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A Hybrid Adaptive Covariance Inflation Method for EnKF-Based ENSO Prediction

JOURNAL OF CLIMATE 2025年第2期38卷 627-643页

作者： Gong, Mengmeng Zhang, Liang Gao, Chuan Wang, Haiyan Chen, Xingrong Zhang, Xuefeng Tianjin Univ Sch Marine Sci & Technol Tianjin Peoples R China Chinese Acad Sci Inst Oceanol Key Lab Ocean Observat & Forecasting Key Lab Ocean Circulat & Waves Qingdao Peoples R China Natl Marine Environm Forecasting Ctr Beijing Peoples R China

The ensemble-based data assimilation method is usually used for the initialization of El Ni & ntilde;o-Southern Oscillation (ENSO) prediction. Because of sampling errors caused by a finite ensemble, imperfect physical parameterizations, and other factors, the multiplicative covariance inflation method is commonly employed in the standard ensemble Kalman filter (EnKF) to increase the prior variance and alleviate filter divergence. Given computational resource constraints, utilizing larger ensemble sizes to minimize sampling errors in high-dimensional oceanic or atmospheric models poses a challenge. The authors propose a new hybrid adaptive covariance inflation scheme in small ensembles and apply this method to an intermediate coupled model (ICM) used at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS), named the IOCAS ICM for ENSO prediction. Hybrid refers to performing both prior and posterior inflation. Results show that the hybrid t-X adaptive inflation scheme performs best within the ICM framework, which can reduce the analysis errors by 46% for the daily SST anomaly compared to the standard EnKF algorithm using the fixed multiplicative covariance inflation factor. The t-X adaptive algorithm enhances the standard EnKF's forecasting ability by optimizing the initial forecast field and improving internal model errors. This method notably improves the prediction skill for the Ni & ntilde;o-1 1 2 SST anomaly, particularly in phase transitions. Regarding SST anomaly prediction, the advantages of the hybrid t-X adaptive method over the standard EnKF scheme mainly occur in the equatorial eastern Pacific and south boundaries of the ICM.

关键词： Atmosphere-ocean interaction ENSO Climate prediction probability forecasts/models/distribution

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The Importance of Perturbation Rank in Ensemble Simulations

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MONTHLY WEATHER REVIEW 2025年第2期153卷 247-261页

作者： Wu, Pin-ying Kawabata, Takuya Duc, Le Japan Soc Promot Sci Tokyo Tokyo Japan Meteorol Res Inst Tsukuba Ibaraki Japan Univ Tokyo Bunkyo Tokyo Japan

Ensemble simulations involve perturbations of error sources in numerical models to represent uncertainties. The rank of the perturbation matrix is expected to be equal to the ensemble size;thus, each ensemble member may have an independent perturbation. However, ensembles without independent perturbations for each member have been commonly adopted, and their impacts remain unknown. This study explores how a lack of perturbation rank affects the quality of an ensemble and its estimates. Focusing on mesoscale weather forecasts, lateral boundary conditions (LBCs) were considered since they are a primary source of uncertainty in regional models. Two sets of 1000-member ensemble simulations of Typhoon Hagibis (2019) were performed with different ranks of LBC perturbations (LBPs). A lack of perturbation rank caused members to cluster based on the given LBC because the LBCs strongly constrained the simulated states. This clustering is a sign of poor orthogonality, that is, a deficiency in the effective ensemble size. Clustering also implies distortion in the ensemble error space. A resulting spurious probability distribution can occur even when the ensemble mean and spread are valid. From the perspective of physical space, spurious probability distributions are caused by the clustering of typhoon locations and synoptic-scale environmental conditions owing to the lack of an LBP rank. Additionally, using positive and negative perturbation pairs was one cause of the perturbation rank defect. Our findings highlight the importance of the perturbation rank, particularly for high-order estimates of probability distributions with large

关键词： Numerical weather prediction/forecasting probability forecasts/models/distribution Ensembles

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Ensemble Predictability of Week 3/4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation

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WEATHER AND FORECASTING 2024年第11期39卷 1531-1544页

作者： Jennrich, Gregory Straus, David Chelliah, Muthuvel Baggett, Cory Earth Resources Technol Inc Laurel MD USA George Mason Univ AOES Dept Fairfax VA 22030 USA NOAA Climate Predict Ctr College Pk MD USA

Forecasting the week 3/4 period presents many challenges, resulting in a need for improvements to forecast skill. At this distance from initial conditions, numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize more predictable large-scale circulation regimes to make forecasts of temperature and precipitation anomalies, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of k-means cluster analysis on geopotential heights and their ability to make skillful regime predictions in the week 3/4 period. Using 14-day running means of for the wintertime December-February (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing the compositing of any reanalysis or observation variable to form cluster maps. Maps of 500-hPa height, 2-m temperature, precipitation, and storm-track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster-derived forecasts. Our preliminary results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather for this challenging forecast time scale.

