The Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) consists of an Earthviewing reflected solar (RS) spectrometer that will measure the Earth-reflected solar radiation from International Space Station with an SI-traceable radiometric uncertainty of 0.3% (1-sigma). The high-accuracy CPF measurements will provide an in-orbit reference for intercalibrating other spaceflight RS instruments. The CPF intercalibration team has been tasked to develop a state-of-the-art approach to calibrate the shortwave channel (300-5000 nm) of the Clouds and the Earth’s Radiant Energy System (CERES) instrument and the reflective solar bands (RSB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, both onboard the NOAA-20 satellite, against the CPF benchmark measurements. The aimed intercalibration methodology uncertainty for both the target instruments is also 0.3%. To meet this stringent intercalibration accuracy, the CPF team has developed methods for mitigating the impacts of spatial, spectral, and angular differences between the intercalibration footprints from the CPF and target instruments. To further alleviate uncertainty, the CPF team will employ Polarization Distribution Models (PDMs) to characterize the polarization state of the Earth-reflected radiance as a function of the intercalibration footprint scene type, solar and viewing geometry, and wavelength. The PDMs will assist in identifying low-polarized scene radiances for meticulously intercalibrating the polarization sensitive VIIRS instrument against the significantly-less polarization-sensitive CPF instrument. This paper will highlight the CPF mission overview, the details of the CPF intercalibration approach, and additional outcomes of the CPF intercalibration studies that may benefit the broader remote sensing community.
A principal component based accurate fast vector radiative transfer model with very high resolution in the UV (200 nm) to NIR (2500 nm) has been developed. This model greatly reduces the number of necessary radiative transfer calculations, and no time-consume convolution process is needed to get the final radiance spectrum. The error in the obtained Stokes component is usually smaller than 0.1% and is much smaller than the uncertainty in the measured solar irradiance.
In this paper, we will present a Single Field-of-view (FOV) Sounder Atmospheric Product (SiFSAP) and a Climate Fingerprinting Sounder Product (ClimFiSP). Both products are derived from hyperspectral Infrared remote sensors such as Atmospheric Infrared Sounder (AIRS) and Cross-track Infrared Sounder (CrIS). Compared to the current operational AIRS and CrIS level-2 algorithms, the SiFSAP algorithm has 3 advantages, which are listed in the technical review abstract. We have developed a ClimFiSP product, which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. Again, the ClimFiSP algorithm overcomes many issues associated with traditional level-1 to level-2 and then to level-3 approach. It can be used to derive climate change signals from multiple satellite sensors using consistent radiative kernels and a robust spectral fingerprinting method. We have applied this method to both AIRS and CrIS data and generated decade-long climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP are being transitioned to NASA data centers for routine generations of both level-2 and level-3 products.
A Principal Component-based Radiative Transfer Model (PCRTM) has been developed to simulate hyper-spectral remote sensing data in the cloudy atmosphere from far IR to visible and UV spectral regions quickly and accurately. Multi-scatterings of multiple layers of clouds/aerosols are included in the model. The PCRTM model is capable of simulating top of atmospheric radiance or reflectance spectral from 50 wavenumber to 30000 wavenumber. We will describe applications of the PCRTM model for solving various atmospheric remote sensing problems such as atmospheric temperature, moisture, and trace gas profiles retrievals, spectral fingerprinting, inter-satellite calibration, and instrument trade studies.
Fast and accurate radiative transfer model is the key for satellite data analysis, numerical weather prediction, and observation system simulation experiments for climate study applications. We have developed a Principal Component-based radiative transfer model (PCRTM) which can simulate radiative transfer in the cloudy atmosphere from far IR to visible and UV spectral regions quickly and accurately. Multi-scattering of multiple layers of clouds/aerosols is included in the model. A hybrid stream discrete multiple scattering scheme is used to minimizing the number calculation need. The computation speed is 3 to 4 orders of magnitude faster than the medium speed correlated-k option MODTRAN5 and LBLRTM. The PCRTM calculated radiance spectra agree with the Modtran and LBLRTM within 0.02%. Application of this model to various hyperspectral instrument will be shown and discuss.
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