Algorithm Development of Temperature and Humidity Profile Retrievals for Long-Term HIRS Observations
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
... icantly 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. Remote Sens. 2016, 8, 280 2 of 17 obtaining atmospheric data since then onboard the subsequent NOAA series of satellites and on the meteorological operational satellite program (Metop) series operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). Routine microwave soundings of the atmosphere began in 1998 with the Advanced Microwave Sounding Unit A (AMSU-A) and Unit B (AMSU-B), and more recently with the microwave humidity sounder (MHS) and the Advanced Technology Microwave Sounder (ATMS). Hyperspectral sounders, such as the Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-track Infrared Sounder (CrIS), on recent satellites marked the new era of satellite infrared sounders. Among these satellite soundings, HIRS observations span the longest time period (1978 to present). The HIRS instrument has twenty channels, including twelve channels in the longwave regime, seven channels in the shorter wave regime, and one shortwave channel. The HIRS footprint is approximately 20 km and 10 km at nadir for the HIRS/2 and HIRS/3 instruments, respectively. Among the longwave channels, channels 1 to 7 are in the carbon dioxide (CO 2 ) absorption band to measure atmospheric temperatures from near-surface to stratosphere, channel 8 is a window channel for surface temperature observation, channel 9 is an ozone channel, and channels 10-12 are for water vapor signals at the near-surface, mid-troposphere, and upper troposphere, respectively. In the present study, temperature and humidity profiles are derived from these HIRS longwave channel observations for long-term studies. HIRS observations have been used to derive temperature and humidity profiles since its initial operation. For example, in the early years of HIRS observations, a physically-based satellite temperature sounding retrieval system was developed at Goddard Laboratory for Atmospheric Sciences to determine atmospheric temperature profiles along with several surface variables . Later, a physical-statistical algorithm, named the Improved Initialization Inversion (3I) , for retrieving meteorological parameters from TIROS-N satellite data at a spatial resolution of 100 km was built. Updated from an earlier version, the International Advanced Television and Infrared 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  . NOAA has been maintaining an operational HIRS sounding product system [5, 6] . These studies advanced our knowledge on the advantages and limitations of the HIRS observations. However, as many of the past studies were geared toward operational or near-real-time weather applications, the produced datasets may not be suitable for climate research. 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 development is different from weather applications in that the long-term consistency of the algorithm and data is a key component. The project consists of several aspects of development, including inter-satellite calibration, retrieval algorithm development, and evaluation of the consistency of the retrievals with independent observational sources. The focus of the present study is on the retrieval algorithm development. One of the major drivers of the development is to build a temperature and humidity dataset that can be used in the construction of relevant CDR products, such as cloud products in the International Satellite Cloud Climatology Project (ISCCP) [7, 8] . This requires the temperature and humidity dataset to have a long-term consistency for different climate regimes. During the past three decades the atmospheric CO 2 concentration increased substantially. The increase of CO 2 has a significant impact on HIRS channel radiances [9, 10] . The development of the retrieval scheme aims to account for the effect of CO 2 on the long-term HIRS observation and to obtain consistency between the upper air retrievals and observations from conventional sources (i.e., homogeneous radiosonde observations in the troposphere and Global Positioning System Radio Occultation (GPS RO) temperatures in the stratosphere). In the following sections the retrieval scheme is described. The retrievals at standard pressure levels are compared with observations not used during algorithm training, and the results are discussed.