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Overview

A Method for D aily T emperature D ata I nterpolation and Q uality C ontrol B ased on the S elected P ast E vents Presentation for the 6 th Seminar for Homogenization and Quality Control in Climatological Databases Gregor Vertačnik Budapest, May 2008. Overview. Purpose

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Overview

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  1. A Methodfor Daily Temperature Data Interpolationand Quality Control Basedonthe Selected Past Events Presentation for the 6th Seminar for Homogenization and Quality Control in Climatological Databases Gregor Vertačnik Budapest, May 2008

  2. Overview • Purpose • Description • The selection of similar days • Interpolation • Examples • Issues and disadvantages • Conclusion

  3. Purpose • Missing data interpolation and quality control of daily air temperature series at climatological stations (T7, T14, T21, Tmin, Tmax) • In simple methods (e.g. with monthly correction factors) the same climate statistics regardless the weather type at given day is used • Typical temperature diurnal ranges and spatial patterns in complex terrain (Slovenia): • Northeasterly föhn wind, Bora (warm and windy littoral, cold interior) • Temperature inversion in valleys and basins (colder nights, fog, larger/smaller diurnal range) • Aim of a new method: the use of climate statistics for the corresponding weather type • Interpolation improvement: • Reduction of interpolation error (standard deviation) • Better mean values for longer periods (month)

  4. Climatological stations in the complex terrain of northwestern Slovenia, 1980: mountain-, plateau-, slope-, valley/basin-stations

  5. An example of strong horizontal temperature (Tmax, yellow) gradient due to daily precipitation gradient (blue), August 29, 2003 (Val Canale flood). Stations below 1000 m are marked by a red circle.

  6. Description • Temperature interpolation at the target station on the chosen (target) day • Two-step method: • Selection of the most similar days to the interpolated one • Interpolation • Use of temperature ranges and the spatial pattern: • Measurements before/after and at the interpolation time (e.g. for Tmin, T21 the day before, Tmin, T7, T14) • User-defined or the best-correlated nearby stations

  7. The selection of similar days • Minimum weighted Euclidean distance • Input: temperature data at reference stations at the target and a similar day • Weights based on Pearson correlation coefficient • Two types of similarity: • Range and spatial pattern (weather phenomena) • Absolute values (air mass) Temperature time series on Rudno polje (Pokljuka) 18-19 July, 2007 (T0) and arbitrary similar series (T1, T2)

  8. Basic weights: • Normalized (standardized) average temperature deviation of a similar day from the target day: • Normalized (standardized) weighted Euclidean distance between a similar and the target day:

  9. Interpolation • Basis: mean differences between the values of the reference variable at a reference station and the interpolated variable at the target station in the set of similar days • Temperature estimation for each reference station • Weighted mean of estimations • Corrected for the number of days with the data An example of temp. estimatations at the reference stations, the final result and the measured value (in brackets)

  10. Examples • Minimum temperature in Portorož 2006-2007: • Reference stations: • Bilje • Bilje, Postojna • Reference variable: Tmin • Var. for the selection of simil. days: • Tmin • Tmin, T21_y, T14 • T14_y, T21_y, T7, Tmin, T14, Tmax • 30 similar days • Reference period: 1991-2005 • p1=1, p2=1, p3=2,kdev=0.5 Standard deviation of the error in °C (Bilje + monthly correct. 1.80, Postojna + monthly correct. 2.35) Topography in western Slovenia with marked station locations

  11. Minimum temperature at Ljubljana Airport, 2003-2007: • 5 reference stations (highest correlation) • Reference variable: minimum temperature • Reference period: 1995-2002 • Var. for the selection of similar days: T21 (the day before), T7, Tmin, T14, Tmax • p1=1, p2=1, p3=2,kdev=0.97 Standard deviation depending on the value of kdev Standard deviation of the error series Result comparison, March 2004

  12. Issues and disadvantages • The choice of the weighting factors (depend on variables, stations) • How many days to select and variables to include? • Homogenous series strongly prefered! (possible solution iterative process?) • Time consuming • Sometimes impossible to infer on local phenomena (lack of stations): • wind (e.g. Karavanke föhn) • valley/basin fog • showers and thunderstorms  the reason for a large part of the variance remained unexplained (other meteorological variables and data at the target station should be included)

  13. Conclusion • Lower interpolation error compared to the most simple method (monthly correction factors) • More stations and variables for the selection of similar days usually improve interpolation results • The method is unable to recognize some local weather phenomena → other meteorological variables should be included • Optimal parameter values vary from case to case • Homogenous series strongly prefered!

  14. Many thanks for your attention!

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