Weather and Forecasting最新文献

{"title":"Forecast Applications of GLM Gridded Products: A Data Fusion Perspective","authors":"Kevin C. Thiel, Kristin M. Calhoun, Anthony E. Reinhart","doi":"10.1175/waf-d-23-0078.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0078.1","url":null,"abstract":"\u0000The recently deployed GOES-R series Geostationary Lightning Mapper (GLM) provides forecasters with a new, rapidly-updating lightning data source to diagnose, forecast, and monitor atmospheric convection. Gridded GLM products have been developed to improve operational forecast applications, with variables including Flash Extent Density (FED), Minimum Flash Area (MFA), and Total Optical Energy (TOE). While these gridded products have been evaluated, there is a continual need to integrate these products with other datasets available to forecasters such as radar, satellite imagery, and ground-based lightning networks. Data from the Advanced Baseline Imager (ABI), Multi-Radar Multi-Sensor (MRMS) system, and one ground-based lightning network were compared against gridded GLM imagery from GOES-East and GOES-West in case studies of two supercell thunderstorms, along with a bulk study from 13 April through 31 May 2019, to provide further validation and applications of gridded GLM products from a data fusion perspective. Increasing FED and decreasing MFA corresponded with increasing thunderstorm intensity from the perspective of ABI infrared imagery and MRMS vertically integrated reflectivity products, and was apparent for more robust and severe convection. Flash areas were also observed to maximize between clean-IR brightness temperatures of 210 to 230 K, and isothermal reflectivity at −10 °C of 20 to 30 dBZ. TOE observations from both GLMs provided additional context of local GLM flash rates in each case study, due to their differing perspectives of convective updrafts.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47524595","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":"Synoptic and mesoscale aspects of exceptional fire weather during the New Year period 2019-20 in southeastern New South Wales, Australia","authors":"Paul Fox‐Hughes","doi":"10.1175/waf-d-23-0007.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0007.1","url":null,"abstract":"\u0000Extreme fire weather and fire behavior occurred during the New Year’s Eve period 30-31 December 2019 in southeast New South Wales, Australia. Fire progressed rapidly during the late evening and early morning periods, and significant extreme pyrocumulonimbus behavior developed, sometimes repeatedly in the same area. This occurred within a broader context of an unprecedented fire season in eastern Australia. Several aspects of the synoptic and mesoscale meteorology are examined, to identify contributions to fire behavior during this period. The passage of a cold front through the region was a key factor in the event, but other processes contributed to the severity of fire weather. Additional important features during this period included the movement of a negatively tilted upper tropospheric trough, the interaction of the front with topography and the occurrence of low-level overnight jets and of horizontal boundary layer rolls in the vicinity of the fireground.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47991585","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":"User-responsive diagnostic forecast evaluation approaches: Application to tropical cyclone predictions","authors":"Barbara Brown, Louisa Nance, Christopher Williams, Kathryn Newman, James Franklin, Edward Rappaport, Paul Kucera, Robert Gall","doi":"10.1175/waf-d-23-0072.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0072.1","url":null,"abstract":"\u0000The Hurricane Forecast Improvement Project1 (HFIP) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010–2014 in the period preceding the hurricane season (defined as August through October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43331002","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":"Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations","authors":"Kyle K. Hugeback, W. Gallus, Hugo N. Villegas Pico","doi":"10.1175/waf-d-23-0023.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0023.1","url":null,"abstract":"\u0000The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41636664","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":"Winter Precipitation Type from Microwave Radiometers in New York State Mesonet Profiler Network","authors":"B. Shrestha, June Wang, J. Brotzge, N. Bain","doi":"10.1175/waf-d-23-0035.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0035.1","url":null,"abstract":"\u0000Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts.\u0000\u0000\u0000Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43928406","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":"Conditional Ensemble Model Output Statistics for Postprocessing of Ensemble Precipitation Forecasting","authors":"Yan Ji, Xiefei Zhi, Lu-ying Ji, Tingbo Peng","doi":"10.1175/waf-d-22-0190.