Weather and Forecasting最新文献_第2页

Forecasting Hourly Wildfire Risk: Enhancing Fire Danger Assessment using Numerical Weather Prediction 预报每小时野火风险：利用数值天气预报加强火险评估

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-04-10 DOI: 10.1175/waf-d-23-0226.1

Christopher Rodell, Rosie Howard, P. Jain, N. Moisseeva, Timothy Chui, Roland Stull

{"title":"Forecasting Hourly Wildfire Risk: Enhancing Fire Danger Assessment using Numerical Weather Prediction","authors":"Christopher Rodell, Rosie Howard, P. Jain, N. Moisseeva, Timothy Chui, Roland Stull","doi":"10.1175/waf-d-23-0226.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0226.1","url":null,"abstract":"\u0000Wildfire agencies use Fire Danger Rating Systems (FDRS) to deploy resources and issue public safety measures. The most widely used FDRS is the Canadian Fire Weather Index (FWI) System, which uses weather inputs to estimate the potential for wildfires to start and spread. Current FWI forecasts provide a daily numerical value, representing potential fire severity at an assumed midafternoon time for peak fire activity. This assumption, based on typical diurnal weather patterns, is not always valid. To address this, we developed an hourly FWI (HFWI) system using numerical weather prediction. We validate HFWI against the traditional daily FWI (DFWI) by comparing HFWI forecasts with observation-derived DFWI values from 917 surface fire weather stations in western North America. Results indicate strong correlations between forecasted HFWI and the observation-derived DFWI. A positive mean bias in the daily maximum values of HFWI compared to the traditional DFWI suggests that HFWI can better capture severe fire weather variations regardless of when they occur. We confirm this by comparing HFWI with hourly Fire Radiative Power (FRP) satellite observations for nine wildfire case studies in Canada and the United States. We demonstrate HFWI’s ability to forecast shifts in fire danger timing, especially during intensified fire activity in the late evening and early morning hours, while allowing for multiple periods of increased fire danger per day—a contrast to the conventional DFWI. This research highlights the HFWI system’s value in improving fire danger assessments and predictions, hopefully enhancing wildfire management, especially during atypical fire behavior.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718091","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}

引用次数: 0

Statistical predictability of Euro-Mediterranean subseasonal anomalies: The TeWA approach 欧洲-地中海副季节性异常的统计可预测性：TeWA 方法

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-04-04 DOI: 10.1175/waf-d-23-0061.1

Darío Redolat, R. Monjo

{"title":"Statistical predictability of Euro-Mediterranean subseasonal anomalies: The TeWA approach","authors":"Darío Redolat, R. Monjo","doi":"10.1175/waf-d-23-0061.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0061.1","url":null,"abstract":"\u0000It is widely known from energy balances that global oceans play a fundamental role in atmospheric seasonal anomalies via coupling mechanisms. However, numerical weather prediction models still have limitations in long-term forecasting due to their nonlinear sensitivity to initial deep oceanic conditions. As the Mediterranean climate has highly unpredictable seasonal variability, we designed a complementary method by supposing that (1) delayed teleconnection patterns provide information about ocean–atmosphere coupling on subseasonal timescales through the lens of (2) partially predictable quasi-periodic oscillations, since (3) forecast signals can be extracted by smoothing noise in a continuous lead-time horizon. To validate these hypotheses, subseasonal predictability of temperature and precipitation was analyzed at 11 reference stations in the Mediterranean area in the 1993–2021 period. The novel method, presented here, consists of combining lag-correlated teleconnections (15 indices) with self-predictability techniques of residual quasi-oscillation based on Wavelet (cyclic) and ARIMA (linear) analyses. The prediction skill of this Teleconnection-Wavelet-ARIMA (TeWA) combination was cross-validated and compared to that of the SEAS5-ECMWF model (3 months ahead). Results show that the proposed TeWA approach improves the predictability of first-month temperature and precipitation anomalies by 50–70% compared with the forecast of SEAS5. On a moving-averaged daily scale, the optimum prediction window is 30 days for temperature and 16 days for precipitation. The predictable ranges are consistent with atmospheric bridges in teleconnection patterns (e.g., ULMO) and are reflected by spatial correlation with SST. Our results suggest that combinations of the TeWA approach and numerical models could boost new research lines in subseasonal-to-seasonal forecasting.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744875","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}

引用次数: 0

The Impact on Simulated Bow Echoes of Changing Grid Spacing from 3 km to 1 km in the WRF Model 将 WRF 模型中的网格间距从 3 公里改为 1 公里对模拟弓回声的影响

