stardex statistical and regional dynamical downscaling of ...the purpose of deliverable d11 is the...
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STARDEX
STAtistical and Regional dynamical Downscaling of EXtremes for European regions
EVK2-CT-2001-00115
Deliverable D11
ANALYSIS OF THE RESULTS OF THE COMPARISON BETWEEN THE REPRESENTATION OF EXTREMES
USING STATION DATA, UPSCALED STATION DATA AND NCEP-NCAR RE-ANALYSIS DATA.
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FOREWORD
The STARDEX project on STAtistical and Regional dynamical Downscaling of Extremes for European regions is a research project supported by the European Commission under the Fifth Framework Programme and contributing to the implementation of the Key Action “global change, climate and biodiversity” within the Environment, Energy and Sustainable Development. STARDEX will provide a rigorous and systematic inter-comparison and evaluation of statistical and dynamical downscaling methods for the construction of scenarios of extremes. The more robust techniques will be identified and used to produce future scenarios of extremes for European case-study regions for the end of the 21st century. These will help to address the vital question as to whether extremes will occur more frequently in the future. For more information about STARDEX, contact the project co-ordinator Clare Goodess ([email protected]) or visit the STARDEX web site: http://www.cru.uea.ac.uk/projects/stardex/ STARDEX is part of a co-operative cluster of projects exploring future changes in extreme events in response to global warming. The other members of the cluster are MICE and PRUDENCE. This research is highly relevant to current climate related problems in Europe. More information about this cluster of projects is available through the MPS Portal: http://www.cru.uea.ac.uk/projects/mps/ STARDEX is organised into five workpackages including Workpackage 2 on ‘Observational analysis of changes in extremes, their causes and impacts’ which was responsible for the production of this deliverable (D9). Workpackage 2 is co-ordinated by András Bárdossy from the Institut für Wasserbau, University of Stuttgart, Germany. STARDEX PROJECT MEMBERS UEA University of East Anglia, UK KCL King’s College London, UK FIC Fundación para la Investigación del Clima, Spain UNIBE University of Berne, Switzerland CNRS Centre National de la Recherche Scientifique, France ARPA-SMR Servizio Meteorologico Regionale, ARPA-SMR Emilia-Romagna, Italy ADGB University of Bologna, Italy DMI Danish Meteorological Institute, Denmark ETH Swiss Federal Institute of Technology, Switzerland FTS Fachhochschule Stuttgart – Hochschule für Technik, Germany USTUTT-IWS Institut für Wasserbau, Germany AUTH University of Thessaloniki, Greece
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D11 AUTHORS AND VERSION HISTORY
Lead author: Panagiotis Maheras, AUTH Contributing authors: Christina Anagnostopoulou, AUTH Konstantia Tolika, AUTH Final Version : 3 November 2003
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CONTENTS
STARDEX PROJECT MEMBERS..............................................................................2
1. INTRODUCTION ..................................................... ERROR! BOOKMARK NOT DEFINED. 2. DATA AND METHOD............................................. ERROR! BOOKMARK NOT DEFINED. 3. RESULTS .................................................................. ERROR! BOOKMARK NOT DEFINED. 4. SUMMARY AND CONCLUSIONS ........................ ERROR! BOOKMARK NOT DEFINED. 5. REFERENCIES: ........................................................ ERROR! BOOKMARK NOT DEFINED. APPENDIX: Partner contributions from AUTH
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1. Introduction
The purpose of deliverable D11 is the analysis of the results of the comparison between the representation of extremes using station data, upscaled station data and NCEP-NCAR re-analysis data over Greece.
2. Data The data set used consists of time series of NCEP-NCAR re-analysis data with
grid point resolution of 2.5oX2.5o, time series of daily temperature values (maximum, minimum and mean) and precipitation from 21 stations. The data of those 21 stations was also used for the development of the upscaled station grids. The study period runs from 1958 to 2000.
Figure1. Geographical distribution of the grid points under study.
