Quality control and bias adjustment of crowdsourced wind speed observations

Abstract

Wind observations collected at citizen weather stations (CWSs) could be an invaluable resource in climate and meteorology studies, yet these observations are underutilised because scientists do not have confidence in their quality. These wind speed observations have systematic biases, likely caused by improper instrumentation and station sitings. Such systematic biases introduce spatial inconsistencies that prevent comparison of these stations spatially and limit the possible usage of the data. In this paper, we address these issues by improving and developing new methods for identifying suspect observations and adjusting systematic biases. Our complete quality control and bias adjustment procedure consists of four steps: (a) performing within-station quality control tests to check the plausible range and the temporal consistency of observations, (b) adjusting the systematic bias using empirical quantile mapping, (c) implementing between-station quality control to compare observations from neighbouring stations to identify spatially inconsistent observations, and (d) providing estimates of the true wind when CWSs falsely report zero wind speeds, as a complement to the bias adjustment. We apply these methods to CWSs from the Weather Observation Website (WOW) in the Netherlands, comparing the crowdsourced data with official data, and statistically assessing the improvements in data quality after each step. The results demonstrate that the crowdsourced wind speed data are more comparable with official data after quality control checks and bias adjustment steps. Our quality assessment methods therefore give confidence in CWSs, converting their observations into a usable data product and an invaluable resource for applications in need of additional wind observations.

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

“…Hence, higher ta in CWS data during daytime, likely resulting from radiative errors, are now better filtered with the new spatial buddy check. Summarizing, using information from neighbouring CWS to filter likely faulty values in the whole data set is beneficial, also highlighted by others (e.g., de Vos et al, 2019;Båserud et al, 2020;Nipen et al, 2020;Chen et al, 2021).…”

“…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.…”

Evidence of horizontal urban heat advection in London using six years of data from a citizen weather station network

“…Others have used CWS to validate urban climate simulations (Hammerberg et al 2018), drive indoortemperatures in urban climate simulations (Jin et al 2021), and model air temperatures in European cities using machine learning (Venter et al 2020, 2021, Vulova et al 2020, Zumwald et al 2021. Research using CWS has not been limited to urban temperature; some have used them to monitor (urban) precipitation (de Vos et al 2019(de Vos et al , 2020 or wind speed (Droste et al 2020)-allowing for the development of an innovative quality-check for crowd-sourced wind data (Chen et al 2021).…”

Quality management system and design of an integrated mesoscale meteorological network in Central Italy

“…This test and other buddy checks are implemented in the Titan library developed by MET Norway (Båserud et al, 2020). Many other different methods for providing spatial estimates can be found in the literature (e.g., Chen et al, 2021; Durre et al, 2010; Kondragunta & Shrestha, 2006; Steinacker et al, 2011) and they are summarized in Table S5.…”

“…This test and other buddy checks are implemented in the Titan library developed by MET Norway (Båserud et al, 2020). Many other different methods for providing spatial estimates can be found in the literature (e.g., Chen et al, 2021;Durre et al, 2010;Kondragunta & Shrestha, 2006;Steinacker et al, 2011) and they are summarized in Table S5. By adopting the complex quality control approach by Gandin (1988), it is recommended to compute multiple spatial consistency tests at the same time and exploit different methodologies (or the same, but varying parameters).…”