scholarly journals Evaluating U.S. East Coast Winter Storms in a Multimodel Ensemble Using EOF and Clustering Approaches

2019 ◽  
Vol 147 (6) ◽  
pp. 1967-1987 ◽  
Author(s):  
Minghua Zheng ◽  
Edmund K. M. Chang ◽  
Brian A. Colle
Keyword(s):  
Lead Time ◽  
East Coast ◽  
Time Frame ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Winter Storms ◽  
East Coast Winter Storms ◽  
Prediction Systems ◽  
Environmental Prediction ◽  
The Individual

Abstract Empirical orthogonal function (EOF) and fuzzy clustering tools were applied to generate and validate scenarios in operational ensemble prediction systems (EPSs) for U.S. East Coast winter storms. The National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), and Canadian Meteorological Centre (CMC) EPSs were validated in their ability to capture the analysis scenarios for historical East Coast cyclone cases at lead times of 1–9 days. The ECMWF ensemble has the best performance for the medium- to extended-range forecasts. During this time frame, NCEP and CMC did not perform as well, but a combination of the two models helps reduce the missing rate and alleviates the underdispersion. All ensembles are underdispersed at all ranges, with combined ensembles being less underdispersed than the individual EPSs. The number of outside-of-envelope cases increases with lead time. For a majority of the cases beyond the short range, the verifying analysis does not lie within the ensemble mean group of the multimodel ensemble or within the same direction indicated by any of the individual model means, suggesting that all possible scenarios need to be taken into account. Using the EOF patterns to validate the cyclone properties, the NCEP model tends to show less intensity and displacement biases during 1–3-day lead time, while the ECMWF model has the smallest biases during 4–6 days. Nevertheless, the ECMWF forecast position tends to be biased toward the southwest of the other two models and the analysis.

scholarly journals Assessing the Ensemble Predictability of Precipitation Forecasts for the January 2015 and 2016 East Coast Winter Storms

2017 ◽  
Vol 32 (3) ◽  
pp. 1057-1078 ◽  
Author(s):  
Steven J. Greybush ◽  
Seth Saslo ◽  
Richard Grumm
Keyword(s):  
East Coast ◽  
Initial Conditions ◽  
Wave Pattern ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Winter Storms ◽  
Position Errors ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
East Coast Winter Storms

Abstract The ensemble predictability of the January 2015 and 2016 East Coast winter storms is assessed, with model precipitation forecasts verified against observational datasets. Skill scores and reliability diagrams indicate that the large ensemble spread produced by operational forecasts was warranted given the actual forecast errors imposed by practical predictability limits. For the 2015 storm, uncertainties along the western edge’s sharp precipitation gradient are linked to position errors of the coastal low, which are traced to the positioning of the preceding 500-hPa wave pattern using the ensemble sensitivity technique. Predictability horizon diagrams indicate the forecast lead time in terms of initial detection, emergence of a signal, and convergence of solutions for an event. For the 2016 storm, the synoptic setup was detected at least 6 days in advance by global ensembles, whereas the predictability of mesoscale features is limited to hours. Convection-permitting WRF ensemble forecasts downscaled from the GEFS resolve mesoscale snowbands and demonstrate sensitivity to synoptic and mesoscale ensemble perturbations, as evidenced by changes in location and timing. Several perturbation techniques are compared, with stochastic techniques [the stochastic kinetic energy backscatter scheme (SKEBS) and stochastically perturbed parameterization tendency (SPPT)] and multiphysics configurations improving performance of both the ensemble mean and spread over the baseline initial conditions/boundary conditions (IC/BC) perturbation run. This study demonstrates the importance of ensembles and convective-allowing models for forecasting and decision support for east coast winter storms.

scholarly journals Probabilistic Heat Wave Forecast Based on a Large-Scale Circulation Pattern Using the TIGGE Data

2020 ◽  
Vol 35 (2) ◽  
pp. 367-377
Author(s):  
Hyun-Ju Lee ◽  
Woo-Seop Lee ◽  
Jong Ahn Chun ◽  
Hwa Woon Lee
Keyword(s):  
South Korea ◽  
Heat Wave ◽  
Large Scale ◽  
Heat Waves ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Large Scale Circulation ◽  
Circulation Patterns ◽  
Wave Forecast ◽  
Prediction Systems

