On the Characterization and Mitigation of Noise in Space-Borne Microwave Sounding Instruments

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

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Publication Details

Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2025-01-01
DOI
https://doi.org/10.1109/tgrs.2025.3616644
Citations
2
Primary Topic
Speech and Audio Processing
Type
article
Field-Weighted Citation Impact
2.97
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article

On the Characterization and Mitigation of Noise in Space-Borne Microwave Sounding Instruments

James Kam, Alexandra Bringer, Cheng‐Hsuan Lyu, Matthew Sammons et al.
2 citations
IEEE Transactions on Geoscience and Remote Sensing
Speech and Audio Processing
2.97
article

On the Characterization and Mitigation of Noise in Space-Borne Microwave Sounding Instruments

James Kam, Alexandra Bringer, Cheng‐Hsuan Lyu, Matthew Sammons, James Fuentes, Xingming Liang, Ninghai Sun, Saji Abraham, Hu Yang, Edward Kim
article en
2 citations

Abstract

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

IEEE Transactions on Geoscience and Remote SensingVol. 63
Goddard Space Flight Center (US), Wyle (United States) (US), Northrop Grumman (United States) (US), Stinger Ghaffarian Technologies (United States) (US), The Eden Rivers Trust (GB), Astrophysics Science Division (US), Earth System Science Interdisciplinary Center (US), Science Systems and Applications (United States) (US), University of Maryland, College Park (US), Morgan State University (US), Philadelphia University (US)
Openalex Percentile: Top 8%
Speech and Audio Processing
2.97
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