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.

Development of a track-pattern-based medium-range tropical cyclone forecasting system for the western North Pacific

2021 ◽  
Author(s):  
Hung Ming Cheung ◽  
Chang-Hoi Ho ◽  
Minhee Chang ◽  
Dasol Kim ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Correlation Coefficients ◽  
Machine Learning Techniques ◽  
Track Forecast ◽  
Dynamical Models ◽  
Pattern Correlation ◽  
Medium Range ◽  
Track Pattern

AbstractDespite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: (1) clustering historical tracks similar to that of an operational five-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; (2) deriving the two environmental variables forecasted by dynamical models; (3) evaluating pattern correlation coefficients between the two environmental fields from step (1) and those from dynamical model for a lead times of 6–8 days; and (4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step (1) and the pattern correlation coefficients obtained from step (3). TCs that formed in the WNP and lasted for at least seven days, during the 9-year period 2011–2019 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate non-linearity in the present model for improving medium-range track forecasts.

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 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.

OBEST: An Observation-Based Ensemble Subsetting Technique for Tropical Cyclone Track Prediction

2016 ◽  
Vol 31 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lin Dong ◽  
Fuqing Zhang
Keyword(s):  
Tropical Cyclone ◽  
Lead Times ◽  
Track Forecast ◽  
Forecast Errors ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Weather Forecasts ◽  
Ensemble Mean ◽  
Medium Range ◽  
Environmental Prediction

Abstract An observation-based ensemble subsetting technique (OBEST) is developed for tropical cyclone track prediction in which a subset of members from either a single- or multimodel ensemble is selected based on the distance from the latest best-track position. The performance of OBEST is examined using both the 2-yr hindcasts for 2010–11 and the 2-yr operational predictions during 2012–13. It is found that OBEST outperforms both the simple ensemble mean (without subsetting) and the corresponding deterministic high-resolution control forecast for most forecast lead times up to 5 days. Applying OBEST to a superensemble of global ensembles from both the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction yielded a further reduction in track forecast errors by 5%–10% for lead times of 24–120 h.

scholarly journals On the Ability of Global Ensemble Prediction Systems to Predict Tropical Cyclone Track Probabilities

2010 ◽  
Vol 25 (2) ◽  
pp. 659-680 ◽  
Author(s):  
Sharanya J. Majumdar ◽  
Peter M. Finocchio
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Ensemble Prediction ◽  
Deterministic Models ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Prediction Systems

Abstract The ability of ensemble prediction systems to predict the probability that a tropical cyclone will fall within a certain area is evaluated. Ensemble forecasts of up to 5 days issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Met Office (UKMET) were evaluated for the 2008 Atlantic and western North Pacific seasons. In the Atlantic, the ECMWF ensemble mean was comparable in skill to a consensus of deterministic models. Dynamic “probability circles” that contained 67% of the ECMWF ensemble captured the best track in ∼67% of all cases for 24–84-h forecasts, and were slightly underdispersive beyond 96 h. In contrast, the Goerss predicted consensus error (GPCE) was overdispersive. The addition of the UKMET ensemble yielded improvements in the short range and degradations for longer-range forecasts. The ECMWF ensemble performed similarly when the size was reduced from 50 to 20. On average, it produced a lower measure of independence between its members than an ensemble comprising different deterministic models. The 67% circles normally captured the best track during straight-line motion, but less so for sharply turning tracks. In contrast to the Atlantic, the ECMWF ensemble (and GPCE) was unable to capture sufficient verifications within the 67% probability circles in the western North Pacific, in part because of a less skillful ensemble mean (and consensus). Though further evaluations are necessary, the results demonstrate the potential for ensemble prediction systems to enhance probabilistic forecasts, and for The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) to be embraced by the operational and research communities.

scholarly journals Are We Reaching the Limit of Tropical Cyclone Track Predictability in the Western North Pacific?

2021 ◽  
pp. 1-46
Author(s):  
Hui Yu ◽  
Guomin Chen ◽  
Cong Zhou ◽  
Wai Kin Wong ◽  
Mengqi Yang ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Lead Time ◽  
Western North Pacific ◽  
Current Trend ◽  
Forecast Accuracy ◽  
Exponential Model ◽  
Track Forecast ◽  
The Past ◽  
Position Errors

AbstractThe annual-mean position errors (PE) of tropical cyclone (TC) track forecasts from three forecast agencies (RSMC-Tokyo, CMA, and JTWC) are analyzed to document the past improvements and project future tendency in track forecast accuracy for TCs in the western North Pacific. An improvement of 48 h (2-day) in lead time has been achieved in the past thirty years, but with noticeable stepwise periods of improvements with superposed short-term fluctuations. The stepwise improvement features differ among the three forecast agencies, but are highly related to the development of objective forecast guidance and the application strategy. As demonstrated by an exponential model for the growth of PEs with lead time for TCs of tropical storm category and above, the improvements in the past ten years have mainly been due to the reduction in analysis errors rather than the reduction in the error growth rate. If the current trend continues, a further 2-day improvement in TC track forecast lead times may be projected for the coming fifteen years up to 2035, and we certainly have not reached yet the limit of TC track predictability in the western North Pacific.

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.

scholarly journals Validation of the Aura High Resolution Dynamics Limb Sounder geopotential heights

2014 ◽  
Vol 7 (2) ◽  
pp. 1001-1025
Author(s):  
L. L. Smith ◽  
J. C. Gille
Keyword(s):  
High Resolution ◽  
System Model ◽  
Weather Forecasts ◽  
Atmospheric Research ◽  
Earth Observing System ◽  
Medium Range ◽  
High Bias ◽  
Environmental Prediction ◽  
The Tropics ◽  
Data Screening

Abstract. Global satellite observations from the EOS Aura spacecraft's High Resolution Dynamics Limb Sounder (HIRDLS) of temperature and geopotential height (GPH) are discussed. The accuracy, resolution and precision of the HIRDLS version 7 algorithms are assessed and data screening recommendations are made. Comparisons with GPH from observations, reanalyses and models including European Center for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis, Goddard Earth Observing System Model (GEOS) version 5, and EOS Aura Microwave Limb Sounder (MLS) illustrate the HIRDLS GPH have a precision ranging from 2 m to 30 m and an accuracy of ±100 m. Comparisons indicate HIRDLS GPH may have a slight low bias in the tropics and a slight high bias at high latitudes. Geostrophic winds computed with HIRDLS GPH qualitatively agree with winds from other data sources including ERA-Interim, NCEP and GEOS-5.

Five-day track forecast skills of WRF model for the western North Pacific tropical cyclones

2021 ◽  
Author(s):  
Jihong Moon ◽  
Jinyoung Park ◽  
Dong-Hyun Cha ◽  
Yumin Moon
Keyword(s):  
Tropical Cyclones ◽  
North Pacific ◽  
Western North Pacific ◽  
Forecast Error ◽  
Wrf Model ◽  
Analysis Data ◽  
Track Forecast ◽  
Steering Vector ◽  
Group 2 ◽  
Group 1

AbstractIn this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF model) are verified. We utilize 12 km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. One hundred and twenty-five forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moves northward in subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

Sign in / Sign up

Export Citation Format

Share Document