scholarly journals Opportunity for Early Warnings of Typhoon Lekima from Two Global Ensemble Model Forecasts of Formation with 7-Day Intensities along Medium-Range Tracks

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1162
Author(s):  
Hsiao-Chung Tsai ◽  
Russell L. Elsberry ◽  
Wei-Chia Chin ◽  
Timothy P. Marchok
Keyword(s):  
Water Resource Management ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Position Errors ◽  
Intensity Prediction ◽  
Prediction Technique ◽  
Medium Range ◽  
Decision Making Processes ◽  
Mean Vector ◽  
Joint Typhoon Warning Center

Typhoon Lekima (2019) with its heavy rains and floods is an excellent example of the need to provide the earliest possible warnings of the formation, intensification, and subsequent track before a typhoon makes landfall along a densely populated coast. To demonstrate an opportunity to provide early (10 days in advance) warnings of the threat of Typhoon Lekima, the ensemble models from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Predictions have been used to provide time-to-formation timing and positions along the weighted-mean vector motion track forecasts. In addition, the seven-day intensity forecasts after the formation using a weighted analog intensity prediction technique are provided. A detailed description of one European Center ensemble forecast is provided to describe the methodology for estimating the formation time and generating the intensity forecasts. Validation summary tables of the formation timing and position errors, and the intensity errors versus the Joint Typhoon Warning Center intensities, are presented. The availability of these ensemble forecasts would have been an opportunity to issue alerts/watches/warnings of Lekima even seven days in advance of when Lekima became a Tropical Storm. These ensemble forecasts also represent an opportunity to extend support on the 5–15 day timescale for the decision-making processes of water resource management and hydrological operations.

scholarly journals Tropical Cyclone Track Sensitivity in Deformation Steering Flow

2018 ◽  
Vol 146 (10) ◽  
pp. 3183-3201 ◽  
Author(s):  
Ryan D. Torn ◽  
Travis J. Elless ◽  
Philippe P. Papin ◽  
Christopher A. Davis
Keyword(s):  
Tropical Cyclones ◽  
Cyclone Track ◽  
Wind Fields ◽  
Tropical Cyclone Track ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Position Errors ◽  
Steering Flow ◽  
Ensemble Mean ◽  
Medium Range

Abstract Previous studies have suggested that tropical cyclones (TCs) in deformation steering flows can be associated with large position errors and uncertainty. The goal of this study is to evaluate the sensitivity of position forecasts for three TCs within deformation wind fields [Debby (2012), Joaquin (2015), and Lionrock (2016)] using the ensemble-based sensitivity technique applied to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts. In all three cases, the position forecasts are sensitive to uncertainty in the steering wind within 500 km of the 0-h TC position. Subsequently, the TC moves onto either side of the axis of contraction due to the ensemble perturbation steering flow. As a TC moves away from the saddle point, the ensemble members subsequently experience different ensemble-mean steering winds, which act to move the TC away from the ensemble-mean TC position along the axis of dilatation. By contrast, the position forecasts appear to exhibit less sensitivity to the steering wind more than 500 km from the initial TC position, even though the TC may interact with these features later in the forecast. Furthermore, forecasts initialized at later times are characterized by significantly lower position errors and uncertainty once it becomes clear on which side of the axis of contraction the TC will move. These results suggest that TCs in deformation steering flow could be inherently unpredictable and may benefit from densely sampling the near-storm steering flow and TC structure early in their lifetimes.

scholarly journals Advanced Global Model Ensemble Forecasts of Tropical Cyclone Formation, and Intensity Predictions along Medium-Range Tracks

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1002
Author(s):  
Russell L. Elsberry ◽  
Hsiao-Chung Tsai ◽  
Wei-Chia Chin ◽  
Timothy P. Marchok
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Future Application ◽  
Track Forecast ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Position Errors ◽  
Medium Range ◽  
Primary Focus ◽  
Environmental Prediction