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0190.1","url":null,"abstract":"\u0000Forecasts produced by EPSs provide the potential state of the future atmosphere and quantify uncertainty. However, the raw ensemble forecasts from a single EPS are typically characterized by underdispersive predictions, especially for precipitation that follows a right-skewed gamma distribution. In this study, censored and shifted gamma distribution ensemble model output statistics (CSG-EMOS) is performed as one of the state-of-the-art methods for probabilistic precipitation postprocessing across China. Ensemble forecasts from multiple EPSs, including the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction, and the Met Office, are collected as raw ensembles. A conditional CSG EMOS (Cond-CSG-EMOS) model is further proposed to calibrate the ensemble forecasts for heavy-precipitation events, where the standard CSG-EMOS is insufficient. The precipitation samples from the training period are divided into two categories, light- and heavy-precipitation events, according to a given precipitation threshold and prior ensemble forecast. Then individual models are, respectively, optimized for adequate parameter estimation. The results demonstrate that the Cond-CSG-EMOS is superior to the raw EPSs and the standard CSG-EMOS, especially for the calibration of heavy-precipitation events. The spatial distribution of forecast skills shows that the Cond-CSG-EMOS outperforms the others over most of the study region, particularly in North and Central China. A sensitivity testing on the precipitation threshold shows that a higher threshold leads to better outcomes for the regions that have more heavy-precipitation events, i.e., South China. Our results indicate that the proposed Cond-CSG-EMOS model is a promising approach for the statistical postprocessing of ensemble precipitation forecasts.\u0000\u0000\u0000Heavy-precipitation events are of highly socioeconomic relevance. But it remains a great challenge to obtain high-quality probabilistic quantitative precipitation forecasting (PQPF) from the operational ensemble prediction systems (EPSs). Statistical postprocessing is commonly used to calibrate the systematic errors of the raw EPSs forecasts. However, the non-Gaussian nature of precipitation and the imbalance between the size of light- and heavy-precipitation samples add to the challenge. This study proposes a conditional postprocessing method to improve PQPF of heavy precipitation by performing calibration separately for light and heavy precipitation, in contrast to some previous studies. Our results indicate that the conditional model mitigates the underestimation of heavy precipitation, as well as with a better calibration for the light- and moderate-precipitation.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49179026","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":"Equity, Inclusion, and Justice: An Opportunity for Action for AMS Publications Stakeholders","authors":"","doi":"10.1175/waf-d-23-0133.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0133.1","url":null,"abstract":"","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47606949","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":"Evaluation of Multi-Week Tropical Cyclone Forecasts in the Philippines","authors":"Maria Czarina M. Tierra, Tzu‐Ting Lo, Hsiao-Chung Tsai, M. Villafuerte","doi":"10.1175/waf-d-22-0173.1","DOIUrl":"https://doi.org/10.1175/waf-d-22-0173.1","url":null,"abstract":"\u0000In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional timescales covered by weather forecasting in the Philippines, this study has examined the multi-week (i.e., from Week-1 to Week-4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely: NCEP Climate Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 06 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multi-week TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p<0.05) positional bias toward the right of observed tracks beyond Week-1, and that they tend to propagate slower than observations especially in Week-1 and Week-2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multi-week TC forecasts in the Philippines.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45793164","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":"ProxyVis – a Proxy for Nighttime Visible Imagery Applicable to Geostationary Satellite Observations","authors":"G. Chirokova, J. Knaff, M. Brennan, Robert T. Demaria, M. Bozeman, S. N. Stevenson, J. Beven, E. Blake, Alan Brammer, James W. E. Darlow, M. DeMaria, S. Miller, C. Slocum, Debra A. Molenar, D. Hillger","doi":"10.1175/waf-d-23-0038.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0038.1","url":null,"abstract":"\u0000Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options.\u0000ProxyVis was trained on the VIIRS Day/Night Band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery.\u0000ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training.\u0000ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES- 16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738096","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":"A Comparison of the Impacts of Inner-Core, In-Vortex, and Environmental Dropsondes on Tropical Cyclone Forecasts during the 2017-2020 Hurricane Seasons","authors":"Sarah D. Ditchek, J. Sippel","doi":"10.1175/waf-d-23-0055.1","DOIUrl":"https://doi.org/10.1175/waf-d-23-0055.1","url":null,"abstract":"\u0000This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multi-storm configuration of the Hurricane Weather Research and Forecasting model (HWRF) and covered active periods of the 2017–2020 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve’s sampling strategy in 2018 that added dropsondes outside of the inner core.","PeriodicalId":49369,"journal":{"name":"Weather and Forecasting","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46469929","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}