ML-driven cross-calibration of microwave and infrared instruments

Moradi, Isaac; Kalluri, Satya

doi:10.1088/3049-4753/ae1cce

i

ML-driven cross-calibration of microwave and infrared instruments

2025
By Moradi, Isaac ; Kalluri, Satya

Details

Journal Title:

Machine Learning: Earth
Personal Author:

Moradi, Isaac ; Kalluri, Satya
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CISESS (Cooperative Institute for Satellite Earth System Studies)
Description:

Microwave (MW) and infrared (IR) observations are fundamental for weather forecasting, climate monitoring, and atmospheric sounding. Traditionally, inter-calibration efforts have focused on harmonizing measurements between similar instruments or within the same spectral domain. In this study, we explore the use of machine learning (ML) to directly cross-calibrate MW and IR observations, leveraging data from the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) onboard the NOAA-20 (N20) and Suomi-NPP (NPP) satellites. We demonstrate that an ML-model trained using CrIS observations as input (2211 channels) can predict the ATMS observations (22 channels) with a bias less than 0.1 K and a the coefficients of determination generally greater than 90%. Our results highlight the potential of ML-based cross-calibration approaches for bridging instruments across different spectral regions, provided that the input observations contain all necessary information to reconstruct the target observations. The proposed methodology offers a path forward for the cross-calibration of instruments operating in different spectral domains.
Keywords:

Infrared Radiation Microwaves Remote Sensing Infrared Radiation Remote Sensing Satellite observations
Source:

Mach. Learn.: Earth 1 (2025) 015012
DOI:

https://doi.org/10.1088/3049-4753/ae1cce
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA24NESX432C0001
License:

https://creativecommons.org/publicdomain/zero/1.0/
Rights Information:

CC BY
Compliance:

Submitted
Main Document Checksum:

urn:sha-512:b4d0431c1a653a546deecb4826a7720a7e4b73a567b15c52dfc10f304c6c4ca470df02863b59986cf0b101b0afb7888e3444a5563594771f57bcf9dcd2347a1e
Download URL:

https://repository.library.noaa.gov/view/noaa/73398/noaa_73398_DS1.pdf
File Type:

[PDF - 2.54 MB ]

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