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Downscaling in sub-daily scale – inventory of methods Joanna Wibig University of Lodz, POLAND VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Outline: • • • • • Dynamical downscaling Weather generators Disaggregators Evaluation procedures Summary VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste dynamic downscaling Regional climate models in relatively high resolution (both in space and time) MOS technics: DC or bias corrections Disaggregation, if necessary VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste DC + frequency adjustment procedure There is an important limitation of DC that a change in precipitation frequency is generally not considered and the future frequency is assumed to be identical to today’s. Olsson J, Gidhagen L, Gamerith V, Gruber G, Hoppe H, Kutschera P, 2012, Sustainability, 2012, 4, 866-887; VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Modeling of a diurnal cycle of precipitation An example of the estimated diurnal cycle of precipitation amount from observation and RCA3 simulations with 4 different resolutions for the ‘Malexander’ station Walther, A., et al., 2011 VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Comparison of different DC and bias correction precedures Räisänen, Räty, Clim.Dyn., September 2012 online first VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Weather generators VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste RainSim • • • • • Burton, A., et al.., 2008: Rainsim: A spatialtemporal stochastic rainfall modelling system. Environ. Mod. & Soft., 23, 1356-1369. Rainfall only Daily or hourly Poisson cluster models: NSRP, GNSRP, BLRP Single or multi-site locations Models are calibrated separately within different weather states Name of software: RainSim V3 Developer: School of Civil Engineering and Geosciences, Newcastle University, NE1 7RU, UK Contact: Aidan Burton, School of Civil Engineering and Geosciences, Newcastle University, NE1 7RU, UK, [email protected] Hardware: PC with windows 2000 or XP VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste WGEN (ClimGen) Washington State University http://www.bsyse.wsu.edu/CS_Suite /ClimGen/index.html Daily resolution • Precipitation • Maximum and Minimum temperature • Solar radiation • Maximum and Minimum relative humidity • Maximum and Minimum dew point temperature • Windspeed • Vapor pressure deficit • Reference evapotranspiration (Penman-Monteith, Priestley-Taylor, Hargreaves). 1 to 1440 minute resolution • Storm events (precipitation intervals) VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste • ClimGen, is a weather generator of a WGEN type • ClimGen generates precipitation, daily maximum and minimum temperature, solar radiation, air humidity, and wind speed. • ClimGen usesWeibull distribution to generate precipitation amounts instead of the Gamma distribution used by WGEN. • In ClimGen, all generation parameters are calculated for each site of interest • ClimGen can be applied to any location with enough data to parameterize the program. • ClimGen uses quadratic spline functions chosen to ensure that: The continuity of the daily average values across month boundaries, The continuity of the first derivative across month boundaries. VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste RMAWGEN • • • • • Cordano E., Eccel, E., 2011. RMAWGEN (R Multi-site Auto-regressive Weather GENerator): a package to generate daily time series from monthly mean values. http://CRAN.Rproject.org/package=RMAWGEN Auto-regressive models R-language Daily resolution Multi-site Temperature, precipitation, wet, dry, hot spells, others VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste GLIMCLIM http://www.homepages.ucl.ac.uk/~ucakarc/work /glimclim.html • A GLM for daily observations of a climate variable is defined by setting up a probability distribution for each of the daily values. Each observation is regarded as a realization or a sample from its own distribution. • A typical assumption in GLMs is that all of the observations are drawn from the same family of distributions, for example, normal, Poisson, or gamma. • A GLM is essentially a multiple regression model for the chosen family of distributions; the regression-like approach enables the individual distributions to change with time site and external factors. Inference (judging if a possible factor has a genuine effect on the studied phenomenon) is carried out using likelihood-based methods. Such methods implicitly take into account the family of distributions being used. It is therefore important to choose a realistic distribution for a specific climate variable. VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Disaggregators VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste K-nearest neighbours resampling approach for disaggregation to multisite hourly data Mezghani, Hingrey, 2009, A combined downscaling-disaggregation weather generator for stochastic generation of multisite hourly weather variables over complex terrain: Development and multi-scale validation for the Upper Rhone River basin, J.Hydrology,377:245-260 M. Sharif, D. H. Burn and K.M. Wey, 2007, Daily and Hourly Weather Data Generation using a K-Nearest Neighbour Approach Challenges for Water Resources Engineering in a Changing World, Winnipeg, Manitoba, August 22 – 24, 2007 End User Needs for Regional Climate Change Scenarios, 7-9 March 2012, Kiel Disaggregation to finer scales with stochastic methods Onof, Arnbjerg-Nielsen, 2009 Atmospheric Research 92 350–363 Hingray, Ben Haha, 2005. Atmospheric Research 77, 152–175. Ormsbee, L.E., 1989, J. Hydraul. Eng., ASCE 115 (4), 507– 525. End User Needs for Regional Climate Change Scenarios, 7-9 March 2012, Kiel Disaggregation by adjusting The proportional adjusting procedure: k ~ ~ X s X s Z / X j j 1 The linear adjusting procedure: k ~ ~ X s X s s Z X j j 1 The power adjusting procedure: ~ ~ X s X s Z / X j j 1 k Koutsojannis & Onof, 2001, J. Hydrology: 246:109-122 s / s VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste M.-L. Segond *, C. Onof, H.S. Wheater, 2006, J. Hydrology 331, 674– 689 VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Validation methods VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste STATISTICAL MEASURES: Mean (monthly, seasonal, annual) and standard deviations Daily averages (or totals): mean (on wet days) , standard deviations, skewness Minimum, maximum, selected percentiles, distribution checking frequency of days with precipitation crossing selected thresholds Dry/wet spells Cold/hot spells Frequency of days with wind maximum exceeding thresholds VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste TEMPORAL CONSISTENCY: trends Autocorrelations with lag 1 (persistency) SPATIAL CONSISTENCY: Anscombe residuals: Pearson residuals: M.-L. Segond *, C. Onof, H.S. Wheater, 2006, J. Hydrology 331, 674– 689 VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Cross – validation principles Räisänen, Räty, Clim.Dyn., September 2012 online first VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste Mean square error Continuous ranked probability score Out of range score The frequency of cases in which Tver is below the lowest or above the highest of the all Tproj values VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste To be continued ….. VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste