Predictability of tropical cyclone intensity: scale-dependent forecast error growth in high-resolution stochastic kinetic-energy backscatter ensembles

2015 ◽  
Vol 142 (694) ◽  
pp. 43-57 ◽  
Author(s):  
Falko Judt ◽  
Shuyi S. Chen ◽  
Judith Berner
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Kinetic Energy ◽  
Forecast Error ◽  
Error Growth ◽  
Tropical Cyclone Intensity ◽  
Intensity Scale ◽  
Cyclone Intensity

scholarly journals Improving Tropical Cyclone Intensity Forecasts with PRIME

2017 ◽  
Vol 32 (4) ◽  
pp. 1353-1377 ◽  
Author(s):  
Kieran T. Bhatia ◽  
David S. Nolan ◽  
Andrea B. Schumacher ◽  
Mark DeMaria
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Forecast Error ◽  
Absolute Error ◽  
Equation Model ◽  
Logistic Growth ◽  
Growth Equation ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity

Abstract The Prediction of Intensity Model Error (PRIME) forecasting scheme uses various large-scale meteorological parameters as well as proxies for initial condition uncertainty and atmospheric flow stability to provide operational forecasts of tropical cyclone intensity forecast error. PRIME forecasts of bias and absolute error are developed for the Logistic Growth Equation Model (LGEM), Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), Hurricane Weather Research and Forecasting Interpolated Model (HWFI), and Geophysical Fluid Dynamics Laboratory Interpolated Hurricane Model (GHMI). These forecasts are evaluated in the Atlantic and east Pacific basins for the 2011–15 hurricane seasons. PRIME is also trained with retrospective forecasts (R-PRIME) from the 2015 version of each model. PRIME error forecasts are significantly better than forecasts that use error climatology for a majority of forecast hours, which raises the question of whether PRIME could provide more than error guidance. PRIME bias forecasts for each model are used to modify intensity forecasts, and the corrected forecasts are compared with the original intensity forecasts. For almost all basins, forecast intervals, and versions of PRIME, the bias-corrected forecasts achieve significantly lower errors than the original intensity forecasts. PRIME absolute error and bias forecasts are also used to create unique ensembles of the four models. These PRIME-modified ensembles are found to frequently outperform the intensity consensus (ICON), the equally weighted ensemble of DSHP, LGEM, GHMI, and HWFI.

scholarly journals Relating the Skill of Tropical Cyclone Intensity Forecasts to the Synoptic Environment

2013 ◽  
Vol 28 (4) ◽  
pp. 961-980 ◽  
Author(s):  
Kieran T. Bhatia ◽  
David S. Nolan
Keyword(s):  
Tropical Cyclone ◽  
Environmental Conditions ◽  
Forecast Error ◽  
Statistical Significance ◽  
Absolute Error ◽  
Tropical Cyclone Intensity ◽  
Hurricane Intensity ◽  
Cyclone Intensity ◽  
Additional Information ◽  
Tropical Cyclone Intensity Forecasts

Abstract Prior knowledge of the performance of a tropical cyclone intensity forecast holds the potential to increase the value of forecasts for end users. The values of certain dynamical parameters, such as storm speed, latitude, current intensity, potential intensity, wind shear magnitude, and direction of the shear vector, are shown to be related to the error of an individual model forecast. The varying success of each model in the different environmental conditions represents a source of additional information on the reliability of an individual forecast beyond average forecast error. Three hurricane intensity models that were operational for the duration of the five hurricane seasons between 2006 and 2010, as well as the National Hurricane Center official forecast (OFCL), are evaluated for 24-, 48-, and 72-h forecasts in the Atlantic Ocean. The performance of each model is assessed by computing the mean absolute error, bias, and percent skill relative to a benchmark model. The synoptic variables are binned into physically meaningful ranges and then tested individually and in combinations to capture the different regimes that are conducive to forecasts with higher or lower error. The results address conventional wisdom about which environmental conditions lead to better forecasts of hurricane intensity and highlight the different strengths of each model. The statistical significance established between the different bins in each model as well as the corresponding bins for other models indicates there is the potential for error predictions to accompany tropical cyclone intensity forecasts.

