Lightning Prediction for Space Launch Using Machine Learning Based Off of Electric Field Mills and Lightning Detection and Ranging Data

Abstract

Last year in 2019, USAF had 48 projected launches out of CC, twice as much as the current standard, 24. Even with such lofty goals, the USAF fell short in one of its busiest years in space launches, not even reaching 24 launches. KSC and CCAFS are in an area of frequent lightning strikes, which is the leading cause for delayed and cancelled space launches and the main obstacle in meeting launch goals. The 45th WS ensures that any weather safety requirements are met during pre-launch and actual space launch. The 45th WS analyzes 31 EFMs data among other sources to predict lightning occurrence. This study hopes to build upon previous EFM studies while taking a different approach.

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Document Details

Document Type
Technical Report
Publication Date
Mar 26, 2020
Accession Number
AD1101340

Entities

People

  • Anson Cheng

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Programming
  • Data Science
  • Data Sets
  • Detection
  • Dimensionality Reduction
  • Electric Fields
  • Experimental Design
  • Governments
  • Information Science
  • Machine Learning
  • Neural Networks
  • Recurrent Neural Networks
  • Sampling
  • Statistical Algorithms
  • United States Government

Fields of Study

  • Physics

Readers

  • Atmospheric Science/Meteorology
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • Space