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Evaluation of a dynamic downscaling of precipitation over the Norwegian mainland
Orskaug E.a, Scheel I.b, Frigessi A.c,a, Guttorp P.d,a, Haugen J. E.e, Tveito O. E.e, Haug O.a
a Norwegian Computing Center, Oslo, Norway
b Department of Mathematics, University of Oslo, Oslo, Norway
c Department of Biostatistics, University of Oslo, Oslo, Norway
d University of Washington, Seattle, USA
e The Norwegian Meteorological Institute, Oslo, Norway
Motivation
► Climate research produces an increasing number of data sets combining different GCMs, CO2 emission scenarios and downscaling techniques.
► For impact studies, but also as an issue of separate interest, the quality of these data need to be verified.
Goal
► We want to compare downscaled ERA-40 reanalysis data (RCM) against observations of Norwegian precipitation.▪ How good are the RCM data?▪ Where (in the distribution) does the RCM differ from
the observations?▪ Where (geographically) does the RCM perform
best/worst?
Why is this work important?
► It assesses the quality of a dynamic downscaled data and highlights which areas these data capture reality and where there are deviations from the truth.
► Another aim is to show how standard methods of statistical testing may be used to assess dynamic downscaling.
Data
Model data► RCM model, dynamically
downscaled HIRHAM model, forced by ERA-40 reanalysis data from the ENSEMBLES project.
► Spatial resolution of 25 x 25 km2.
► Reliant on the downscaling, still supposed to possess properties similar to real weather locally over longer time periods.
Observations
► Interpolations (1 x 1 km2) from a triangulation of the official measurement stations operated by The Norwegian Meteorological Institute.
► Aggregated to 25 x 25 km2 scale by collecting 1 x 1 km2 grid cells with centre points within the RCM cell, the mean is representing the precipitation within that grid cell.
Data – The RCM
► The RCM from the ENSEMBLES project
Data – properties for both data sets
► Climate variable: precipitation
► Time period: 1961 – 2000
► Time scale: Daily, seasonal
► Resolution: 25 x 25 km2
► Number of grid cells: 777 grid cells covering Norway
Methods for comparison
► Evaluate the distributions1. Global measure:
Kolmogorov Smirnov test
2. Local measures:
Comments
► Drizzle effect avoided: conditioned on wet days; i.e. days with precipitation below a small, positive threshold (0.5 mm/day) are discarded.
► Day-to-day correlation in the RCM is partly lost due to downscaling, hence the distributions have to be compared instead of comparing day by day.
► Separate tests for each grid cell and each season.
Kolmogorov-Smirnov test
► K-S two sample test is used to check whether the empirical distributions from the RCM and the observations are equal.
► To avoid the problem of tied data, a small, random normally distributed number, N(0, σ2), is added to each data point.
σ = 1e-7
Kolmogorov-Smirnov test – Results
► The null hypothesis of equality of the distributions are rejected for almost all grid cells for all the four seasons.
► Global picture: the RCM does not have the same distribution as the observations.
► Next: want to find out where the distributions differ; local measures.
Methods for comparison
► Evaluate the distributions1. Global measure:
Kolmogorov Smirnov test
2. Local measures:
Test equality of quantiles
Construction of the 2 x 2 contigency table
0.05-quantile – Results
► Hardly any rejections of null hypothesis of equality.
► For low quantiles: the RCM reproduces the observations well both season- and nationwide.
0.95-quantile – Results
► Mainly rejections of the null hypothesis of equality.
► Overall picture: the RCM underestimates high precipitation.
Generalized Pareto Distribution (GPD)
►
GPD – Results
► One-year return levels from GPD are more similar than expressed through the Kolmogorov-Smirnov test.
► But still: tendency that the RCM underestimates high precipitation.
Wet day frequency
► Wet day frequency = Proportion of wet days (among all days in the data)
► A wet day is defined to be above 0.5 mm/day for both data sets.
► The equality of the wet day frequency is tested by permutation testing.
Wet day frequency – Results
► Mainly rejections of the null hypothesis of equality.
► Total picture: Wet day frequency of the RCM is greater than for the observations.
Summary
► Small amounts of rainfall: the RCM shows good agreement with the observations.
► When rainfall amounts is beyond the first quartile, the agreement disappear.
► The RCM has too many and too small rain events for all seasons.
► This work is accepted for publication in Tellus A.
An improvement/correction of the RCM is needed.
What to do next?
► We want to add a statistical correction method to the output of the RCM, especially improve the right tail.
► Simple linear regression was tried out, but did not improve the results.
► We are currently working on a more complex transformation with spatial corrections.