Abstract Forecasting extreme events is important for having more time to prepare and mitigate high-impact events because those are expected to become more frequent, intense, and persistent around the globe in the future under the warming atmosphere. This study evaluates the probabilistic predictability of the heat wave index (HWI) associated with large-scale circulation patterns for predicting heat waves over South Korea. The HWI, reflecting heat waves over South Korea, was defined as the vorticity difference at 200 hPa between the South China Sea and northeast Asia. The forecast of up to 15 days from five ensemble prediction systems and the multimodel ensemble has been used to predict the probabilistic HWI during the summers of 2011–15. The ensemble prediction systems consist of different five operational centers, and the forecast skill of the probability of heat waves occurrence was assessed using the Brier skill score (BSS), relative operating characteristics (ROC), and reliability diagram. It was found that the multimodel ensemble is capable of better predicting the large-scale circulation patterns leading to heat waves over South Korea than any other single ensemble system through all forecast lead times. We concluded that the probabilistic forecast of the HWI has promise as a tool to take appropriate and timely actions to minimize the loss of lives and properties from imminent heat waves.

Sub-seasonal Monsoon Onset forecasting over West Africa

2020 ◽  
Author(s):  
Elisabeth Thompson ◽  
Caroline Wainwright ◽  
Linda Hirons ◽  
Felipe Marques de Andrade ◽  
Steven Woolnough
Keyword(s):  
West Africa ◽  
Lead Time ◽  
Monsoon Onset ◽  
Ensemble Prediction ◽  
Spatial Averaging ◽  
Seasonal Forecasts ◽  
Research And Innovation ◽  
Prediction Systems ◽  
Operational Methods ◽  
Programme Grant

<p>Skilful onset forecasts are highly sought after in West Africa, due to the importance of monsoon onset for agriculture, disease prevalence and energy provision. With research on the sub-seasonal timescale bridging the gap between weather and seasonal forecasts, sub-seasonal forecasts may provide useful information in the period preceding monsoon onset. This study explores sub-seasonal monsoon onset forecasts over West Africa using three operational ensemble prediction systems (ECMWF, UKMO, and NCEP) from the Sub-seasonal to Seasonal (S2S) prediction project database in order to determine the spatial scale and lead time at which sub-seasonal forecasts can provide useful monsoon onset information. Current research and operational methods of determining onset are identified and compared. The effect of spatial averaging on onset forecasting and skill is explored by comparing regional [Coast, Forest, Transition and North] and local forecasts at 4 major cities over Ghana.</p><p> </p><p>‘This work was supported by UK Research and Innovation as part of the Global Challenges Research Fund, African SWIFT programme, grant number NE/P021077/1’</p>

scholarly journals Must Surprise Snowstorms be a Surprise?

2008 ◽  
Vol 55 ◽  
pp. 251-268
Author(s):  
M. Steven Tracton
Keyword(s):  
East Coast ◽  
State Of The Art ◽  
Value Added ◽  
Ensemble Prediction ◽  
Massachusetts Institute ◽  
Massachusetts Institute Of Technology ◽  
Personal Involvement ◽  
Modeling Systems ◽  
Institute Of Technology ◽  
Prediction Systems

Abstract Today, even with state-of-the-art observational, data assimilation, and modeling systems run routinely on supercomputers, there are often surprises in the prediction of snowstorms, especially the “big ones,” affecting coastal regions of the mid-Atlantic and northeastern United States. Little did the author know that lessons from Fred Sanders' synoptic meteorology class at the Massachusetts Institute of Technology (1967) would later (late 1980s) inspire him to pursue practical issues of predictability in the context of the development of ensemble prediction systems, strategies, and applications for providing information on the inevitable case-dependent uncertainties in forecasts. This paper is a brief qualitative and somewhat colloquial overview, based upon this author's personal involvement and experiences, intended to highlight some basic aspects of the source and nature of uncertainties in forecasts and to illustrate the sort of value added information ensembles can provide in dealing with uncertainties in predictions of East Coast snowstorms.

scholarly journals Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation

2020 ◽  
Vol 148 (6) ◽  
pp. 2591-2606 ◽  
Author(s):  
Luying Ji ◽  
Xiefei Zhi ◽  
Clemens Simmer ◽  
Shoupeng Zhu ◽  
Yan Ji
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Diagnostic Evaluation ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Object Based ◽  
Forecast Technique ◽  
Point To Point ◽  
The Individual

Abstract We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

scholarly journals A Hybrid Stochastically Perturbed Parametrization Scheme in a Convection-Permitting Ensemble

