scholarly journals The similarity-based method: a new object detection method for deterministic and ensemble weather forecasts

2019 ◽  
Vol 16 ◽  
pp. 209-213
Author(s):  
Lucie Rottner ◽  
Philippe Arbogast ◽  
Mayeul Destouches ◽  
Yamina Hamidi ◽  
Laure Raynaud
Keyword(s):  
Detection Method ◽  
Weather Prediction ◽  
Object Oriented ◽  
Small Scale ◽  
Object Oriented Method ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Mesoscale Structures ◽  
Ensemble Weather Forecasts ◽  
Meteorological Models

Abstract. A new object-oriented method has been developed to detect hazardous phenomena predicted by Numerical Weather Prediction (NWP) models. This method, called similarity-based method, is looking for specific meteorological objects in the forecasts, which are defined by a reference histogram representing the meteorological phenomena to be detected. The similarity-based method enables to cope with small scale unpredictable details of mesoscale structures in meteorological models and to quantify the uncertainties on the location of the predicted phenomena. Applied to ensemble forecasts, the similarity-based method can be viewed as a particular case of neighborhood processing, allowing spatialized probabilities to be computed. An application to rainfall detection using forecasts from the AROME deterministic and ensemble models is presented.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

scholarly journals Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression

2015 ◽  
Vol 143 (3) ◽  
pp. 955-971 ◽  
Author(s):  
Kira Feldmann ◽  
Michael Scheuerer ◽  
Thordis L. Thorarinsdottir
Keyword(s):  
Bayesian Model Averaging ◽  
Forecast Error ◽  
Gaussian Random Field ◽  
Rank Correlation ◽  
Model Averaging ◽  
Small Scale ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Scale Modelling ◽  
Gaussian Regression

Abstract Statistical postprocessing techniques are commonly used to improve the skill of ensembles from numerical weather forecasts. This paper considers spatial extensions of the well-established nonhomogeneous Gaussian regression (NGR) postprocessing technique for surface temperature and a recent modification thereof in which the local climatology is included in the regression model to permit locally adaptive postprocessing. In a comparative study employing 21-h forecasts from the Consortium for Small Scale Modelling ensemble predictive system over Germany (COSMO-DE), two approaches for modeling spatial forecast error correlations are considered: a parametric Gaussian random field model and the ensemble copula coupling (ECC) approach, which utilizes the spatial rank correlation structure of the raw ensemble. Additionally, the NGR methods are compared to both univariate and spatial versions of the ensemble Bayesian model averaging (BMA) postprocessing technique.

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

scholarly journals Practical Predictability of Supercells: Exploring Ensemble Forecast Sensitivity to Initial Condition Spread

2018 ◽  
Vol 146 (8) ◽  
pp. 2361-2379 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Louis J. Wicker
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Object Oriented ◽  
Comprehensive Analysis ◽  
Initial Condition ◽  
Forecast System ◽  
Model Assumption ◽  
Ensemble Forecasts ◽  
Severe Thunderstorms ◽  
Perfect Model

Abstract As convection-allowing ensembles are routinely used to forecast the evolution of severe thunderstorms, developing an understanding of storm-scale predictability is critical. Using a full-physics numerical weather prediction (NWP) framework, the sensitivity of ensemble forecasts of supercells to initial condition (IC) uncertainty is investigated using a perfect model assumption. Three cases are used from the real-time NSSL Experimental Warn-on-Forecast System for Ensembles (NEWS-e) from the 2016 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. The forecast sensitivity to IC uncertainty is assessed by repeating the simulations with the initial ensemble perturbations reduced to 50% and 25% of their original magnitudes. The object-oriented analysis focuses on significant supercell features, including the mid- and low-level mesocyclone, and rainfall. For a comprehensive analysis, supercell location and amplitude predictability of the aforementioned features are evaluated separately. For all examined features and cases, forecast spread is greatly reduced by halving the IC spread. By reducing the IC spread from 50% to 25% of the original magnitude, forecast spread is still substantially reduced in two of the three cases. The practical predictability limit (PPL), or the lead time beyond which the forecast spread exceeds some prechosen threshold, is case and feature dependent. Comparing to past studies reveals that practical predictability of supercells is substantially improved by initializing once storms are well established in the ensemble analysis.

scholarly journals Comparative Evaluation of Three Schaake Shuffle Schemes in Postprocessing GEFS Precipitation Ensemble Forecasts

2018 ◽  
Vol 19 (3) ◽  
pp. 575-598 ◽  
Author(s):  
Limin Wu ◽  
Yu Zhang ◽  
Thomas Adams ◽  
Haksu Lee ◽  
Yuqiong Liu ◽  
...  
Keyword(s):  
The United States ◽  
Spatiotemporal Variability ◽  
Precipitation Event ◽  
Precipitation Pattern ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Temporal Correlations ◽  
Extreme Precipitation Events ◽  
Proper Size ◽  
Ensemble Weather Forecasts