Two uncorrelated areas in the Greek region were selected for the analysis of the results of this study, due to their differences in the orography and the influence of the sea: western and eastern Greece. The selected stations of the first area (Western-Greece) are Kerkyra, Ioannina, Agrinio and Kalamata, while selected stations of the second area (Eastern – Greece) are Alexandroupoli, Mytilini, Samos and Rodos. The grid points used for the analysis are the closest to the above stations (Table 1).
10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 32.5 35.030.0
32.5
35.0
37.5
40.0
42.5
45.0
47.5
50.0
1 2 3 4 5 6
7 8 9 10 11 12
13 14 15 16 17 18
19 20 21 22 23 24
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Table 1: Stations and the corresponded grid points West Greece
East Greece
Station Log. Lat. Alt.(m) Grid Station Log. Lat. Alt.(m) Grid
Kerkyra 19.92 39.62 4 14 Alexandroupoli 25.92 40.85 3.5 16 Ioannina 20.85 39.67 484 14 Mytilini 26.5 39.07 4.8 17 Agrinio 21.45 38.62 25 8 Samos 26.97 37.77 7.3 11 Kalamata 22.02 37.07 11.1 9 Rodos 28.08 36.38 11.5 11
3. Methodology We first produced grid point values (upscaling of station data) at exactly the
same grids employed by the NCEP-NCAR data points. Upscaling of station data to NCEP-NCAR data points obtained using the BLUE (Best Linear Unbiased Estimation) methods that, by definition, represent the best linear methodology for spatial data interpolation (Delhomme, 1978; De Marsily, 1986). Kriging is the most applied of the BLUE methods in geosciences (Delhomme, 1978; Gampolati and Volpi, 1979).
Kriging is the interpolation method performed by a system of linear equations that combine original data with values of the semi-variogram as derived by couples of points with original data and couples of all points with original data and the point for which the estimation is performed.
The performance of the method used was investigated by comparing time series of the observed maximum and minimum temperatures at the stations with the corresponding estimated time series at the grid points nearest to the station locations. The comparison of the annual and seasonal core STARDEX Diagnostic Extremes Indices for the three datasets was done using the Taylor diagrams (Taylor, 2001).
4. Results 4.1 Average and Standard Deviation (Stdev) BIASES.
• Temperature From table 2 it becomes evident that biases for the tmax90p index vary from grid
to grid and from season to season. On the other hand, the behavior of the tmin10p index is clearer. For the grids in the western part of the Greek area (Grid 8, Grid 9, Grid 14) the NCEP values are higher, as compared to the upscaled data (positive average biases), while for the grids in the eastern part of Greece the NCEP underestimate the upscaled grids (Table 2). Concerning stdev. biases it seems that the variability of the two data series for this index is almost the same (the biases are small). The results for the other temperature indices are also shown in Table 2.
• Precipitation The analysis of the biased averages of the indices prec90p, 641CDD, 644R5d and
646SDII (Table 3) demonstrated that the NCEP values are lower than the upscaled values in most of the cases both on annual and seasonal basis. On the contrary, for the 691R90N index the NCEP and upscaled data are quite similar (small average biases), while for the 692R90N the NCEP values are higher than the upscaled values.
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The analysis of the stdev. biases revealed that the NCEP data variability is smaller than the variability of the upscaled data, except of the cases of the 644R5d and 692R90N where the biases are noticeable large, negative for the former and positive for the latter case 4.2 Taylor Diagrams
• Temperature West Region
The variation of the correlation skill and the standard deviation ratio from the western Greek area is presented in figures 2-5 on annual and seasonal basis. On annual basis, the skill is higher for the temperature indices for the NCEP-Uobs. The highest correlation (0.8) is for tmax90p. The standard deviation ratio (inter-annual variability) is generally high for tmax90p and the 144HWDI and low for the other two indices. From the seasonal analysis of the taylor diagrams it can be said that the NCEP-skill varies from season to season and from grid to grid. For example in the case of winter (Ioannina –Grid 14) the standard deviation for the NCEP-Uobs is about correct for all the indices. Also the correlation skill reaches the value of 0.9 in autumn for the tmax90p. East Region
The same analysis was performed for the east part of the Greek region (figures 6-9), where the highest correlation skills are found for the tmin10p (>0.8) on an annual basis. This index shows the highest correlations for all seasons (especially the NCEP-Uobs). In the case of the autumn (Alexandroupoli – Grid 16) the correlation of tmin10p is a little bit higher than 0.9. It is also noticed that both symbols of the NCEP-Uobs and NCEP-Station are closer to the unit circle. Exceptions are the case of Samos-Grid 11 and Rodos-Grid 11 in summer.
