0000Short-lived and poorly organized convective cells, often called weakly forced thunderstorms (WFT), are a common phenomenon during the warm season across the eastern and southeastern United States. While typically benign, wet downbursts emanating from such convection can have substantial societal impacts, including tree, power line, and property damage from strong outflow winds. Observational studies have documented the occurrence of severe (25.7 m s‒1 or higher) wind speeds from wet downbursts, but the frequency of severe downbursts, including the spatial extent and temporal duration of severe winds, remains unclear. The ability for modern observing networks to reliably observe such events is also unknown; however, answering these questions is important for improving forecast skill and verifying convective warnings accurately. This study attempts to answer these questions by drawing statistical inferences from 97 high-resolution idealized simulations of single-cell downburst events. It was found that while 35% of the simulations featured severe winds, the spatial and temporal extent of such winds is limited - on the order of 10 km2 or less and persisting for around 5 minutes on average. Furthermore, through a series of simulated network experiments, it is postulated that the probability that a modern mesonet observes a severe wind gust given a severe downburst is around 1%. From these results, a statistical argument is made that most tree impacts associated with pulse convection are likely caused by sub-severe winds. Several implications for forecasting, warning, and verifying WFT events fall out from these discussions.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141016557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving beyond the Aerosol Climatology of WRF-Solar: A Case Study over the North China Plain","authors":"Wenting Wang, Hongrong Shi, Disong Fu, Mengqi Liu, Jiawei Li, Yunpeng Shan, Tao Hong, Dazhi Yang, Xiangao Xia","doi":"10.1175/waf-d-23-0203.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0203.1","url":null,"abstract":"\u0000Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%.\u0000\u0000\u0000Numerical weather prediction (NWP) is the “go-to” approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141033980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Objective Verification of the Weather Prediction Center’s Mesoscale Precipitation Discussions","authors":"Erica Bower, Michael J. Erickson, James A. Nelson, M. Klein, Andrew Orrison","doi":"10.1175/waf-d-23-0199.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0199.1","url":null,"abstract":"\u0000The Weather Prediction Center (WPC) issues Mesoscale Precipitation Discussions (MPDs) to highlight regions where heavy rainfall is expected to pose a threat for flash flooding. Issued as short-term guidance, the MPD consists of a graphical depiction of the threat area and a technical discussion of the forecasted meteorological and hydrological conditions conducive to heavy rainfall and the potential for a flash flood event. MPDs can be issued either during or in anticipation of an event and typically are valid for up to 6 hours. This study presents an objective verification of WPC’s MPDs issued between 2016 and 2022, complete with a climatology, false alarm analysis, and contingency table-based skill scores (e.g. critical success index, fractional coverage, etc). Regional and seasonal differences become evident when MPDs are assessed based on these groupings. MPDs improved in basic skill scores between 2016 and 2020, with slight decline in scores for 2021 and 2022. The false alarm ratio of MPDs has decreased between 2016 and 2021. The most dramatic improvement over the period occurs in the MPDs in the winter season (December, January, and February) and along theWest Coast (primarily atmospheric river events). The accuracy of MPDs in this group has quadrupled when measured by fractional coverage, and the false alarm rate is approximately one fifth of the 2016 value. Skill during active monsoon seasons tends to decrease, partially due to the large size of MPDs issued for monsoon-related flash flooding events.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}