Abstract Natural weather systems possess certain spatiotemporal variability and correlations. Preserving these spatiotemporal properties is a significant challenge in postprocessing ensemble weather forecasts. To address this challenge, several rank-based methods, the Schaake Shuffle and its variants, have been developed in recent years. This paper presents an extensive assessment of the Schaake Shuffle and its two variants. These schemes differ in how the reference multivariate rank structure is established. The first scheme (SS-CLM), an implementation of the original Schaake Shuffle method, relies on climatological observations to construct rank structures. The second scheme (SS-ANA) utilizes precipitation event analogs obtained from a historical archive of observations. The third scheme (SS-ENS) employs ensemble members from the Global Ensemble Forecast System (GEFS). Each of the three schemes is applied to postprocess precipitation ensemble forecasts from the GEFS for its first three forecast days over the mid-Atlantic region of the United States. In general, the effectiveness of these schemes depends on several factors, including the season (or precipitation pattern) and the level of gridcell aggregation. It is found that 1) the SS-CLM and SS-ANA behave similarly in spatial and temporal correlations; 2) by a measure for capturing spatial variability, the SS-ENS outperforms the SS-ANA, which in turn outperforms the SS-CLM; and 3), overall, the SS-ANA performs better than the SS-CLM. The study also reveals that it is important to choose a proper size for the postprocessed ensembles in order to capture extreme precipitation events.

Severe Weather Prediction Using Storm Surrogates from an Ensemble Forecasting System

2016 ◽  
Vol 31 (1) ◽  
pp. 255-271 ◽  
Author(s):  
Ryan A. Sobash ◽  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Kathryn R. Fossell ◽  
Morris L. Weisman
Keyword(s):  
Extreme Values ◽  
Weather Prediction ◽  
Severe Weather ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Smoothing Kernel ◽  
Risk Areas ◽  
Forecasting System ◽  
Computational Resources

Abstract Probabilistic severe weather forecasts for days 1 and 2 were produced using 30-member convection-allowing ensemble forecasts initialized by an ensemble Kalman filter data assimilation system during a 32-day period coinciding with the Mesoscale Predictability Experiment. The forecasts were generated by smoothing the locations where model output indicated extreme values of updraft helicity, a surrogate for rotating thunderstorms in model output. The day 1 surrogate severe probability forecasts (SSPFs) produced skillful and reliable predictions of severe weather during this period, after an appropriate calibration of the smoothing kernel. The ensemble SSPFs exceeded the skill of SSPFs derived from two benchmark deterministic forecasts, with the largest differences occurring on the mesoscale, while all SSPFs produced similar forecasts on synoptic scales. While the deterministic SSPFs often overforecasted high probabilities, the ensemble improved the reliability of these probabilities, at the expense of producing fewer high-probability values. For the day 2 period, the SSPFs provided competitive guidance compared to the day 1 forecasts, although additional smoothing was needed to produce the same level of skill, reducing the forecast sharpness. Results were similar using 10 ensemble members, suggesting value exists when running a smaller ensemble if computational resources are limited. Finally, the SSPFs were compared to severe weather risk areas identified in Storm Prediction Center (SPC) convective outlooks. The SSPF skill was comparable to the SPC outlook skill in identifying regions where severe weather would occur, although performance varied on a day-to-day basis.

scholarly journals Compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts

2021 ◽  
Author(s):  
Michał Z. Ziemiański ◽  
Damian K. Wójcik ◽  
Bogdan Rosa ◽  
Zbigniew P. Piotrowski
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Small Scale ◽  
Dynamical Core ◽  
Weather Forecasts ◽  
The Core ◽  
Convective Scale ◽  
Realistic Representation ◽  
General Improvement ◽  
Forecasting System

AbstractThis paper presents the semi-implicit compressible EULAG as a new dynamical core for convective-scale numerical weather prediction. The core is implemented within the infrastructure of the operational model of the Consortium for Small Scale Modeling (COSMO), forming the NWP COSMO-EULAG model (CE). This regional high-resolution implementation of the dynamical core complements its global implementation in the Finite-Volume Module of ECMWF’s Integrated Forecasting System. The paper documents the first operational-like application of the dynamical core for realistic weather forecasts. After discussing the formulation of the core and its coupling with the host model, the paper considers several high-resolution prognostic experiments over complex Alpine orography. Standard verification experiments examine the sensitivity of the CE forecast to the choice of the advection routine and assess the forecast skills against those of the default COSMO Runge-Kutta dynamical core at 2.2 km grid size showing a general improvement. The skills are also compared using satellite observations for a weak-flow convective Alpine weather case-study, showing favorable results. Additional validation of the new CE framework for partly convection-resolving forecasts using 1.1 km, 0.55 km, 0.22 km, and 0.1 km grids, designed to challenge its numerics and test the dynamics-physics coupling, demonstrates its high robustness in simulating multi-phase flows over complex mountain terrain, with slopes reaching 85 degrees, and the flow’s realistic representation.

