Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.
Abstract. The number of personal weather stations (PWS) with data available online through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation for high intensity events of different durations. Due to unknown errors and biases of the observations rainfall amounts of the PWS network are not considered directly. Instead, only their temporal order is assumed to be correct. The crucial step is to find the stations with informative measurements. This is done in two steps, first by selecting the locations using time series of indicators of high precipitation amounts. The remaining stations are checked whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the percentiles of the PWS network are accurate. These percentiles are then translated to precipitation amounts using the distribution functions which were interpolated using the information from German National Weather Service (DWD) data only. The suggested procedure was tested for the State of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amounts of 1, 3, 6, 12 and 24 hours. For each aggregation nearly 200 intense events were evaluated. The results show that filtering the secondary observations is necessary as the interpolation error after filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest time aggregations.
<p>On 14 and 15 July 2021, heavy and prolonged precipitation caused flooding in large areas in western Germany and adjacent regions. The Ahr River valley in the Federal State of Rhineland-Palatinate was particularly affected, with numerous fatalities and large-scale damage. Due to the spatio-temporal variability of precipitation and failure of several gauging stations, the estimation of the flood triggering areal precipitation as well as determination of peak discharges is associated with high uncertainties.</p> <p>In this study, we present results where data from opportunistic sensors (commercial microwave links (CML) and personal weather stations (PWS)) were used to interpolate hourly precipitation sums for the Ahr catchment. The data from the opportunistic sensors was quality controlled, filtered and interpolated using the methods from Graf et al. (2021). This precipitation data was compared to a gauge adjusted weather radar product from the German Weather Service DWD as well as interpolated rain gauge data. In order to determine the maximum discharges at the gauges in the Ahr, flood was simulated with the water balance model LARSIM (Large Area Runoff Simulation Model) using the aforementioned precipitation products as input data.</p> <p>The results show that the areal precipitation obtained from opportunistic sensors yielded higher sums than the gauge adjusted radar products and the interpolated gauge data, especially in the northern part of the Ahr catchment where the station density of the conventional rain gauges was not sufficient to capture the spatial variability of this extreme event. Furthermore, the modelled run-offs using the precipitation input from opportunistic sensors yielded higher and more plausible peak discharges than the ones with the gauge adjusted weather radar product. This suggests that the radar underestimated precipitation due to attenuation. The difference in the resulting peak discharges point to the fact that due to the saturated soils any additional precipitation during the flood event in July 2021 lead to a direct run-off effect.</p> <p>&#160;</p> <p>References:</p> <p>Graf, M., El Hachem, A., Eisele, M., Seidel, J., Chwala, C., Kunstmann, H., & B&#225;rdossy, A. (2021). Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales. Journal of Hydrology: Regional Studies, 37, 100883.</p>
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