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A Geospatial Verification Method for Severe Convective Weather Warnings: Implications for Current and Future Warning Methods

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WEATHER AND FORECASTING 2024年第5期39卷 689-704页

作者： Stumpf, Gregory J. Stough, Sarah M. Colorado State Univ Cooperat Inst Res Atmosphere Ft Collins CO 80523 USA NOAA Meteorol Dev Lab NWS Off Sci & Technol Integrat Silver Spring MD 20910 USA Univ Oklahoma Cooperat Inst Severe & High Impact Weather Res & Norman OK USA NOAA Natl Severe Storms Lab Norman OK USA

Legacy National Weather Service veri fi cation techniques, when applied to current static severe convective warnings, exhibit limitations, particularly in accounting for the precise spatial and temporal aspects of warnings and severe convective events. Consequently, they are not particularly well suited for application to some proposed future National Weather Service warning delivery methods considered under the Forecasting a Continuum of Environmental Threats (FACETs) initiative. These methods include threats -in -motion (TIM), wherein warning polygons move nearly continuously with convective hazards, and probabilistic hazard information (PHI), a concept that involves augmenting warnings with rapidly updating probabilistic plumes. A new geospatial veri fi cation method was developed and evaluated, by which warnings and observations are placed on equivalent grids within a common reference frame, with each grid cell being represented as a hit, miss, false alarm, or correct null for each minute. New measures are computed, including false alarm area and location -speci fi c lead time, departure time, and false alarm time. Using the 27 April 2011 tornado event, we applied the TIM and PHI warning techniques to demonstrate the bene fi ts of rapidly updating warning areas, showcase the application of the geospatial veri fi cation method within this novel warning framework, and highlight the impact of varying probabilistic warning thresholds on warning performance. Additionally, the geospatial veri fi cation method was tested on a storm -based warning dataset (2008 - 22) to derive annual, monthly, and hourly statistics.

关键词： Thunderstorms Forecast veri fi cation/skill probability forecasts/models/distribution Statistical techniques Operational forecasting

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Investigating the Near-Surface Wind Fields of Downbursts Using a Series of High-Resolution Idealized Simulations

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WEATHER AND FORECASTING 2024年第7期39卷 1065-1086页

作者： Moore, Andrew NOAA NCEP Storm Predict Ctr Norman OK 73069 USA

Short-lived and poorly organized convective cells, often called weakly forced thunderstorms (WFTs), 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 fl ow 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}O(10) } O (10) km2 2 or less and persisting for around 5 min 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 subsevere winds. Several implications for forecasting, warning, and verifying WFT events fall out from these discussions.

关键词： Convective storms Forecasting Operational forecasting probability forecasts/models/distribution Cloud-resolving models Idealized models

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Spatiotemporal Convolutional Approach for the Short-Term Forecast of Hourly Heavy Rainfall probability Integrating Numerical Weather Predictions and Surface Observations

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WEATHER AND FORECASTING 2024年第3期597卷 597-612页

作者： Liu, Xi Zheng, Yu Zhuang, Xiaoran Wang, Yaqiang Li, Xin Bei, Zhang Zhang, Wenhua Nanjing Joint Inst Atmospher Sci China Meteorol Adm Key Lab Transportat Meteorol Nanjing Peoples R China Chinese Acad Meteorol Sci State Key Lab Severe Weather Beijing Peoples R China Chinese Acad Meteorol Sci Inst Artificial Intelligence Meteorol Beijing Peoples R China Jiangsu Meteorol Observ Nanjing Peoples R China

The 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 h. 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 h and the entire forecast hours, respectively, at a threshold of 5 mm h21. Noting 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 compared with 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.

关键词： Rainfall Nowcasting probability forecasts/models/distribution Deep learning Machine learning

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Critical Effects of Precipitation on Future Colorado River Flow

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JOURNAL OF CLIMATE 2024年第16期37卷 4079-4093页

作者： Hoerling, Martin P. Eischeid, Jon K. Diaz, Henry F. Rajagopolan, Balaji Kuhn, Eric Univ Colorado Boulder Cooperat Inst Res Environm Sci Boulder CO 80309 USA Univ Colorado Boulder Dept Civil Environm & Architectural Engn Boulder CO 80309 USA NOAA Phys Sci Lab Boulder CO USA Univ Hawai i Manoa Dept Geog & Environm Honolulu HI USA Univ Colorado Boulder Getches Wilkinson Ctr Colorado River Res Grp Boulder CO USA

Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000-20 is possible. Here, we analyze new simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026-50 when revised guidelines would operate. We constrain projected flows for Lees Ferry, the gauge through which 85% of the river flow passes, using its estimated sensitivity to meteorological variability together with CMIP6-projected precipitation and temperature changes. The critical importance of precipitation, especially its natural variability, is emphasized. Model projections indicate increased precipitation in the upper Colorado River basin due to climate change, which alone increases river flows by 5%-7% (relative to a 2000-20 climatology). Depending on the river's temperature sensitivity, this wet signal compensates for some, if not all, of the depleting effects of basin warming. Considerable internal decadal precipitation variability (;5% of the climatological mean) is demonstrated, driving a greater range of plausible Colorado River flow changes for 2026-50 than previously surmised from treatment of temperature impacts alone: the overall precipitation-induced Lees Ferry flow changes span from -25% to 140%, contrasting with a range from -30% to -5% from expected warming effects only. Consequently, extreme low and high flows are more likely. Lees Ferry flow projections, conditioned on initial drought states akin to 2000-20, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than in recent decades.

关键词： Climate Change Hydrology probability forecasts/models/distribution

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Evaluation of Probabilistic forecasts of Binary Events with the Neighborhood Brier Divergence Skill Score

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MONTHLY WEATHER REVIEW 2024年第5期152卷 1201-1222页

作者： Stein, Joel Toop, Abiens Meteo France Toulouse France

A procedure for evaluating the quality of probabilistic forecasts of binary events has been developed. This is based on a two-step procedure: pooling of forecasts on the one hand and observations on the other hand, on all the points of a neighborhood in order to obtain frequencies at the neighborhood length scale and then to calculate the Brier divergence for these neighborhood frequencies. This score allows the comparison of a probabilistic forecast and observations at the neighborhood length scale, and therefore, the rewarding of event forecasts shifted from the location of the observed event by a distance smaller than the neighborhood size. A new decomposition of this score generalizes that of the Brier score and allows the separation of the generalized resolution, reliability, and uncertainty terms. The neighborhood Brier divergence skill score (BDnSS) measures the performance of the probabilistic forecast against the sample climatology. BDnSS and its decomposition have been used for idealized and real cases in order to show the utility of neighborhoods when comparing at different scales the performances of ensemble forecasts between themselves or with deterministic forecasts or of deterministic forecasts between themselves.

关键词： Forecast veri fi cation/skill Operational forecasting Probabilistic quantitative precipitation forecasting (PQPF) probability forecasts/models/distribution

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Statistical Response of ENSO Complexity to Initial Condition and Model Parameter Perturbations

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JOURNAL OF CLIMATE 2024年第21期37卷 5629-5651页

作者： Andreou, Marios Chen, Nan Univ Wisconsin Madison Dept Math Madison WI 53706 USA

Studying the response of a climate system to perturbations has practical significance. Standard methods in computing the trajectory-wise deviation caused by perturbations may suffer from the chaotic nature that makes the model error dominate the true response after a short lead time. Statistical response, which computes the return described by the statistics, provides a systematic way of reaching robust outcomes with an appropriate quantification of the uncertainty and extreme events. In this paper, information theory is applied to compute the statistical response and find the most sensitive perturbation direction of different El Ni & ntilde;o-Southern Oscillation (ENSO) events to initial value and model parameter perturbations. Depending on the initial phase and the time horizon, different state variables contribute to the most sensitive perturbation direction. While initial perturbations in sea surface temperature (SST) and thermocline depth usually lead to the most significant response of SST at short and long ranges, respectively, initial adjustment of the zonal advection can be crucial to trigger strong statistical responses at medium range around 5-7 months, especially at the transient phases between El Ni & ntilde;o and La Ni & ntilde;a. It is also shown that the response in the variance triggered by external random forcing perturbations, such as the wind bursts, often dominates the mean response, making the resulting most sensitive direction very different from the trajectory-wise methods. Finally, despite the strong non-Gaussian climatology distributions, using Gaussian approximations in the information theory is efficient and accurate for computing the statistical response, allowing the method to be applied to sophisticated operational systems.

关键词： Atmosphere-ocean interaction El Nino ENSO La Nina Statistical techniques probability forecasts/models/distribution

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Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific

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WEATHER AND FORECASTING 2024年第6期39卷 833-846页

作者： Higgins, Timothy B. Subramanian, Aneesh C. Chapman, Will E. Lavers, David A. Winters, Andrew C. Univ Colorado Dept Atmospher & Ocean Sci Boulder CO 80309 USA Natl Ctr Atmospher Res Boulder CO USA European Ctr Medium Range Weather Forecasting Reading England

Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a " perfect model, " of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Paci fi c. Our results demonstrate signi fi cant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for speci fi c areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Paci fi c jet. These fi ndings of the source of skillful subseasonal forecasts over the U.S. West Coast could have implications for water management in these regions susceptible to drought and fl ooding extremes. Additionally, they may offer valuable insights for governments and industries on the U.S. West Coast seeking to make informed decisions based on extended weather prediction.