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-04-02 DOI: 10.1175/waf-d-23-0192.1

Dylan J. Dodson, W. Gallus

{"title":"The Impact on Simulated Bow Echoes of Changing Grid Spacing from 3 km to 1 km in the WRF Model","authors":"Dylan J. Dodson, W. Gallus","doi":"10.1175/waf-d-23-0192.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0192.1","url":null,"abstract":"\u0000Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) model with 3-km and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivity were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753331","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}

引用次数: 0

Radar Outage Costs and the Value of Alternate Datasets 雷达故障成本和替代数据集的价值

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-19 DOI: 10.1175/waf-d-23-0165.1

S. Rudlosky, Joseph Patton, Eric Palagonia, John Y. N. Cho, J. Kurdzo

{"title":"Radar Outage Costs and the Value of Alternate Datasets","authors":"S. Rudlosky, Joseph Patton, Eric Palagonia, John Y. N. Cho, J. Kurdzo","doi":"10.1175/waf-d-23-0165.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0165.1","url":null,"abstract":"\u0000Quantifying the costs of radar outages allows value to be attributed to the alternate datasets that help mitigate outages. When radars are offline, forecasters rely more heavily on nearby radars, surface reports, numerical weather prediction models, and satellite observations. Monetized radar benefit models allow value to be attributed to individual radars for mitigating the threat to life from tornadoes, flash floods, and severe winds. Eighteen radars exceed $20 million in annual benefits for mitigating the threat to life from these convective hazards. The Jackson, MS radar (KJAN) provides the most value ($41.4 million), with the vast majority related to tornado risk mitigation ($29.4 million). During 2020-2023, the average radar is offline for 2.57% of minutes or 9.27 days per year, and experiences an average of 58.9 outages per year lasting 4.32 hours on average. Radar outage cost estimates vary by location and convective hazard. Outage cost estimates concentrate at the top, with 8, 2, 4, and 5 radars exceeding $1 million in outage costs during 2020, 2021, 2022, and 2023, respectively. The KJAN radar experiences outage frequencies of 4.92% and 5.50% during 2020 and 2023, resulting in outage cost estimates > $2 million both years. Combining outage cost estimates for all radars suggests that approximately $29.1 million in annual radar outage costs may be attributable as value to alternative datasets for helping to mitigate radar outage impacts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229222","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}

引用次数: 0

Spatiotemporal convolutional approach for the short-term forecast of hourly heavy rainfall probability integrating numerical weather predictions and surface observations 结合数值天气预报和地面观测数据的时空卷积法短期预报每小时暴雨概率

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-13 DOI: 10.1175/waf-d-23-0068.1

Xi Liu, Yu Zheng, Xiaoran Zhuang, Yaqiang Wang, Xin Li, Zhang Bei, Wenhua Zhang

{"title":"Spatiotemporal convolutional approach for the short-term forecast of hourly heavy rainfall probability integrating numerical weather predictions and surface observations","authors":"Xi Liu, Yu Zheng, Xiaoran Zhuang, Yaqiang Wang, Xin Li, Zhang Bei, Wenhua Zhang","doi":"10.1175/waf-d-23-0068.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0068.1","url":null,"abstract":"\u0000The accurate prediction of short-term rainfall, and in particular the forecast of hourly heavy rainfall (HHR) probability, remains challenging for numerical weather prediction (NWP) models. Here, we introduce a deep learning (DL) model, PredRNNv2-AWS, a convolutional recurrent neural network designed for deterministic short-term rainfall forecasting. This model integrates surface rainfall observations and atmospheric variables simulated by the Precision Weather Analysis and Forecasting System (PWAFS). Our DL model produces realistic hourly rainfall forecasts for the next 13 hours. Quantitative evaluations show that the use of surface rainfall observations as one of the predictors achieves higher performance (threat score) with 263% and 186% relative improvements over NWP simulations for the first 3 hours and the entire forecast hours, respectively, at a threshold of 5 mm/h. Noted that the optical-flow method also performs well in the initial hours, its predictions quickly worsen in the final hours compared to other experiments. The machine learning model, LightGBM, is then integrated to classify HHR from the predicted hourly rainfall of PredRNNv2-AWS. The results show that PredRNNv2-AWS can better reflect actual HHR conditions than PredRNNv2 and PWAFS. A representative case demonstrates the superiority of PredRNNv2-AWS in predicting the evolution of the rainy system, which substantially improves the accuracy of the HHR prediction. A test case involving the extreme flood event in Zhengzhou exemplifies the generalizability of our proposed model. Our model offers a reliable framework to predict target variables that can be obtained from numerical simulations and observations, e.g., visibility, wind power, solar energy, and air pollution.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247347","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}

