Weather and Forecasting最新文献_第3页

Improving the Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling 改进热带气旋风暴内海面温度冷却的统计表示方法

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-04 DOI: 10.1175/waf-d-23-0115.1

Joshua B. Wadler, J. Cione, Samantha Michlowitz, Benjamin Jaimes de la Cruz, Lynn K. Shay

{"title":"Improving the Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling","authors":"Joshua B. Wadler, J. Cione, Samantha Michlowitz, Benjamin Jaimes de la Cruz, Lynn K. Shay","doi":"10.1175/waf-d-23-0115.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0115.1","url":null,"abstract":"\u0000This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner-core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the pre-storm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJcm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJcm−2 (SST cooling is always less than 2 °C) which demonstrates the importance of initial oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical-dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140080336","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

On the Importance of Regime-Specific Evaluations for Numerical Weather Prediction Models as Demonstrated using the High Resolution Rapid Refresh (HRRR) Model 利用高分辨率快速刷新（HRRR）模式证明特定区域评估对数值天气预报模式的重要性

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-03-01 DOI: 10.1175/waf-d-23-0177.1

Temple R. Lee, Sandip Pal, Ronald D. Leeper, Tim Wilson, Howard J. Diamond, Tilden P. Meyers, David D. Turner

{"title":"On the Importance of Regime-Specific Evaluations for Numerical Weather Prediction Models as Demonstrated using the High Resolution Rapid Refresh (HRRR) Model","authors":"Temple R. Lee, Sandip Pal, Ronald D. Leeper, Tim Wilson, Howard J. Diamond, Tilden P. Meyers, David D. Turner","doi":"10.1175/waf-d-23-0177.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0177.1","url":null,"abstract":"\u0000The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings which can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different scenarios, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions (i.e., different near-surface heating rates (), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)) to evaluate the High-Resolution Rapid Refresh (HRRR) model, which is a 3-km model used for operational weather forecasting in the U.S. On days with small (large) , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identifying model deficiencies which supports improvements to the parameterization schemes used therein. A similar, regime-specific model verification approach may also be used to help evaluate other geophysical models.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083560","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

Identifying ZDR Columns in Radar Data with the Hotspot Technique 利用热点技术识别雷达数据中的 ZDR 柱

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-01-26 DOI: 10.1175/waf-d-23-0146.1

John Krause, Vinzent Klaus

引用次数: 0

Improving afternoon thunderstorm prediction over complex terrain with the assimilation of dense ground-based observations: Four cases in the Taipei Basin 利用密集的地面观测资料同化改进复杂地形上的午后雷暴预报：台北盆地的四个案例

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-01-24 DOI: 10.1175/waf-d-23-0149.1

Shu‐Chih Yang, Yi-Pin Chang, Hsiang-Wen Cheng, Kuan‐Jen Lin, Ya-Ting Tsai, Jing-Shan Hong, Yu-Chi Li

{"title":"Improving afternoon thunderstorm prediction over complex terrain with the assimilation of dense ground-based observations: Four cases in the Taipei Basin","authors":"Shu‐Chih Yang, Yi-Pin Chang, Hsiang-Wen Cheng, Kuan‐Jen Lin, Ya-Ting Tsai, Jing-Shan Hong, Yu-Chi Li","doi":"10.1175/waf-d-23-0149.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0149.1","url":null,"abstract":"\u0000In this study, we investigate the impact of assimilating densely distributed Global Navigation Satellite System (GNSS) zenith total delay (ZTD) and surface station (SFC) data on the prediction of very short-term heavy rainfall associated with afternoon thunderstorm (AT) events in the Taipei Basin. Under weak synoptic-scale conditions, four cases characterized by different rainfall features are chosen for investigation. Experiments are conducted with a 3-hour assimilation period, followed by 3-hour forecasts. Also, various experiments are performed to explore the sensitivity of AT initialization.\u0000Data assimilation experiments are conducted with a convective-scale Weather Research and Forecasting-local ensemble transform Kalman filter (WRF-LETKF) system. The results show that ZTD assimilation can provide effective moisture corrections. Assimilating SFC wind and temperature data could additionally improve the near-surface convergence and cold bias, further increasing the impact of ZTD assimilation. Frequently assimilating SFC data every 10 minutes provides the best forecast performance especially for rainfall intensity predictions. Such a benefit could still be identified in the earlier forecast initialized two hours before the start of the event. Detailed analysis of a case on 22 July 2019 reveals that frequent assimilation provides initial conditions that can lead to fast vertical expansion of the convection and trigger an intense AT.\u0000This study proposes a new metric using the fraction skill score to construct an informative diagram to evaluate the location and intensity of heavy rainfall forecast and display a clear characteristic of different cases. Issues of how assimilation strategies affect the impact of ground-based observations in a convective ensemble data assimilation system and AT development are also discussed.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139600759","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

Operational Aviation Icing Forecast Algorithm for the Korea Meteorological Administration 韩国气象局航空结冰业务预报算法

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-01-22 DOI: 10.1175/waf-d-23-0160.1

