Establishing a high-resolution precipitation dataset for the Alps

Transcription

1 Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Establishing a high-resolution precipitation dataset for the Alps F. A. Isotta, C. Lukasczyk, and C. Frei 14 th September 2011

2 Content Introduction Status of data collection Gridding - first results Next steps 2

3 Content Introduction Status of data collection Gridding - first results Next steps 3

4 EURO4M at MeteoSwiss General objectives Develop and analyze a gridded dataset of daily precipitation, covering the entire Alpine region and resolving significant heavy precipitation events of the past 40 years. Compare and evaluate other EURO4M datasets with gridded station data focusing on extreme events; e.g. evaluate high-resolution model based reanalyses. Build on/extend previous activities at ETH (Swiss Federal Institute of Technology Zurich), see Frei and Schär, 1998 (> 6000 stations). Upgrade to up-to-date data quality status (if feasible). Enhance station density. Re-establish formal data agreements. NASA 4

5 Content Introduction Status of data collection Gridding - first results Next steps 5

6 Data collection Domain: Lon E, lat N (including flatland & adjacent hill ranges) Countries: Austria, Croatia, France, Germany, Italy, Slovenia and Switzerland Typical inter-station distance: km Daily sums (ca. 06:00-06:00), (1961-) 1971-recent September

7 Data contributions Austria Lebensministerium ~1300 (960) stored Croatia Meteorological service ~200 (?) Data transfer in progress France Météo-France ~2500 (?) Negotiating Germany DWD ~1140 (~950) Data storage in process Italy Regional & national services Climatological Archive for Northern Italy ( Total # ( instantaneous ) ~615 (450) stored Improve data density Slovenia Hidrometeorološki Zavod ~264 (170) stored Switzerland MeteoSwiss ~700 (440) stored Only a part of the (available) data from rain-gauges can be used (quality, e.g UTC,...). 7

8 Stations density 16 # stations per 1000 km2 and year Austria Germany Switzerland Croatia Italy Slovenia # stations/1000 km Year France: no formal agreement yet. Croatia: better density with new delivery (in progress). 8

9 Italy 16 # stations per 1000 km2 and year Aosta Piemonte Liguria Lombardia Emilia-Romagna Veneto Trentino Alto Adige Friuli # stations/1000 km Year 9

10 Altitudinal distribution Altitudinal distribution fraction of rain-gauges and surface area mmsl 10

11 Content Introduction Status of data collection Gridding - first results Next steps 11

12 Gridding method Combination of local regression (PRISM, for monthly climatology) and distance angular weighting (SYMAP, scaled daily values). Special features: - weighting for directional isolation accounts for clustering - adaptive search radius improves flexibility with variable station density - climatological scaling introduces small-scale topographic effects and reduces biases from non-representative station distributions PRISM = Precipitation-elevation Regression on Indipendent Slopes Model (Daly et al. 1994, 1997; Schwarb et al. 2001) SYMAP = Synagraphic Mapping System (Shepard 1968, 1984; Frei & Schär 1998) 12

13 Higher spatial resolution Precipitation/Extreme events (precipitation sum, mm): km grid 5 km grid Transnational Hydrological modelling 13

14 Higher observational density E-Obs vs preliminary results (Precipitation sum, mm, 22 August 2005) E-Obs (ENSEMBLES) ~20 km grid spacing (Europe), coarse input data (Haylock et al., 2008; Klok and Klein Tank, 2008; van den Besselaar et al., 2011) Alpine Grid 5 km grid spacing, high-resolution station data 14

15 Extreme events Transnational Hydrological modelling European standard grid ETRS89-LAEA 15

16 Content Introduction Status of data collection Gridding - first results Next steps 16

17 Next steps Derive indices of precipitation extremes (dry and wet) 90% - Quantile daily precipitation (mm) Mean maximum annual dry-day period (d) Frei and Schmidli (2005) 20 km grid, daily, Implement basic data quality procedures Check for extreme/impossible value Check for long period without precipitation Crossvalidation (spatial consistency) 17

18 Next steps Evaluate regional re-analyses and climate models (RCMs) Mean Precipitation (mm, DJF) MeteoSwiss 2006 OBS=PRISM 50 km HADAM3H=GCM 50km RCMs driven by GCM Evaluate other precipitation datasets of EURO4M Frei et al. 2003, 2005, 2006 Continue data collection efforts (France and Italy) Preliminary version of gridded dataset by March

19 Conclusions Transnational dataset, consistent climatology over the domain. Huge effort to collect all the data: Political obstacle Concern of the meteorological offices about station data distribution and competition to already existing (or in development) products (e.g. gridded climatological datasets). 19

20 Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Thank you for your attention!