THIS GRANT IS A CONTINUATION OF N00014-12-1-0912 Physics Constrained Stochastic-Statistical Models for Extended Range Environmental Prediction

Abstract

Funding is provided to create new simplified models which predict for the first time the key features of the MJO, new physics constrained nonlinear low order stochastic modeling, new physics constrained data mining of large data sets to reveal intermittency as well as low frequency variability, and finally, novel strategies for real time data assimilation of complex turbulent signals such as moisture coupled tropical waves.

Document Details

Document Type
DoD Grant Award
Publication Date
Nov 23, 2016
Source ID
N000141612161

Entities

People

  • Andrew Majda

Organizations

  • New York University
  • Office of Naval Research
  • United States Navy

Tags

Fields of Study

  • Physics

Readers

  • Distributed Systems and Data Platform Development
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms