During the Mt Kelud February 2014 eruption the ash cloud was detectable on 13–14 February in the infrared with the reverse absorption technique by, for example, the Advanced Very High Resolution ...Radiometer (AVHRR/3). The Infrared Atmospheric Sounding Interferometer (IASI) observed the ash cloud also on 15 February when AVHRR did not detect any ash signal. The differences between ash detection with AVHRR/3 and IASI are discussed along with the reasons for the differences, supported by radiative transfer modelling. The effect of concurrent ice clouds on the ash detection and the ash signal in the IASI measurements is demonstrated. Specifically, a radiative transfer model is used to simulate IASI spectra with ash-only, with ice cloud only and with both ash and ice clouds. It is shown that modelled IASI spectra with ash and ice clouds reproduce the measured IASI spectra better than ash-only- or ice-only-modelled spectra. The ash and ice modelled spectra that best reproduce the IASI spectra contain about a factor of 12 less ash than the ash-only spectra that come closest to reproducing the measured spectra.
Due to its comparatively high spatial resolution and its daily repeat frequency, the tropospheric nitrogen dioxide product provided by the TROPOspheric Monitoring Instrument (TROPOMI) onboard the ...Sentinel-5 Precursor platform has attracted significant attention for its potential for urban-scale monitoring of air quality. However, the exploitation of such data in, for example, operational assimilation of local-scale dispersion models is often complicated by substantial data gaps due to cloud cover or other retrieval limitations. These challenges are particularly prominent in high-latitude regions where significant cloud cover and high solar zenith angles are often prevalent. Using the example of Norway as a representative case for a high-latitude region, we here evaluate the spatiotemporal patterns in the availability of valid data from the operational TROPOMI tropospheric nitrogen dioxide (NO2) product over five urban areas (Oslo, Bergen, Trondheim, Stavanger, and Kristiansand) and a 2.5 year period from July 2018 through November 2020. Our results indicate that even for relatively clean environments such as small Norwegian cities, distinct spatial patterns of tropospheric NO2 are visible in long-term average datasets from TROPOMI. However, the availability of valid data on a daily level is limited by both cloud cover and solar zenith angle (during the winter months), causing the fraction of valid retrievals in each study site to vary from 20% to 50% on average. A temporal analysis shows that for our study sites and the selected period, the fraction of valid pixels in each domain shows a clear seasonal cycle reaching a maximum of 50% to 75% in the summer months and 0% to 20% in winter. The seasonal cycle in data availability shows the inverse behavior of NO2 pollution in Norway, which typically has its peak in the winter months. However, outside of the mid-winter period we find the TROPOMI NO2 product to provide sufficient data availability for detailed mapping and monitoring of NO2 pollution in the major urban areas in Norway and see potential for the use of the data in local-scale data assimilation and emission inversions applications.
Purpose
: To individually benchmark the incident electron parameters in a Monte Carlo model of an Elekta linear accelerator operating at 6 and 15 MV. The main objective is to establish a simplified ...but still precise benchmarking procedure that allows accurate dose calculations of advanced treatment techniques.
Methods
: TheEGSnrc Monte Carlo user codes BEAMnrc and DOSXYZnrc are used for photon beam simulations and dose calculations, respectively. A 5 × 5 cm2 field is used to determine both the incident electron energy and the electron radial intensity. First, the electron energy is adjusted to match the calculated depth dose to the measured one. Second, the electron radial intensity is adjusted to make the calculated dose profile in the penumbrae region match the penumbrae measured by GafChromic EBT film. Finally, the mean angular spread of the incident electron beam is determined by matching calculated and measured cross-field profiles of large fields. The beam parameters are verified for various field sizes and shapes.
Results
: The penumbrae measurements revealed a non-circular electron radial intensity distribution for the 6 MV beam, while a circular electron radial intensity distribution could best describe the 15 MV beam. These electron radial intensity distributions, given as the standard deviation of a Gaussian distribution, were found to be 0.25 mm (in-plane) and 1.0 mm (cross-plane) for the 6 MV beam and 0.5 mm (both in-plane and cross-plane) for the 15 MV beam. Introducing a small mean angular spread of the incident electron beam has a considerable impact on the lateral dose profiles of large fields. The mean angular spread was found to be 0.7° and 0.5° for the 6 and 15 MV beams, respectively.
Conclusions
: The incident electron beam parameters in a Monte Carlo model of a linear accelerator could be precisely and independently determined by the benchmarking procedure proposed. As the dose distribution in the penumbra region is insensitive to moderate changes in electron energy and angular spread, accurate penumbra measurements is feasible for benchmarking the electron radial intensity distribution. This parameter is particularly important for accurate dosimetry of mlc-shaped fields and small fields.
