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Evaluation of a dynamic downscaling of precipitation over the Norwegian mainland

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

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Evaluation of a dynamic downscaling of precipitation over the Norwegian mainland

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  1. 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

  2. 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.

  3. Goal • Wewant to comparedownscaled ERA-40 reanalysis data (RCM) againstobservationsof Norwegian precipitation. • How goodarethe RCM data? • Where (in thedistribution) doesthe RCM differ from theobservations? • Where (geographically) doesthe RCM perform best/worst?

  4. 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.

  5. Data Model data Observations Interpolations (1 x 1 km2) from a triangulationoftheofficialmeasurementstationsoperated by The Norwegian MeteorologicalInstitute. Aggregated to 25 x 25 km2scale by collecting 1 x 1 km2 grid cellswithcentre points withinthe RCM cell, themean is representingtheprecipitationwithinthat grid cell. • RCM model, dynamicallydownscaled HIRHAM model, forced by ERA-40 reanalysis data from the ENSEMBLES project. • Spatial resolution of 25 x 25 km2. • Reliant on thedownscaling, still supposed to possess properties similar to real weatherlocally over longer time periods.

  6. Data – The RCM • The RCM from the ENSEMBLES project

  7. Data – properties for both data sets • Climate variable: precipitation • Time period: 1961 – 2000 • Time scale: Daily, seasonal • Resolution: 25 x 25 km2 • Numberof grid cells: 777 grid cellscovering Norway

  8. Methods for comparison • Evaluatethedistributions • Global measure: Kolmogorov Smirnov test • Localmeasures:

  9. 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.

  10. Kolmogorov-Smirnov test • K-S two sample test is used to checkwhethertheempiricaldistributions from the RCM and theobservationsareequal. • To avoidthe problem oftied data, a small, random normallydistributednumber, N(0, σ2), is added to each data point. σ = 1e-7

  11. Kolmogorov-Smirnov test – Results • The null hypothesisofequalityofthedistributionsarerejected for almost all grid cells for all thefourseasons. • Global picture: the RCM does not have the same distribution as theobservations. • Next: want to findoutwherethedistributionsdiffer; localmeasures.

  12. Methods for comparison • Evaluatethedistributions • Global measure: Kolmogorov Smirnov test • Localmeasures:

  13. Test equality of quantiles Construction of the 2 x 2 contigency table

  14. 0.05-quantile – Results • Hardlyanyrejectionsof null hypothesisofequality. • For lowquantiles: the RCM reproduces theobservationswellbothseason- and nationwide.

  15. 0.95-quantile – Results • Mainlyrejectionsofthe null hypothesisofequality. • Overall picture: the RCM underestimateshighprecipitation.

  16. Generalized Pareto Distribution (GPD)

  17. GPD – Results • One-yearreturnlevels from GPD are more similarthanexpressedthroughtheKolmogorov-Smirnov test. • Butstill: tendencythatthe RCM underestimateshighprecipitation.

  18. 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.

  19. Wet day frequency – Results • Mainlyrejectionsofthe null hypothesisofequality. • Total picture: Wetdayfrequencyofthe RCM is greaterthan for theobservations.

  20. 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.

  21. 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.

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