Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for...

The core information for this publication's citation.: 
Xu Liu, W. Wu, B. Wielicki, Q. Yang, S. H. Kizer, X. Huang, X. Chen, S. Kato, Y. L. Shea, and M. Mlynczak (2017), Spectrally Dependent CLARREO Infrared Spectrometer Calibration Requirement for Climate Change Detection, J. Climate, 30, 3979-3998, doi:10.1175/JCLI-D-16-0704.1.
Abstract: 

Detecting climate trends of atmospheric temperature, moisture, cloud, and surface temperature requires accurately calibrated satellite instruments such as the Climate Absolute Radiance and Refractivity Observatory (CLARREO). Previous studies have evaluated the CLARREO measurement requirements for achieving climate change accuracy goals in orbit. The present study further quantifies the spectrally dependent IR instrument calibration requirement for detecting trends of atmospheric temperature and moisture profiles. The temperature, water vapor, and surface skin temperature variability and the associated correlation time are derived using the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The results are further validated using climate model simulation results. With the derived natural variability as the reference, the calibration requirement is established by carrying out a simulation study for CLARREO observations of various atmospheric states under all-sky conditions. A 0.04-K (k 5 2; 95% confidence) radiometric calibration requirement baseline is derived using a spectral fingerprinting method. It is also demonstrated that the requirement is spectrally dependent and that some spectral regions can be relaxed as a result of the hyperspectral nature of the CLARREO instrument. Relaxing the requirement to 0.06 K (k 5 2) is discussed further based on the uncertainties associated with the temperature and water vapor natural variability and relatively small delay in the time to detect for trends relative to the baseline case. The methodology used in this study can be extended to other parameters (such as clouds and CO2) and other instrument configurations.

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Research Program: 
Atmospheric Composition Modeling and Analysis Program (ACMAP)
Interdisciplinary Science Program (IDS)
Modeling Analysis and Prediction Program (MAP)
Radiation Science Program (RSP)