The use of personal weather station observations to improve precipitation estimation and interpolation

Abstract

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.

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

“…To address sources of uncertainties associated with CWS data and to remove erroneous data from a data set of crowdsourced CWS observations, a number of studies has developed QC procedures, either relying on reference data from professionally-operated weather stations (PRWS), or using statistical approaches that are independent of additional meteorological observations. Several QC procedures for CWS that make use of PRWS data have been developed, all with different complexity and focusing on different variables: for ta (e.g., Bell 2015;Meier et al, 2017;Hammerberg et al, 2018;Cornes et al, 2020), for precipitation (Bárdossy et al, 2021), for wind speed (Droste et al, 2020;Chen et al, 2021), and for multiple variables (Clark et al, 2018;Mandement and Caumont 2020). Recently, Båserud et al (2020) introduced an automatic QC package for ta and precipitation, which aims at identifying possibly faulty values from meteorological observations based on a series of (spatial) tests.…”

Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC)

“…Generally speaking, some precipitation data QC checks consider each single observation separately (Upton and Rahimi, 2003;Taylor and Loescher, 2013;Blenkinsop et al, 2017), whereas more complex ones also take into account data from neighbouring stations (Steinacker et al, 2011;Scherrer et al, 2011) or multi-source data, such as weather radar data (Yeung al., 2014;Baserud et al, 2020) and output from a numerical weather prediction model (Qi et al, 2016). Recently, due to the increased utilization of crowdsourced observations, specific QC methods applicable for this type of precipitation data have been developed (de Vos et al, 2019;Bárdossy et al, 2021;Niu et al, 2021).…”

The Impact of a Lack of Government Strategies for Sustainable Water Management and Land Use Planning on the Hydrology of Water Bodies: Lessons Learned from the Disappearance of the Aculeo Lagoon in Central Chile

“…The fact that some of these records are not collected by a government agency does not mean that they cannot be used for research purposes. In fact, there are countless studies that have conducted remarkable investigations based on personal or family owned rain gauges (e.g., [46][47][48][49][50][51]). Therefore, considering government and/or personal rain gauges closer to the lagoon is very important for the development of modeling strategies because of the strong precipitation recycling effects observed in water bodies along Chile [52], which can be enhanced during ENSO warmings.…”