Wavelet spectra and filtering of tropical cyclone forecast errors

2018 ◽  
Vol 16 (03) ◽  
pp. 1850011
Author(s):  
D. H. Smith
Keyword(s):  
Tropical Cyclone ◽  
Feature Detection ◽  
Local Error ◽  
Forecast Errors ◽  
Cyclone Track ◽  
Sea Level Pressure ◽  
Compression Performance ◽  
Wavelet Expansions ◽  
Ensemble Mean ◽  
Structural Differences

Ensemble mean forecast errors during a tropical cyclone event are probed with a spherical wavelet transform constructed by the lifting scheme. Coefficient spectra and associated filtered error components are examined during the forecast, with an emphasis on feature detection, for mean sea level pressure and wind components. Leading wavelet coefficients within a reference circle centered on the estimated cyclone track demonstrate a clear affinity for local error extrema, reflecting the transform’s feature detection capacity. Compression performance of the transform is also demonstrated by truncated wavelet expansions, which exhibit contrasting behavior reflecting fundamental structural differences between the wind and pressure error fields.

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.

Impact of Resolution and Design on the U.S. Navy Global Ensemble Performance in the Tropics

2011 ◽  
Vol 139 (7) ◽  
pp. 2145-2155 ◽  
Author(s):  
Carolyn A. Reynolds ◽  
Justin G. McLay ◽  
James S. Goerss ◽  
Efren A. Serra ◽  
Daniel Hodyss ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Kinetic Energy ◽  
Ensemble Prediction ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Ensemble Performance ◽  
Ensemble Mean ◽  
The Tropics ◽  
The U.S

Abstract The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.

scholarly journals Assimilation of Tropical Cyclone Track and Wind Radius Data with an Ensemble Kalman Filter

2015 ◽  
Vol 30 (4) ◽  
pp. 1050-1063 ◽  
Author(s):  
Masaru Kunii
Keyword(s):  
Kalman Filter ◽  
Tropical Cyclone ◽  
Ensemble Kalman Filter ◽  
Shape Parameters ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Sea Level Pressure ◽  
Position Information ◽  
Position Data ◽  
Minimum Sea Level Pressure

Abstract Improving tropical cyclone (TC) forecasts is one of the most important issues in meteorology, but TC intensity forecasting is a challenging task. Because the lack of observations near TCs usually results in degraded accuracy of the initial fields, utilizing TC advisory data in data assimilation typically has started with an ensemble Kalman filter (EnKF). In this study, TC minimum sea level pressure (MSLP) and position information were directly assimilated using the EnKF, and the impacts of these observations were investigated by comparing different assimilation strategies. Another experiment with TC wind radius data was carried out to examine the influence of TC shape parameters. Sensitivity experiments indicated that the direct assimilation of TC MSLP and position data yielded results that were superior to those based on conventional assimilation of TC MSLP as a standard surface pressure observation. Assimilation of TC radius data modified the outer circulation of TCs closer to observations. The impacts of these TC parameters were also evaluated by using the case of Typhoon Talas in 2011. The TC MSLP, position, and wind radius data led to improved TC track forecasts and therefore to improved precipitation forecasts. These results imply that initialization with these TC-related observations benefits TC forecasting, offering promise for the prevention and mitigation of natural disasters caused by TCs.

scholarly journals Impact of Enhanced Satellite-Derived Atmospheric Motion Vector Observations on Numerical Tropical Cyclone Track Forecasts in the Western North Pacific during TPARC/TCS-08

2011 ◽  
Vol 50 (11) ◽  
pp. 2309-2318 ◽  
Author(s):  
Howard Berger ◽  
Rolf Langland ◽  
Christopher S. Velden ◽  
Carolyn A. Reynolds ◽  
Patricia M. Pauley
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
North Pacific ◽  
Western North Pacific ◽  
Forecast Errors ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Rapid Scan ◽  
Atmospheric Motion ◽  
The Impact

