Climate Data Records (CDRs) Derived from Multiple Hyperspectral Remote Sensors

Hyperspectral observations from satellite-based sensors provide high information content for the Earth’s atmospheric and surface properties. Traditionally, long-term climate products are derived by performing spatial and temporal averaging of level-2 satellite products. It is a time-consuming process to generate level-2 data products since modern hyperspectral satellite sensors have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in level-2 retrieval algorithms can lead to errors in the climate products when fusing data from different satellite sensors. We have developed a radiometrically consistent spectral fingerprinting method, which overcomes the above-mentioned shortcomings, to derive climate change signals from multiple satellite sensors using spatiotemporally averaged level-1 data. A key component to this work is a set of observational-based radiative kernels produced from Cross-track Infrared Sounder (CrIS) level-1 data using a Single field-of-view (SFOV) Sounder Atmospheric Product (SiFSAP) retrieval algorithm. Only limited CrIS level-1 data (e.g., 1-2 years of data) are needed to the derive radiative kernels. Our fast and accurate Principal Component-based Radiative Model (PCRTM) enables us to perform SFOV retrievals under all sky conditions and provides radiative kernels (including those for clouds) needed by the spectral fingerprinting method. Once these kernels and associated atmospheric and surface parameters are generated, they can be applied to different IR hyperspectral sounders such as Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). In this presentation, we will describe the basic methodology, the details of the algorithm, and results. We will show results of twenty years of climate data records (CDRs) derived from AIRS on NASA Aqua satellite and CrIS on SNPP and NOAA20 satellites. These global daily (day and night) and monthly long-term CDRs will be processed and stored on a 0.5 by 0.5 degree grided at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) and will be released for public access in the next few months. These long-term CDRs include: 1) vertical profiles of atmospheric temperature, water vapor, ozone, and carbon monoxide; 2) height, temperature, particle size, and optical depths for ice and water clouds, and 3) surface skin temperature and surface emissivity spectra. We plan to continue the CDR generation when data from future JPSS hyperspectral data are available. The same method can be applied to study future hyperspectral remote sensors such as CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF), Surface Biology and Geology (SBG), and Traceable Radiometry Underpinning Terrestrial- and Helio-Studies (TRUTHS).