• Precipitation West Region
Figures 10-13 present the variation of the correlation skill and the standard deviation ratio from the western Greek area for precipitation indices. On average, the correlation skill is lower then 0.5 on an annual basis while the inter-annual variability of both sets (NCEP-Uobs and NCEP – Stat) is quite low. The correlation skills for all the indices are also low on a seasonal basis (values <0.5), except of the case of the 641CDD where the correlation skill is between 0.9 and 0.95. East Region
In the eastern part of the Greek area the annual analysis of the precipitation indices appears in figures 14-17. Only the symbols of the 692R90N are further away of the unit circle (high inter-annual variability), where all the other symbols are lower from the unit circle (low inter-annual variability). The seasonal correlation variability is generally low but the index 641CDD presents again higher values in the case of winter. Also the 692R90N index symbols are far away of the unit circle, which shows a high seasonal variability.
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Table 2: Biases between NCEP data and Upscaling grid point data, for extreme temperature indices, on an annual and seasonal basis
BIAS 8
Average BIAS 9
Average BIAS 11 Average
BIAS 14 Average
BIAS 16 Average
BIAS 17 Average
BIAS 8 SD BIAS 9 SD BIAS 11 SD BIAS 14 SD BIAS 16 SD BIAS 17 SD
tmax90p Winter -1.9 1.0 -2.7 -3.0 -2.7 -2.1 -0.1 -0.4 0.0 0.0 -0.1 0.0 tmax90p Spring -2.0 2.2 -0.4 -1.5 -1.0 1.2 -0.1 0.2 0.4 0.3 0.4 0.4 tmax90p Summer -2.3 3.3 0.2 -0.3 -0.5 1.5 -0.4 0.4 0.3 0.4 0.2 0.4 tmax90p Autumn -1.1 1.5 0.1 -0.8 -0.8 0.8 0.0 0.3 0.2 0.3 0.2 0.6 tmax90p Year -1.3 2.8 0.1 -0.8 -0.9 0.7 -0.1 0.2 0.4 0.4 0.2 0.2
tmin10p Winter 1.8 1.2 -2.8 0.4 -1.9 -3.3 0.1 -0.3 0.0 0.1 0.0 0.0 tmin10p Spring 2.1 0.8 -2.3 0.2 -1.4 -2.8 0.1 0.1 0.1 0.3 0.2 0.2 tmin10p Summer 2.6 0.5 -1.9 0.0 -1.3 -2.7 0.0 -0.1 -0.4 -0.3 -0.1 -0.1 tmin10p Autumn 1.9 0.8 -3.1 0.1 -1.6 -3.2 -0.2 -0.4 0.0 -0.1 0.2 0.0 tmin10p Year 2.0 1.0 -2.5 0.4 -1.7 -3.1 -0.2 -0.3 0.0 -0.1 0.0 0.1
125Fd Winter 0.0 -1.7 3.4 -3.0 10.4 20.3 0.0 -1.5 2.9 -0.5 1.7 5.1 125Fd Spring 0.0 -0.3 0.4 0.0 2.0 5.0 -0.3 -0.8 0.7 0.0 0.9 2.1 125Fd Summer 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 125Fd Autumn 0.0 0.0 0.0 0.0 0.9 1.9 0.0 0.3 0.2 0.0 1.5 2.0 125Fd Year 0.0 -2.0 3.9 -3.0 13.2 27.2 -0.3 -1.3 2.8 0.3 1.9 4.7
144HWDI Winter 2.9 -0.9 -2.0 4.0 0.1 1.9 0.8 -0.5 0.1 0.3 1.4 2.3 144HWDI Spring 7.5 -2.1 4.2 1.0 1.8 2.7 2.6 2.9 2.7 -1.2 -1.8 -0.3 144HWDI Summer 0.8 -2.3 9.6 1.9 2.5 3.3 6.0 -1.7 5.8 1.2 0.0 0.8 144HWDI Autumn 3.6 -4.7 2.4 0.2 1.0 2.5 0.9 -2.3 -1.6 -0.6 -2.9 -1.6 144HWDI Year 15.2 -9.1 15.2 7.6 5.1 10.9 12.7 6.0 10.9 0.0 0.8 0.6
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Table 3: Biases between NCEP data and Upscaling grid point data, for extreme precipitation indices, on an annual and seasonal basis
BIAS 8 Average
BIAS 9 Average
BIAS 11 Average
BIAS 14 Average
BIAS 16 Average
BIAS 17 Average
BIAS 8 SD
BIAS 9 SD
BIAS 11 SD
BIAS 14 SD
BIAS 16 SD
BIAS 17 SD
