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Title Improved nowcasting of heavy precipitation using satellite and weather radar data
Author Vestergaard, Jacob Schack (Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Supervisor Nielsen, Allan Aasbjerg (Geodesy, National Space Institute, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Larsen, Rasmus (Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2011
Abstract Global climate changes in recent years have caused a higher frequency of heavy precipitative events in Denmark, due to the increase in the atmospheric temperature. Therefore, a desire to nowcast these events has emerged. Nowcasting is the discipline of short term forecasting (0–3 hours) meteorological events and works on a smaller scale than the numerical weather models typically used for forecasting. Six dates over the last few years (2007–2010) exhibiting extreme weather phenomena in Denmark have been selected, ranging from heavy snow fall to extreme downpour. Data used in the nowcasting come from the Meteosat-8 satellite and from weather radars operated by the Danish Meteorological Institute (DMI). The supplied data are used for development of a nowcast system specifically designed for heavy precipitative events in Denmark. This includes a statistical approach to identification of ground truth using linear multivariate statistical methods, such as canonical correlation analysis. A method for learning a discriminative dictionary of satellite image patches is applied for classification and prediction of heavy precipitation. An operational setting is simulated by use of leave-one-out cross validation, where the nowcast model is built on data from five dates and evaluated on the sixth. While the nowcasting abilities degrade when increasing the nowcast length above 0.5 hours, probably due to the diversity of the six weather situations, the method proves successful in classifying heavy precipitative events as they occur.
Abstract Over de seneste år er temperaturen i atmosfæren forøget og dermed også frekvensen af kraftige byger i Danmark. Et ønske om nowcasting af disse hændelser er derfor opstået. Nowcasting er forudsigelse af meteorologiske begivenheder kort tid inden de forekommer (0–3 timer) og opererer på en mindre skala end de numeriske vejrmodeller, der typisk bruges til vejrudsigter. Seks dage inden for de sidste få år (2007–2010) er blevet udvalgt, da disse dage har indeholdt elementer af ekstremt vejr på en dansk målestok, fra kraftigt snefald i efteråret til ekstremt regnfald i sommervarmen. Til nowcastingen bruges data fra Meteosat-8 vejrsatelliten og radar data fra vejrradarer opereret af Danmarks Meteorologisk Institut (DMI). Data fra disse hændelser bruges til udvikling af et nowcasting system, specifikt designet til kraftigt nedbør i Danmark. Analysen inkluderer en statistisk tilgang til identifikation af faktiske data ved hjælp af lineære multivariate statistiske metoder, såsom kanonisk korrelations analyse. En metode til læring af en diskriminativ basis af satellit billedpatches er anvendt til klassifikation og prædiktion af kraftig nedbør. Et operationelt scenarie er simuleret ved brug af “leave-one-out” krydsvalidering, hvor nowcast modellen er bygget på data fra fem datoer og evalueret på den sjette. Modellens evner til prædiktion forringes for nowcast længder over en halv time, formentlig på grund af diversiteten i de seks vejrsituationer. Dog viser metoden sig at være særdeles anvendelig til at klassificere kraftige nedbørshændelser, når de opstår.
Imprint Technical University of Denmark (DTU) : Kgs. Lyngby, Denmark
ISBN 9788764308730
Pages 177
Series IMM-M.Sc.-2011
Fulltext
Original PDF 1F3CEd01.pdf (31.17 MB)
Admin Creation date: 2011-10-10    Update date: 2011-10-10    Source: dtu    ID: 285070    Original MXD