Articles | Volume 12, issue 12
https://doi.org/10.5194/amt-12-6341-2019
https://doi.org/10.5194/amt-12-6341-2019
Research article
 | 
03 Dec 2019
Research article |  | 03 Dec 2019

An experimental 2D-Var retrieval using AMSR2

David Ian Duncan, Patrick Eriksson, and Simon Pfreundschuh

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Cited articles

Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith, D. K., and Gombos, D.: A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications, B. Am. Meteor. Soc., 92, 157–174, https://doi.org/10.1175/2010BAMS2946.1, 2011. a
Backus, G. E. and Gilbert, J. F.: Numerical Applications of a Formalism for Geophysical Inverse Prob., Geophys. J. R. Astron. Soc., 13, 247–276, https://doi.org/10.1111/j.1365-246X.1967.tb02159.x, 1967. a, b
Baron, P., Ricaud, P., Delanoë, J., Eriksson, P., Merino, F., and Murtagh, D.: Studies for the Odin sub-millimetre radiometer. II. Retrieval methodology, Can. J. Phys., 80, 341–356, https://doi.org/10.1139/p01-150, 2002. a
Berg, W.: GPM GMI Common Calibrated Brightness Temperatures Collocated L1C 1.5 hours 13 km V05, Tech. rep., NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA, https://doi.org/10.5067/GPM/GMI/GPM/1C/05, 2016. a
Berg, W., Bilanow, S., Chen, R., Datta, S., Draper, D., Ebrahimi, H., Farrar, S., Jones, W. L., Kroodsma, R., McKague, D., Payne, V., Wang, J., Wilheit, T., and Yang, J. X.: Intercalibration of the GPM Microwave Radiometer Constellation, J. Atmos. Oceanic Tech., 33, 2639–2654, https://doi.org/10.1175/JTECH-D-16-0100.1, 2016. a
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Short summary
The overlapping beams of some satellite observations contain spatial information that is discarded by most data processing techniques. This study applies an established technique in a new way to improve the spatial resolution of retrieval targets, effectively using the overlapping information to achieve a higher ultimate resolution. It is argued that this is a more optimal use of the total information available from current microwave sensors, using AMSR2 as an example.
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