Marchok vortex tracker outputs from the European Centre for Medium-Range Weather Forecasts ensemble (ECEPS) and National Centers for Environmental Prediction ensemble (GEFS) are utilized to provide the Time-to-Formation (T2F of 25 kt or 35 kt) timing and positions along the weighted-mean vector motion (WMVM) track forecasts, and our weighted analog intensity Pacific (WAIP) technique provides 7-day intensity forecasts after the T2F. Example T2F(35) forecasts up to 5 days in advance of two typhoons and one non-developer in the western North Pacific are described in detail. An example T2F forecast of pre-Hurricane Kiko in the eastern North Pacific indicated that Hawaii would be under threat by the end of the 15-day ECEPS WMVM track forecast. An example T2F forecast of pre-Hurricane Lorenzo in the eastern Atlantic demonstrates that both the ECEPS and GEFS predict up to 5 days in advance that the precursor African wave will become a Tropical Storm off the west coast and will likely become a hurricane. Validations of the T2F(25) and T2F(35) timing and position errors are provided for all ECEPS and GEFS forecasts of the two typhoons and Hurricanes Kiko and Lorenzo. If the T2F timing errors are small (<1 day), the T2F position errors along the WMVM track forecasts will be small (<300 km). Although the primary focus is on the western North Pacific, the examples from the Atlantic and eastern/central North Pacific indicate the potential for future application in other basins.

scholarly journals Nonhomogeneous Boosting for Predictor Selection in Ensemble Postprocessing

2016 ◽  
Vol 145 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Jakob W. Messner ◽  
Georg J. Mayr ◽  
Achim Zeileis
Keyword(s):  
Regression Coefficients ◽  
Maximum Temperature ◽  
Ensemble Forecasts ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Medium Range ◽  
Important Input ◽  
Boosting Algorithm ◽  
Input Variables

Abstract Nonhomogeneous regression is often used to statistically postprocess ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input, but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients, while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at five central European stations.

New Methods for Data Storage of Model Output from Ensemble Simulations

2019 ◽  
Vol 147 (2) ◽  
pp. 677-689 ◽  
Author(s):  
Peter D. Düben ◽  
Martin Leutbecher ◽  
Peter Bauer
Keyword(s):  
Data Storage ◽  
Climate Modeling ◽  
Model Resolution ◽  
Ensemble Spread ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Ensemble Simulations ◽  
New Methods ◽  
Weather And Climate ◽  
Medium Range

Abstract Data storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users increases with increasing model resolution and the use of larger forecast ensembles. If precision of data is reduced, cost can be reduced accordingly. In this paper, three new methods to allow a reduction in precision with minimal loss of information are suggested and tested. Two of these methods rely on the similarities between ensemble members in ensemble forecasts. Therefore, precision will be high at the beginning of forecasts when ensemble members are more similar, to provide sufficient distinction, and decrease with increasing ensemble spread. To keep precision high for predictable situations and low elsewhere appears to be a useful approach to optimize data storage in weather forecasts. All methods are tested with data of operational weather forecasts of the European Centre for Medium-Range Weather Forecasts.

Predicting Wind Power with Reforecasts

2015 ◽  
Vol 30 (6) ◽  
pp. 1655-1662 ◽  
Author(s):  
Markus Dabernig ◽  
Georg J. Mayr ◽  
Jakob W. Messner
Keyword(s):  
Wind Power ◽  
Systematic Errors ◽  
Decision Makers ◽  
Energy Market ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Medium Range ◽  
Operational Models ◽  
Nwp Model

Abstract Energy traders and decision-makers need accurate wind power forecasts. For this purpose, numerical weather predictions (NWPs) are often statistically postprocessed to correct systematic errors. This requires a dataset of past forecasts and observations that is often limited by frequent NWP model enhancements that change the statistical model properties. Reforecasts that recompute past forecasts with a recent model provide considerably longer datasets but usually have weaker setups than operational models. This study tests the reforecasts from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) for wind power predictions. The NOAA reforecast clearly performs worse than the ECMWF reforecast, the operational ECMWF deterministic and ensemble forecasts, and a limited-area model of the Austrian weather service [Zentralanstalt für Meteorologie und Geodynamik (ZAMG)]. On the contrary, the ECMWF reforecast has, of all tested models, the smallest squared errors and one of the highest financial values in an energy market.

