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, aidan.burton@ncl.ac.ukHardware: 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

~/~

The linear adjusting procedure:

k

jjsss XZXX

1

~~

ssk

jjss XZXX

/

1

~/~

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