引用次数: 0

Predicting the Intensity of Tropical Cyclones over the Western North Pacific Using Dual-Branch Spatio-Temporal Attention Convolutional Network 利用双分支时空注意力卷积网络预测北太平洋西部热带气旋的强度

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-12 DOI: 10.1175/waf-d-23-0191.1

Wei Tian, Yuanyuan Chen, Ping Song, Haifeng Xu, Liguang Wu, K. T. C. Lim Kam Sian, Yonghong Zhang, Chunyi Xiang

{"title":"Predicting the Intensity of Tropical Cyclones over the Western North Pacific Using Dual-Branch Spatio-Temporal Attention Convolutional Network","authors":"Wei Tian, Yuanyuan Chen, Ping Song, Haifeng Xu, Liguang Wu, K. T. C. Lim Kam Sian, Yonghong Zhang, Chunyi Xiang","doi":"10.1175/waf-d-23-0191.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0191.1","url":null,"abstract":"\u0000This paper proposes a Spatio-temporal Attention Convolutional Network (STACPred) that leverages deep learning techniques to model the spatio-temporal features of tropical cyclones (TC) and enable real-time prediction of their intensity. The proposed model employs dual branches to concurrently extract and integrate features from intensity heatmaps and satellite cloud imagery. Additionally, a Residual Attention (RA) module is integrated into the three-channel cloud imagery convolution process to automatically respond to high wind speed regions. TC’s longitude, latitude, and radius of winds are injected into the multi-timepoint prediction model to assist in the prediction task. Furthermore, a Rolling Mechanism (RM) is employed to smooth the fluctuation of losses, achieving accurate prediction of TC intensity. We use several TC records to evaluate and validate the universality and effectiveness of the model. The results indicate that STAC-Pred achieve satisfactory performance. Specifically, the STAC-Pred model improves prediction performance by 47.69% and 28.26% compared to the baseline (official institutions) at 3 and 6-hour intervals, respectively.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251159","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}

引用次数: 0

Forecasting High Wind Events in the HRRR Model over Wyoming and Colorado. Part I: Evaluation of Wind Speeds and Gusts 怀俄明州和科罗拉多州上空 HRRR 模式的大风事件预报。第一部分：风速和阵风评估

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-08 DOI: 10.1175/waf-d-23-0036.1

Ethan Collins, Z. Lebo, Robert Cox, Christopher L. Hammer, Matthew D. Brothers, B. Geerts, Robert Capella, Sarah McCorkle

{"title":"Forecasting High Wind Events in the HRRR Model over Wyoming and Colorado. Part I: Evaluation of Wind Speeds and Gusts","authors":"Ethan Collins, Z. Lebo, Robert Cox, Christopher L. Hammer, Matthew D. Brothers, B. Geerts, Robert Capella, Sarah McCorkle","doi":"10.1175/waf-d-23-0036.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0036.1","url":null,"abstract":"\u0000Strong wind events cause significant societal damage ranging from loss of property and disruption of commerce to loss of life. Over portions of the United States, the strongest winds occur in the cold season and may be driven by interactions with the terrain (downslope winds, gap flow, and mountain wave activity). In Part I of this two-part series, we evaluate the High-Resolution Rapid Refresh (HRRR) model wind speed and gust forecasts for the 2016-2022 winter months over Wyoming and Colorado, an area prone to downslope windstorms and gap flows due to its complex topography. The HRRR model exhibits a positive bias for low wind speeds/gusts and a large negative bias for strong wind speeds/gusts. In general, the model misses many strong wind events, but when it does predict strong winds, there is a high false alarm probability. An analysis of proxies for surface winds is conducted. Specifically, 700-mb and 850-mb geopotential height gradients are found to be good proxies for strong wind speeds and gusts at two wind-prone locations in Wyoming. Given the good agreement between low-level height gradients and surface wind speeds yet a strong negative bias for strong wind speeds and gusts, there is a potential shortcoming in the boundary layer physics in the HRRR model with regard to predicting strong winds over complex terrain, which is the focus of Part II. Lastly, the sites with the largest strong wind speed bias are found to mostly sit on the leeward side of high mountains, suggesting that the HRRR model performs poorly in the prediction of downslope windstorms.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257392","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}

引用次数: 0

Steering Flow Sensitivity in Forecast Models for Hurricane Ian (2022) 飓风伊恩（2022 年）预测模型中的转向流敏感性

