Coupled initialization in an ocean-atmosphere tropical cyclone prediction system

2013 ◽  
Vol 140 (678) ◽  
pp. 82-95 ◽  
Author(s):  
Paul A. Sandery ◽  
Terence J. O'Kane
Keyword(s):  
Tropical Cyclone ◽  
Prediction System ◽  
Ocean Atmosphere ◽  
Cyclone Prediction

scholarly journals A review of the development and implementation of a tropical cyclone prediction system for North Indian Ocean in a multi-model ensemble framework

MAUSAM ◽  
2021 ◽  
Vol 72 (1) ◽  
pp. 57-76
Author(s):  
S. SARANYA GANESH ◽  
S. ABHILASH ◽  
S. JOSEPH ◽  
A. DEY ◽  
R. MANDAL ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
North Indian Ocean ◽  
Prediction System ◽  
Model Ensemble ◽  
Cyclone Prediction

Performance of a Dynamic Initialization Scheme in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC)

2011 ◽  
Vol 26 (5) ◽  
pp. 650-663 ◽  
Author(s):  
Eric A. Hendricks ◽  
Melinda S. Peng ◽  
Xuyang Ge ◽  
Tim Li
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Initial Conditions ◽  
Layer Structure ◽  
North Pacific Ocean ◽  
Surface Wind ◽  
Naval Research ◽  
Prediction System ◽  
Ocean Atmosphere ◽  
Initialization Scheme

Abstract A dynamic initialization scheme for tropical cyclone structure and intensity in numerical prediction systems is described and tested. The procedure involves the removal of the analyzed vortex and, then, insertion of a new vortex that is dynamically initialized to the observed surface pressure into the numerical model initial conditions. This new vortex has the potential to be more balanced, and to have a more realistic boundary layer structure than by adding synthetic data in the data assimilation procedure to initialize the tropical cyclone in a model. The dynamic initialization scheme was tested on multiple tropical cyclones during 2008 and 2009 in the North Atlantic and western North Pacific Ocean basins using the Naval Research Laboratory’s tropical cyclone version of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS-TC). The use of this initialization procedure yielded significant improvements in intensity forecasts, with no degradation in track performance. Mean absolute errors in the maximum sustained surface wind were reduced by approximately 5 kt for all lead times up to 72 h.

The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System – Tropical Cyclone ensemble (COAMPS-TC ensemble)

2021 ◽  
Author(s):  
William A. Komaromi ◽  
Patrick A. Reinecke ◽  
James D. Doyle ◽  
Jonathan R. Moskaitis
Keyword(s):  
Tropical Cyclone ◽  
Boundary Conditions ◽  
Skill Score ◽  
Horizontal Resolution ◽  
Naval Research ◽  
Lead Times ◽  
Prediction System ◽  
Intensity Forecast ◽  
Probabilistic Forecasts ◽  
Ocean Atmosphere

AbstractThe 11-member Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) ensemble has been developed by the Naval Research Laboratory (NRL) to produce probabilistic forecasts of tropical cyclone (TC) track, intensity and structure. All members run with a storm-following inner grid at convection-permitting 4-km horizontal resolution. The COAMPS-TC ensemble is constructed via a combination of perturbations to initial and boundary conditions, the initial vortex, and model physics to account for a variety of different sources of uncertainty that affect track and intensity forecasts. Unlike global model ensembles, which do a reasonable job capturing track uncertainty but not intensity, mesoscale ensembles such as the COAMPS-TC ensemble are necessary to provide a realistic intensity forecast spectrum.The initial and boundary condition perturbations are responsible for generating the majority of track spread at all lead times, as well as the intensity spread from 36-120 h. The vortex and physics perturbations are necessary to produce meaningful spread in the intensity prediction from 0-36 h. In a large sample of forecasts from 2014-2017, the ensemble-mean track and intensity forecast is superior to the unperturbed control forecast at all lead times, demonstrating a clear advantage to running an ensemble versus a deterministic forecast. The spread-skill relationship of the ensemble is also examined, and is found to be very well calibrated for track, but is under-dispersive for intensity. Using a mixture of lateral boundary conditions derived from different global models is found to improve upon the spread-skill score for intensity, but it is hypothesized that additional physics perturbations will be necessary to achieve realistic ensemble spread.

scholarly journals Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization

2011 ◽  
Vol 24 (12) ◽  
pp. 2963-2982 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Silvio Gualdi ◽  
Enrico Scoccimarro ◽  
Simona Masina
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Wind Shear ◽  
Large Scale ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Convective Available Potential Energy ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Prediction System

Abstract This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992–2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs’ occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

scholarly journals REAL-TIME TROPICAL CYCLONE PREDICTION USING COAMPS-TC

2012 ◽  
pp. 15-28 ◽  
Author(s):  
J. D. DOYLE ◽  
Y. JIN ◽  
R. M. HODUR ◽  
S. CHEN ◽  
H. JIN ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Cyclone Prediction
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