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Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations

WEATHER AND FORECASTING 2024年第2期39卷 387-402页

作者： Wilson, Katie A. Burke, Patrick C. Gallo, Burkely T. Skinner, Patrick S. Lindley, T. Todd Gravelle, Chad Bieda III, Stephen W. Madden, Jonathan G. Monroe, Justin W. Guerra, Jorge E. Morris, Dale A. Natl Severe Storms Lab NOAA OAR Norman OK 73072 USA Univ Oklahoma Cooperat Inst Severe & High Impact Weather Res & O Norman OK 73019 USA RAND Corp Santa Monica CA 90401 USA NOAA NWS Storm Predict Ctr Norman OK USA NWS Weather Forecast Off NOAA Norman OK USA NOAA NWS Southern Reg Headquarters Ft Worth TX USA NOAA NWS Headquarters Silver Spring MD USA RCSDF Denver CO USA NOAA NWS Warning Decis Training Div Norman OK USA

The 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. SIGNIFICANCE STATEMENT: A first -of -its -kind experiment brought together forecasters from local weather forecast offices and national centers to examine the

关键词： Social Science Ensembles Forecasting techniques Numerical weather prediction/forecasting Operational forecasting probability forecasts/models/distribution

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Improving High-Resolution Ensemble Forecast (HREF) System Mesoscale Snowband forecasts with Random Forests

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WEATHER AND FORECASTING 2023年第9期38卷 1695-1706页

作者： Radford, Jacob T. Lackmann, Gary M. North Carolina State Univ Dept Marine Earth & Atmospher Sci Raleigh NC 27695 USA Colorado State Univ Cooperat Inst Res Atmosphere Ft Collins CO 80523 USA

Mesoscale snowbands are impactful winter weather phenomena but are challenging to predict due to small-scale forcings and ingredients. Previous work has established that even deterministic convection-allowing models (CAMs) often struggle to represent these features with much precision and recommended the application of ingredients-based or probabilistic forecast strategies. Based on these recommendations, we develop and evaluate four different models for fore-casting snowbands. The first model, referred to as the "HREF threshold probability" model, detects band development in High-Resolution Ensemble Forecast (HREF) system members' 1000-m simulated reflectivities, then uses these detections to calculate a snowband probability. The second model is a random forest incorporating features explicitly linked to snow -bands, such as the detection of bands in each HREF member and statistical summaries of simulated reflectivity and the categorical snow field. The third model is a random forest model incorporating snowband ingredients, such as midtropo-spheric frontogenesis, moist symmetric stability, and vertical velocity. Last, the fourth model combines the features of the explicit and implicit random forests. Binary band predictions based upon the HREF threshold probability model resulted in a critical success index 27% higher than the average HREF member. The explicit feature random forest model further improved performance by an additional 11%, with statistics of the reflectivity field holding the most predictive value. The implicit and combined random forests slightly underperformed the explicit random forest, perhaps due to a large number of noisy, correlated features. Ultimately, we demonstrate that simple probabilistic snowband forecasting strategies can yield substantial improvements over deterministic *** STATEMENT: Mesoscale snowbands have the potential for major societal impacts but are difficult to predict due to their small spatial scales. Previou

关键词： Snowbands Forecast verification/skill probability forecasts/models/distribution Artificial intelligence Classification Decision trees

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Coherent Bimodal Events in Ensemble forecasts of 2-m Temperature

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WEATHER AND FORECASTING 2023年第10期38卷 1953-1970页

作者： Bertossa, Cameron Hitchcock, Peter Degaetano, Arthur Plougonvenc, Riwal Univ Wisconsin Dept Atmospher & Ocean Sci Madison WI 53715 USA Cornell Univ Dept Earth & Atmospher Sci Ithaca NY 14850 USA PSL Res Univ Sorbonne Univ Inst Polytech Paris LMD IPSLENSCNRSEcole Polytech Paris France

A previous study has shown that a large portion of subseasonal-to-seasonal European Centre for Medium Range Weather forecasts (ECMWF) ensemble forecasts for 2-m temperature exhibit properties of univariate bimodality, in some locations occurring in over 30% of forecasts. This study introduces a novel methodology to identify "bimodal events," meteorological events that trigger the development of spatially and temporally correlated bimodality in forecasts. Understanding such events not only provides insight into the dynamics of the meteorological phenomena causing bimodal events, but also indicates when Gaussian interpretations of forecasts are detrimental. The methodology that is developed allows one to systematically characterize the spatial and temporal scales of the derived bimodal events, and thus uncover the flow states that lead to them. Three distinct regions that exhibit high occurrence rates of bimodality are studied: one in South America, one in the Southern Ocean, and one in the North Atlantic. It is found that bimodal events in each region appear to be triggered by synoptic processes interacting with geographically specific processes: in South America, bimodality is often related to Andes blocking events;in the Southern Ocean, bimodality is often related to an atmospheric Rossby wave interacting with sea ice;and in the North Atlantic, bimodality is often connected to the displacement of a persistent subtropical high. This common pattern of large-scale circulation anomalies interacting with local boundary conditions suggests that any deeper dynamical understanding of these events should incorporate such interactions.

关键词： Nonlinear dynamics Atmosphere-ocean interaction Statistical techniques Ensembles probability forecasts/models/distribution Atmosphere-land interaction

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Parametric Postprocessing of Dual-Resolution Precipitation forecasts

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WEATHER AND FORECASTING 2023年第8期38卷 1313-1322页

作者： Szabo, Marianna Gascon, Estibaliz Baran, Sandor Univ Debrecen Fac Informat Debrecen Hungary Univ Debrecen Doctoral Sch Informat Debrecen Hungary European Ctr Medium Range Weather Forecasts Reading England

All major weather centers issue ensemble forecasts, which differ both in ensemble size and spatial resolu-tion, even while covering the same domain. These parameters directly determine both the forecast skill of the prediction and the computation cost. In the past few years, the plans for upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather forecasts (ECMWF) from a single forecast with 9-km resolu-tion and a 51-member ensemble with 18-km resolution induced an extensive study of the forecast skill of both raw and postprocessed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investi-gate the predictive performance of the censored shifted gamma (CSG) ensemble model output statistic (EMOS) approach for statistical postprocessing with the help of dual-resolution, 24-h, precipitation accumulation ensemble forecasts over Europe with various forecast horizons. We consider the operational 50-member ECMWF ensemble as of high resolution and extend it with a low-resolution (29-km grid), 200-member experimental forecast. The investigated dual-resolution com-binations consist of subsets of these two forecast ensembles with equal computational cost, which is equivalent to the cost of the operational ensemble. Our case study verifies that, compared with the raw ensemble combinations, EMOS postpro-cessing results in a significant improvement in forecast skill and that skill is statistically indistinguishable between any of the analyzed mixtures of dual-resolution combinations. Furthermore, the semilocally trained CSG EMOS provides an efficient alternative to the state-of-the-art quantile mapping without requiring additional historical data.

关键词： Ensembles Forecasting Postprocessing probability forecasts/models/distribution

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A Statistical Forecast Model for Extratropical Cyclones Including Intensity and Precipitation Type

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MONTHLY WEATHER REVIEW 2023年第11期151卷 2851-2867页

作者： Cavanagh, Rebekah Oliver, Eric. J. Dalhousie Univ Halifax NS B3H 4R2 Canada Environm & Climate Change Canada Meteorol Serv Canada Toronto ON M3H 5T4 Canada

Winter extratropical cyclones (ETCs) are dominant features of winter weather on the east coast of North America. These storms are characterized by high winds and heavy precipitation (rain, snow, and ice). ETCs are well predicted by numerical weather prediction models (NWPs) at short-to midrange forecast lead times, but prediction on seasonal time scales is lacking. We develop a set of multiple linear regression models, using stepwise regression and cross validation, to predict the number of storms expected to affect a specific location throughout the winter storm season. Each model in the set predicts a specific storm type (e.g., snow, rain, or bomb storms). This set of models is applied in a probabilistic forecast framework that uses the probability density function of the prediction in combination with climatological mean storm activity. The resulting forecast makes statements about the likelihood of below-average, average, or above-average activity for all storms and for each of the type-specific subsets of storms. Though this forecast framework could in theory be applied anywhere, we demonstrate its skill in forecasting the characteristics of the winter storm season experienced in Halifax, Nova Scotia, Canada.

