Estimation of Wind Speed and Roughness Length Using Smartphones: Method and Quality Assessment

Abstract

Crowdsourced data is now seen as a potential source of high-resolution observations in the atmospheric sciences. In this paper we investigate a potential data source, wind observations obtained using anemometers connected to handheld smartphones. The aim of this paper is twofold: to assess the quality of raw and height extrapolated wind measurements from the handheld anemometer against professional-grade SYNOP stations, and to use this data of opportunity to infer a more accurate estimation of terrain roughness lengths. Roughness lengths are essential in numerical weather prediction; however, they are often poorly determined. Roughness lengths are also necessary when correcting near-surface wind observations for height offsets. For the analysis we performed a series of field experiments measuring wind profiles using handheld anemometers at roughly 2 m above ground. These raw measurements were then extrapolated to 10 m height using roughness lengths from three different sources. The extrapolation enabled us to compare the quality of roughness lengths estimated from smartphone measurements to those from traditional sources, as well as assess the quality of these wind measurements against the professional-grade stations. We find the handheld wind measurements are comparable in quality to wind measurements from SYNOP stations at 10 m height, and that for some cases the handheld measurements can be more representative than SYNOP stations only about a kilometre away. To determine the roughness lengths, we examine a method based on the turbulent intensity derived from the high-frequency signal of the smartphone wind measurements. Under certain circumstances, the roughness lengths obtained with the approach presented here are superior to traditional sources.

A transformer neural network for predicting near‐surface temperature

“…The use of crowd‐sourced observations in atmospheric sciences, NWP included, is becoming increasingly widespread. In recent years, several study areas of interest have emerged, such as the use of pressure observations from smartphones (Hintz et al, 2019; Mass & Madaus, 2014; McNicholas & Mass, 2018), the quantification of the urban heat island effect (Meier et al, 2017; Steeneveld et al, 2011), the use of crowd‐sourced wind observations (Droste et al, 2020; Hintz et al, 2020), the use of vehicle observations for assimilation in NWP models (Siems‐Anderson et al, 2020), and the use of PWSs for post‐processing of NWP forecasts (Nipen et al, 2020). Here, we are using crowd‐sourced observations from PWSs in order to post‐process near‐surface temperature forecasts.…”

Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation

“…The application of opportunistic datasets in NWP has been a popular area of research in recent years (Hintz et al, 2019a). Observations from personal weather stations (PWSs) (Chapman et al, 2017;Meier et al, 2017;Nipen et al, 2020;Steeneveld et al, 2011;Wolters & Brandsma, 2012) and smartphones (Droste et al, 2017;Hintz et al, 2020Hintz et al, , 2021Hintz 2019b;Madaus & Mass, 2017;Overeem et al, 2013) are commonly obtained through crowdsourcing. Such observations may be inaccurate when compared with traditional scientific observations.…”

Exploring the characteristics of a vehicle-based temperature dataset for convection-permitting numerical weather prediction

“…The application of opportunistic datasets in NWP has been a popular area of research in recent years (Hintz et al, 2019a). Observations from personal weather stations (PWSs) (Steeneveld et al, 2011;Wolters and Brandsma, 2012;Chapman et al, 2017;Meier et al, 2017;Nipen et al, 2020) and smartphones (Overeem et al, 2013;Droste et al, 2017;Madaus and Mass, 2017;Hintz et al, 2019bHintz et al, , 2020Hintz et al, , 2021 are commonly obtained through crowdsourcing. Such observations may be inaccurate when compared with traditional scientific observations.…”