Journal Article10.1002/JOC.2312
A comparison of statistical downscaling methods suited for wildfire applications
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TL;DR: In this article, two statistical downscaling methods, the daily bias corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA), were validated over the western US using global reanalysis data.
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Abstract: Place-based data is required in wildfire analyses, particularly in regions of diverse terrain that foster not only strong gradients in meteorological variables, but also complex fire behaviour. However, a majority of downscaling methods are inappropriate for wildfire application due to the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA) that directly incorporate daily data from global climate models, were validated over the western US using global reanalysis data. While both methods outperformed results obtained from direct interpolation from reanalysis, MACA exhibited additional skill in temperature, humidity, wind, and precipitation due to its ability to jointly downscale temperature and dew point temperature, and its use of analog patterns rather than interpolation. Both downscaling methods exhibited value added information in tracking fire danger indices and periods of extreme fire danger; however, MACA outperformed the daily BCSD due to its ability to more accurately capture relative humidity and winds. Copyright © 2011 Royal Meteorological Society
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References
North american regional reanalysis
Fedor Mesinger,Geoff DiMego,Eugenia Kalnay,Kenneth E. Mitchell,Perry Shafran,Wesley Ebisuzaki,Dusan Jovic,John S. Woollen,Eric Rogers,Ernesto Hugo Berbery,Michael Ek,Yun Fan,Robert Grumbine,Wayne Higgins,Hong Li,Ying Lin,Geoff Manikin,David F. Parrish,Wei Shi +18 more
TL;DR: The North American Regional Reanalysis (NARR) project as mentioned in this paper uses the NCEP Eta model and its Data Assimilation System (at 32-km-45-layer resolution with 3-hourly output) to capture regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.
A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain
TL;DR: In this article, the authors present an analytical model that distributes point measurements of monthly and annual precipitation to regularly spaced grid cells in midlatitude regions, using a combination of climatological and statistical concepts to analyze orographic precipitation.
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Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs
TL;DR: In this article, six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macro-scale hydrology model applied at much higher spatial resolution than the climate model.
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