For more than a decade, the remote sensing community has called for longwave spectral measurements of the Earth system to facilitate closing the radiation budget. In an effort to address this large gap in Earth-observing capability, NASA has supported the development and implementation of the Polar Radiant Energy in the Far-Infrared (FIR) Experiment (PREFIRE), which realizes two miniaturized thermal IR spectrometers (TIRSs), one on each of two polar orbiting small satellites with different ascending node crossing times. The spectral radiances measured by the TIRS instruments are utilized to extract cloud presence, atmospheric state, surface properties, and the associated top-of-atmosphere (TOA) spectral fluxes at both poles. This article describes the major system and algorithm components and introduces the geophysical products. PREFIRE data will be used to inform and improve ice-sheet and coupled-Earth system models to better predict the future state of the Earth’s poles.

We have developed a$k$-coefficient retrieval approach for Atmospheric Infrared Sounder (AIRS) observations, using AIRS cloud-cleared radiances (ACCRs) as input. This new approach takes advantage of the available ACCR, reduces computational expense, offers an efficient and accurate cirrus cloud retrieval alternative for hyperspectral infrared (IR) observations, and is potentially applicable to the compilation of a long-term cirrus cloud climatology from hyperspectral IR observations. The retrieval combines a lookup-table method coupled to a residual minimization scheme using observed cloudy and cloud-cleared AIRS radiances as input. Six AIRS channels between 766 and 832$\hbox{cm}^{-1}$with minimal water vapor absorption/emission have been selected, and their spectral radiances have been demonstrated to be sensitive to both cirrus cloud optical depth$(\tau_{c})$and ice crystal effective particle size$(D_{e})$. The capability of the$k$-coefficient approach is demonstrated by comparison with a more accurate retrieval program, which combines the delta-four stream (D4S) approximation with the currently operational Stand-alone AIRS Radiative Transfer Algorithm (SARTA). The distribution patterns and the range of retrieved cloud parameters from the$k$-coefficient approach are nearly identical to those from SARTA$+$D4S retrievals, with minor differences traced to uncertainties in parameterized cloudy radiances in the$k$-coefficient approach and in the ACCR. The$k$-coefficient approach has also been applied to four AIRS granules over North Central China, Mongolia, and Siberia containing a significant presence of cirrus clouds, and its results are quantitatively compared to simultaneous Moderate Resolution Imaging Spectroradiometer/Aqua cirrus cloud retrievals. Finally, AIRS retrieved$\tau_{c}$and$D_{e}$are consistent with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat derived values for semitransparent cirrus clouds, with more significant differences in thicker cirrus and multilayer clouds.