Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS Observations

Shi, Lei; Matthews, Jessica; Ho, Shu-peng; Yang, Qiong; Bates, John

doi:10.3390/rs8040280

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS Observations

2016
By Shi, Lei ; Matthews, Jessica ; Ho, Shu-peng ; ...
Source: Remote Sens. 8(4), 1-17

[PDF-3.56 MB]

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Journal Title:

Remote Sensing
Personal Author:

Shi, Lei ; Matthews, Jessica ; Ho, Shu-peng ; Yang, Qiong ; Bates, John

Shi, Lei ; Matthews, Jessica ; Ho, Shu-peng ; Yang, Qiong ; Bates, John Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) ; CICS (Cooperative Institute for Climate and Satellites) ; JISAO (Joint Institute for the Study of the Atmosphere and Ocean)

NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) ; CICS (Cooperative Institute for Climate and Satellites) ; JISAO (Joint Institute for the Study of the Atmosphere and Ocean) Less -
Description:

A project for deriving temperature and humidity profiles from High-resolution Infrared Radiation Sounder (HIRS) observations is underway to build a long-term dataset for climate applications. The retrieval algorithm development of the project includes a neural network retrieval scheme, a two-tiered cloud screening method, and a calibration using radiosonde and Global Positioning System Radio Occultation (GPS RO) measurements. As atmospheric profiles over high surface elevations can differ significantly from those over low elevations, different neural networks are developed for three classifications of surface elevations. The significant impact from the increase of carbon dioxide in the last several decades on HIRS temperature sounding channel measurements is accounted for in the retrieval scheme. The cloud screening method added one more step from the HIRS-only approach by incorporating the Advanced Very High Resolution Radiometer (AVHRR) observations to assess the likelihood of cloudiness in HIRS pixels. Calibrating the retrievals with radiosonde and GPS RO reduces biases in retrieved temperature and humidity. Except for the lowest pressure level which exhibits larger variability, the mean biases are within ±0.3 °C for temperature and within ±0.2 g/kg for specific humidity at standard pressure levels, globally. Overall, the HIRS temperature and specific humidity retrievals closely align with radiosonde and GPS RO observations in providing measurements of the global atmosphere to support other relevant climate dataset development.
Keywords:

[+]

Algorithms Atmospheric Temperature Humidity Quantitative Research Remote Sensing
Source:

Remote Sens. 8(4), 1-17
DOI:

https://doi.org/10.3390/rs8040280
Document Type:

Journal Article
Funding:

Grant no. NA14NES432003
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CC BY
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urn:sha-512:5733da0f11bb6738538c09ee63ea4a498c108a865610d920643d4dee2b610d16a003c6c9d83d070f16b6802ed853c74766ba603402d412ad2fbbd44eaaaf821f
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Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS Observations

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