1. Introduction
Numerous national and regional studies have been conducted to assess measurement Ž
errors in solid, mixed and liquid precipitation e.g., Hamon, 1973; Tammelin, 1975; Goodison, 1978; Allerup and Madsen, 1979,1980,1986; Allerup et al., 1997; Aune and
Førland, 1985; Golubev, 1986; Sevruk, 1986; Goodison and Yang, 1995; Yang et al., .
1995 . In 1971, an intercomparison study dealing with errors of liquid precipitation was Ž
. initiated by the World Meteorological Organization WMO , and the analyses resulted in
construction of a statistical correction model generally applicable for correction of liquid Ž
. precipitation WMO, 1982 . A similar WMO intercomparison study was initiated in
1986 leading to various suggestions for statistical correction models generally applicable Ž
. for correction of solid and mixed precipitation WMO, 1998 . Within the framework of
Ž the above mentioned WMO studies, a statistical model for correction of liquid Allerup
. Ž
. and Madsen, 1979 as well as solid and mixed precipitation Allerup et al., 1997 was
developed, in the following referred to as ‘‘the Comprehensive Model for Correction of Precipitation’’.
In the present study, and as required by the comprehensive model, the meteorological Ž .
Ž . parameters needed for the correction to be carried out are:
i rain intensity,
ii Ž .
proportion of snow, and averages of continuous measurements during precipitation, iii Ž .
wind speed and iv air temperature, all measured at the gauge station. If continuous measurements during precipitation are not available at the meteorological stations, some
Ž studies use daily averages of maximum and minimum temperature instead Yang et al.,
. Ž
. 1999 . WMO 1998 recommends that the meteorological parameters should be mea-
sured at the gauge station, and wind speed be recorded at gauge level. However, in many countries existing networks of rain gauge stations do not offer such on-site measure-
ments, and measurements must be extrapolated from other nearby weather stations. An important question when using this extrapolation procedure is how can the error on the
estimated correction factor be evaluated. The aim of this paper is to answer, on the one
Ž . Ž .
hand whether missing on-site observations of wind speed V , temperature T , rain Ž .
Ž . intensity I and snow fraction a , i.e. the fraction of precipitation amount fallen as
snow, can be substituted by remote measurements, and, if so, how far away these data can be collected if the accuracy must be within certain confidence limits.
The motivation behind the study is that there is a practical need for correcting daily precipitation amounts in Denmark, operationally as well as historical data. For this
reason the comprehensive correction model was implemented, using a system of gauges for operational correction of point precipitation. For the application of this system,
Denmark was subdivided into 12 sub-regions each of them being as homogeneous as possible with respect to wind speed, temperature, and precipitation patterns. This
assumption seems reasonable, considering the limited geographical extent of Denmark
Ž .
and the structure of a typical atmospheric pressure system Petersen et al., 1981 . Ž
. An automatic weather station basic station is placed in the center of each of the
Ž .
sub-regions see map in Fig. 1 . At the precipitation stations a , V, T and I information is not available, and thus the question arises whether a , V, T and I can be extrapolated
from a remote site, e. g., the automatic weather station.
Fig. 1. Map of automatic weather stations in Denmark.
Data from the 12 automatic weather stations were used. Wind speed recorded at 10 m level, air temperature, and amount, as well as duration of precipitation were recorded
hourly. The shelter conditions were well described at all stations and taken into account when estimating the true wind speed V at gauge level. The precipitation type for
estimation of a was observed every 3 h at nearby weather stations.
The criteria for ‘‘accepting’’ remote a , V, T and I information will be defined in detail below. Both temporal and spatial aspects enter into such analyses, and it could be
anticipated that non-isotropic properties in the spatial distribution of wind speed will influence ‘‘where’’ missing wind information can be collected adequately.
It is, nevertheless, the intention to make these substitution procedures operational in ordinary databases in meteorological offices, and therefore the ambition is to create
general rules for substitution of a , V, T and I information, depending only on distance.
2. Methods
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