Development and Application of a Statistically-Based Quality Control for Crowdsourced Air Temperature Data

Napoly, Adrien; Grassmann, Tom; Meier, Fred; Fenner, Daniel

doi:10.3389/feart.2018.00118

Cited by 93 publications

(130 citation statements)

References 33 publications

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“…The inspection of raw PWS time series for all parameters shows major departures compared to SWS time series, which confirms the necessity of a quality control as already stated in previous studies (Bell et al, 2013;Muller et al, 2015;Meier et al, 2017;Napoly et al, 2018). Measurements provided by PWSs have a lot of uncertainties due to heterogeneous and unknown environmental conditions.…”

Section: Data Processingsupporting

confidence: 81%

Contribution of personal weather stations to the observation of deep-convection features near the ground

Mandement¹,

Caumont²

2019

Preprint

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The lack of observations near the surface is often cited as a limiting factor in the observation and prediction of deep convection. Recently, networks of personal weather stations (PWSs) measuring pressure, temperature and humidity in nearreal time have been rapidly developing. Even if they suffer from quality issues, their high temporal resolution and their higher spatial density than standard weather station (SWS) networks have aroused interest in using them to observe deep convection.In this study, the PWSs contribution to the observation of deep-convection features near the ground is evaluated. Four cases 5 of deep convection in 2018 over France were considered using data from Netatmo, a PWS manufacturer. A fully automatic PWS processing algorithm, including PWS quality control, was developed. After processing, the mean number of observations available increased by a factor of 134 in mean sea level pressure (MSLP), of 11 in temperature and of 14 in relative humidity over the areas of study. Near-surface SWS analyses, and analyses comprising standard and personal weather stations (SPWS) were built. The usefulness of crowdsourced data was proven both objectively and subjectively for deep convection observation. 10Objective validations of SWS and SPWS analyses by leave-one-out cross-validation (LOOCV) were performed using SWSs as the validation dataset. Over the four cases, LOOCV root mean square errors (RMSEs) decreased for all parameters in SPWS analyses compared to SWS analyses. RMSEs decreased by 73 to 77 % in MSLP, 12 to 23 % in temperature and 17 to 21 % in relative humidity. Subjectively, fine-scale structures showed up in SPWS analyses, while partly or not at all visible in SWS observations only. MSLP jumps accompanying squall lines or individual cells were observed, as well as wake lows at the rear 15 of these lines. Temperature drops and humidity rises accompanying most of the storms were observed sooner and at a finer resolution in SPWS analyses than in SWS analyses. The virtual potential temperature was spatialized at an unprecedented spatial resolution. It gave the opportunity to observe cold pool propagation and secondary convective initiation over areas with high virtual potential temperatures, i.e. favorable locations for near surface parcel lifting.

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Section: Data Processingsupporting

confidence: 81%

Contribution of personal weather stations to the observation of deep-convection features near the ground

Mandement¹,

Caumont²

2019

Preprint

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“…For QC, the statistically-based methods of 'CrowdQC v1.2.0' were applied, which are independent of reference T data. Quality-controlled CWS data at level O1 (see Napoly et al 2018) were used in all analyses.…”

Section: Meteorological Data and Processingmentioning

confidence: 99%

Contrasting changes of urban heat island intensity during hot weather episodes

Fenner

Holtmann

Meier

et al. 2019

Environ. Res. Lett.

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Cities typically exhibit higher air temperatures than their rural surroundings, a phenomenon known as the urban heat island (UHI) effect. Contrasting results are reported as to whether UHI intensity (UHII) is exacerbated or reduced during hot weather episodes (HWEs). This contrast is investigated for a four-year period from 2015 to 2018, utilising a set of observational data from high-quality meteorological stations, as well as from hundreds of crowdsourced citizen weather stations, located in the urban region of Berlin, Germany. It can be shown that if HWEs, defined here as the ten percent hottest days or nights during May-September, are identified via daytime conditions, or by night-time conditions at inner-city sites, then night-time UHII is exacerbated. However, if HWEs are identified via night-time conditions at rural sites, then night-time UHII is reduced. These differences in UHII change can be linked with prevalent weather conditions, namely radiation, cloud cover, wind speed, precipitation, and humidity. This highlights that, beside land cover changes, future changes in weather conditions due to climate change will control UHIIs, and thus heat-stress hazards in cities.

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“…The increasing number of professional and amateur weather stations which provide meteorological information at high spatial and temporal resolution (i.e., sub-daily data), make fully automatic quality control (QC) and homogenization methods an essential tool for identifying and removing spurious measurements, as well as for adjusting the inhomogeneities from the series (Hunziker et al, 2018;Napoly et al, 2018). Numerous air temperature homogenization studies have been dedicated to monthly and daily data, including extremes, and focusing mainly on local and country scales.…”

Section: Introductionmentioning

confidence: 99%

Homogenization of a combined hourly air temperature dataset over Romania

Dumitrescu

Cheval

Guijarro

2019

Intl Journal of Climatology

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Daily and sub‐daily homogenization of climate variables have been intensively investigated in the last decades, but to the best of our knowledge, this is the first study on homogenization of hourly temperature in Romania. This paper describes the creation of a homogenized hourly air temperature data set at a country scale by combining data from four independent meteorological networks. The air temperature measurements for the period 2009 and 2017 were obtained from the following networks: Romanian National Meteorological Administration (ANM), National Network for Monitoring Air Quality (RNMCA), Regional Basic Synoptic Network (RBSN), and Meteorological Terminal Aviation Routine Weather Report network (METAR). The climatological limits, persistence, temporal variation (step test), and spatial consistency were the quality control tests used to isolate the errors due to malfunctioning of the temperature sensors, data coding or transmission. The Climatol homogenization method was successfully applied for identifying and correcting any suspicious values. The missing data were filled by considering the similarities between each station and the reference series. Comparing the output with the original data, it is apparent that the removal of the break points, correction and homogenization resulted in a new data set with statistical properties very similar to the raw data, but more reliable for climate research due to the increased homogeneity. Eventually, the procedure can be implemented in operational use for collecting more data from other networks.

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Development and Application of a Statistically-Based Quality Control for Crowdsourced Air Temperature Data

Cited by 93 publications

References 33 publications

Contribution of personal weather stations to the observation of deep-convection features near the ground

Contribution of personal weather stations to the observation of deep-convection features near the ground

Contrasting changes of urban heat island intensity during hot weather episodes

Homogenization of a combined hourly air temperature dataset over Romania

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