A deep-learning model to predict thunderstorms within 400 km2 South Texas domains
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

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

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

The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.
i

A deep-learning model to predict thunderstorms within 400 km2 South Texas domains

Filetype[PDF-2.89 MB]


Details You May Also Like

Details:

  • Journal Title:
    Meteorological Applications
  • Personal Author:
  • NOAA Program & Office:
    NWS (National Weather Service)
  • Description:
    A deep-learning neural network (DLNN) model was developed to predict thunderstorm occurrence within 400 km2 South Texas domains for up to 15 hr (±2 hr accuracy) in advance. The input features were chosen primarily from numerical weather prediction model output parameters/variables; cloud-to-ground lightning served as the target. The deep-learning technique used was the stacked denoising autoencoder (SDAE) in order to create a higher order representation of the features. Logistic regression was then applied to the SDAE output to train the predictive model. An iterative technique was used to determine the optimal SDAE architecture. The performance of the optimized DLNN classifiers exceeded that of the corresponding shallow neural network models, a classifier via a combination of principal component analysis and logistic regression, and operational weather forecasters, based on the same data set.
  • Keywords:
  • Source:
    Meteorological Applications, 27(2)
  • DOI:
  • Document Type:
    Journal Article
  • Place as Subject:
    Texas
  • Rights Information:
    CC BY
  • 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

Version 3.27.2