2019 ◽  
Vol 147 (6) ◽  
pp. 2217-2230 ◽  
Author(s):  
Clemens Wastl ◽  
Yong Wang ◽  
Aitor Atencia ◽  
Christoph Wittmann
Keyword(s):  
Ensemble Prediction ◽  
Limited Area ◽  
Perturbation Approach ◽  
Perturbation Scheme ◽  
Shallow Convection ◽  
Physical Processes ◽  
Parametrization Scheme ◽  
Prediction Systems ◽  
The Individual ◽  
Stochastically Perturbed

Abstract Model error in ensemble prediction systems is often represented by either a tendency perturbation approach or a process-based parameter perturbation scheme. In this paper a novel hybrid stochastically perturbed parameterization (HSPP) scheme is proposed and implemented in the Convection Permitting Limited Area Ensemble Forecasting (C-LAEF) system developed at the Zentralanstalt für Meteorologie und Geodynamik (ZAMG). In HSPP, the individual parameterization tendencies of the physical processes radiation, shallow convection, and microphysics are perturbed stochastically by a spatially and temporally varying pattern. Uncertainties in the turbulence scheme are considered by perturbing key parameters on the process level. The proposed scheme HSPP features several advantages compared to the popular stochastically perturbed parameterization tendencies (SPPT) scheme: it considers a more physically consistent relationship between different parameterization schemes, deals with uncertainties especially adapted to the individual physical processes, respects conservation laws of energy and moisture, and eliminates the tapering function that has to be introduced to the SPPT scheme because of mainly numerical reasons. The hybrid scheme HSPP is evaluated over one summer and one winter month and compared to a reference ensemble without any stochastic physics perturbations and to two versions of the SPPT scheme. The results show that HSPP significantly increases the ensemble spread of temperature, humidity, wind speed, and pressure, especially in the lower levels of the atmosphere where a tapering function is active in the original SPPT approach. Precipitation verification yields a generally improved probabilistic performance of the HSPP scheme in summer when convection is dominating, which has also been demonstrated in a case study.

On the nature of rapidly deepening Canadian East Coast winter storms

1989 ◽  
Vol 27 (1) ◽  
pp. 87-107 ◽  
Author(s):  
Ronald E. Stewart ◽  
Norman R. Donaldson
Keyword(s):  
East Coast ◽  
Winter Storms ◽  
East Coast Winter Storms

scholarly journals Applying Fuzzy Clustering to a Multimodel Ensemble for U.S. East Coast Winter Storms: Scenario Identification and Forecast Verification

2017 ◽  
Vol 32 (3) ◽  
pp. 881-903 ◽  
Author(s):  
Minghua Zheng ◽  
Edmund K. M. Chang ◽  
Brian A. Colle ◽  
Yan Luo ◽  
YueJian Zhu
Keyword(s):  
Fuzzy Clustering ◽  
East Coast ◽  
State Variables ◽  
Clustering Method ◽  
Multimodel Ensemble ◽  
Forecast Uncertainty ◽  
Analysis Group ◽  
Fuzzy Clustering Method ◽  
Medium Range ◽  
Environmental Prediction

Abstract This article introduces a method for objectively separating and validating forecast scenarios within a large multimodel ensemble for the medium-range (3–7 day) forecasts of extratropical cyclones impacting the U.S. East Coast. The method applies fuzzy clustering to the principal components (PCs) of empirical orthogonal function (EOF) analysis on mean sea level pressure (MSLP) from a 90-member combination of the global ensembles from the National Centers for Environmental Prediction, the Canadian Meteorological Center, and the European Centre for Medium-Range Weather Forecasts. Two representative cases are presented to illustrate the applications of this method. Application to the 26–28 January 2015 event demonstrates that the forecast scenarios determined by the fuzzy clustering method are well separated and consistent in different state variables (i.e., MSLP, 500-hPa geopotential height, and total precipitation). The fuzzy clustering method and an existing ensemble sensitivity method are applied to the 26–28 December 2010 event to investigate forecast uncertainty, which demonstrates that these two methods are complementary to each other and can be used in the operations together to track the evolution of forecast uncertainty. For past cases one can define a cluster close to the analysis based on the projection of the analysis onto the PC base of clustering. This analysis group is validated using conventional validation metrics for both cases examined, and this analysis group has fewer errors than the other groups as well as the multimodel ensemble mean and individual model means.

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