scholarly journals On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

2016 ◽  
Vol 73 (9) ◽  
pp. 3739-3747 ◽  
Author(s):  
Kerry Emanuel ◽  
Fuqing Zhang
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Forecast Skill ◽  
Error Growth ◽  
Tropical Cyclone Intensity ◽  
Model Framework ◽  
Cyclone Intensity ◽  
Perfect Model ◽  
Tropical Cyclone Intensity Forecasts ◽  
Initial Intensity

Abstract The skill of tropical cyclone intensity forecasts has improved slowly since such forecasts became routine, even though track forecast skill has increased markedly over the same period. In deciding whether or how best to improve intensity forecasts, it is useful to estimate fundamental predictability limits as well as sources of intensity error. Toward that end, the authors estimate rates of error growth in a “perfect model” framework in which the same model is used to explore the sensitivities of tropical cyclone intensity to perturbations in the initial storm intensity and large-scale environment. These are compared to estimates made in previous studies and to intensity error growth in real-time forecasts made using the same model, in which model error also plays an important role. The authors find that error growth over approximately the first few days in the perfect model framework is dominated by errors in initial intensity, after which errors in forecasting the track and large-scale kinematic environment become more pronounced. Errors owing solely to misgauging initial intensity are particularly large for storms about to undergo rapid intensification and are systematically larger when initial intensity is underestimated compared to overestimating initial intensity by the same amount. There remains an appreciable gap between actual and realistically achievable forecast skill, which this study suggests can best be closed by improved models, better observations, and superior data assimilation techniques.

scholarly journals Numerical Simulation of Rapid Weakening of Hurricane Joaquin with Assimilation of High-Definition Sounding System Dropsondes during the Tropical Cyclone Intensity Experiment: Comparison of Three- and Four-Dimensional Ensemble–Variational Data Assimilation

2019 ◽  
Vol 34 (3) ◽  
pp. 521-538 ◽  
Author(s):  
Shixuan Zhang ◽  
Zhaoxia Pu
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Inner Core ◽  
Surface Wind ◽  
Naval Research ◽  
Tropical Cyclone Intensity ◽  
High Definition ◽  
Background Error ◽  
Cyclone Intensity

Abstract Observations from High-Definition Sounding System (HDSS) dropsondes, collected for Hurricane Joaquin during the Office of Naval Research Tropical Cyclone Intensity (TCI) field experiment in 2015, are assimilated into the NCEP Hurricane Weather Research and Forecasting (HWRF) Model. The Gridpoint Statistical Interpolation (GSI)-based hybrid three-dimensional and four-dimensional ensemble–variational (3DEnVar and 4DEnVar) data assimilation configurations are compared. The assimilation of HDSS dropsonde observations can help HWRF initialization by generating consistent analysis between wind and pressure fields and can also compensate for the initial maximum surface wind errors in the absence of initial vortex intensity correction. Compared with GSI–3DEnVar, the assimilation of HDSS dropsonde observations using GSI–4DEnVar generates a more realistic initial vortex intensity and reproduces the rapid weakening (RW) of Hurricane Joaquin, suggesting that the assimilation of high-resolution inner-core observations (e.g., HDSS dropsonde data) based on an advanced data assimilation method (e.g., 4DEnVar) can potentially outperform the vortex initialization scheme currently used in HWRF. Additionally, the assimilation of HDSS dropsonde observations can improve the simulation of vortex structure changes and the accuracy of the vertical motion within the TC inner-core region, which is essential to the successful simulation of the RW of Hurricane Joaquin with HWRF. Additional experiments with GSI–4DEnVar in different configurations also indicate that the performance of GSI–4DEnVar can be further improved with a high-resolution background error covariance and a denser observational bin.

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