Minute Averaged Data Research Articles

Performance of Vehicle Add-on Mobile Monitoring System PM2.5 measurements during wildland fire episodes.

Fine particulate matter (PM2.5) resulting from wildland fire is a significant public health risk in the United States (U.S.). The existing stationary monitoring network and the tools used to alert the public of smoke conditions, such as the Air Quality Index or NowCast, are not optimized to capture actual exposure concentrations in impacted communities given that wildland fire smoke plumes have characteristically steep exposure concentration gradients that can vary over fine spatiotemporal scales. In response, we developed and evaluated a lightweight, universally attachable mobile PM2.5 monitoring system to provide supplemental, real-time air quality information during wildfire incidents and prescribed burning activities. We retroactively assessed the performance of the mobile monitor compared to nearby (100-1500 m) stationary low-cost sensors and regulatory monitors using 1 minute averaged data collected during two large wildfires in the western U.S. and during one small, prescribed burn in the Midwest. The mobile measurements were highly correlated (R 2 > 0.85) with the stationary network during the large wildfires. Further, 1 minute averaged mobile measurements differed from three collocated stationary instruments by <25% on average for fourteen out of fifteen total passages. For the small, prescribed burn, rapidly changing conditions near the fire border complicated the comparison of mobile and stationary measurements but the spatial maximum concentrations measured by both instruments were consistent. In general, this work highlights the value of using portable sensor technologies to address the monitoring challenges presented by dynamic wildland fire conditions and demonstrates the value in combining mobile monitoring with stationary data where possible.

Comparing measurements of the horizontal wind speed of a 2D Multi-Lidar and a cup anemometer

Wind measurements of a 2D Multi-Lidar and a mast mounted cup anemometer are compared in this study. Average wind speed and direction as well as the turbulence intensity of the wind speed are considered. Data analysis is mainly performed using standard regression analysis on 10 minute average data and the calculation of the power spectral density. The results show a good agreement regarding wind speed and direction and the turbulence intensity of the horizontal wind.

Wind Speed Modelling and Short-Term Prediction Using Wavelets

The mathematical method of wavelets is explained and used to predict wind conditions using short-term data collected at a site and referred to long term data from, say, a meteorological station. We model the response time-series in terms of a multiscale wavelet decomposition of the explanatory time-series. Preliminary results of this method, using hourly 10 minute averaged data from six locations in the British Isles, allow comparison with a linear regression method in terms of prediction errors over 21 days.

Wind Speed Power Spectrum Analysis for Bushland, Texas, USA

Numerous papers and publications on wind turbulence have referenced the wind speed spectrum presented by Isaac Van der Hoven in his article entitled Power Spectrum of Horizontal Wind Speed Spectrum in the Frequency Range from 0.0007 to 900 Cycles per Hour (Journal of Meteorology, Vol. 14, 1957, p. 160). Van der Hoven used data measured at different heights between 91 and 125 meters, and represented the high frequency end of the spectrum with data from the peak wind speed hour of hurricane Connie (13 August 1955). Because the data were collected in such special conditions we should question the use of his power spectrum in the wind industry. We computed a power spectrum for Bushland, Texas, from 13 years of hourly average data, 1 year of 5 minute average data, and 2 particularly gusty day's 1 second average data all collected at a height of 10 metres. While the general shape is similar to the Van der Hoven spectrum, few of his peaks were found in the Bushland spectrum.