ISSN:
1436-5065
Source:
Springer Online Journal Archives 1860-2000
Topics:
Geography
,
Physics
Notes:
Summary The quality control of rain amount data is usually based on a posteriori defined sets of empirical thresholds, e.g. when crosscheking neighbouring observations. While these procedures are well suited for very long time series, it is difficult to adopt the rules for time series of special observation periods with higher resolution in time. We propose a linear Kalman filter and present several methods, how to fit it onto the stochastic structure of the time series in the presence of many zeroes. These zeroes may indicate light rain or no rain and, therefore, are considered censored. They do not fit into the stochastic structure of the non-zero values and have to be treated separately. Fitting one out of four model parameters of the Kalman filter also determinates two other dependent ones. Only one model parameter has to be known from independent sources. The fitting algorithms are tested with artificial rain rate time series with known stochastic structure and several zero rain data points. Furthermore, applied to time series of observed hourly rain amounts for 4 consecutive winter months, the Kalman filter shows its sensitivity for faulty data points. The detection of conspicuous data by the method of Kalman filtering is discussed.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01030213
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