AbstractEnhanced atmospheric motion vectors (AMVs) produced from the geostationary Multifunctional Transport Satellite (MTSAT) are assimilated into the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) to evaluate the impact of these observations on tropical cyclone track forecasts during the simultaneous western North Pacific Ocean Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (TPARC) and the Tropical Cyclone Structure—2008 (TCS-08) field experiments. Four-dimensional data assimilation is employed to take advantage of experimental high-resolution (space and time) AMVs produced for the field campaigns by the Cooperative Institute for Meteorological Satellite Studies. Two enhanced AMV datasets are considered: 1) extended periods produced at hourly intervals over a large western North Pacific domain using routinely available MTSAT imagery and 2) limited periods over a smaller storm-centered domain produced using special MTSAT rapid-scan imagery. Most of the locally impacted forecast cases involve Typhoons Sinlaku and Hagupit, although other storms are also examined. On average, the continuous assimilation of the hourly AMVs reduces the NOGAPS tropical cyclone track forecast errors—in particular, for forecasts longer than 72 h. It is shown that the AMVs can improve the environmental flow analyses that may be influencing the tropical cyclone tracks. Adding rapid-scan AMV observations further reduces the NOGAPS forecast errors. In addition to their benefit in traditional data assimilation, the enhanced AMVs show promise as a potential resource for advanced objective data-targeting methods.

scholarly journals Use of Multiensemble Track Clustering to Inform Medium-Range Tropical Cyclone Forecasts

2020 ◽  
Vol 35 (4) ◽  
pp. 1407-1426
Author(s):  
Alex M. Kowaleski ◽  
Jenni L. Evans
Keyword(s):  
Tropical Cyclone ◽  
Forecast Errors ◽  
Mean Error ◽  
Ensemble Mean ◽  
Cluster Properties ◽  
The Mean ◽  
Regression Mixture ◽  
Small Clusters ◽  
The Relationship ◽  
Average Cluster

AbstractTropical cyclone ensemble track forecasts from 153 initialization times during 2017–18 are clustered using regression mixture models. Clustering is performed on a four-ensemble dataset [ECMWF + GEFS + UKMET + CMC (EGUC)], and a three-ensemble dataset that excludes the CMC (EGU). For both datasets, five-cluster partitions are selected to analyze, and the relationship between cluster properties (size, ensemble composition) and 96–144-h cluster-mean error is evaluated. For both datasets, small clusters produce very large errors, with the least populous cluster producing the largest error in more than 50% of forecasts. The mean of the most populous EGUC cluster outperforms the most accurate (EGU) ensemble mean in only 43% of forecasts; however, when the most populous EGUC cluster from each forecast contains ≥30% of the ensemble population, its average cluster-mean error is significantly reduced compared to when the most populous cluster is smaller. Forecasts with a highly populous EGUC cluster also appear to have smaller EGUC-, EGU-, and ECMWF-mean errors. Cluster-mean errors also vary substantially by the ensembles composing the cluster. The most accurate clusters are EGUC clusters that contain threshold memberships of ECMWF, GEFS, and UKMET, but not CMC. The elevated accuracy of EGUC CMC-excluding clusters indicates the potential utility of including the CMC in clustering, despite its large ensemble-mean errors. Pruning ensembles by removing members that belong to small clusters reduces 96–144-h forecast errors for both EGUC and EGU clustering. For five-cluster partitions, a pruning threshold of 10% affects 49% and 35% of EGUC and EGU ensembles, respectively, improving 69%–74% of the forecasts affected by pruning.

Evaluation of a Multigrid Barotropic Tropical Cyclone Track Model

2003 ◽  
Vol 131 (8) ◽  
pp. 1629-1636 ◽  
Author(s):  
Jonathan Vigh ◽  
Scott R. Fulton ◽  
Mark DeMaria ◽  
Wayne H. Schubert
Keyword(s):  
Tropical Cyclone ◽  
Computational Cost ◽  
Track Forecast ◽  
Forecast Errors ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Barotropic Model ◽  
Atlantic Hurricane ◽  
Hurricane Season ◽  
A Current

Abstract The performance of a multigrid barotropic tropical cyclone track model (MUDBAR) is compared to that of a current operational barotropic model (LBAR). Analysis of track forecast errors for the 2001 Atlantic hurricane season shows that MUDBAR gives accuracy similar to LBAR with substantially lower computational cost. Despite the use of a barotropic model, the MUDBAR forecasts show skill relative to climatology and persistence (CLIPER) out to 5 days.

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