Improving satellite-retrieved surface radiative fluxes in polar regions using a smart sampling approach

• Published in: The Cryosphere
• Main Authors: K. Van Tricht, S. Lhermitte, I. V. Gorodetskaya, N. P. M. van Lipzig
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2016
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Greenland; Pine Island Glacier; Antarc*; Antarctica; glacier; Pine Island; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-10-2379-2016; https://doaj.org/article/d9c7a8993dad4f739c40c3bb2bb3f45e

The surface energy budget (SEB) of polar regions is key to understanding the polar amplification of global climate change and its worldwide consequences. However, despite a growing network of ground-based automatic weather stations that measure the radiative components of the SEB, extensive areas remain where no ground-based observations are available. Satellite remote sensing has emerged as a potential solution to retrieve components of the SEB over remote areas, with radar and lidar aboard the CloudSat and CALIPSO satellites among the first to enable estimates of surface radiative long-wave (LW) and short-wave (SW) fluxes based on active cloud observations. However, due to the small swath footprints, combined with a return cycle of 16 days, questions arise as to how CloudSat and CALIPSO observations should be optimally sampled in order to retrieve representative fluxes for a given location. Here we present a smart sampling approach to retrieve downwelling surface radiative fluxes from CloudSat and CALIPSO observations for any given land-based point-of-interest (POI) in polar regions. The method comprises a spatial correction that allows the distance between the satellite footprint and the POI to be increased in order to raise the satellite sampling frequency. Sampling frequency is enhanced on average from only two unique satellite overpasses each month for limited-distance sampling < 10 km from the POI, to 35 satellite overpasses for the smart sampling approach. This reduces the root-mean-square errors on monthly mean flux estimates compared to ground-based measurements from 23 to 10 W m −2 (LW) and from 43 to 14 W m −2 (SW). The added value of the smart sampling approach is shown to be largest on finer temporal resolutions, where limited-distance sampling suffers from severely limited sampling frequencies. Finally, the methodology is illustrated for Pine Island Glacier (Antarctica) and the Greenland northern interior. Although few ground-based observations are available for these remote areas, important ...