关键词： Extratropical cyclones Winter/cool season Regression analysis probability forecasts/models/distribution Seasonal forecasting Statistical forecasting

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Interpretation of Probabilistic Surface Ozone forecasts: A Case Study for Philadelphia

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WEATHER AND FORECASTING 2023年第10期38卷 1895-1906页

作者： Balashov, Nikolay V. Huff, Amy k. Thompson, Anne m. NASA Goddard Space Flight Ctr Global Modeling & Assimilat Off Greenbelt MD 20771 USA Univ Maryland Earth Syst Sci Interdisciplinary Ctr College Pk MD 20742 USA IM Syst Grp College Pk MD USA NASA Goddard Space Flight Ctr Earth Sci Div Greenbelt MD USA Univ Maryland Baltimore Cty Baltimore MD USA

The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult, deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model [the Regression in Self Organizing Map (REGiS)] is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for the Philadelphia, Pennsylvania, metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-h-average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000-11 ozone-season (1 May-30 September) data, calibrated using 2012-14 data, and evaluated using 2015-18 data. Assessment of the calibration data with the Pierce skill score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the U.S. national air quality model and operational "expert" forecasts over the evaluation period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health. SIGNIFICANCE STATEMENT: Because probabilistic forecasting is becoming more prevalent in the field of air quality, the purpose of this article is to draw attention to

关键词： Forecast verification/skill Forecasting techniques probability forecasts/models/distribution Statistical forecasting Air quality Ozone

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Using the Orthogonal Conditional Nonlinear Optimal Perturbations Approach to Address the Uncertainties of Tropical Cyclone Track forecasts Generated by the WRF Model

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WEATHER AND FORECASTING 2023年第10期38卷 1907-1933页

作者： Zhang, Han Duan, Wansuo Zhang, Yichi Chinese Acad Sci LASG Inst Atmospher Phys Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

The orthogonal conditional nonlinear optimal perturbations (O-CNOPs) approach for measuring initial uncertainties is applied to the Weather Research and Forecasting (WRF) Model to provide skillful forecasts of tropical cyclone (TC) tracks. The hindcasts for 10 TCs selected from 2005 to 2020 show that the ensembles generated by the O-CNOPs have a greater probability of capturing the true TC tracks, and the corresponding ensemble forecasts signifi-cantly outperform the forecasts made by the singular vectors, bred vectors, and random perturbations in terms of both deterministic and probabilistic skills. In particular, for two unusual TCs, Megi (2010) and Tembin (2012), the ensembles generated by the O-CNOPs successfully reproduce the sharp northward-turning track in the former and the counterclockwise loop track in the latter, while the ensembles generated by the other methods fail to do so. Moreover, additional attempts are performed on the real-time forecasts of TCs In-Fa (2021) and Hinnamnor (2022), and it is shown that O-CNOPs are very useful for improving the accuracy of real-time TC track forecasts. Therefore, O-CNOPs, together with the WRF Model, could provide a new platform for the ensemble forecasting of TC tracks with much higher skill.

关键词： Tropical cyclones Uncertainty Ensembles probability forecasts/models/distribution Nonlinear models

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Assessment of an Experimental Version of fvGFS for TC Genesis Forecasting Ability in the Western North Pacific

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WEATHER AND FORECASTING 2023年第11期38卷 2271-2287页

作者： Lin, Shu-jeng Hsu, Huang-hsiung Tu, Chia-ying Chih, Cheng-hsiang Chinese Culture Univ Dept Atmospher Sci Taipei Taiwan Acad Sinica Res Ctr Environm Changes Taipei Taiwan Natl Taiwan Univ Dept Atmospher Sci Taipei Taiwan

