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The absence of sunlight during the winter in the High Arctic results in a strong surface-based atmospheric temperature inversion, especially during clear skies and light surface wind conditions. The inversion suppresses turbulent heat transfer between the ground and the boundary layer. As a result, the difference between the surface air temperature, measured at a height of 2 m , and the ground skin temperature can exceed several degrees Celsius. Such inversions occur very frequently in polar regions, are of interest to understand the mechanisms responsible for surface–atmosphere heat, mass, and momentum exchanges, and are critical for satellite validation studies. In this paper we present the results of operations of two commercial remotely piloted aircraft systems, or drones, at the Polar Environment Atmospheric Research Laboratory, Eureka, Nunavut, Canada, at 80 ∘N latitude. The drones are the Matrice 100 and Matrice 210 RTK quadcopters manufactured by DJI and were flown over Eureka during the February–March field campaigns in 2017 and 2020. They were equipped with a temperature measurement system built on a Raspberry Pi single-board computer, three platinum-wire temperature sensors, a Global Navigation Satellite System receiver, and a barometric altimeter. We demonstrate that the drones can be effectively used in the extremely challenging High Arctic conditions to measure vertical temperature profiles up to 75 m above the ground and sea ice surface at ambient temperatures down to - 46 ∘C. Our results indicate that the inversion lapse rates within the 0–10 m altitude range above the ground can reach values of ∼ 10–30 ∘C(100m)-1 (∼ 100–300 ∘Ckm-1). The results are in good agreement with the coincident surface air temperatures measured at 2, 6, and 10 m levels at the National Oceanic and Atmospheric Administration flux tower at the Polar Environment Atmospheric Research Laboratory. Above 10 m more gradual inversion with order-of-magnitude smaller lapse rates is recorded by the drone. This inversion lapse rate agrees well with the results obtained from the radiosonde temperature measurements. Above the sea ice drone temperature profiles are found to have an isothermal layer above a surface-based layer of instability, which is attributed to the heat flux through the sea ice. With the drones we were able to evaluate the influence of local topography on the surface-based inversion structure above the ground and to measure extremely cold temperatures of air that can pool in topographic depressions. The unique technical challenges of conducting drone campaigns in the winter High Arctic are highlighted in the paper. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) provides a daily, spatially resolved (initially 7×7 km 2 , upgraded to 7×5.6 km 2 in August 2019) global dataset of CO columns; however, due to the relative sparseness of reliable ground-based data sources, it can be challenging to characterize the validity and accuracy of satellite data products in remote regions such as the high Arctic. In these regions, satellite intercomparisons can supplement model- and ground-based validation efforts and serve to verify previously observed differences. In this paper, we compare the CO products from TROPOMI, the Atmospheric Chemistry Experiment (ACE) Fourier transform spectrometer (FTS), and a high-Arctic ground-based FTS located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut (80.05 ∘ N, 86.42 ∘ W). A global comparison of TROPOMI reference profiles scaled by the retrieved total column with ACE-FTS CO partial columns for the period from 28 November 2017 to 31 May 2020 displays excellent agreement between the two datasets (R=0.93) and a small relative bias of -0.83±0.26% (bias ± standard error of the mean). Additional comparisons were performed within five latitude bands: the north polar region (60 to 90 ∘ N), northern mid-latitudes (20 to 60 ∘ N), the equatorial region (20 ∘ S to 20 ∘ N), southern mid-latitudes (60 to 20 ∘ S), and the south polar region (90 to 60 ∘ S). Latitudinal comparisons of the TROPOMI and ACE-FTS CO datasets show strong correlations ranging from R=0.93 (southern mid-latitudes) to R=0.86 (equatorial region) between the CO products but display a dependence of the mean differences on latitude. Positive mean biases of 7.93±0.61% and 7.21±0.52% were found in the northern and southern polar regions, respectively, while a negative bias of -9.41±0.55% was observed in the equatorial region. To investigate whether these differences are introduced by cloud contamination, which is reflected in the TROPOMI averaging kernel shape, the latitudinal comparisons were repeated for cloud-covered pixels and clear-sky pixels only, as well as for the unsmoothed and smoothed cases. Clear-sky pixels were found to be biased higher with poorer correlations on average than clear + cloudy scenes and cloud-covered scenes only. Furthermore, the latitudinal dependence on the biases was observed in both the smoothed and unsmoothed cases. To provide additional context to the global comparisons of TROPOMI with ACE-FTS in the Arctic, both