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Prediction of cloud ice signatures in submillimeter emission spectra by means of ground-based radar and in-situ microphysical data
Chalmers University of Technology, Department of Radio and Space Science, Gothenburg.
Chalmers University of Technology, Department of Radio and Space Science, Gothenburg.
LuleƄ University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.ORCID iD: 0000-0001-6389-1160
2007 (English)In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 133, no Suppl.2, 151-162 p.Article in journal (Refereed) Published
Abstract [en]

Submillimetre down-looking radiometry is a promising technique for global measurements of cloud ice properties. There exist no observation data of sufficient size that can be used for detailed pre-launch studies of such an instrument and other means must be found to obtain data to optimise the instrument design and similar tasks. Several aspects of the observations make traditional retrieval methods not suitable and nonlinear multidimensional regression techniques (e.g. Bayesian Monte Carlo integration and neural networks) must be applied. Such methods are based on a retrieval database and to be successful the database must mimic relevant real conditions closely. A method to generate such databases of high quality is described here. Correct vertical distributions of cloud ice are obtained by basic data from ground-based radars. Cloud ice particle microphysical properties are generated randomly where statistical parameters are selected to mimic in situ measurement data closely. Atmospheric background fields from ECMWF are perturbed to account for variation on sub-grid scales. All these data, together with sensor characteristics, are fed into a state-of-the-art radiative transfer simulator (ARTS). The method was validated by a successful comparison with AMSU data.

Place, publisher, year, edition, pages
2007. Vol. 133, no Suppl.2, 151-162 p.
National Category
Aerospace Engineering
Research subject
Space Technology
Identifiers
URN: urn:nbn:se:ltu:diva-12426DOI: 10.1002/qj.151Local ID: b93eb320-8eae-11dc-a188-000ea68e967bOAI: oai:DiVA.org:ltu-12426DiVA: diva2:985377
Note
Validerad; 2007; 20071109 (sbuehler)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-21Bibliographically approved

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