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Remote Sens. 2016, 8(4), 280; doi:10.3390/rs8040280

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

1
NOAA’s National Centers for Environmental Information (NCEI), 151 Patton Avenue, Asheville, NC 28801, USA
2
Cooperative Institute for Climate and Satellites—North Carolina (CICS-NC), North Carolina State University, 151 Patton Avenue, Asheville, NC 28801, USA
3
COSMIC Project Office, University Corporation for Atmospheric Research, Boulder, CO 80307, USA
4
Joint Institute for the Study of the Atmosphere and Ocean, Seattle, WA 98105, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Wenze Yang, Viju John, Hui Lu, Richard Müller and Prasad S. Thenkabail
Received: 16 January 2016 / Revised: 18 March 2016 / Accepted: 21 March 2016 / Published: 25 March 2016
(This article belongs to the Special Issue Satellite Climate Data Records and Applications)
View Full-Text   |   Download PDF [3648 KB, uploaded 25 March 2016]   |  

Abstract

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. View Full-Text
Keywords: temperature; humidity; HIRS; retrieval algorithms and methods; satellite observation temperature; humidity; HIRS; retrieval algorithms and methods; satellite observation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Shi, L.; Matthews, J.L.; Ho, S.-P.; Yang, Q.; Bates, J.J. Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS Observations. Remote Sens. 2016, 8, 280.

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