关键词： Climate classification/regimes Operational forecasting probability forecasts/models/distribution Postprocessing Clustering

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Deep Learning for Postprocessing Global Probabilistic forecasts on Subseasonal Time Scales

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MONTHLY WEATHER REVIEW 2024年第3期152卷 667-687页

作者： Horat, Nina Lerch, Sebastian Karlsruhe Inst Technol Karlsruhe Germany Heidelberg Inst Theoret Studies Heidelberg Germany

Subseasonal weather forecasts are becoming increasingly important for a range of socioeconomic activities. However, the predictive ability of physical weather models is very limited on these time scales. We propose four postprocessing methods based on convolutional neural networks to improve subseasonal forecasts by correcting systematic errors of numerical weather prediction models. Our postprocessing models operate directly on spatial input fields and are therefore able to retain spatial relationships and to generate spatially homogeneous predictions. They produce global probabilistic tercile forecasts for biweekly aggregates of temperature and precipitation for weeks 3-4 and 5-6. In a case study based on a public forecasting challenge organized by the World Meteorological Organization, our postprocessing models outperform the bias -corrected forecasts from the European Centre for Medium -Range Weather forecasts (ECMWF), and achieve improvements over climatological forecasts for all considered variables and lead times. We compare several model architectures and training modes and demonstrate that all approaches lead to skillful and well -calibrated probabilistic forecasts. The good calibration of the postprocessed forecasts emphasizes that our postprocessing models reliably quantify the forecast uncertainty based on deterministic input information in the form of ECMWF ensemble mean forecast fields only.

关键词： Postprocessing Neural networks Subseasonal variability probability forecasts/models/distribution Deep learning

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ProbSevere Version 3: Improved Exploitation of Data Fusion and Machine Learning for Nowcasting Severe Weather

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WEATHER AND FORECASTING 2024年第12期39卷 1937-1958页

作者： Cintineo, John l. Pavolonis, Michael j. Sieglaff, Justin m. Univ Wisconsin Madison Cooperat Inst Meteorol Satellite Studies Madison WI 53705 USA NOAA NESDIS Ctr Satellite Applicat & Res Ctr Satellite Applicat & Res Madison WI USA

National Oceanic and Atmospheric Administration (NOAA)/Cooperative Institute for Meteorological Satellite Studies (CIMSS) ProbSevere, or the "probability of severe" models, provide next-hour probabilistic guidance on severe weather in the United States (large hail, strong wind gusts, and tornadoes) using machine learning and meteorological data from remotely sensed platforms and short-term numerical weather models. ProbSevere has been widely used by NOAA's National Weather Service (NWS) since 2016. ProbSevere version 3 employs multiplatform, multiscale storm object identification and tracking to collect storm features and then uses gradient boosting decision trees to make predictions of convective hazards from the extracted predictors. ProbSevere v3 was trained, validated, and tested on data from 2018 to 2023. Results demonstrate improved performance over ProbSevere v2 for the majority of the United States across all seasons, spanning a wide range of meteorological regimes. The greatest improvements are for the hail, wind, and any severe models, whereas the tornado model demonstrated modest improvement. ProbSevere v3 models were evaluated by NWS forecasters in simulated real-time experiments from 2021 to 2023. The majority of forecasters preferred ProbSevere v3 over v2 due to improved confidence in predictions and greater lead times for the issuance of severe weather warnings. An analysis of predictor importance revealed several radar, lightning, satellite, and numerical model fi elds that strongly contribute to the model predictions, which is consistent with past research and knowledge of severe thunderstorm forecasting. This reinforces the importance of environmental data fusion for nowcasting severe weather associated with midlatitude convection. SIGNIFICANCE STATEMENT: This study details the updates made to a U.S.-wide, real-time, probabilistic severe- weather guidance system aimed to improve forecaster confidence and the accuracy of the U.S. National Weather Se

关键词： Thunderstorms Remote sensing Nowcasting probability forecasts/models/distribution Machine learning Model interpretation and visualization

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A Warning System for the Laseyer Windstorm in a Narrow and Deep Valley of Northeastern Switzerland

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WEATHER AND FORECASTING 2024年第10期39卷 1487-1502页

作者： Sprenger, Michael Mahlstein, Irina Caratsch, Andrina Mueller, Marielle Swiss Fed Inst Technol Zurich Switzerland MeteoSwiss Zurich Switzerland Appenzeller Bahnen Herisau Switzerland

