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.
Highlights
Satellite soundings have been providing global measurements of the atmospheric temperature and humidity for several decades with operational measurements dating back to 1972
If these effects are not considered in the temperature profile retrieval, it can lead to an underestimation of tropospheric temperatures and an overestimation of stratospheric temperatures in the High-resolution Infrared Radiation Sounder (HIRS) measurements in an increasing CO2 environment
The development combines a neural network technique with the modeling of long-term CO2 effect and a calibration scheme based on conventional observations
Summary
Satellite soundings have been providing global measurements of the atmospheric temperature and humidity for several decades with operational measurements dating back to 1972. Temperature and humidity profiles are derived from these HIRS longwave channel observations for long-term studies. In the early years of HIRS observations, a physically-based satellite temperature sounding retrieval system was developed at Goddard Laboratory for Atmospheric. Observation Satellite Operational Vertical Sounder (ATOVS) Processing Package (IAPP) was developed for retrieving atmospheric temperature and moisture profiles, total ozone, and other parameters in real-time [4]. To build a long-term dataset for climate applications, development of a Climate Data Record (CDR) for temperature and humidity profiles from inter-satellite calibrated HIRS data is underway. The retrievals at standard pressure levels are compared with observations not used during algorithm training, and the results are discussed
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