We evaluated the ability of the fvGFS with a 13-km resolution in simulating tropical cyclone genesis (TCG) by conducting hindcast experiments for 42 TCG events over 2018-19 in the western North Pacific (WNP). We observed an improved hit rate with a lead time of between 5 and 4 days;however, from 4-to 3-day lead time, no consistent improvement in the temporal and spatial errors of TCG was obtained. More "Fail" cases occurred when and where a low-level easterly background flow prevailed: from mid-August to September 2018 and after October 2019 and mainly in the eastern WNP. In "Hit" cases, 850-hPa streamfunction and divergence, 200-hPa divergence, and genesis potential index (GPI) provided favorable TCG conditions. However, the Hit-Fail case differences in other suggested factors (vertical wind shear, 700-hPa moisture, and SST) were nonsignificant. By contrast, the reanalysis used for validation showed only significant difference in 850-hPa streamfunction. We stratified the background flow of TCG into four types. The monsoon trough type (82%) provided the most favorable environmental conditions for successful hindcasts, followed by the subtropical high (45%), easterly (17%), and others (0%) types. These results indicated that fvGFS is more capable of enhancing monsoon trough circulation and provides a much better environment for TCG development but is less skillful in other types of back-ground flow that provides weaker large-scale forcing. The results suggest that the most advanced high-resolution weather forecast models such as the fvGFS warrant further improvement to properly simulate the subtle circulation features (e.g., mesoscale convection system) that might provide seeds for *** STATEMENT: This study provides an evaluation of tropical cyclone genesis prediction skill of fvGFS. Favorable large-scale environmental factors for successful prediction are identified. Skill dependence on environmental factors provides guidance for evaluating the reliabil

关键词： North Pacific Ocean Cyclogenesis/cyclolysis Tropical cyclones Forecast verification/skill Hindcasts probability forecasts/models/distribution

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Information-Based Probabilistic Verification Scores for Two-Dimensional Ensemble Forecast Data: A Madden-Julian Oscillation Index Example

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MONTHLY WEATHER REVIEW 2023年第9期151卷 2245-2255页

作者： Takaya, Yuhei Komatsu, Kensuke K. Hino, Hideitsu Vitart, Frederic Japan Meteorol Agcy Meteorol Res Inst Tsukuba Ibaraki Japan Univ Tokyo Atmosphere & Ocean Res Inst Kashiwa Chiba Japan Inst Stat Math Tachikawa Tokyo Japan European Ctr Medium Range Weather Forecasts Reading Berks England Japan Meteorol Agcy Osaka Reg Headquarters Osaka Japan

Probabilistic forecasting is a common activity in many fields of the Earth sciences. Assessing the quality of probabilistic forecasts}probabilistic forecast verification}is therefore an essential task in these activities. Numerous methods and metrics have been proposed for this purpose;however, the probabilistic verification of vector variables of ensemble forecasts has received less attention than others. Here we introduce a new approach that is applicable for verifying ensemble forecasts of continuous, scalar, and two-dimensional vector data. The proposed method uses a fixed-radius near neighbors search to compute two information-based scores, the ignorance score (the logarithmic score) and the information gain, which quantifies the skill gain from the reference forecast. Basic characteristics of the proposed scores were examined using idealized Monte Carlo simulations. The results indicated that both the continuous ranked probability score (CRPS) and the proposed score with a relatively small ensemble size (,25) are not proper in terms of the forecast dispersion. The proposed verification method was successfully used to verify the Madden-Julian oscillation index, which is a twodimensional quantity. The proposed method is expected to advance probabilistic ensemble forecasts in various *** STATEMENT: In the Earth sciences, stochastic future states are estimated by solving a large number of forecasts (called ensemble forecasts) based on physical equations with slightly different initial conditions and stochastic parameters. The verification of probabilistic forecasts is an essential part of forecasting and modeling activity in the Earth sciences. However, there is no information-based probabilistic verification score applicable for vector variables of ensemble forecasts. The purpose of this study is to introduce a novel method for verifying scalar and twodimensional vector variables of ensemble forecasts. The proposed method offers a new approach to

关键词： Madden-Julian oscillation Ensembles Forecast verification/skill probability forecasts/models/distribution

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Communicating Properties of Changes in Lagged Weather forecasts

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WEATHER AND FORECASTING 2022年第1期37卷 125-142页

作者： Jewson, Stephen Scher, Sebastian Messori, Gabriele Lambda Climate Res Ltd London England Know Ctr GmbH Graz Austria Stockholm Univ Dept Meteorol Stockholm Sweden Stockholm Univ Bolin Ctr Climate Res Stockholm Sweden Uppsala Univ Dept Earth Sci Uppsala Sweden Ctr Nat Hazards & Disaster Sci Uppsala Sweden

Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.

关键词： Forecasting probability forecasts/models/distribution Statistical forecasting Decision making Decision support Economic value

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