关键词： Atmospheric river Forecast veri fi cation/skill Hindcasts probability forecasts/models/distribution Seasonal forecasting

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A Hail Climatology for Canada Using a Lightning Proxy

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JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY 2024年第10期63卷 1227-1240页

作者： Brunet, Dominique Brimelow, Julian Environm & Climate Change Canada Meteorol Res Div Toronto ON Canada Western Univ Northern Hail Project London ON Canada

Hail is a significant weather hazard in Canada, but its spatial and temporal distribution is poorly understood. We compiled a Canadian hail report database for 2005-22, containing 7000 unique entries with estimates of the timing and location of the hail reports and estimated hail diameter. We developed a methodology to construct an estimate of the hail climatology across Canada using manual hail observations at airports and a lightning proxy. First, we estimated the probability of hail occurrence at airport locations across the country at any given hour using Bayesian inference. Next, we interpolated in space the probabilities to obtain smooth prior probabilities of hail occurrence at any location in Canada. Then, we refined these probabilities using lightning flash density as a proxy for the likelihood of hail, severe hail (diameter greater than 20 mm), or significant severe hail (diameter greater than 50 mm). Finally, we aggregated the posterior probabilities of hail, severe hail, and significant severe hail over time and space and compared them with the number of reports found in the 2005-22 Canadian hail database. Our results indicate that the posterior probabilities of hail are not consistent with the observed hail reports and suggest that there are many gaps in hail reporting in Canada.

关键词： Lightning Climatology Hail probability forecasts/models/distribution

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Clustering-Based Spatial Interpolation of Parametric Postprocessing models

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WEATHER AND FORECASTING 2024年第11期39卷 1591-1604页

作者： Baran, Sandor Lakatos, Maria Univ Debrecen Fac Informat Debrecen Hungary Univ Debrecen Doctoral Sch Informat Debrecen Hungary

Since the start of the operational use of ensemble prediction systems, ensemble-based probabilistic forecasting has become the most advanced approach in weather prediction. However, despite the persistent development of last three decades, ensemble forecasts still often suffer from the lack of calibration and might exhibit systematic bias, which calls for some form of statistical postprocessing. Nowadays, one can choose from a large variety of postprocessing proaches, where parametric methods provide full predictive distributions of the investigated weather quantity. Parameter estimation in these models is based on training data consisting of past forecast-observation pairs;thus, postprocessed casts are usually available only at those locations where training data are accessible. We propose a general clustering-based interpolation technique of extending calibrated predictive distributions from observation stations to any location in the semble domain where there are ensemble forecasts at hand. Focusing on the ensemble model output statistics (EMOS) postprocessing technique, in a case study based on 10-m wind speed ensemble forecasts of the European Centre Medium-Range Weather forecasts, we demonstrate the predictive performance of various versions of the suggested method and show its superiority over the regionally estimated and interpolated EMOS models and the raw ensemble forecasts as well.

关键词： Ensembles probability forecasts/models/distribution Postprocessing Clustering Interpolation schemes

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Warn-on-Forecast System: From Vision to Reality

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WEATHER AND FORECASTING 2024年第1期39卷 75-95页

作者： Heinselman, Pamela L. Burke, Patrick C. Wicker, Louis J. Clark, Adam J. Kain, John S. Gao, Jidong Yussouf, Nusrat Jones, Thomas A. Skinner, Patrick S. Potvin, Corey K. Wilson, Katie A. Gallo, Burkely T. Flora, Montgomery L. Martin, Joshua Creager, Gerry Knopfmeier, Kent H. Wang, Yunheng Matilla, Brian C. Dowell, David C. Mansell, Edward R. Roberts, Brett Hoogewind, Kimberly A. Stratman, Derek R. Guerra, Jorge Reinhart, Anthony E. Kerr, Christopher A. Miller, William NOAA OAR Natl Severe Storms Lab Norman OK 73072 USA Univ Oklahoma Cooperat Inst Severe & High Impact Weather Res & O Norman OK 73019 USA Univ Oklahoma Sch Meteorol Norman OK 73019 USA NOAA NWS NCEP Storm Predict Ctr Norman OK USA NOAA Global Syst Lab Earth Syst Res Labs Boulder CO USA Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD USA Rand Corp Santa Monica CA USA 16th Weather Squadron Bellevue NE USA CoreLogic Irvine CA 92618 USA IBSS Corp Silver Spring MD USA Project Canary Denver CO USA

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models;2) ensemble-and variational based assimilation of weather radar, satellite, and conventional observations;and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction. SIGNIFICANCE STATEMENT: The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to me

关键词： Forecast verification/skill Numerical weather prediction/forecasting probability forecasts/models/distribution Short-range prediction Data assimilation Decision support

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