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Title: On the Use of Ocean Dynamic Temperature for Hurricane Intensity Forecasting

Abstract

Sea surface temperature (SST) and the Tropical Cyclone Heat Potential (TCHP) are metrics used to incorporate the ocean's influence on hurricane intensification in the National Hurricane Center's Statistical Hurricane Intensity Prediction Scheme (SHIPS). While both SST and TCHP serve as useful measures of the upper-ocean heat content, they do not accurately represent ocean stratification effects. Here we show that replacing SST in the SHIPS framework with a dynamic temperature (Tdy), which accounts for the oceanic negative feedback to the hurricane's intensity arising from storm-induced vertical mixing and sea-surface cooling, improves the model performance. While the model with SST and TCHP explains nearly 41% of the variance in 36-hr intensity changes, replacing SST with Tdy increases the variance explained to nearly 44%. Our results suggest that representation of the oceanic feedback, even through relatively simple formulations such as Tdy, may improve the performance of statistical hurricane intensity prediction models such as SHIPS.

Authors:
 [1];  [2];  [3];  [3];  [4]
  1. Marine Sciences Laboratory, Pacific Northwest National Laboratory, Seattle, Washington
  2. Physical Oceanography Division, Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida
  3. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
  4. Earth Systems Science, Pacific Northwest National Laboratory, Richland, Washington
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1438976
Report Number(s):
PNNL-SA-129386
Journal ID: ISSN 0882-8156; KP1703010
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Weather and Forecasting; Journal Volume: 33; Journal Issue: 2
Country of Publication:
United States
Language:
English

Citation Formats

Balaguru, Karthik, Foltz, Gregory R., Leung, L. Ruby, Hagos, Samson M., and Judi, David R.. On the Use of Ocean Dynamic Temperature for Hurricane Intensity Forecasting. United States: N. p., 2018. Web. doi:10.1175/WAF-D-17-0143.1.
Balaguru, Karthik, Foltz, Gregory R., Leung, L. Ruby, Hagos, Samson M., & Judi, David R.. On the Use of Ocean Dynamic Temperature for Hurricane Intensity Forecasting. United States. doi:10.1175/WAF-D-17-0143.1.
Balaguru, Karthik, Foltz, Gregory R., Leung, L. Ruby, Hagos, Samson M., and Judi, David R.. Sun . "On the Use of Ocean Dynamic Temperature for Hurricane Intensity Forecasting". United States. doi:10.1175/WAF-D-17-0143.1.
@article{osti_1438976,
title = {On the Use of Ocean Dynamic Temperature for Hurricane Intensity Forecasting},
author = {Balaguru, Karthik and Foltz, Gregory R. and Leung, L. Ruby and Hagos, Samson M. and Judi, David R.},
abstractNote = {Sea surface temperature (SST) and the Tropical Cyclone Heat Potential (TCHP) are metrics used to incorporate the ocean's influence on hurricane intensification in the National Hurricane Center's Statistical Hurricane Intensity Prediction Scheme (SHIPS). While both SST and TCHP serve as useful measures of the upper-ocean heat content, they do not accurately represent ocean stratification effects. Here we show that replacing SST in the SHIPS framework with a dynamic temperature (Tdy), which accounts for the oceanic negative feedback to the hurricane's intensity arising from storm-induced vertical mixing and sea-surface cooling, improves the model performance. While the model with SST and TCHP explains nearly 41% of the variance in 36-hr intensity changes, replacing SST with Tdy increases the variance explained to nearly 44%. Our results suggest that representation of the oceanic feedback, even through relatively simple formulations such as Tdy, may improve the performance of statistical hurricane intensity prediction models such as SHIPS.},
doi = {10.1175/WAF-D-17-0143.1},
journal = {Weather and Forecasting},
number = 2,
volume = 33,
place = {United States},
year = {Sun Apr 01 00:00:00 EDT 2018},
month = {Sun Apr 01 00:00:00 EDT 2018}
}