Calibration of weather radar data in different space ...

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Remote Sensing and Hydrology 2000 (Proceedings o f a s y m p o s i u m held at Santa Fe, N e w M e x i c o , U S A , April 2 0 0 0 ) . I A H S Publ. no. 267, 2 0 0 1 .

Calibration of weather radar data in different space and time scales H. B E R G M A N N Institute A-8010

for Hydraulics Graz, Austria

and Hydrology,

Technical

University

of Graz, Mandellslrafie

9,

e-mail: christophe.mch@,ioanneurri.ac,at

R. SCHATZL, H. P O Z A R N I K Styrian

Government,

Faia-Hydrographical

Service,

Slempfergasse

7, A-8010

Graz,

Austria

C. A. R U C H & T. H A R U M Institute A-8010

for Hydrogeology Graz, Austria

and Géothermie,

Joanneutn

Research,

Elisabethstrafie

16/11,

Abstract This paper describes a new method for calibrating weather radar data with raingauge data. Since calibration with raingauges in the hydrographical network is a scale problem, data from a high-density raingauge net are used to develop a calibration algorithm. This algorithm is then transferred to the region of interest. In this paper the method is described and applied for an advective event, which is considered at different space and time scales. K e y w o r d s Austria; calibration; "EinfluBfunktionenverfahren"; grid; Pollau basin, Austria; w e a t h e r radar data; scale; Styria

INTRODUCTION Precipitation data are required for many hydrological analyses and the demand for precipitation fields on a regular grid is growing dramatically as hydrological models become increasingly linked to geographic information systems. Historically, most methods for estimating areal and gridded precipitation from point data have fallen into three major groups: graphical, topographical, and numerical. However, obtaining reliable estimates is particularly difficult when the areal coverage provided by the surrounding precipitation stations is sparse or when precipitation characteristics vary greatly with location. The purpose of this study is to present a simple method for distributing point measurement on different time and space scales using radar technology offering fine spatial ( 1 k m ) and time (10 min) scales as well as the possibility of measuring precipitation values at different horizontal levels. Radar precipitation values are calibrated over a small catchment with a high raingauge density and extended spatial results are compared with interpolated data in larger areas. 2

WEATHER RADAR DATA The great advantage of radar data compared with raingauge data lies in its very high spatial ( l x l k m p i x e l ) and temporal resolution (10 min, and in the near future 5 min). The fact that radar data are grid data and therefore very suitable for the new generation rainfall-runoff models, which are mainly based on the use of GIS, could suggest that 2

Calibration of weather radar data in different space and time scales

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radar data are a perfect instrument for recording precipitation. However, comparisons between radar data and point measured raingauge data show that radar data are affected b y some error sources (Kôck, 1999); some of the most important of these errors are considered here. There is a basic distinction between: (a) errors at the radar station due to errors in the configuration of the station or attenuation of the radar beam through the random errors in the transformation of the reflectivity measured at the radar station into rain rates (so called Z-R relation), and (b) errors which influence the radar beam itself such as attenuation of the radar ray through mountains, ground clutter or absorption through several precipitation fronts. These errors mean that the quantitative use of radar data for hydrological purposes are accompanied by great problems. Also, it should be noted that radar data in Austria were only twodimensional until the end of 1998; the radar scanned the volume over each pixel ( 2 x 2 km in this old version) in steps of 1 km height but only the maximum value of this volume was projected to the ground (so called maximum projection), a fact, which caused additional errors especially during convective events (Bergmann et al., 1998a,b). Since the beginning of 1999, three-dimensional radar data have been available, and it is now possible to obtain radar data for any height (1 km steps), a development that should allow important improvements. Nevertheless it is necessary to calibrate radar data with raingauge data measured at the surface. A calibration method developed at the Institute of Hydraulics and Hydrology is presented here together with a practical example.

C A L I B R A T I O N O F R A D A R DATA The most practical means of improving radar data is by calibration with raingauge data. Up to now it has been usual to compare raingauge values with the radar value for the pixel in which raingauge is situated, whereas some methods also use the radar values of neighbouring pixels as a control for plausibility. These comparisons were made at different time (10 min to 1 day) and spatial scales. Thus for each pixel where a raingauge is situated, a factor of improvement can be calculated using different methods; this/these factor(s) can then be interpolated across the whole catchment area to obtain improvement of the radar data for all relevant pixels. Important problems with these methods are first that point raingauge values are compared with areal weather radar values, and secondly the disadvantage that only a fraction of the radar information can be used for the comparison. For this reason a new method for the calibration of radar data was developed at the Institute of Hydraulics and Hydrology; the principle is to make the comparison together with the calibration on the basis of the grid of the weather radar. Therefore it is necessary to transform the raingauge values into grid data b y a suitable method. The so-called "EinfluBfunktionenverfahren" developed at the Institute is used. But whatever method is used for transformation of the point raingauge values into grid values with a pixel size of 1 x 1 km, there is a scale problem, since the raingauge net of the Hydrographical Services in Austria has a density of about one raingauge per 100 k m . The solution to the problem lies in using raingauge data from a high density raingauge net, where the density is comparable with the information density of the weather radar. For this purpose the measuring net of the Pdllau hydrological research basin was chosen; seven raingauges are available in 2

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H. Bergmann et al.

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the 58.3 k m catchment (Fig. 1). The calibration algorithm found in Pôllau can b e transferred to the regions of interest; in this paper the Raab River basin with 18 raingauges in 2020 k m (Fig. 2) is considered. 2

Fig. 1 Hydrological research basin Pôllau with weather radar grid ( l x l km); • raingauges. imp:: ffr :::: rf~H~ " —H-F

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