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Lookup NU author(s): Professor Andras BardossyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The number of personal weather stations (PWSs)with data available through the internet is increasing graduallyin many parts of the world. The purpose of this studyis to investigate the applicability of these data for the spatialinterpolation of precipitation using a novel approach basedon indicator correlations and rank statistics. Due to unknownerrors and biases of the observations, rainfall amounts fromthe PWS network are not considered directly. Instead, it isassumed that the temporal order of the ranking of these datais correct. The crucial step is to find the stations which fulfilthis condition. This is done in two steps – first, by selectingthe locations using the time series of indicators of highprecipitation amounts. Then, the remaining stations are thenchecked for whether they fit into the spatial pattern of theother stations. Thus, it is assumed that the quantiles of theempirical distribution functions are accurate.These quantiles are then transformed to precipitationamounts by a quantile mapping using the distribution functionswhich were interpolated from the information from theGerman NationalWeather Service (DeutscherWetterdienst –DWD) data only. The suggested procedure was tested for thestate of Baden-Württemberg in Germany. A detailed crossvalidation of the interpolation was carried out for aggregatedprecipitation amount of 1, 3, 6, 12 and 24 h. For each of thesetemporal aggregations, nearly 200 intense events were evaluated,and the improvement of the interpolation was quantified.The results show that the filtering of observations fromPWSs is necessary as the interpolation error after the filteringand data transformation decreases significantly. The biggestimprovement is achieved for the shortest temporal aggregations.
Author(s): Bardossy A, Seidel J, El Hachem A
Publication type: Article
Publication status: Published
Journal: Hydrology and Earth System Science
Pages: 583 - 601
Online publication date: 03/02/2021
Acceptance date: 23/12/2020
Date deposited: 21/02/2022
ISSN (print): 1027-5606
ISSN (electronic): 1607-7938
Data Source Location: https://doi.org/10.5281/zenodo.4501919
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