Downscaling in sub-daily scale – inventory of methods Joanna Wibig University of Lodz, POLAND...
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Transcript of Downscaling in sub-daily scale – inventory of methods Joanna Wibig University of Lodz, POLAND...
Downscaling in sub-daily scale – inventory of methods
Joanna WibigUniversity 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)
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
MOS technics: DC or bias corrections
Disaggregation, if necessary
DC + frequency adjustment procedure
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
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;
Modeling of a diurnal cycle of precipitation
Walther, A., et al., 2011
An example of the estimated diurnal cycle ofprecipitation amount from observation and RCA3 simulations with 4 differentresolutions for the ‘Malexander’ station
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
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
Weather generators
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
RainSim
• Rainfall only• Daily or hourly• Poisson cluster models: NSRP, GNSRP,
BLRP• Single or multi-site locations• Models are calibrated separately within
different weather states
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
Burton, A., et al.., 2008: Rainsim: A spatial-temporal stochastic rainfall modelling system. Environ. Mod. & Soft., 23, 1356-1369.
Name of software: RainSim V3Developer: School of Civil Engineering and Geosciences, Newcastle University, NE1 7RU, UKContact: Aidan Burton, School of Civil Engineering and Geosciences, Newcastle University, NE1 7RU, UK, [email protected]: PC with windows 2000 or XP
WGEN (ClimGen)
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
Washington State Universityhttp://www.bsyse.wsu.edu/CS_Suite/ClimGen/index.html
• 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
• 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
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.R-project.org/package=RMAWGEN
• 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.
http://www.homepages.ucl.ac.uk/~ucakarc/work/glimclim.htmlGLIMCLIM
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
Disaggregators
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
End User Needs for Regional Climate Change Scenarios, 7-9 March 2012, Kiel
K-nearest neighbours resampling approach for
disaggregation to multisite hourly data
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
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
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 92 350–363Hingray, Ben Haha, 2005. Atmospheric Research 77, 152–175.Ormsbee, L.E., 1989, J. Hydraul. Eng., ASCE 115 (4), 507– 525.
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
The proportional adjusting procedure:
k
jjss XZXX
1
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The linear adjusting procedure:
k
jjsss XZXX
1
~~
ssk
jjss XZXX
/
1
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The power adjusting procedure:
Koutsojannis & Onof, 2001, J. Hydrology: 246:109-122
Disaggregation by adjusting
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
Validation methods
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste
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
To be continued …..
VALUE - Data and Validation Workshop, 19-20 Sep 2012, ICTP, Trieste