Prediction of Atmospheric Noise Temperature at the Deep Space Network With Machine Learning-Reference-Cited by-同舟云学术

Prediction of Atmospheric Noise Temperature at the Deep Space Network With Machine Learning

Published:2022-11 Issue:11 Volume:57 Page:
ISSN:0048-6604
Container-title:Radio Science
language:en
Short-container-title:Radio Science

Author:

Wu Longtao¹^ORCID,Morabito David D.¹^ORCID,Teixeira Joaquim P.¹,Huang Lei¹²^ORCID,Nguyen Hai M.¹^ORCID,Su Hui¹³^ORCID,Soriano Melissa A.¹,Pan Lei¹,Kahan Daniel S.¹,Bhawar Rohini⁴

Affiliation:

1. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

2. Now at University of California, Los Angeles Los Angeles CA USA

3. Now at Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong China

4. Department of Atmospheric and Space Sciences Savitribai Phule Pune University Pune India

Publisher

American Geophysical Union (AGU)

Subject

Electrical and Electronic Engineering,General Earth and Planetary Sciences,Condensed Matter Physics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022RS007483

Reference24 articles.

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2. Dynamical Link Budget in Satellite Communications at Ka-Band: Testing Radiometeorological Forecasts with Hayabusa2 Deep-Space Mission Support Data

3. Optimizing Data Volume Return for Ka-Band Deep Space Links Exploiting Short-Term Radiometeorological Model Forecast

4. Davarian F. Shambayati S. &Slobin S.(2004).Deep space Ka‐band link management and Mars reconnaissance orbiter: Long term weather statistics versus forecasting. InProc. IEEE(Vol.92(12) pp.312–329).

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