prec90p Winter -12.7 -14.5 -15.7 -15.2 -2.5 -12.0 -5.5 -4.3 -4.6 -3.2 -0.3 -4.3 prec90p Spring -8.9 -7.3 -9.4 -8.1 -1.3 -4.2 -1.7 -4.8 -5.5 -4.1 -0.6 -2.7 prec90p Summer -6.0 -6.3 -1.6 -0.5 -0.2 1.9 -6.1 -7.0 -8.7 -5.3 -1.2 -1.9 prec90p Autumn -14.5 -13.3 -12.7 -21.1 -5.6 -10.4 -5.1 -5.5 -8.6 -7.0 -2.1 -3.7 prec90p Year -12.9 -11.8 -14.3 -14.5 -3.4 -9.3 -2.4 -2.7 -3.2 -2.7 -0.9 -2.4
641CDD Winter -1.9 -1.5 -0.2 -3.2 -2.9 -4.6 -0.5 0.6 0.3 -1.2 -3.9 -1.9 641CDD Spring 0.3 3.4 -6.4 -3.3 -3.6 -4.6 2.5 2.4 -2.5 -0.1 0.0 -0.2 641CDD Summer -17.3 3.8 -22.5 -8.6 1.7 -13.7 0.2 2.4 3.2 -2.5 2.7 -3.8 641CDD Autumn -2.5 -0.4 -3.8 0.0 -0.6 -1.5 1.7 0.9 -0.1 2.0 -2.2 0.1 641CDD Year -17.0 5.6 -41.3 -7.6 1.7 -14.5 -2.4 -0.9 -3.2 -3.2 2.7 -8.0
644R5d Winter -46.9 -56.3 -48.4 -51.5 -3.1 -36.7 -25.7 -24.9 -14.1 -20.1 -1.2 -10.4 644R5d Spring -24.6 -16.4 -17.9 -15.8 6.6 1.9 -6.5 -13.6 -14.4 -13.0 3.1 -9.5 644R5d Summer -2.6 -9.4 11.7 5.5 8.5 22.0 -8.2 -9.5 0.7 -5.5 4.4 4.2 644R5d Autumn -47.8 -36.2 -32.1 -61.3 -8.8 -22.0 -16.0 -22.7 -28.3 -30.6 -2.4 -11.1 644R5d Year -61.5 -56.4 -55.8 -70.0 -2.0 -32.2 -20.9 -22.6 -14.7 -27.3 1.8 -11.5
646SDII Winter -4.9 -5.7 -6.6 -6.5 -1.6 -5.2 -1.6 -1.5 -1.5 -1.3 -0.5 -1.2 646SDII Spring -3.3 -2.7 -3.2 -2.8 -0.3 -1.2 -0.4 -1.5 -1.6 -1.4 -0.3 -0.7 646SDII Summer -2.8 -3.0 0.3 0.0 0.0 0.8 -2.5 -3.2 -3.5 -2.5 -0.2 -0.7 646SDII Autumn -5.4 -5.6 -5.4 -8.0 -2.3 -4.6 -1.9 -1.8 -3.4 -2.4 -0.6 -1.5 646SDII Year -4.7 -4.5 -5.5 -5.2 -1.2 -3.4 -0.9 -1.1 -1.2 -0.8 -0.2 -0.4
(Continued)
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Table 3: (Continued) 691R90T Winter -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 691R90T Spring -0.1 -0.1 -0.1 -0.1 0.0 -0.1 0.0 0.0 -0.1 -0.1 0.0 -0.1 691R90T Summer 0.0 -0.1 0.1 -0.1 -0.1 0.0 -0.1 -0.1 -0.1 -0.1 0.0 -0.1 691R90T Autumn -0.1 -0.1 0.0 -0.1 -0.1 -0.1 0.0 0.0 -0.1 -0.1 0.0 0.0 691R90T Year -0.1 -0.1 -0.1 -0.1 0.0 -0.1 0.0 0.0 0.0 0.0 0.0 0.0
692R90N Winter 0.8 0.3 0.5 1.0 0.7 1.2 0.2 0.1 0.4 0.3 0.1 0.4 692R90N Spring 0.5 0.1 1.1 1.5 1.0 1.9 0.4 0.4 0.1 0.7 0.6 0.8 692R90N Summer 0.7 0.0 0.5 1.0 0.7 1.4 0.2 0.9 0.7 1.1 1.1 1.0 692R90N Autumn 0.6 0.2 0.4 1.0 0.3 0.7 0.4 0.2 0.1 0.5 0.3 0.5 692R90N Year 2.5 0.5 2.5 4.6 3.0 5.3 0.7 0.5 1.6 2.4 0.9 2.4
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5. Conclusions From this study it becomes evident that the correlation skills and the standard
deviation ratios vary from grid to grid, from station to station and from season to season. Generally it could be supported that for the temperature indices the correlation skills are much higher both on an annual and a seasonal basis. Also the NCEP-Ubs set are closer to the unit circle in the Taylor diagram, suggesting that both the annual and the seasonal variability of this couple are more correct? Representative? as compared to the NCEP-Station set.
6. References Delhomme J. P., 1978: Application de la théorie des variables régionalisées dans les
sciences de l’eau. Bulletin de BRGM, 3(4): 341-375. De Marisily G., 1986a: Quantitative Hydrogeology. Academic Press: 464 pages Gampolati G., Volpi G., 1979: A Conceptual Deterministic Analysis of the Kriging
Technique in Hydrology. Water Resources Research, 15 (3): 625-629. Taylor E. K., 2001: Summarizing multiple aspects of model performance in a single
diagram. Journal of Geophysical Research, Vol. 106: 7183-7192.
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Figure 2: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue symbols) and NCEP-Stat Kerkyra (Red symbols), on an annual and seasonal basis
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Figure 3: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Ioannina (Red symbols), on an annual and seasonal basis
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Figure 4: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Agrinio (Red symbols), on an annual and seasonal basis
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Figure 5: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue symbols) and NCEP-Stat Kalamata (Red symbols), on an annual and seasonal basis
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Figure 6: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue symbols) and NCEP-Stat Alexandroupoli (Red symbols), on an annual and seasonal basis
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Figure 7: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue symbols) and NCEP-Stat Mytilini (Red symbols), on an annual and seasonal basis
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Figure 8: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Samos (Red symbols), on an annual and seasonal basis
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Figure 9: Taylor diagrams for extreme temperature indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Rodos (Red symbols), on an annual and seasonal basis
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Figure 10: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue symbols) and NCEP-Stat Kerkyra (Red symbols), on an annual and seasonal basis
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Figure 11: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Ioannina (Red symbols), on an annual and seasonal basis
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Figure 12: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Agrinio (Red symbols), on an annual and seasonal basis
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Figure 13: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Kalamata (Red symbols), on an annual and seasonal basis
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Figure 14: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue symbols) and NCEP-Stat Alexandroupoli (Red symbols), on an annual and seasonal basis
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Figure 15: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Mytilini (Red symbols), on an annual and seasonal basis
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Figure 16: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue
symbols) and NCEP-Stat Samos (Red symbols), on an annual and seasonal basis
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Figure 17: Taylor diagrams for extreme precipitation indices, for NCEP-Uobs (Blue symbols) and NCEP-Stat Rodos (Red symbols), on an annual and seasonal basis