Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Diagnosing Tropical Cyclone Rapid Intensification Using Kernel Methods and Reanalysis Datasets

Filetype[PDF-305.57 KB]


Details You May Also Like

Details:

  • Journal Title:
    Procedia Computer Science
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    Tropical cyclone rapid intensification (RI) continues to be a problem that eludes operational forecasters. Recent work in this area has revealed the value of applying machine learning techniques to classifying storms as RI or non-RI at 24-hours lead time. However, that work showed that differing reanalysis datasets represented the storms in unique ways, offering different discrimination capability and unique predictor sets that are important for RI. The scope of this research is to identify factors important for RI that are consistent among three reanalysis datasets, as these are likely the fields that will provide the greatest discrimination capability. An S-mode rotated principal component analysis was used to formulate unique patterns within RI and non-RI storms, and the resulting RPC scores were used to train a support vector machine classification algorithm that yielded binary RI occurrence output. Base-state meteorological variables (geopotential height, temperature, u and v wind components, vertical velocity, and relative humidity) at single horizontal levels were tested individually as predictors for the SVM. Base-state fields that were consistently good at discriminating RI events from non-RI events among all three reanalysis datasets were deemed most useful for RI classification and will be considered for future forecast applications.
  • Source:
    Procedia Computer Science, 61, 422-427.
  • DOI:
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA11OAR4320199
  • Rights Information:
    CC BY-NC-ND
  • Compliance:
    Submitted
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files

More +

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.26