Spatial Interpolation of daily Temperature and Precipitation for the Fennoscandia
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1 Spatial Interpolation of daily Temperature and Precipitation for the Fennoscandia Cristian Lussana and Ole Einar Tveito (1) Norwegian Meteorological Institute, Oslo th EUMETNET Data Management Workshop, St. Gallen, Switzerland, 28th 30th October 2015
2 Norwegian Meteorological Institue
3 Nordic Gridded Climate Dataset NGCD An observation gridded dataset for temperature and precipitation covering Finland, Sweden and Norway. - Spatial resolution 1Km x 1Km - CRS: EPSG Projection ETRS89 / ETRS-LAEA - Temporal resolution: daily - Time range: Data sources: ECA&D, eklima.met.no, SMHI, FMI Nordic observation gridded dataset will be an outcome of the Nordic Framework for Climate Services (SMHI, FMI, MI, (DMI,IMO)) NGCD first versions: 2 from MET Norway, 1 from FMI and 1 from SMHI Norwegian Meteorological Institue
4 RR daily precipitation + 24h PREC, Element descriptions in ECA&D: Norway: id=rr2 (D-1) 06UTC -> D 06UTC; Sweden: id=rr9 D 06UTC -> (D+1) 06UTC; Finland: id=rr5 D > (D+1) 07.30UTC; Norwegian Meteorological Institue
5 TG daily mean temperature + Daily mean temperature, Element descriptions in ECA&D: Norway: id=tg9 (D-1) 6UTC->D 6UTC; Sweden: id=tg6 average using TN,TX,06,12,18; Finland: id=tg6 average using 8 observations; Norwegian Meteorological Institue
6 MET Norway Daily mean Temperature Residual Kriging (RK) Optimal Interpolation (OI) Daily accumulated precipitation Multi-Scale Optimal Interpolation (MSOI) Both OI products includes an automatic data quality control procedure (described in poster #14, Data Quality Control of Temperature and Precipitation in-situ observations based on Spatial Interpolation, Cristian Lussana and Ole Einar Tveito) 6
7 TEMP1d: Residual Kriging Residual kriging: ^ n T = TS + TD Kriging (or any spatial interpolation method) 0 )u(t λt(u ) )u( )u( mm )u( ii i 1 Trend predictors: Altitude (station) Mean altitude within a 20 km circle around the station Minimum altitude within a 20 km circle around the station Longitude Latitude Linear stepwise regression is used to define the trend. External trend/drift (linear regression) 7
8 Grids of the independent variables Latitude Longitude DEM DEM_MEAN DEM_MIN 8
9 Coefficients terrain Coefficients position Regression coefficients Month -0.5 ALT DEM_MEAN DEM_MIN LAT LONG 9
10 Trend climatological first guess 10
11 TEMP1d: OI Large(coarser) scale trend estimation + OI introduces the Local(finer) scale 11
12 TEMP1d: OI NGCD senorge2 12
13 MET Norway TEMP1d - Evaluation BLACK= Uncertainty gridpoints RED= Uncertainty, Large scale only BLUE= Uncertainty station points Norwegian Meteorological Institue
14 MET Norway TEMP1d - Evaluation BLACK= Uncertainty gridpoints Norwegian Meteorological Institue
15 MET Norway TEMP1d - Evaluation influence of station density/distribution Norwegian Meteorological Institue
16 MET Norway TEMP1d - Evaluation influence of station density/distribution Norwegian Meteorological Institue
17 MET Norway TEMP1d - Evaluation Norwegian Meteorological Institue
18 Spatial Interpolation Method based on Multi-scale Optimal Interpolation (Prec) Step 0: Identification of Precipitation Events (Observed Areas of Precipitation) (given the Station distribution) Event A Event B Event C Events D,... 18
19 Spatial Interpolation Method based on Multi-scale Optimal Interpolation (Prec) Given a single Event, the spatial interpolation is based on an iterative process: Coarser scale Finer scale OI OI OI Given a predefined (horizontal) spatial scale. OI assumptions: Additive error model: obs scale = truth scale +err scale back scale = truth scale +err scale Gaussian errors: err scale = N(0,CovMat) CovMat = f(scale,vertical coord) OI (through leave-one-out cross validation) is used to optimize the influence of the vertical coordinate in the error covariance matrix 19
20 Multi-Scale Optimal Interpolation Step-by-step: from coarser to local scale 20
21 Multi-Scale Optimal Interpolation Step-by-step: predicted field 21
22 Multi-Scale Optimal Interpolation Evolution in time 22
23 Multi-Scale Optimal Interpolation Consistency along the borders 23
24 Case study: the New Year's Day Storm 1992 Aune, B., and K. Harstveit. "The storm of January 1st 1992." DNMI Rapport NR 23 (1992): Norwegian Meteorological Institue
25 the New Year's Day Storm Norwegian Meteorological Institue
26 the New Year's Day Storm Norwegian Meteorological Institue
27 Summary Within the NFCS, NORDGRID activity, we're establishing several observation-based gridded dataset of daily precipitation and temperature for the Fennoscandia region covering the period Given the station distribution we expect to correctly describe the TEMP/PREC state down to the meso-beta scale (20-200Km). Bayesian/Residual Kriging spatial interpolation of precipitation and temperature show encouraging results. Temp: on the average, Temperature analysis uncertainty is estimated to be between 0.6 C in the summer and 1.5 C in the winter. Prec: Visual inspection of precipitation fields show realistic feature. Quantitative evaluation needed. 27
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