Eun-Tae Kim, Jung-Hoon Kim, Soo-Hyun Kim, Cyril Morcrette

{"title":"Operational Aviation Icing Forecast Algorithm for the Korea Meteorological Administration","authors":"Eun-Tae Kim, Jung-Hoon Kim, Soo-Hyun Kim, Cyril Morcrette","doi":"10.1175/waf-d-23-0160.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0160.1","url":null,"abstract":"\u0000In this study, we developed and evaluated the Korean Forecast Icing Potential (K-FIP), an in-flight icing forecast system for the Korea Meteorological Administration (KMA) based on the Simplified Forecast Icing Potential (SFIP) algorithm. The SFIP is an algorithm used to post-process numerical weather prediction (NWP) model forecasts for predicting potential areas of icing based on the fuzzy logic formulations of four membership functions: temperature, relative humidity, vertical velocity, and cloud liquid water content. In this study, we optimized the original version of the SFIP for the global NWP model of the KMA through three important updates using 34 months of pilot reports for icing: using total cloud condensates, reconstructing membership functions, and determining the best weight combination for input variables. The use of all cloud condensates and the reconstruction of these membership functions resulted in a significant improvement in the algorithm compared with the original. The weight combinations for the KMA's global model were determined based on the performance scores. While several sets of weights performed equally well, this process identified the most effective weight combination for the KMA model, which is referred to as the K-FIP. The K-FIP demonstrated the ability to successfully predict icing over the Korean Peninsula using observations made by research aircraft from the National Institute of Meteorological Sciences of the KMA. Eventually, the K-FIP icing forecasts will provide better forecasts of icing potentials for safe and efficient aviation operations in South Korea.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139608894","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

WoFS and the Wisdom of the Crowd: The Impact of the Warn-on-Forecast System on Hourly Forecasts during the 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiment WoFS 和群众的智慧：预报警告系统对 2021 年 NOAA 危险天气试验台春季预报实验期间每小时预报的影响

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-01-18 DOI: 10.1175/waf-d-23-0033.1

Burkely T. Gallo, Adam J. Clark, I. Jirak, David A. Imy, Brett Roberts, Jacob Vancil, Kent Knopfmeier, P. Burke

{"title":"WoFS and the Wisdom of the Crowd: The Impact of the Warn-on-Forecast System on Hourly Forecasts during the 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiment","authors":"Burkely T. Gallo, Adam J. Clark, I. Jirak, David A. Imy, Brett Roberts, Jacob Vancil, Kent Knopfmeier, P. Burke","doi":"10.1175/waf-d-23-0033.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0033.1","url":null,"abstract":"\u0000During 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 2200–2300 UTC, 2300–0000 UTC, 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.\u0000The 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 over-forecasted. 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.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614010","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 of Analysis Correction-based Additive Inflation on subseasonal tropical prediction in the Navy Earth System Prediction Capability 基于分析修正的加法膨胀对海军地球系统预测能力中热带副季节预测的影响

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-01-09 DOI: 10.1175/waf-d-23-0046.1

Stephanie S. Rushley, M. Janiga, William Crawford, Carolyn A. Reynolds, William Komaromi, J. McLay

{"title":"The Impact of Analysis Correction-based Additive Inflation on subseasonal tropical prediction in the Navy Earth System Prediction Capability","authors":"Stephanie S. Rushley, M. Janiga, William Crawford, Carolyn A. Reynolds, William Komaromi, J. McLay","doi":"10.1175/waf-d-23-0046.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0046.1","url":null,"abstract":"\u0000Accurately simulating the Madden-Julian Oscillation (MJO), which dominates intraseasonal (30-90 day) variability in the tropics, is critical to predicting tropical cyclones (TCs) and other phenomena at extended-range (2-3 week) timescales. MJO biases in intensity and propagation speed are a common problem in global coupled models. For example, the MJO in the Navy Earth System Prediction Capability (ESPC), a global coupled model, has been shown to be too strong and too fast, which has implications for the MJO-TC relationship in that model.\u0000The biases and extended-range prediction skill in the operational version of the Navy ESPC are compared to experiments applying different versions of Analysis Correction-based Additive Inflation (ACAI) to reduce model biases. ACAI is a method in which time-mean and stochastic perturbations based on analysis increments are added to the model tendencies with the goals of reducing systematic error and accounting for model uncertainty. Over the extended boreal summer (May-November), ACAI reduces the root mean squared error (RMSE) and improves the spread-skill relationship of the total tropical and MJO-filtered OLR and low-level zonal winds. While ACAI improves skill in the environmental fields of low-level absolute vorticity, potential intensity, and vertical wind shear, it degrades the skill in the relative humidity, which increases the positive bias in the Genesis Potential Index (GPI) in the operational Navy ESPC. Northern Hemisphere integrated TC genesis biases are reduced (increased number of TCs) in the ACAI experiments, which is consistent with the positive GPI bias in the ACAI simulations.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139443531","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

Comparison of Clustering Approaches in a Multi-Model Ensemble for U.S. East Coast Cold Season Extratropical Cyclones 美国东海岸冷季外热带气旋多模式集合中的聚类方法比较

