New Satellite Error Simulator Released

John Xun Yang and His Daughter

© BAMS

When testing of new satellites, models, and data assimilation is done, simulated errors are used. These are usually based on empirical errors, which do not represent the full range of errors. To fix this problem, CISESS Scientist John Xun Yang has collaborated with other NOAA and CISESS scientist to develop an error inventory simulator, the Satellite Error Representation and Realization system (SatERR).

SatERR is designed to simulate a wide spectrum of observation errors, from instrument measurement to model assimilation errors. To be more specific, SatERR encompasses four distinct categories of satellite observation errors: 

● Measurement Errors: These errors stem from instrument imperfections, including biases and noise in the radiance measurements. 

● Observation Operator Errors: This category accounts for inaccuracies resulting from deficiencies in the forward model, such as those encountered in radiative transfer models (RTMs) used during satellite observation assimilation. 

● Representativeness Errors: These errors arise from unresolved scales and processes, often tied to model parameterization and scale resolution. 

● Preprocessing Errors: This category pertains to errors incurred during the data prescreening process. 

SatERR's comprehensive framework allows for the detailed analysis and mitigation of these errors, enhancing the accuracy and reliability of satellite observations for crucial applications in weather, climate, and environmental sciences.

Figure: Schematic of the different process steps in SatERR.

Citation: Yang, John Xun, Yalei You, William Blackwell, Cheng Da, Eugenia Kalnay, Christopher Grassotti, Quanhua (Mark) Liu, Ralph Ferraro, Huan Meng, Cheng-Zhi Zou, Shu-Peng Ho, Jifu Yin, Veljko Petkovic, Timothy Hewison, Derek Posselt, Antonia Gambacorta, David Draper, Sidharth Misra, Rachael Kroodsma, and Min Chen, 2024: SatERR: A community error inventory for satellite microwave observation error representation and uncertainty quantification, Bull. Amer. Meteor. Soc., 105(1), E2316–E2335, https://doi.org/10.1175/BAMS-D-22-0207.1.

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