Detection and tracking of tropical cyclone using NCEP-GFS model analysis and forecasts

Abstract The impact of assimilating synoptic surveillance dropwindsonde data on the analysis and forecast of the structure and intensity of Tropical Storm Karen (2013) was examined. Data-denial experiments were conducted using the NCEP hybrid 3D ensemble–variational GSI and forecasts were made using the NCEP GFS model. The assimilation of dropwindsonde data resulted in a slightly more tilted tropical cyclone vortex, stronger vertical wind shear, and more upper-tropospheric dry air west of Karen in the initial conditions. These differences grew with time in the GFS forecasts, and resulted in a weaker and more sheared vortex by 24 h in the forecast that included the dropwindsonde data. After 24 h, the cyclone reintensified in the experiment where dropwindsonde data were excluded, likely because of moist processes in a favorable region for synoptic-scale ascent ahead of a baroclinic trough. In contrast, the forecast including the dropwindsonde data kept Karen weak and also did a better job forecasting the structure and track of Karen. These results suggest that differences in the analysis and short-term evolution of Karen and the environment due to the dropwindsonde data played a role in the longer-term structure and intensity of the cyclone, including the distribution and magnitude of associated diabatic heating. These results strongly suggest that a systematic study be undertaken to examine the impact of these data on tropical cyclone structure and intensity, since previous work has focused largely on the impact on track.

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Western North Pacific Tropical Cyclone Tracks in CMIP5 Models: Statistical Assessment Using a Model-Independent Detection and Tracking Scheme

Journal of Climate ◽

10.1175/jcli-d-18-0785.1 ◽

2019 ◽

Vol 32 (21) ◽

pp. 7191-7208 ◽

Cited By ~ 9

Author(s):

Samuel S. Bell ◽

Savin S. Chand ◽

Suzana J. Camargo ◽

Kevin J. Tory ◽

Chris Turville ◽

...

Keyword(s):

Tropical Cyclone ◽

North Pacific ◽

Western North Pacific ◽

Clustering Algorithm ◽

Vertical Wind Shear ◽

The Philippines ◽

Cmip5 Models ◽

Climate Projections ◽

Detection And Tracking ◽

Model Independent

Abstract Past studies have shown that tropical cyclone (TC) projection results can be sensitive to different types of TC tracking schemes, and that the relative adjustments of detection criteria to accommodate different models may not necessarily provide a consistent platform for comparison of projection results. Here, future climate projections of TC activity in the western North Pacific basin (WNP, defined from 0°–50°N and 100°E–180°) are assessed with a model-independent detection and tracking scheme. This scheme is applied to models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) forced under the historical and representative concentration pathway 8.5 (RCP8.5) conditions. TC tracks from the observed records and independent models are analyzed simultaneously with a curve-clustering algorithm, allowing observed and model tracks to be projected onto the same set of clusters (k = 9). Four of the nine clusters were projected to undergo significant changes in TC frequency. Straight-moving TCs in the South China Sea were projected to significantly decrease. Projected increases in TC frequency were found poleward of 20°N and east of 160°E, consistent with changes in ascending motion, as well as vertical wind shear and relative humidity respectively. Projections of TC track exposure indicated significant reductions for southern China and the Philippines and significant increases for the Korean peninsula and Japan, although very few model TCs reached the latter subtropical regions in comparison to the observations. The use of a fundamentally different detection methodology that overcomes the detector/tracker bias gives increased certainty to projections as best as low-resolution simulations can offer.

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Mathematical model analysis of missile detection and tracking

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

10.1109/iccasm.2010.5622919 ◽

2010 ◽

Author(s):

Zhenmin Qiao ◽

Minchai Hao

Keyword(s):

Mathematical Model ◽

Model Analysis ◽

Detection And Tracking ◽

Mathematical Model Analysis

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Comparison of Aura MLS Water Vapor Measurements with GFS and NAM Analyses in the Upper Troposphere–Lower Stratosphere

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2009jtecha1317.1 ◽

2010 ◽

Vol 27 (2) ◽

pp. 274-289 ◽

Cited By ~ 2

Author(s):

Le Van Thien ◽

William A. Gallus ◽

Mark A. Olsen ◽

Nathaniel Livesey

Keyword(s):

Water Vapor ◽

North American ◽

Lower Stratosphere ◽

Model Analysis ◽

Weather Research And Forecasting ◽

Upper Troposphere ◽

Mixing Ratios ◽

Four Seasons ◽

Gfs Model ◽

The Tropics

Abstract Water vapor mixing ratios in the upper troposphere and lower stratosphere measured by the Aura Microwave Limb Sounder (MLS) version 2.2 instrument have been compared with Global Forecast System (GFS) analyses at five levels within the 300–100-hPa layer and North American Mesoscale (NAM) model analyses at six levels within the 300–50-hPa layer over the two years of 2005 and 2006 at four analysis times (e.g., 0000, 0600, 1200, and 1800 UTC). Probability density functions of the vapor mixing ratios suggest that both analyses are often moister than Aura MLS values, but NAM model analyses agree somewhat better with Aura MLS measurements than GFS model analyses over the same North American domain at the five common levels. Examining five subsets of the global GFS domain, the GFS model analysis is moister than Aura MLS estimates everywhere but at 150 and 100 hPa in all regions outside of the tropics. NAM model analysis water vapor mixing ratios exceeded the Aura MLS values at all levels from 250 to 150 hPa in all four seasons of both years and some seasons at 100 and 50 hPa. Moist biases in winter and spring of both years were similar at all levels, but these moist biases in summer and fall were smaller in 2005 than in 2006 at all levels. These differences may be due to the change in the NAM from using the Eta Model to using the Weather Research and Forecasting model (WRF) in June 2006.

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