scholarly journals Predictability of a Mediterranean Tropical-Like Storm Downstream of the Extratropical Transition of Hurricane Helene (2006)

2013 ◽  
Vol 141 (6) ◽  
pp. 1943-1962 ◽  
Author(s):  
Florian P. Pantillon ◽  
Jean-Pierre Chaboureau ◽  
Patrick J. Mascart ◽  
Christine Lac
Keyword(s):  
Tropical Cyclone ◽  
Lead Time ◽  
Large Scale ◽  
Synoptic Situation ◽  
British Isles ◽  
Extratropical Transition ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Warm Core ◽  
Medium Range

Abstract The extratropical transition (ET) of a tropical cyclone is known as a source of forecast uncertainty that can propagate far downstream. The present study focuses on the predictability of a Mediterranean tropical-like storm (Medicane) on 26 September 2006 downstream of the ET of Hurricane Helene from 22 to 25 September. While the development of the Medicane was missed in the deterministic forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) initialized before and during ET, it was contained in the ECMWF ensemble forecasts in more than 10% of the 50 members up to 108-h lead time. The 200 ensemble members initialized at 0000 UTC from 20 to 23 September were clustered into two nearly equiprobable scenarios after the synoptic situation over the Mediterranean. In the first and verifying scenario, Helene was steered northeastward by an upstream trough during ET and contributed to the building of a downstream ridge. A trough elongated farther downstream toward Italy and enabled the development of the Medicane in 9 of 102 members. In the second and nonverifying scenario, Helene turned southeastward during ET and the downstream ridge building was reduced. A large-scale low over the British Isles dominated the circulation in Europe and only 1 of 98 members forecasted the Medicane. The two scenarios resulted from a different phasing between Helene and the upstream trough. Sensitivity experiments performed with the Méso-NH model further revealed that initial perturbations targeted on Helene and the upstream trough were sufficient in forecasting the warm-core Medicane at 84- and 108-h lead time.

scholarly journals Sensitivity, Structure, and Dynamics of Singular Vectors Associated with Hurricane Helene (2006)

2012 ◽  
Vol 69 (2) ◽  
pp. 675-694 ◽  
Author(s):  
Simon T. K. Lang ◽  
Sarah C. Jones ◽  
Martin Leutbecher ◽  
Melinda S. Peng ◽  
Carolyn A. Reynolds
Keyword(s):  
Three Dimensional ◽  
Growth Dynamics ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Singular Vectors ◽  
Medium Range ◽  
Flow Features ◽  
Forecasting System ◽  
Diabatic Processes ◽  
The Impact

Abstract The sensitivity of singular vectors (SVs) associated with Hurricane Helene (2006) to resolution and diabatic processes is investigated. Furthermore, the dynamics of their growth are analyzed. The SVs are calculated using the tangent linear and adjoint model of the integrated forecasting system (IFS) of the European Centre for Medium-Range Weather Forecasts with a spatial resolution up to TL255 (~80 km) and 48-h optimization time. The TL255 moist (diabatic) SVs possess a three-dimensional spiral structure with significant horizontal and vertical upshear tilt within the tropical cyclone (TC). Also, their amplitude is larger than that of dry and lower-resolution SVs closer to the center of Helene. Both higher resolution and diabatic processes result in stronger growth being associated with the TC compared to other flow features. The growth of the SVs in the vicinity of Helene is associated with baroclinic and barotropic mechanisms. The combined effect of higher resolution and diabatic processes leads to significant differences of the SV structure and growth dynamics within the core and in the vicinity of the TC. If used to initialize ensemble forecasts with the IFS, the higher-resolution moist SVs cause larger spread of the wind speed, track, and intensity of Helene than their lower-resolution or dry counterparts. They affect the outflow of the TC more strongly, resulting in a larger downstream impact during recurvature. Increasing the resolution or including diabatic effects degrades the linearity of the SVs. While the impact of diabatic effects on the linearity is small at low resolution, it becomes large at high resolution.

