A case study of GOES-15 imager bias characterization with a numerical weather prediction model

Lu REN

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PDF(2703 KB)
Front. Earth Sci. ›› 2016, Vol. 10 ›› Issue (3) : 409-418. DOI: 10.1007/s11707-016-0579-y
RESEARCH ARTICLE
RESEARCH ARTICLE

A case study of GOES-15 imager bias characterization with a numerical weather prediction model

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Abstract

The infrared imager onboard the Geostationary Operational Environmental Satellite 15 (GOES-15) provides temporally continuous observations over a limited spatial domain. To quantify bias of the GOES-15 imager, observations from four infrared channels (2, 3, 4, and 6) are compared with simulations from the numerical weather prediction model and radiative transfer model. One-day clear-sky infrared observations from the GOES-15 imager over an oceanic domain during nighttime are selected. Two datasets, Global Forecast System (GFS) analysis and ERA-Interim reanalysis, are used as inputs to the radiative transfer model. The results show that magnitudes of biases for the GOES-15 surface channels are approximately 1 K using two datasets, whereas the magnitude of bias for the GOES-15 water vapor channel can reach 5.5 K using the GFS dataset and 2.5 K using the ERA dataset. The GOES-15 surface channels show positive dependencies on scene temperature, whereas the water vapor channel has a weak dependence on scene temperature. The strong dependence of bias on sensor zenith angle for the GOES-15 water vapor channel using GFS analysis implies large biases might exist in GFS water vapor profiles.

Keywords

data assimilation / NWP / GOES imager / bias

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Lu REN. A case study of GOES-15 imager bias characterization with a numerical weather prediction model. Front. Earth Sci., 2016, 10(3): 409‒418 https://doi.org/10.1007/s11707-016-0579-y

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Acknowledgements

The author thanks the University of Michigan for providing excellent experimental facilities.

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2016 Higher Education Press and Springer-Verlag Berlin Heidelberg
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