Eidgenössisches Departement des Innern EDIBundesamt für Meteorologie und Klimatologie MeteoSchweiz

The renewed Alpine precipitation grid dataset

Developments and analyses

David Masson, Francesco Isotta, Christoph Frei

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Outline

I. Alpine precipitation grid dataset: current status

II. Towards a new method of spatial precipitation analysis

III. Preliminary considerations on interannual to decadal variations

Eidgenössisches Departement des Innern EDIBundesamt für Meteorologie und Klimatologie MeteoSchweiz

PART I:

Alpine precipitation grid dataset:

Current status

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Alpine precipitation station dataset

Domain: European Alps and adjacent flatland regions: 4.8-17.5E / 43-49N, (47.6N in France) Station data: seven countries, > 8500 time series in total, ~ 5500 measurements each day Period: 1971-2008Quality check: specific quality control procedure (for gross errors)

Stations in the dataset, height in meter

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Alpine precipitation station dataset

Grid: grid spacing 5x5 km (ETRS89-LAEA)Quantification of interpolation errors based on leave-one-out crossvalidation

Precipitation sum (mm), 17-19 July 1987

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Web site for data disseminationEURO4M Deliverable D1.1

Terms and conditions of use: Data access is subject of formal registrationand agreement of the conditions of use

www.meteoswiss.chSearch for «Alpine precipitation»

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Publication

The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-

gauge data

Francesco A. Isotta,a Christoph Frei,a Viktor Weilguni,b Melita Perčec Tadić,c Pierre Lassègues,d Bruno Rudolf,e Valentina Pavan,f Carlo Cacciamani, f Gabriele Antolini,f Sara

M. Ratto,g Michela Munari,h Stefano Micheletti,i Veronica Bonati,j Cristian Lussana,kChristian Ronchi,l Elvio Panettieri,m Gianni Marigo,n Gregor Vertačnik, o

Submitted to the International Journal of Climatology, 4.4.2013

23-25 August 1987 (mm) 20-22 August 2005 (mm)

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Climatology of daily statistics

Statistical indices of daily precipitation, period 1971-2008(a) Frequency of wet days (≥ 1 mm, fraction), (b) mean precipitation on wet days (mm/day), (c) mean of annual maximum daily precipitation (mm/day), (d) fraction of precipitation from days with moderate to high intensity (≥ 75% percentile on wet-days, fraction).

Eidgenössisches Departement des Innern EDIBundesamt für Meteorologie und Klimatologie MeteoSchweiz

Part II:

Towards a new method of spatial precipitation analysis

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Motivation: Climatological precipitation (1971-2008)

(mm/day)Winter

• Complex Alpine topography

• Circulation regimes

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• Topographic dataset: Shuttle Radar Topography Mission (SRTM)• Smoothing of the topography using Gaussian Kernel at various spatial scales• Use topographic height and gradient as precipitation predictor

Multi-scale precipitation ~ topography relationship

1km 25km 75km

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Geostrophic wind:

Interaction between winds and topography

Weather type CAP9-09-winter, 5km scale

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Circulation regimes ~ precipitation relationship

• Stratification of precipitation data using circulation regimes• Use of the CAP9 weather type classification (COST 733)• 9 weather types ˣ 4 seasons = 36 classes

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Interpolation of climatological precipitation

Which predictors are relevant for the interpolation?

Test of different models:•Topographical height only•Topographical height at 3 spatial scales (1km, 25km, 75km)•Topographical height and directional gradient at 3 spatial scales

•Stochastic interpolation (Kriging) vs. linear regression

Study over a quasi 2D cross-section of the Alps

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Topographical height only, linear regression(mm/day)

50km

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Topographical height at 3 spatial scales(mm/day)

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Topographical height and directional gradient at 3 spatial scales(mm/day)

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Ordinary kriging

• Stochastic prediction• No topographic predictors!• Weather type CAP9-9 winter• Climatological average

(mm/day)

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(mm/day)

KED with height and directional gradient at 3 spatial scales

• KED=Kriging with External Drift• Weather type CAP9-9 winter• Climatological average

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Performance for climatological map

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KED, 13.02.1990(mm/day)

Interpolation of daily precipitation

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Daily performance for an entire weather class

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Outlook

•Comparison with seasonal vs. weather type stratification

•Interpolation over the entire Alps

•Publication submission this summer

Conclusion

•Reliable interpolation at high altitudes requires a model of precipitation-height relationship

•Topographic height alone is a poor predictor for this relationship over mesoscale domains

•Improvement is obtained with:• additional predictors at coarser

space scales• predictors of slope along

prevailing wind direction

•KED with predictors from 3 scales and slope is the best interpolation method

Eidgenössisches Departement des Innern EDIBundesamt für Meteorologie und Klimatologie MeteoSchweiz

Part III:

Preliminary considerations on interannual to decadal variations

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Alpine Precip Trends (1971-2008)

wetter

dryer

Theil-Sen Slope (colour, end/beginning), Mann-Kendall Test (black contour α=5%)

„Raw“ Alpine Grid Dataset DJF

residual data errorsinhomogeneitiesSmall-scale noise network changes

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Alpine Precip Trends (1971-2008)

wetter

dryer

Theil-Sen Slope (colour, end/beginning), Mann-Kendall Test (black contour α=5%)

„Filtered“ Alpine Grid Dataset DJF

PCA-Filtering (Projection in Phasespace)Reduction in dimensionality from ~6000 stations to 15 PC-modesA robust basis for studying interannual variations

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Alpine Precip Trends (1971-2008)