satellite datasets were compared against measurements from the ground-based PEARL-FTS. Comparisons of TROPOMI with smoothed PEARL-FTS total columns in the period of 3 March 2018 to 27 March 2020 display a strong correlation (R=0.88); however, a positive mean bias of 14.7±0.16% was also found. A partial column comparison of ACE-FTS with the PEARL-FTS in the period from 25 February 2007 to 18 March 2020 shows good agreement (R=0.79) and a mean positive bias of 7.89±0.21% in the ACE-FTS product relative to the ground-based FTS. The magnitude and sign of the mean relative differences are consistent across all intercomparisons in this work, as well as with recent ground-based validation efforts, suggesting that the current TROPOMI CO product exhibits a positive bias in the high-Arctic region. However, the observed bias is within the TROPOMI mission accuracy requirement of ±15% , providing further confirmation that the data quality in these remote high-latitude regions meets this specification. [ABSTRACT FROM AUTHOR]

Upper-atmosphere winds from a meteor radar and a field-widened Michelson interferometer, co-located at the Polar Environment Atmospheric Research Laboratory in Eureka, Nunavut, Canada (80° N, 86° W) are compared. The two instruments implement different wind-measuring techniques at similar heights and have very different temporal and spatial observational footprints. The meteor radar provides winds averaged over a ∼ 300 km horizontal area in 3 km vertical bins between 82 and 97 km on a 1 h cadence. The E-Region Wind Interferometer II (ERWIN) provides airglow-weighted winds (averaged over volumes of ∼ 8 km in height by ∼ 5 km radius) from three nightglow emissions (O(1S) , oxygen green line, 557.7 nm , 97 km ; an O2 line, 866 nm , 94 km ; and an OH line, 843 nm , 87 km) on a ∼ 5 min cadence. ERWIN's higher precision (1–2 ms-1 for the O(1S) and OH emissions and ∼ 4 ms-1 for the O2 emissions) and higher cadence allows more substantive comparisons between winds measured by meteor radar and Doppler shifts in airglow emissions than previously possible for similar meteor radar/airglow Doppler shift comparisons using Fabry–Perot interferometers. The best correlation is achieved using Gaussian weighting of meteor radar winds with peak height and vertical width being optimally determined. Peak heights agree well with co-located SABER airglow observations. Offsets between the two instruments are ∼ 1–2 ms-1 for the O2 and O(1S) emissions and less than 0.3 ms-1 for the OH emission. Wind directions are highly correlated with a ∼ 1:1 correspondence. On average, meteor radar wind magnitudes are ∼ 40 % larger than those from ERWIN. Gravity wave airglow brightness weighting of observations is discussed. Non-quadrature phase offsets between the airglow weighting and gravity wave associated wind and temperature perturbations will result in enhanced or reduced layer-weighted wind amplitudes. [ABSTRACT FROM AUTHOR]

Measurements of Pollution In The Troposphere (MOPITT) is an instrument on NASA's Terra satellite that has measured tropospheric carbon monoxide (CO) from early 2000 to the present day. Validation of data from satellite instruments like MOPITT is often conducted using ground-based measurements to ensure the continued accuracy of the space-based instrument's measurements and its scientific results. Previous MOPITT validation studies generally found a larger bias in the MOPITT data poleward of 60 ∘ N. In this study, we use data from 2006 to 2019 from the Bruker IFS 125HR Fourier Transform Infrared spectrometer (FTIR) located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada, to validate the MOPITT version 8 (V8) retrievals. These comparisons utilize mid- and near-infrared FTIR measurements made as part of the Network for the Detection for Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), respectively. All MOPITT version 8 retrievals within a radius of 110 km (1 ∘) from the PEARL Ridge Laboratory and within a 24 h time interval are used in this validation study. MOPITT retrieval products include those from the near-infrared (NIR) channel, the thermal infrared (TIR) channel, and a joint product from the thermal and near-infrared (TIR–NIR) channels. Each channel's detector has 4 pixels. We calculated the MOPITT pixel-to-pixel biases for each pixel, which were found to vary based on the season and surface type (land or water). The systematic bias for pixel 1 over land is larger than that for other pixels, which can reach up to 20 ppb. We use a small-region approximation method to find filtering criteria. We then apply the filters to the MOPITT dataset to minimize the MOPITT pixel bias and the number of outliers in the dataset. The sensitivity of each MOPITT pixel and each product is examined over the Canadian high Arctic. We then follow the methodologies recommended by NDACC and TCCON for the comparison between the FTIR and satellite total column retrievals. MOPITT averaging kernels are used to weight the NDACC and TCCON retrievals and take into account the different vertical sensitivities between the satellite and PEARL FTIR measurements. We use a modified Taylor diagram to present