Ultraviolet (UV) SO2 cameras have become a common tool to measure and monitor SO2 emission rates, mostly from volcanoes but also from anthropogenic sources (e.g., power plants or ships). Over the ...past decade, the analysis of UV SO2 camera data has seen many improvements. As a result, for many of the required analysis steps, several alternatives exist today (e.g., cell vs. DOAS based camera calibration; optical flow vs. cross-correlation based gas-velocity retrieval). This inspired the development of Pyplis (Python plume imaging software), an open-source software toolbox written in Python 2.7, which unifies the most prevalent methods from literature within a single, cross-platform analysis framework. Pyplis comprises a vast collection of algorithms relevant for the analysis of UV SO2 camera data. These include several routines to retrieve plume background radiances as well as routines for cell and DOAS based camera calibration. The latter includes two independent methods to identify the DOAS field-of-view (FOV) within the camera images (based on (1) Pearson correlation and (2) IFR inversion method). Plume velocities can be retrieved using an optical flow algorithm as well as signal cross-correlation. Furthermore, Pyplis includes a routine to perform a first order correction of the signal dilution effect (also referred to as light dilution). All required geometrical calculations are performed within a 3D model environment allowing for distance retrievals to plume and local terrain features on a pixel basis. SO2 emission rates can be retrieved simultaneously for an arbitrary number of plume intersections. Hence, Pyplis provides a state-of-the-art framework for more efficient and flexible analyses of UV SO2 camera data and, therefore, marks an important step forward towards more transparency, reliability and inter-comparability of the results. Pyplis has been extensively and successfully tested using data from several field campaigns. Here, the main features are introduced using a dataset obtained at Mt. Etna, Italy on 16 September 2015.
During the Mt. Kelud Feb 2014 eruption the ash cloud was detectable on 13--14 Feb in the infrared with the reverse absorption technique by, for example, the Advanced Very High Resolution Radiometer ...(AVHRR/3). The Infrared Atmospheric Sounding Interferometer (IASI) observed the ash cloud also on 15 Feb when AVHRR did not detect any ash signal. The differences between ash detection with AVHRR/3 and IASI are discussed and the reasons for the differences supported with radiative transfer modelling. The effect of conccurent ice clouds on the ash detection and the ash signal in the IASI measurements is demonstrated. Speciï¬cally, a radiative transfer model is used to simulate IASI spectra with ash only, with ice cloud only and with both ash and ice clouds. It is shown that modelled IASI spectra with ash and ice clouds better reproduce the measured IASI spectra than ash only or ice only modelled spectra. The ash and ice modelled spectra that best reproduce the IASI spectra contain about a factor of 14 less ash than the ash only spectra that come closest to reproducing the measured spectra.
Satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) show significant potential for monitoring the spatiotemporal variability of NO2, however they ...typically provide vertically integrated measurements over the tropospheric column. In this study, we introduce a machine learning approach entitled ‘S-MESH’ (Satellite and ML-based Estimation of Surface air quality at High resolution) that allows for estimating daily surface NO2 concentrations over Europe at 1 km spatial resolution based on eXtreme gradient boost (XGBoost) model using primarily observation-based datasets over the period 2019–2021. Spatiotemporal datasets used by the model include TROPOMI NO2 tropospheric vertical column density, night light radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS), Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer (MODIS), observations of air quality monitoring stations from the European Environment Agency database and modeled meteorological parameters such as planetary boundary layer height, wind velocity, temperature. The overall model evaluation shows a mean absolute error of 7.77 μg/m3, a median bias of 0.6 μg/m3 and a Spearman rank correlation of 0.66. The model performance is found to be influenced by NO2 concentration levels, with the most reliable predictions at concentration levels of 10–40 μg/m3 with a bias of <40%. The spatial and temporal error analyses indicate the spatial robustness of the model across the study area, with better prediction accuracy during the winter months and the associated higher NO2 concentrations. Despite the complexity and the continental scale of the study area, the XGBoost-based model shows fast execution potential in providing daily estimates of surface NO2 concentrations over Europe. The Shapley Additive exPlanations (SHAP) value analysis highlights TROPOMI NO2 tropospheric column density as the main source of information in deriving surface NO2 concentrations, indicating its significant potential for such studies. The SHAP values also indicate the importance of anthropogenic emission proxy inputs such as VIIRS night lights, in complementing TROPOMI NO2 values for deriving higher resolution and detailed spatial patterns of NO2 variations.
