Application of non-linear techniques for daily weather data reconstruction and downscaling coarse climate data for local predictions

Simple item page
cg.contributor.affiliationInternational Potato Centeren
cg.contributor.crpClimate Change, Agriculture and Food Security
cg.identifier.projectCCAFS: FP4_MarkSim
cg.number21en
cg.subject.ccafsDATA AND TOOLS FOR ANALYSIS AND PLANNINGen
dc.contributor.authorQuiróz, R.en
dc.contributor.authorPosadas, A.en
dc.contributor.authorYarleque, C.en
dc.contributor.authorHeidinger, Halineen
dc.contributor.authorRaymundo, R.en
dc.contributor.authorCarbajal, M.en
dc.contributor.authorCruz, Marianaen
dc.contributor.authorGuerrero, J.en
dc.contributor.authorMares, V.en
dc.contributor.authorSilvestre, Elisabethen
dc.contributor.authorJones, C.en
dc.contributor.authorCarvalho, Leila M. V.en
dc.contributor.authorDinku, T.en
dc.date.accessioned2012-08-30T12:42:15Zen
dc.date.available2012-08-30T12:42:15Zen
dc.identifier.urihttps://hdl.handle.net/10568/21724
dc.titleApplication of non-linear techniques for daily weather data reconstruction and downscaling coarse climate data for local predictionsen
dcterms.abstractDownscaling techniques aim at resolving the scale discrepancy between climate change scenarios and the resolution demanded for impact assessments. Requirements for downscaled climate, to be useful for end users, include reliable representation of precipitation intensities, temporal and spatial variability, and physical parameters consistency. This report summarizes the results of the proof of concept phase in the development and testing of a novel data reconstruction method and a downscaling algorithm based on the multiplicative random cascade disaggregation method using rainfall signals at different spatial and temporal resolutions. The Wavelet Transformed-based Multi-Resolution Analysis (WT-MRA) was used for reconstructing the historical daily rainfall data needed as input for the downscaling methodology, using satellite-derived proxy data. Comparisons with presently used software showed that in all the cases; that is, the reconstructed, generated daily or downscaled daily data, the products developed outperformed the control test by either generating more accurate outcomes or by demanding significantly less parameterizing data.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationQuiroz R, Posadas A, Yarlequé, C, Heidinger H, Raymundo R, Carbajal M, Cruz M, Guerrero J, Mares V, Silvestre E, Jones C, Carvalho LV. de, Dinku T. 2012. Application of non-linear techniques for daily weather data reconstruction and downscaling coarse climate data for local predictions. CCAFS Working Paper 21. Copenhagen, Denmark: CCAFS.en
dcterms.isPartOfCCAFS Working Paperen
dcterms.issued2012
dcterms.languageen
dcterms.publisherCGIAR Research Program on Climate Change, Agriculture and Food Securityen
dcterms.subjectclimateen
dcterms.subjectprecipitationen
dcterms.subjectweather dataen
dcterms.typeWorking Paper

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
ccafs-wp-21-non-linear-daily-weather-data.pdf
Size:
6.41 MB
Format:
Adobe Portable Document Format
Description:
Working paper
Download

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

CCAFS Working Papers
CIP Working Papers