Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition
Abstract
The Deep Space Climate Observatory (DSCOVR) is designed to study the daytime Earth radiation budget by means of onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC imager observes in several shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and total broadband windows. Calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers. These properties have to be co-located with EPIC imager pixels to provide scene identification and to select anisotropic directional models, which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. The highest quality observation is selected by means of an aggregated rating which incorporates several factors such as the nearest time relative to EPIC observation, lowest viewing zenith angle, and others. This process provides a smoother transition and avoids abrupt changes in the merged composite data. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into the EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. Within every EPIC footprint, the PSF-weighted average radiances and cloud properties are computed for each cloud phase and then stored within five data subsets (clear-sky, water cloud, ice cloud, total cloud, and no retrieval). Overall, the composite product has been generated for every EPIC observation from June 2015 to December 2016, typically 300-500 composites per month, which makes it useful for many climate applications.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.A33D2393K
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0370 Volcanic effects;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3311 Clouds and aerosols;
- ATMOSPHERIC PROCESSES;
- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES