Estimation of Airport Reference Temperatures

Transcription

1 Estimation of Airport Reference Temperatures Ole Einar Tveito and Gudmund A. Dalsbø (P.O. Box 43, N-0313 OSLO, NORWAY) ABSTRACT The paper describes an approach to apply newly developed spatial interpolation techniques utilizing Geographic Information Technology to derive design values for planning and dimensioning. In this case the design values are airport reference temperatures. The reference values obtained by spatial interpolation are compared with the values estimated by traditional manual calculations using the most representative observations. The spatial interpolation method provides reference values most likely to be as reliable as the manual methods. It is flexible, reasonable fast, and easy to apply for any period (within the period 1957-present) at any location in mainland Norway. No. 9/2005 Oslo,

2 Objective The motivation of this investigation has been to calculate and evaluate reference temperatures (highest mean monthly maximum temperature (TAXM)) at Norwegian airports by applying an objective spatial interpolation technique. Reference temperatures have until now been determined manually by using data from synoptic or METAR observations. For climatological reasons it is preferred to use data representing a complete normal period of 30 years ( ), but at many airports complete series are not available. Furthermore, several airports have limited opening hours and do not necessarily observe at the time of the day when the maximum temperature is likely to occur. The reference temperatures for these airports have therefore been determined through a manual evaluation of data for every day. This process is very time-demanding, so according to International Civil Aviation Organization (ICAO) regulations a minimum period of five years has been used. The objective of this investigation is therefore to establish an objective, consistent and efficient methodology for determining reference temperatures for any airport, using the same data period. One main objective at the Norwegian Meteorological Institute during the last years has been to develop techniques for spatial distribution of climate elements, and by that establish gridded datasets providing information about certain climate elements at any location. Different types of reference values based on climate statistics are important for planning in several different sectors in the society, especially in relation to safety issues. This preliminary investigation is therefore also meant as a contribution to a possible approach using Geographic Information Technology-based methodology to derive such design values. Data and definitions In article of Annex 14 to the Convention on International Civil Aviation it is stated that a reference temperature shall be determined for all aerodromes. This is defined in article as "the monthly mean of the daily maximum temperatures for the hottest month of the year (the hottest month being that which has the highest monthly mean temperature). The temperature should be averaged over a period of years." According to Annex 3, article "climatological information should normally be based on observations made over a period of at least five years". The Norwegian airport operator AVINOR AS has defined a list of 49 airports where reference temperatures should be estimated. These are listed in table 1. The reference temperature is defined as the highest monthly mean value of daily maximum temperature (TAXM). The reference temperature Tˆ is defined as: TAXM j = yearn k = year1 i= 1 ( year year + 1) n N ( j) TAX 1 i N( j) Tˆ = max { TAXM } where i is day-index, N(j) number of days in month j. TAX is daily maximum air temperature (the highest temperature during 24 hours UTC). In all regions in Norway the highest j 2

3 TAXM occurs in July or August. In practise therefore only values for July and August will be analysed. Spatial interpolation of temperature The spatial variability of air temperature is strongly related to terrain and landscape characteristics. Tveito et al. (2000) developed a spatial interpolation method for monthly mean temperatures in Fennoscandia utilizing these relations in a residual kriging approach. The principle of residual kriging is to regard the spatial variability of temperature as a two component process. One component is a deterministic model, describing the influence of physiographical and geographical parameters. This model could be developed as a conceptual model, but is usually established using multiple linear regression with physiographical and geographical parameters as predictor variables 1. The resulting trend expression is then used to remove the influence of terrain and other geographical parameters. The resulting residuals can then be interpolated in space applying geostatistical methods, which are based on stochastic theory. This is therefore regarded as the stochastic component. The residual field is close to fulfil the assumptions which geostatistics are based on (e.g. Cressie 1993, Isaaks and Srivastava 1989). In the work by Tveito et al. (2000) five independent parameters describing the deterministic trend were identified. These are the altitude of the location, mean altitude within a 20 kilometre radius around the location, and the lowest altitude within this radius. The two last parameters are the geographical position, expressed in longitude and latitude. Distance to the sea is probably also a significant predictor (e.g. Benestad and Hanssen-Bauer, 2003), but this parameter was not included in the Fennoscandian model. Individual trend expressions are established for each month. The method has proven to be robust and reliable, and is also applied for daily values (Tveito et al, 2005). The five trend variables for the 49 airports are listed in table 1. Spatial interpolation procedure The data used as input to the spatial interpolation is monthly mean values of daily maximum temperatures. Temperatures from all weather stations are applied. A typical station distribution is shown in figure 2, showing all weather stations and the location of the airports. Normally approximately 160 weather stations observing temperature are operational. The temperatures at the weather stations are de-trended using the monthly trend expressions derived by Tveito et al. (2000). The remaining residuals are interpolated in space, and values for the airport are extracted. The inverse trend expression is then applied to estimate monthly TAXM at each airport. This procedure is repeated for each single July and August month for the period The distinction between parameters and variables can here be a bit confusing. Terrain properties such as elevation, slope, aspect etc. are parameters at a specific location. When analysing the influence of these parameters at several locations, they are regarded as variables in that analysis. 3

4 Table 1: List of airports, position and geographical parameters used in the temperature estimation. The UTM coordinates are only used in the spatial interpolation of the stochastic component. NAME ALT(M) LAT LONG MEAN ALT. MIN. ALT UTM_EAST UTM_NORTH Alta Andøya Banak Bardufoss Bergen Berlevåg Bodø Brønnøysund Båtsfjord Fagernes Florø Førde Hammerfest Harstad/Narvik/Evenes Hasvik Haugesund Honningsvåg Kirkenes Kristiansand Kristiansund Leknes Mehamn Mo i Rana Molde Mosjøen Namsos Narvik Gardermoen Rygge Røros Rørvik Røst Sandane Sandnessjøen Sogndal Stavanger Stokmarknes Svalbard* Svolvær Sørkjosen Torp Tromsø Trondheim Vadsø Vardø Værøy Ørland Ørsta-Volda Ålesund *Svalbard Airport is outside the area where spatial interpolation is valid, and spatial interpolation is therefore not applied for this airport. 4

5 Figure 1 Locations of airports (circles) and weather stations (triangles) used for the spatial interpolation. Airport reference temperatures The monthly mean daily maximum temperatures for each month are post-processed to estimate mean monthly maximum values for each airport. The highest of the mean July and August values for each airport is selected as reference temperature. Values are estimated for two different time periods: the standard normal period and the recent 5-year period The latter is selected since it overlaps with the period used for several manual estimations of reference temperatures at several airports. Table 2 shows the results of the estimations for the periods applying spatial interpolation as well as the manually calculated temperatures at 29 airports. The differences between the manually and spatially interpolated temperatures are also presented. Comparing manually estimated and spatially interpolated values is difficult, since none of them represent the absolute truth. Here the stability of the estimates compared with each other will therefore be addressed. As can be seen from table 2 as well as figure 2 the reference temperatures achieved by spatial interpolation for the period is systematically lower than the manually estimated temperatures. One reason for this might be that most of the manual calculations are 5

6 based on data from the last decade, which in some regions include some of the warmest summers during the last century. This can easily be seen in figure 2 where the estimated temperature based on the five-year period shows a better fit. Figure 3 shows the histograms of the difference, and the differences for the last shorter period are more centred on zero. The histograms show that the distribution of the differences is within 1.5 C, which is acceptable and within the uncertainty of the interpolation method. This is reflected in Figure 4 which shows the distribution of estimation error of mean monthly July temperatures for Fennoscandian stations in the period as results from a cross-validation analysis using the same method. What Figure 4 shows is that the uncertainty of the temperature estimate is approximately ±1 C. Table 2: Estimated reference temperatures (manual, interpolated and ) at 29 airports where manual estimates of reference temperature exist. Manual Spatial Spatial Airport Data period Estimated Estimated Difference Estimated Difference Andøya Båtsfjord Berlevåg Bodø Brønnøysund Evenes Bergen Florø Førde Hammerfest Haugesund Honningsvåg Kjevik Leknes Mo i Rana Molde Mosjøen Narvik Ørsta Volda Rørvik Røst Sandane Sandnessjøen Sogndal Stavanger Sørkjosen Stokmarknes Svolvær Værøy

7 Estimated(61-90) Estimated(98-02) Linear Linear Spatially interpolated Trend line y = x R 2 = Trend line y = x R 2 = Manually estimated Figure 2. Scatterplot of manually vs. spatially interpolated reference temperatures. Squares show spatially interpolated estimates based on the period , while the crosses represent estimates based on the period The solid trend line represent the latter period, while the dashed represents the normal period

8 Frequency 4 Frequency ,5 0,0 0,5 1,0 1,5 2,0-1,0-0,5 0,0 0,5 1,0 1,5 Figure 3. Histograms of the difference between manual and spatial interpolated estimates of reference temperatures at 29 airports for the period (left) end (right) Estimation error Figure 4. Estimation error of mean July temperatures in Fennoscandia (from Tveito et al.,2000). 8

9 Discussion and conclusions An evaluation and comparison of two different estimates, with different sources of uncertainty makes it almost impossible to draw substantial conclusions. However, the rather small difference between the estimates indicates that the spatial interpolation method can be used to establish realistic estimates of reference temperatures. The method is developed for mean monthly temperatures, and is not validated for mean monthly maximum temperatures. The method is however validated for interpolating daily mean temperatures, and show excellent performance especially in the summer season. Daily maximum and daily mean temperatures are not expected to perform very differently in the summer season. This does not mean that the method is perfect. Improved methods for spatial interpolation of temperature are under development, in order to produce high resolution gridded data sets. In the future these grids will probably be an important data source for estimation of different types of reference temperatures. The resulting reference temperature for 48 airports on the Norwegian main-land is presented in table 3. Values based on a five year data period will be very sensitive for anomalous climate variations. Therefore climatological reference values are usually established on the basis of normally 30-year data period. We thus recommend that reference values are established based on the 30-year period As this is the standard normal period these values will be consistent to other climatological reference values. The derived methodology can later easily be applied to establish reference values based on other, more recent data periods, e.g or

10 Table 3: Monthly mean daily maximum temperatures and reference temperatures (REF) at Norwegian airports for the time periods (recommended) and estimated by spatial interpolation Airport July August REF July August REF Alta Andøya Banak Bardufoss Bergen Berlevåg Bodø Brønnøysund Båtsfjord Fagernes Florø Førde Hammerfest Harstad/Narvik/Evenes Hasvik Haugesund Honningsvåg Kirkenes Kristiansand Kristiansund Leknes Mehamn Mo i Rana Molde Mosjøen Namsos Narvik Gardermoen Rygge Røros Rørvik Røst Sandane Sandnessjøen Sogndal Stavanger Stokmarknes Svalbard* * Svolvær Sørkjosen Torp Tromsø Trondheim Vadsø Vardø Værøy Ørland Ørsta-Volda Ålesund *Svalbard airport is outside the area covered by the spatial interpolation. This value is therefore based on synoptic observations from the airport for the period

11 References Benestad, R.E., and I. Hanssen-Bauer (2003) Empirical-based refinement of dynamically downscaled temperature scenarios in southern Norway. met.no Report 07/03 Klima, Norwegian Meteorological Institute, Oslo Cressie, N.O.A. (1991) Spatial statistics, Wiley & Son, New York, 900pp. Isaaks, E.H. and R.M.Srivastava (1989) An introduction to applied Geostatistics, Oxford University Press, New York, 561 pp Tveito, O.E., E.J.Førland, R.Heino, I.Hanssen-Bauer, H.Alexandersson, B.Dahlström, A.Drebs, C.Kern-Hansen, T.Jónsson, E.Vaarby-Laursen and Y.Westman. (2000), Nordic Temperature Maps, DNMI Report 9/00 KLIMA, Norwegian Meteorological Institute, Oslo Tveito,O.E., I.Bjørdal, A.O.Skjelvåg and B.Aune(2005) A GIS-based agro-ecological decision system based on gridded climatology, Met.Apps., 12:1,