the comparison results from each pixel for each product over land and water with NDACC and TCCON measurements. Our results show overall consistency between MOPITT and the NDACC and TCCON measurements. When compared to the FTIR, the NIR MOPITT retrievals have a positive bias of 3 %–10 % depending on the pixel. The bias values are negative for the TIR product, with values between -5 % and 0 %. The joint TIR–NIR products show differences of -4 % to 7 %. The drift in MOPITT biases (in units of % yr -1) relative to NDACC and TCCON varies by MOPITT data product. In the NIR, drifts vs. TCCON are smaller than those vs. NDACC; however, this scenario is reversed for the MOPITT TIR and joint TIR–NIR products. Overall, this study aims to provide detailed validation for MOPITT version 8 measurements in the Canadian high Arctic. [ABSTRACT FROM AUTHOR]

Improving measurements of water vapour in the upper troposphere and lower stratosphere (UTLS) is a priority for the atmospheric science community. In this work, UTLS water vapour profiles derived from Atmospheric Chemistry Experiment (ACE) satellite measurements are assessed with coincident ground-based measurements taken at a high Arctic observatory at Eureka, Nunavut, Canada. Additional comparisons to satellite measurements taken by the Atmospheric Infrared Sounder (AIRS), Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), Microwave Limb Sounder (MLS), Scanning Imaging Absorption Spectrometer for Atmospheric CHartography (SCIAMACHY), and Tropospheric Emission Spectrometer (TES) are included to put the ACE Fourier transform spectrometer (ACE-FTS) and ACE Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (ACE-MAESTRO) results in context. Measurements of water vapour profiles at Eureka are made using a Bruker 125HR solar absorption Fourier transform infrared spectrometer at the Polar Environment Atmospheric Research Laboratory (PEARL) and radiosondes launched from the Eureka Weather Station. Radiosonde measurements used in this study were processed with software developed by the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) to account for known biases and calculate uncertainties in a well-documented and consistent manner. ACE-FTS measurements were within 11 ppmv (parts per million by volume; 13 %) of 125HR measurements between 6 and 14 km. Between 8 and 14 km ACE-FTS profiles showed a small wet bias of approximately 8 % relative to the 125HR. ACE-FTS water vapour profiles had mean differences of 13 ppmv (32 %) or better when compared to coincident radiosonde profiles at altitudes between 6 and 14 km; mean differences were within 6 ppmv (12 %) between 7 and 11 km. ACE-MAESTRO profiles showed a small dry bias relative to the 125HR of approximately 7 % between 6 and 9 km and 10 % between 10 and 14 km. ACE-MAESTRO profiles agreed within 30 ppmv (36 %) of the radiosondes between 7 and 14 km. ACE-FTS and ACE-MAESTRO comparison results show closer agreement with the radiosondes and PEARL 125HR overall than other satellite datasets – except for AIRS. Close agreement was observed between AIRS and the 125HR and radiosonde measurements, with mean differences within 5 % and correlation coefficients above 0.83 in the troposphere between 1 and 7 km. Comparisons to MLS at altitudes around 10 km showed a dry bias, e.g. mean differences between MLS and radiosondes were -25.6 %. SCIAMACHY comparisons were very limited due to minimal overlap between the vertical extent of the measurements. TES had no temporal overlap with the radiosonde dataset used in this study. Comparisons between TES and the 125HR showed a wet bias of approximately 25 % in the UTLS and mean differences within 14 % below 5 km. [ABSTRACT FROM AUTHOR]

Linear depolarization measurement capabilities were added to the CANDAC Rayleigh-Mie-Raman lidar (CRL) at Eureka, Nunavut, in the Canadian High Arctic in 2010. This upgrade enables measurements of the phases (liquid versus ice) of cold and mixed-phase clouds throughout the year, including during polar night. Depolarization measurements were calibrated according to existing methods using parallel- and perpendicular-polarized profiles as discussed in McCullough et al. (2017). We present a new technique that uses the polarization-independent Rayleigh elastic channel in combination with one of the new polarizationdependent channels, and we show that for a lidar with low signal in one of the polarization-dependent channels this method is superior to the traditional method. The optimal procedure for CRL is to determine the depolarization parameter using the traditional method at low resolution (from parallel and perpendicular signals) and then to use this value to calibrate the high-resolution new measurements (from parallel and polarization-independent Rayleigh elastic signals). Due to its use of two high-signal-rate channels, the new method has lower statistical uncertainty and thus gives depolarization parameter values at higher spatial-temporal resolution by up to a factor of 20 for CRL. This method is easily adaptable to other lidar systems which are considering adding depolarization capability to existing hardware. [ABSTRACT FROM AUTHOR]