The Laseyer is a very local and uncommon windstorm in a narrow and steep valley in northeastern Switzerland. Whereas the ambient wind is from west to northwest, the strong surface wind in the valley is from the east, leading to gust speeds that become dangerous for the local train running into the valley to the Wasserauen station. To minimize the risk of derailment and to improve passenger comfort, the train service provider Appenzeller Bahnen (AB) has developed a new warning algorithm in close collaboration with academia (ETH Zurich) and the Swiss national weather service (MeteoSwiss). The aim is not only to accurately predict the Laseyer windstorm several hours in advance but also to reduce the number of false alarms. The new warning system is based on the MeteoSwiss operational ensemble prediction system at 1.1-km horizontal mesh size, which is then used in combination with an observation-based machine learning approach to probabilistically forecast Laseyer events up to 30 h in advance. A particular challenge in developing the new system was to introduce the customer, AB, to the modern concept of probabilistic numerical weather prediction, which requires a careful risk assessment by the customer. Hence, the development of the warning system is a process in which the customer and the warning provider closely collaborate and specify the final warning products to be delivered operationally. The operation of the new warning system during the 2021/22 Laseyer season shows that it is working successfully and also indicates that the warning thresholds in the warning algorithm can be adjusted in the future to minimize false alarms without increasing the number of missed events.

关键词： Forecasting Forecasting techniques Operational forecasting probability forecasts/models/distribution Mountain meteorology Wind gusts

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RainForests: A Machine Learning Approach to Calibrating NWP Precipitation forecasts

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WEATHER AND FORECASTING 2024年第11期39卷 1715-1732页

作者： Trotta, Belinda Owen, Benjamin Liu, Jiaping Weymouth, Gary Gale, Thomas Hume, Timothy Schubert, Anja Canvin, James Mentiplay, Daniel Whelan, jennifer Johnson, Robert Bur Meteorol Melbourne Vic Australia

Probabilistic forecasts derived from ensemble prediction systems (EPSs) have become the standard basis for many products and services produced by modern operational forecasting centers. However, statistical postprocessing is generally required to ensure forecasts have the desired properties expected for probability-based outputs. Precipitation, a core component of any operational forecast, is particularly challenging to calibrate due to its discontinuous nature and the extreme skew in rainfall amounts. A skillful forecasting system must maintain accuracy for low-to-moderate precipitation amounts, but preserve resolvability for high-to-extreme rainfall amounts, which, though rare, are important to forecast accurately in the interest of public safety. Existing statistical and machine learning approaches to rainfall calibration address this problem, but each has drawbacks in design, training approaches, and/or performance. We describe RainForests, a machine learning approach for calibrating ensemble rainfall forecasts using gradient-boosted decision trees. The model is based on the ecPoint system recently developed at ECMWF by Hewson and Pillosu, but uses machine learning models in place of the semisubjective decision trees of ecPoint, along with some other improvements to the model structure. We evaluate RainForests on the Australian domain against some simple benchmarks and show that it outperforms standard calibration approaches both in overall skill and in accurately forecasting high rainfall conditions, while being computationally efficient enough to be used in an operational forecasting system.

关键词： Downscaling Ensembles probability forecasts/models/distribution Postprocessing Decision trees Machine learning

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Probabilistic Forecasting of Cloud-Base Height and Visibility Using Quantile Regression Forests, Based on NWP and Observation Features

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WEATHER AND FORECASTING 2025年第4期40卷 543-559页

作者： Wolters, Dirk Schmeits, Maurice Whan, Kirien Royal Netherlands Meteorol Inst KNMI Utrecht Netherlands

We have applied quantile regression forests (QRFs) to generate probabilistic forecasts of weather conditions associated with low-visibility procedures (LVPs) at Schiphol Airport (Amsterdam, the Netherlands). LVPs are determined by combined thresholds of cloud-base height and (runway) visibility. forecasts of these conditions are critical for airport operations, as they inform operational planning, with the potential of minimizing meteorologically induced disruptions. Using a dataset of 5 years of hourly data, we have performed a forward feature selection and optimized QRF's hyperparameters for this specific application, and evaluated the model's performance for different forecast lead times and different LVP classes. Hereby, LVP forecasts were obtained by combining separate models for cloud-base height and (runway) visibility, applying a Schaake shuffle approach for restoration of the dependencies between these parameters. The verification revealed consistent positive Brier skill scores (BSSs) for the three most common LVP classes: marginal, A, and B. Although the skill was not always positive for the more extreme LVP classes, C and D, we argue that also for these conditions forecasters might derive valuable indications from the forecast system. We demonstrate the operational utility of the system with an example, also illustrating the support of interpretability through the use of Shapley additive explanation (SHAP) values. Our results underscore the potential of QRF for probabilistic forecasts of meteorological conditions, for aviation and other purposes.