Discrete Postprocessing of Total Cloud Cover Ensemble Forecasts

2016 ◽  
Vol 144 (7) ◽  
pp. 2565-2577 ◽  
Author(s):  
Stephan Hemri ◽  
Thomas Haiden ◽  
Florian Pappenberger
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Cloud Cover ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Total Cloud Cover ◽  
Proportional Odds ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Medium Range

Abstract This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model.

scholarly journals Decomposition of a New Proper Score for Verification of Ensemble Forecasts

2015 ◽  
Vol 143 (5) ◽  
pp. 1517-1532 ◽  
Author(s):  
H. M. Christensen
Keyword(s):  
Standard Deviation ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Brier Score ◽  
Continuous Variables ◽  
Ensemble Forecasts ◽  
Atmospheric Flow ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Medium Range

Abstract A new proper score, the error-spread score (ES), has recently been proposed for evaluation of ensemble forecasts of continuous variables. The ES is formulated with respect to the moments of the ensemble forecast. It is particularly sensitive to evaluating how well an ensemble forecast represents uncertainty: is the probabilistic forecast well calibrated? In this paper, it is shown that the ES can be decomposed into its reliability, resolution, and uncertainty components in a similar way to the Brier score. The first term evaluates the reliability of the forecast standard deviation and skewness, rewarding systems where the forecast moments reliably indicate the properties of the verification. The second term evaluates the resolution of the forecast standard deviation and skewness, and rewards systems where the forecast moments vary from the climatological moments according to the predictability of the atmospheric flow. The uncertainty term depends only on the observed error distribution and is independent of the forecast standard deviation or skewness. The decomposition was demonstrated using forecasts made with the European Centre for Medium-Range Weather Forecasts ensemble prediction system, and was able to identify the source of the skill in the forecasts at different latitudes.

Flow-Dependent Reliability: A Path to More Skillful Ensemble Forecasts

2018 ◽  
Vol 99 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
M. J. Rodwell ◽  
D. S. Richardson ◽  
D. B. Parsons ◽  
H. Wernli
Keyword(s):  
Lead Times ◽  
Jet Stream ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Convective Systems ◽  
Flow Type ◽  
Medium Range ◽  
Mesoscale Convective ◽  
Ensemble Weather Forecasts ◽  
Start Dates

AbstractWhile chaos ensures that probabilistic weather forecasts cannot always be “sharp,” it is important for users and developers that they are reliable. For example, they should not be overconfident or underconfident. The “spread–error” relationship is often used as a first-order assessment of the reliability of ensemble weather forecasts. This states that the ensemble standard deviation (a measure of forecast uncertainty) should match the root-mean-square error on the ensemble mean (when averaged over a sufficient number of forecast start dates). It is shown here that this relationship is now largely satisfied at the European Centre for Medium-Range Weather Forecasts (ECMWF) for ensemble forecasts of the midlatitude, midtropospheric flow out to lead times of at least 10 days when averaged over all flow situations throughout the year. This study proposes a practical framework for continued improvement in the reliability (and skill) of such forecasts. This involves the diagnosis of flow-dependent deficiencies in short-range (∼12 h) reliability for a range of synoptic-scale flow types and the prioritization of modeling research to address these deficiencies. The approach is demonstrated for a previously identified flow type, a trough over the Rockies with warm, moist air ahead. The mesoscale convective systems that can ensue are difficult to predict and, by perturbing the jet stream, are thought to lead to deterministic forecast “busts” for Europe several days later. The results here suggest that jet stream spread is insufficient during this flow type, and thus unreliable. This is likely to mean that the uncertain forecasts for Europe may, nevertheless, still be overconfident.

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