scholarly journals A New View of Heat Wave Dynamics and Predictability over the Eastern Mediterranean

2020 ◽  
Author(s):  
Assaf Hochman ◽  
Sebastian Scher ◽  
Julian Quinting ◽  
Joaquim G. Pinto ◽  
Gabriele Messori
Keyword(s):  
Dynamical Systems ◽  
Heat Wave ◽  
Heat Waves ◽  
Eastern Mediterranean ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Stress Index ◽  
Weather Forecasts ◽  
Ensemble Weather Forecasts

Abstract. Skillful forecasts of extreme weather events have a major socio-economic relevance. Here, we compare two complementary approaches to diagnose the predictability of extreme weather: recent developments in dynamical systems theory and numerical ensemble weather forecasts. The former allows us to define atmospheric configurations in terms of their persistence and local dimension, which inform on how the atmosphere evolves to and from a given state of interest. These metrics may be used as proxies for the intrinsic predictability of the atmosphere, which depends exclusively on the atmosphere’s properties. Ensemble weather forecasts inform on the practical predictability of the atmosphere, which primarily depends on the performance of the numerical model used. We focus on heat waves affecting the Eastern Mediterranean. These are identified using the Climatic Stress Index (CSI), which was explicitly developed for the summer weather conditions in this region and differentiates between heat waves (upper decile) and cool days (lower decile). Significant differences are found between the two groups from both the dynamical systems and the numerical weather prediction perspectives. Specifically, heat waves show relatively stable flow characteristics (high intrinsic predictability), but comparatively low practical predictability (large model spread/error). For 500 hPa geopotential height fields, the intrinsic predictability of heat waves is lowest at the event’s onset and decay. We relate these results to the physical processes governing Eastern Mediterranean summer heat waves: adiabatic descent of the air parcels over the region and the geographical origin of the air parcels over land prior to the onset of a heat wave. A detailed analysis of the mid-August 2010 record-breaking heat wave provides further insights into the range of different regional atmospheric configurations conducive to heat waves. We conclude that the dynamical systems approach can be a useful complement to conventional numerical forecasts for understanding the dynamics of Eastern Mediterranean heat waves.

Statistical post-processing of ensemble forecasts at the Belgian met service

2021 ◽  
Author(s):  
Jonathan Demaeyer ◽  
Bert Van schaeybroeck ◽  
Stéphane Vannitsem
Keyword(s):  
Maximum Temperature ◽  
Post Processing ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Operational Application ◽  
Uncertainty Estimates ◽  
The Mean ◽  
Model Output Statistics ◽  
Ensemble Weather Forecasts ◽  
Meteorological Institute

<p>Statistical post-processing of ensemble weather forecasts has become an essential step in the forecasting chain as it enables the correction of biases and reliable uncertainty estimates of ensembles (Gneiting, 2014).  One algorithm recently proposed to perform the correction of ensemble weather forecasts is a linear member-by-member (MBM) Model Output Statistics (MOS) system, post-processing each member of the ECMWF ensemble (Van Schaeybroeck & Vannitsem, 2015). This method consists in correcting the mean and variability of the ensemble members in line with the observed climatology. At the same time, it calibrates the ensemble spread such as to match, on average, the mean square error of the ensemble mean. The MBM method calibrates the ensemble forecasts based on the station observations by minimizing the continuous ranked probability score (CRPS).</p><p><span>Using this method, the Royal Meteorological Institute of Belgium has started in 2020 its new postprocessing program by developing an operational application to perform the calibration of the ECMWF ensemble forecasts at the stations points for the minimum and maximum temperature, and for wind gusts. </span>In this report, we will first describe briefly the postprocessing methods being used and the architecture of the application. We will then present the results over the first few months of operation. Finally, we will discuss the future developments of this application and of the program.</p><p><span><br></span></p><p><span> </span> <span> </span></p><p><strong>Gneiting</strong>, <strong>T.</strong>, 2014: Calibration of medium-range weather forecasts. <em>ECMWF Technical Memorandum</em> <strong>No. 719</strong></p><p><span> </span> <span> </span></p><p><strong>Van Schaeybroeck</strong>, <strong>B.</strong> & <strong>Vannitsem</strong>, <strong>S.</strong>, 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. <em>Quarterly Journal of the Royal Meteorological Society</em>, <strong>141</strong>, 807–818.</p>

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