%0 Conference Paper %A Hobbs, Jonathan %A Braverman, Amy J. %A Fetzer, Eric J. %A Nguyen, Hai %A Lee, Kyo %A Behrangi, Ali %A Teixeira, Joaquim %A Foster, Kelsey %D 2019 %T Simulation-Based Uncertainty Quantification for Level 2 Retrievals %U https://esip.figshare.com/articles/poster/Simulation-Based_Uncertainty_Quantification_for_Level_2_Retrievals/8947400 %R 10.6084/m9.figshare.8947400.v1 %2 https://esip.figshare.com/ndownloader/files/16353842 %K ESIP Summer 2019 %K Uncertainty Quantification %K Remote Sensing %K AIRS %K Atmospheric Sciences %X This work develops methodology, software, and practices for remote sensing retrieval Monte Carlo simulation experiments. We formulate a probabilistic and computational pipeline to resemble the data-generating process for Level 1 and Level 2 satellite products. We illustrate the implementation for the Atmospheric Infrared Sounder (AIRS). This poster was presented in July 2019 at the Earth Science Information Partners (ESIP) Summer Meeting held in Tacoma Washington.

%I ESIP