Search

Crowdsourced data are 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 surface synoptic observation (SYNOP) stations, and to use these 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 with those from traditional sources, as well as to assess the quality of these wind measurements against the professional-grade stations. We find that 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 kilometer away. To determine the roughness lengths, we examine a method that is 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.

Crowdsourced data are 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 surface synoptic observation (SYNOP) stations, and to use these 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 with those from traditional sources, as well as to assess the quality of these wind measurements against the professional-grade stations. We find that 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 kilometer away. To determine the roughness lengths, we examine a method that is 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.

Document describes an apparatus for and a method of controlling multiple time varying processes by a central control device through a use of an adaptive data acquisition scheme. A state estimator is used to extrapolate the current state of each control process as well as an estimate of the uncertainty of each state estimate. The state whose uncertainty is greatest is selected. The selected process is then measured, estimated, and a control variable calculated. Use of this selection criteria allows a single control device to effectively control more time varying processes then if the controls had been determined by a controller using a fixed sampling rate. Statistics for each process are updated with each measurement in order to compensate for the time/varying nature of the processes. (Author)