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-01-09 DOI: 10.1175/waf-d-23-0017.1

Benjamin M. Kiel, B. Colle

{"title":"Comparison of Clustering Approaches in a Multi-Model Ensemble for U.S. East Coast Cold Season Extratropical Cyclones","authors":"Benjamin M. Kiel, B. Colle","doi":"10.1175/waf-d-23-0017.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0017.1","url":null,"abstract":"\u0000Several clustering approaches are evaluated for 1–9-day forecasts using a multi-model ensemble that includes the GEFS, ECMWF, and Canadian ensembles. Six clustering algorithms and three clustering spaces are evaluated using mean sea-level pressure (MSLP) and 12-h accumulated precipitation (APCP) for cool-season extratropical cyclones across the Northeast United States. Using the MSLP cluster membership to obtain the APCP clusters is also evaluated, along with applying clustering determined at one lead time to cluster forecasts at a different lead time. Five scenarios from each clustering algorithm are evaluated using displacement and intensity/amount errors from the scenario nearest to the MSLP and 12-h APCP analyses in the NCEP GFS and ERA5, respectively. Most clustering strategies yield similar improvements over the full ensemble mean and are similar in probabilistic skill except that: (1) Intensity Displacement Space gives lower MSLP displacement and intensity errors; and (2) Euclidean Space and Agglomerative Hierarchical Clustering, when using either full or average linkage, struggle to produce reasonably sized clusters. Applying clusters derived from MSLP to 12-h APCP forecasts is not as skillful as clustering by 12-h APCP directly, especially if several members contain little precipitation. Use of the same cluster membership for one lead time to cluster the forecast at another lead time is less skillful than clustering independently at each forecast lead time. Finally, the number of members within each cluster does not necessarily correspond with the best forecast, especially at the longer lead times, when the probability of the smallest cluster being the best scenario was usually underestimated.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444324","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

Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations 为 NWS 预报业务提供风暴尺度概率指导的合作探索

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2024-01-08 DOI: 10.1175/waf-d-23-0174.1

Katie A. Wilson, P. Burke, Burkely T. Gallo, Patrick S. Skinner, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda, Jonathan G. Madden, Justin W. Monroe, Jorge E. Guerra, Dale A. Morris

{"title":"Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations","authors":"Katie A. Wilson, P. Burke, Burkely T. Gallo, Patrick S. Skinner, T. T. Lindley, Chad M. Gravelle, Stephen W. Bieda, Jonathan G. Madden, Justin W. Monroe, Jorge E. Guerra, Dale A. Morris","doi":"10.1175/waf-d-23-0174.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0174.1","url":null,"abstract":"\u0000The operational utility of the NOAA National Severe Storm Laboratory’s storm-scale probabilistic Warn-on-Forecast System (WoFS) was examined across the watch-to-warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: (1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and (2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447284","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

Verification of the Global Forecast System, North American Mesoscale Forecast System, and High-Resolution Rapid Refresh Model Near-Surface Forecasts by use of the New York State Mesonet 利用纽约州中间网验证全球预报系统、北美中尺度预报系统和高分辨率快速刷新模式近地表预报

IF 2.9 3区地球科学

Weather and Forecasting Pub Date : 2023-12-29 DOI: 10.1175/waf-d-23-0094.1

Lauriana C. Gaudet, Kara J. Sulia, R. Torn, Nick P. Bassill

{"title":"Verification of the Global Forecast System, North American Mesoscale Forecast System, and High-Resolution Rapid Refresh Model Near-Surface Forecasts by use of the New York State Mesonet","authors":"Lauriana C. Gaudet, Kara J. Sulia, R. Torn, Nick P. Bassill","doi":"10.1175/waf-d-23-0094.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0094.1","url":null,"abstract":"Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and High-Resolution Rapid Refresh (HRRR) 2-m temperature, 10-m wind speed, and precipitation accumulation forecasts initialized at 1200 UTC are verified against New York State Mesonet (NYSM) observations from 1 January 2018 through 31 December 2021. NYSM observations at 126 site locations are used to calculate standard error statistics (e.g., forecast error, root mean square error) for temperature and wind speed and contingency table statistics for precipitation across forecast hours, meteorological seasons, and regions. The majority of the focus is placed on the first 18 forecast hours to allow for comparison among all three models. A daily NYSM station-mean temperature error analysis identified a slight cold bias at temperatures below 25°C in the GFS, a cool-to-warm bias as forecast temperatures warm in the HRRR, and a warm bias at temperatures above 30°C in each model. Differences arise when considering temperature biases with respect to lead times and seasons. Wind speeds are over-forecast at all ranges in each season, and forecast wind speeds ≥ 18 m s−1 are rarely observed. Performance diagrams indicate overall good forecast performance at precipitation thresholds of 0.1–1.5 mm, but with a high frequency bias in the GFS and NAM. This paper provides an overview of deterministic forecast performance across NYS, with the aim of sharing common biases associated with temperature, wind speed, and precipitation with operational forecasters and is the first step in developing a real-time model forecast uncertainty prediction tool.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147001","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