关键词： Visibility probability forecasts/models/distribution Statistical forecasting Machine learning

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A User-Focused Approach to Evaluating Probabilistic and Categorical forecasts

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WEATHER AND FORECASTING 2024年第8期39卷 1163-1180页

作者： Loveday, Nicholas Taggart, Robert Khanarmuei, Mohammadreza Bur Meteorol Melbourne Vic Australia Bur Meteorol Sydney NSW Australia Bur Meteorol Brisbane Qld Australia

A user-focused verification fi cation approach for evaluating probability forecasts of binary outcomes (also known as probabilistic classifiers) fiers) is demonstrated that (i) is based on proper scoring rules, (ii) focuses on user decision thresholds, and (iii) provides actionable insights. It is argued that when categorical performance diagrams and the critical success index are used to evaluate overall predictive performance, rather than the discrimination ability of probabilistic forecasts, they may produce misleading results. Instead, Murphy diagrams are shown to provide a better understanding of the overall predictive performance as a function of user probabilistic decision threshold. We illustrate how to select a proper scoring rule, based on the relative importance of different user decision thresholds, and how this choice impacts scores of overall predictive performance and supporting measures of discrimination and calibration. These approaches and ideas are demonstrated using several probabilistic thunderstorm forecast systems as well as synthetic forecast data. Furthermore, a fair method for comparing the performance of probabilistic and categorical forecasts is illustrated using the fixed risk multicategorical (FIRM) score, which is a proper scoring rule directly connected to values on the Murphy diagram. While the methods are illustrated using thunderstorm forecasts, they are applicable for evaluating probabilistic forecasts for any situation with binary outcomes. SIGNIFICANCE STATEMENT: Recently, several papers have presented verification fi cation results for probabilistic forecasts using so-called categorical performance diagrams, which summarize multiple verification fi cation metrics. While categorical performance diagrams measure discrimination ability, we demonstrate how they can potentially lead to incorrect conclusions when evaluating overall predictive performance of probabilistic forecasts. By reviewing recent advances in the statistical lite

关键词： Forecast verification/skill fi cation/skill Model evaluation/performance Thunderstorms Statistical techniques Operational forecasting probability forecasts/models/distribution

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Probabilistic Neural Networks for Ensemble Postprocessing

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MONTHLY WEATHER REVIEW 2024年第7期152卷 1487-1510页

作者： Liu, Pu Dabernig, Markus Atencia, Aitor Wang, Yong Zhao, Yuchu Nanjing Univ Informat Sci & Technol Sch Atmospher Sci Nanjing Peoples R China Nanjing Univ Informat Sci & Technol HuaFeng Res Lab Weather Sci & Applicat Nanjing Peoples R China China Meteorol Adm CMA Earth Syst Modeling & Predict Ctr Beijing Peoples R China GeoSphere Austria Vienna Austria China Meteorol Adm HuaFeng Meteorol Media Grp Co Ltd Beijing Peoples R China

Accurate temperature forecasts are critical for various industries and sectors. We propose a probabilistic neural network (PNN), an extension of the distributional regression network (DRN), for 2-m temperature forecasts, consisting of three variants with different inputs and target variables. The fi rst variant, standardized anomaly probabilistic neural network (SAPNN), employs a two-step approach involving standardized anomalies and global PNN modeling to effectively capture underlying features and anomalies. The second variant, PNN with geographical predictors (PNNGE), incorporates raw and static geographical predictors to enhance predictive performance. The third variant, PNN with station one-hot encoding (PNNEN), utilizes raw with station one-hot encoding predictors to represent geographical information effectively. We compare three PNN variants with two benchmarks: 1) standardized anomaly model output statistics (SAMOS) and 2) three DRN variants identical to those applied to PNN. These evaluations utilize ECMWF data from 2019 to 2020 at 6-h intervals up to 72 h over Hebei, China. Results show that SAPNN and PNNGE are better than SAMOS, while PNNEN notably exhibits a signi fi cant 14% improvement in the continuous ranked probability skill score (CRPSS). Moreover, the PNN variants exhibit comparable or superior performance to DRN regarding forecast accuracy, CRPSS, and reliability, showcasing a better-calibrated spread - error relationship. This study highlights the value of the proposed PNN variants with a distribution output in capturing nonlinear relationships within different sources of predictors and improving temperature forecast skills.

关键词： Statistical techniques Forecasting techniques probability forecasts/models/distribution Numerical weather prediction/forecasting Postprocessing Neural networks

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