•Surface NO2 concentrations at 1 km resolution is estimated over Europe using XGBoost.•Satellite-based input features' potential contribution is observed.•SHAP values indicate highest importance of Sentinel-5P TROPOMI observation.•Finer spatial patterns can be derived using VIIRS nightlight.•XGBoost is a good candidate for continental-scale study areas.
Satellite-based remote sensing might provide a potential way for monitoring the global flight activities and their environment impacts, while the remote sensing community pays less attention on it. ...In this study, we presented a flying aircraft detection algorithm which effectively handles the noise on Landsat 8 OLI cirrus band caused by energetic particles in the South Atlantic Anomaly region, and a new framework based on cloud infrastructure was proposed to map global flying aircraft activities from 2013 to 2020 using Landsat 8 Operational Land Imager (OLI) data. Validation was performed for 254 scenes recorded for various cloudy and surface conditions and vapor contents. The overall percentages of false alarms and omissions for these validation images were 5.37% and 7.80%, respectively. Limited to the resolution of Landsat data, cloud, the size and flight altitude of the aircraft, 42.99% flying aircraft were undetected compared with the FlightRadar24. Instead of using the Google Earth Engine, we employed more flexible cloud computing techniques, Google Cloud Storage and Google Calculation Engine, to construct our framework for the larger volume data. A total of 1.94 million Landsat images were analyzed to obtain the activities maps, and the results showed that globally flying aircraft increased by 25.85% from 2014 to 2019 (the year 2013 was excluded for the low coverage of Landsat scenes), with an annual rate of 4.31%. In 2020, flying aircraft were reduced by 40% compared with 2019 due to the influence of COVID-19 and traveling restrictions, and Europe was the most severely affected by COVID-19, with an 84.59% decline of flying aircraft in April 2020. This study provides a unique long-term supplement to monitor aviation activities and their climate impact.
The enhancement of the UV global irradiance due to snow cover on the ground has been observed at the station of Briançon, in a high‐altitude Alpine valley. The analysis relies on a three‐dimensional ...(3‐D) model, using an elevation map of the area. Without snow, comparison with the results of a 1‐D model shows no detectable effect of topography, within the uncertainty of modeling (2–3%). The 3‐D model relates the enhancement due to snow to the altitude of the snow line. The enhancement is shown to depend on the snow distribution around the site and not on the topography itself. The enhancement was measured at Briançon for nine cloudless days in winter 2002. As expected, it increases with the decrease of the snow line. In erythemal UV the enhancement reaches a maximum of about 22% in the beginning of March, in agreement with the results of the 3‐D model, assuming a snow albedo of 0.3 above the snow line and below the tree line and 0.8 above the tree line. Retrieving an effective surface albedo is a very challenging problem. Very small uncertainties in enhancement (±2%) lead to large uncertainties (±0.05) in effective albedo. Using the snow distribution with a contribution function does not give good results when the snow line is high; this is explained by the low resolution of the map with the rapid variation of the contribution near the site.
The top-of-the-atmosphere (TOA) reflectances of the Visible Infrared Imaging Radiometer (VIIRS) M9 channel and the Moderate Resolution Imaging Spectroradiometer (MODIS) 26 channel have been simulated ...using the libRadtran radiative transfer model and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Mask data. The simulated data were analyzed to quantify the performance differences between the VIIRS M9 and the MODIS 26 channels. Analysis of simulated clear-sky TOA reflectances showed that compared MODIS channel 26, the VIIRS M9 channel always performs better in reducing background reflectance regardless of latitude, season, surface type, vapor content or surface elevation. For mid-latitude, sub-Arctic and tropical regions the VIIRS M9 channel reduce the background reflectance by approximately 66.7%, 52.6% and 41.5%, respectively over the surface type of sandstone, compared to MODIS channel 26. Simulations for cloudy skies showed that both stratus and cumulus clouds contribute less to VIIRS M9 and MODIS band 26 TOA reflectances. Analysis of observed MODIS, VIIRS and CALIOP data was consistent with the simulated results. The VIIRS M9 decreases clear-sky background reflectance by as much as 35.96% and non-cirrus cloud reflectance by 29.86% compared with the MODIS channel 26. The observed reflectances of MODIS and VIIRS cirrus channels for clear-sky, non-cirrus cloud, and cirrus cloud are 0.0133 and 0.0095, 0.020 and 0.015, 0.084 and 0.067 respectively.
•The VIIRS M9 channel always performs better than MODIS channel 26.•The VIIRS M9 decreases background reflectance by 24.67% compared with the MODIS.•The VIIRS M9 increase cirrus cloud reflectance by 1.84% compared with the MODIS.
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