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-06 DOI: 10.1175/waf-d-23-0169.1

F. Colby, Mathew Barlow, Andrew B. Penny

{"title":"Steering Flow Sensitivity in Forecast Models for Hurricane Ian (2022)","authors":"F. Colby, Mathew Barlow, Andrew B. Penny","doi":"10.1175/waf-d-23-0169.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0169.1","url":null,"abstract":"\u0000Hurricane Ian made landfall along Florida’s West Coast on 28 September 2022 at 1905 UTC near the Fort Myers area as a high impact storm. Here, we examine the potential link between track forecast errors near the time of landfall and errors in both the synoptic-scale upper-level flow and a shortwave moving within that flow.\u0000Five-days before the actual landfall (0000 UTC 23 September), most model guidance indicated landfall would occur close to where Ian eventually came ashore. But by 0000 UTC 25 September, model forecasts were all forecasting landfall in the Florida Panhandle. One day later, the models again agreed with each other but for a landfall 100 – 200 km north of Tampa, FL. By 0000 UTC 27 September, forecast models indicated landfall would occur near Tampa. Model forecasts continued shifting to the right and finally converged on Punta Gorda, FL, as the landfall location, less than 24 hours before landfall.\u0000In this short article, we hypothesize that the track of Ian depended on subtle interactions with an extratropical wave in the middle and upper atmosphere. Deterministic and ensemble model forecasts reveal that the interactions were very sensitive to the characteristics of this wave and the synoptic-scale flow in which the wave was embedded. A 1-2 dam difference in the geopotential heights played a major role in whether Ian moved north into the Panhandle or towards the east, making landfall in Central Florida.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261342","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}

引用次数: 0

The First Operational Version of Taiwan Central Weather Bureau’s One-tier Global Atmosphere-Ocean Coupled Forecast System for Seasonal Prediction 台灣中央氣象局全球大氣-海洋一層式季節預測耦合預報系統首度運作版本

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-06 DOI: 10.1175/waf-d-22-0128.1

H. Juang, Tzu-Yu Wu, Pang-Yen Brian Liu, Hsin-Yi Lin, Ching-Teng Lee, M. Kueh, Jia-Fong Fan, Jen-Her River Chen, Mong-Ming Lu, Pay-Liam Lin

{"title":"The First Operational Version of Taiwan Central Weather Bureau’s One-tier Global Atmosphere-Ocean Coupled Forecast System for Seasonal Prediction","authors":"H. Juang, Tzu-Yu Wu, Pang-Yen Brian Liu, Hsin-Yi Lin, Ching-Teng Lee, M. Kueh, Jia-Fong Fan, Jen-Her River Chen, Mong-Ming Lu, Pay-Liam Lin","doi":"10.1175/waf-d-22-0128.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0128.1","url":null,"abstract":"\u0000The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB’s atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcast from 1982 to 2011 and forecast from 2012 to 2019 for analyzing its performance. The results of hindcast and forecast show that the TCWB1T has useful predictions as verified to the observation of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, RPSS, RD, and ROC. And TCWB1T has the same level of skill scores as compared to NCEP CFSv2 and/or ECMWF SEAS5, based on EOF, APC, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skills which are better in winter than summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261434","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}

引用次数: 0

Advanced sea ice modeling for short-term forecasting for Alaska’s coasts 用于阿拉斯加沿岸短期预测的先进海冰模型

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-06 DOI: 10.1175/waf-d-23-0178.1

A. Fujisaki‐Manome, Haoguo Hu, Jia Wang, J. Westerink, D. Wirasaet, Guoming Ling, Mindo Choi, Saeed Moghimi, Edward Myers, Ali Abdolali, Clint Dawson, Carol Janzen

{"title":"Advanced sea ice modeling for short-term forecasting for Alaska’s coasts","authors":"A. Fujisaki‐Manome, Haoguo Hu, Jia Wang, J. Westerink, D. Wirasaet, Guoming Ling, Mindo Choi, Saeed Moghimi, Edward Myers, Ali Abdolali, Clint Dawson, Carol Janzen","doi":"10.1175/waf-d-23-0178.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0178.1","url":null,"abstract":"\u0000In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice model version 6. The regional, high resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted for December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air-ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud and land Elevation Satellite-2 product showed a mean bias of -4.6 cm. Daily 5-day forecast simulations for October 2020-August 2021 presented the model’s promising performance for future implementation in the coupled model system.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140261197","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}

引用次数: 0