The Environment Canada Pan and Parapan American Science Showcase Project

Joe, Paul; Bélair, Stéphane; Bernier, Natacha B.; Bouchet, V. S.; Brook, J. R.; Brunet, Dominique; Burrows, William R.; Charland, Jean‐Pierre; Dehghan, Ali Akbar; Driedger, N.; Duhaime, Carole P.; Evans, Greg J.; Filion, A.-B.; Frenette, R.; Grandpré, J. de; Gültepe, Ismail; Henderson, Dave; Herdt, Alexandria J.; Hilker, Nathan; Huang, Laura; Hung, Eddie; Isaac, George A.; Jeong, Cheol–Heon; Johnston, David H.; Klaassen, Joan; Leroyer, Sylvie; Lin, Hai; Macdonald, Melissa; MacPhee, Josh; Mariani, Zen; Munoz, Tony; Reid, Janti; Robichaud, Alain; Rochon, Y.; Shairsingh, Kerolyn K.; Sills, David; Spaček, Ľuboš; Stroud, Craig; Su, Yushan; Taylor, Neil; Vanos, Jennifer; Voogt, James A.; Wang, Jonathan M.; Wiechers, T.; Wren, Sumi N.; Huang, Yang; Yip, Terry

doi:10.1175/bams-d-16-0162.1

Cited by 34 publications

(42 citation statements)

References 55 publications

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“…Doppler lidars provide precise and accurate backscatter and Doppler velocity measurements at high temporal-and spatial-resolution along the lidar beam's path (radial direction). As such, they have demonstrated the ability to measure fast-evolving meteorological features, such as lake breezes and low-level jets, and cloud microphysical properties [7,[26][27][28][29].…”

Section: Doppler Light Detection and Ranging (Lidar)mentioning

confidence: 99%

A Multi-Year Evaluation of Doppler Lidar Wind-Profile Observations in the Arctic

Mariani¹,

Crawford²,

Casati³

et al. 2020

Remote Sensing

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Doppler light detection and ranging (lidar) wind profilers have proven their capability to measure vertical wind profiles with an accuracy comparable to anemometers and radiosondes. However, most of these comparisons were performed over short time periods or at mid-latitudes. This study presents a multi-year assessment of the accuracy of Doppler lidar wind-profile measurements in the Arctic by comparing them with coincident radiosonde observations, and excellent agreement was observed. The suitability of the Doppler lidar for verification case studies of operational numerical weather prediction (NWP) models during the World Meteorological Organization’s Year of Polar Prediction is also demonstrated, by using Environment and Climate Change Canada’s (ECCC) global environmental multiscale model (GEM-2.5 km and GEM-10 km). Since 2016, identical scanning Doppler lidars were deployed at two supersites commissioned by ECCC as part of the Canadian Arctic Weather Science project. The supersites are located in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) with a third Halo Doppler lidar located in Squamish (50°N, 123°W). Two lidar wind-profile measurement methodologies were investigated; the velocity-azimuth display method exhibited a smaller average bias (−0.27 ± 0.02 m/s) than the Doppler beam-swinging method (–0.46 ± 0.02 m/s) compared to the sonde. Comparisons to ECCC’s NWP models indicate good agreement, more so during the summer months, with an average bias < 0.71 m/s for the higher-resolution (GEM-2.5 km) ECCC models at Iqaluit. Larger biases were found in the mountainous terrain of Whitehorse and Squamish, likely due to difficulties in the model’s ability to resolve the topography. This provides evidence in favor of using high temporal resolution lidar wind-profile measurements to complement radiosonde observations and for NWP model verification and process studies.

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Section: Doppler Light Detection and Ranging (Lidar)mentioning

confidence: 99%

A Multi-Year Evaluation of Doppler Lidar Wind-Profile Observations in the Arctic

Mariani¹,

Crawford²,

Casati³

et al. 2020

Remote Sensing

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“…CRUISER was deployed during two seasons over the Greater Toronto Area (GTA), a metropolitan area encompassing the city of Toronto and four regional municipalities with a population of over 6 million. The summer campaign took place over 9 days in July 2015 (15,16,17,20,21,22,23,27, and 28 July) as part of the Environment Canada Pan and Parapan American Science Showcase (ECPASS) (Joe et al, 2018). The winter campaign took place over 8 days in January 2016 (11,13,14,15,18,19,20, and 21 January) as part of a health exposure mapping study.…”

Section: Overview Of Mobile Measurementsmentioning

confidence: 99%

“…Volatile organic compounds were measured using a protontransfer-reaction time-of-flight mass spectrometer (PTR-TOF-8000, Ionicon Analytik). The operating principles of the PTR-TOF-MS instrument have been described elsewhere (Jordan et al, 2009;Li et al, 2017); further details can be found in the Supplement. Briefly, the PTR-TOF-MS was operated with an E / N value of 140 Td.…”

Section: Proton-transfer-reaction Time-of-flight Mass Spectrometry (Pmentioning

confidence: 99%

Elucidating real-world vehicle emission factors from mobile measurements over a large metropolitan region: a focus on isocyanic acid, hydrogen cyanide, and black carbon

et al. 2018

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A mobile laboratory equipped with state-of-the-art gaseous and particulate instrumentation was deployed across the Greater Toronto Area (GTA) during two seasons. A high-resolution time-of-flight chemical ionization mass spectrometer (HR-TOF-CIMS) measured isocyanic acid (HNCO) and hydrogen cyanide (HCN), and a high-sensitivity laserinduced incandescence (HS-LII) instrument measured black carbon (BC). Results indicate that on-road vehicles are a clear source of HNCO and HCN and that their impact is more pronounced in the winter, when influences from biomass burning (BB) and secondary photochemistry are weakest. Plume-based and time-based algorithms were developed to calculate fleet-average vehicle emission factors (EFs); the algorithms were found to yield comparable results, depending on the pollutant identity. With respect to literature EFs for benzene, toluene, C2 benzene (sum of m-, p-, and o-xylenes and ethylbenzene), nitrogen oxides, particle number concentration (PN), and black carbon, the calculated EFs were characteristic of a relatively clean vehicle fleet dominated by light-duty vehicles (LDV). Our fleet-average EF for BC (median: 25 mg kg −1 fuel ; interquartile range, IQR: 10-76 mg kg −1 fuel ) suggests that overall vehicular emissions of BC have decreased over time. However, the distribution of EFs indicates that a small proportion of high-emitters continue to contribute disproportionately to total BC emissions. We report the first fleet-average EF for HNCO (median: 2.3 mg kg −1 fuel , IQR: 1.4-4.2 mg kg −1 fuel ) and HCN (median: 0.52 mg kg −1 fuel , IQR: 0.32-0.88 mg kg −1 fuel ). The distribution of the estimated EFs provides insight into the real-world variability of HNCO and HCN emissions and constrains the wide range of literature EFs obtained from prior dynamometer studies. The impact of vehicle emissions on urban HNCO levels can be expected to be further enhanced if secondary HNCO formation from vehicle exhaust is considered.Published by Copernicus Publications on behalf of the European Geosciences Union.

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“…Similarly, studies quantifying emissions in which the vehicle stops to obtain wind measurements (Brantley et al, 2014) and the anemometer is placed ahead of and above the vehicle are unlikely to require compensations for the vehicle's flow field. Vehicles outfitted to study severe weather (Curry et al, 2017;Taylor et al, 2011) and urban meteorology (Joe et al, 2018) show resilience to bias by also outfitting vehicles with wind sensors above and ahead of the vehicle. Vehicle-based anemometer measurements should be calibrated for vehicle shape and anemometer placement, but as this can be costly and time-consuming truck-based anemometers should be placed as far forward and as high as possible to obtain the most accurate results.…”

Section: Discussionmentioning

confidence: 99%

Using computational fluid dynamics and field experiments to improve vehicle-based wind measurements for environmental monitoring

Hanlon

Risk

2020

Atmos. Meas. Tech.

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Abstract. Vehicle-based measurements of wind speed and direction are presently used for a range of applications, including gas plume detection. Many applications use mobile wind measurements without knowledge of the limitations and accuracy of the mobile measurement system. Our research objective for this field-simulation study was to understand how anemometer placement and the vehicle's external airflow field affect measurement accuracy of vehicle-mounted anemometers. Computational fluid dynamic (CFD) simulations were generated in ANSYS Fluent to model the external flow field of a research truck under varying vehicle speed and wind yaw angle. The CFD simulations provided a quantitative description of fluid flow surrounding the vehicle and demonstrated that the change in wind speed magnitude from the inlet increased as the wind yaw angle between the inlet and the vehicle's longitudinal axis increased. The CFD results were used to develop empirical speed correction factors at specified yaw angles and to derive an aerodynamics-based correction function calibrated for wind yaw angle and anemometer placement. For comparison with CFD, we designed field tests on a square, 12.8 km route in flat, treeless terrain with stationary sonic anemometers positioned at each corner. The route was driven in replicate under varying wind conditions and vehicle speeds. The vehicle-based anemometer measurements were corrected to remove the vehicle speed and course vector. From the field trials, we observed that vehicle-based wind speed measurements differed in average magnitude in each of the upwind, downwind, and crosswind directions. The difference from stationary anemometers increased as the yaw angle between the wind direction and the truck's longitudinal axis increased, confirming the vehicle's impact on the surrounding flow field and validating the trends in CFD. To further explore the accuracy of CFD, we applied the function derived from the simulations to the field data and again compared these with stationary measurements. From this study, we were able to make recommendations for anemometer placement, demonstrate the importance of applying aerodynamics-based correction factors to vehicle-based wind measurements, and identify ways to improve the empirical aerodynamic-based correction factors.

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The Environment Canada Pan and Parapan American Science Showcase Project

Cited by 34 publications

References 55 publications

A Multi-Year Evaluation of Doppler Lidar Wind-Profile Observations in the Arctic

A Multi-Year Evaluation of Doppler Lidar Wind-Profile Observations in the Arctic

Elucidating real-world vehicle emission factors from mobile measurements over a large metropolitan region: a focus on isocyanic acid, hydrogen cyanide, and black carbon

Using computational fluid dynamics and field experiments to improve vehicle-based wind measurements for environmental monitoring

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