A Comparative Evaluation of Snowflake Particle Size and Shape Estimation Techniques used by the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD)

Measurements of snowflake particle size and shape are important for studying the snow microphysics. While a number of instruments exist that are designed to measure these important parameters, this study focuses on the measurement techniques of three digital video disdrometers: the Precipitation Imaging Package (PIP), the Multi-Angle Snowflake Camera (MASC) and the Two-Dimensional Video Disdrometer (2DVD). To gain a better understanding of the relative strengths and weaknesses of these instruments and to provide a foundation upon which comparisons can be made between studies using data from different instruments, we perform a comparative analysis of the measurement algorithms employed by each of the three instruments by applying the algorithms to snowflake images captured by PIP during the ICEP-POP 2018 field campaign. Our analysis primarily focuses on the measurements of area, equivalent diameter, and aspect ratio. Our findings indicate that area and equi-area diameter measurements using the 2DVD camera setup should be the most accurate, followed by MASC, which is slightly more accurate than PIP. In terms of the precision of the area and equi-area diameter measurements, however, MASC is considerably more precise than PIP or 2DVD, which provide similar precision once the effects of the PIP image compression algorithm are taken into account. Both PIP and MASC use shape-fitting algorithms to measure aspect ratio. While our analysis of the MASC aspect ratio suggests the measurements are reliable, our findings indicate that both the ellipse and rectangle aspect ratios produced by PIP under-performed considerably due to the shortcomings of the PIP shape-fitting techniques. That said, we also demonstrate that reliable measurements of aspect ratio can be retrieved from PIP by reprocessing the PIP images using either the MASC shape-fitting technique or a tensor-based ellipse-fitting technique. Because of differences in instrument design, 2DVD produces measurements of particle horizontal and vertical extent rather than length and width. Furthermore, the 2DVD measurements of particle horizontal extent can be contaminated by horizontal particle motion. Our findings indicate that, although the correction technique used to remove the horizontal motion contamination performs remarkably well with snowflakes despite being designed for use with rain drops, the 2DVD measurements of particle horizontal extent are potentially unreliable.

Scale sensitivity of the Gill circulation, Part II: Off-equatorial case

We investigate the steady dynamical response of the atmosphere on the equatorial β-plane to a steady, localized, mid-tropospheric heating source. Following Part I which investigates the case of an equatorial diabatic heating, we explore the sensitivity of the Gill circulation to the latitudinal location of the heating, together with the sensitivity to its horizontal scale. Again, we focus on characteristics of the response which would be particularly important if the circulation interacted with the hydrologic and energy cycles: overturning circulation and low-level wind. In the off-equatorial case, the intensity of the overturning circulation has the same limit as in the equatorial case for small horizontal extent of the diabatic heating, which is also the limit in the f-plane case. The decrease in this intensity with increasing horizontal scale of the diabatic heating is slightly faster in the off-equatorial case than in the equatorial case, which is due to the increase of rotational winds at the expense of divergent winds. The low-level westerly jet is more intense than in the equatorial case, with larger maximum wind and eastward mass transport that tend to infinity for small horizontal extent of the diabatic heating. In terms of spatial characteristics, this jet has a similar latitudinal extent as in the equatorial case but, unlike in the equatorial case, it extends further equatorward than poleward of the diabatic-heating center. It also extends further eastward than in the equatorial case.

The 2019 Raikoke volcanic eruption – Part 1: Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide

Volcanic eruptions can cause significant disruption to society and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3° N, 153.2° E). The eruption emitted around 1.5 ± 0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the Tropospheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption, but is unable, in its standard configuration, to capture the extent and precise location of very high-concentration regions within the volcanic cloud. Using the Fractional Skill Score as metric for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 days after the eruption where vertical column densities (VCD) of SO2 (in Dobson Units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 days only. The lower skill for these high-concentration regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard diffusion parameters used in NAME by a factor of four results in a slightly increased model skill during the first five days of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. We find that the temporal evolution of the NH-mean SO2 mass burden simulated by NAME strongly depends on the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), both NAME and TROPOMI show a similar peak in SO2 mass burden (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 days in the NH. Our work demonstrates the large potential of using high-resolution satellite retrievals to identify and rectify limitations in dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

Mitigating VHF Lightning Source Retrieval Errors

The problem of inferring the location and time of occurrence of a very high frequency (VHF) lightning source emission from Lightning Mapping Array (LMA) network time-of-arrival (TOA) measurements is closely examined in order to clarify the cause of retrieval errors and to determine how best to mitigate these errors. With regard to this inverse problem, the previous literature lacks a comprehensive discussion of the associated forward problem. Hence, the forward problem is analyzed in this study to better clarify why retrieval errors increase with increasing source horizontal range and/or decreasing source altitude. Further insight is obtained by performing carefully designed Monte Carlo inversion simulations that provide specific retrieval error plots, which in turn lead to clear recommendations for mitigating retrieval errors. Based on all of the numerical results, the following strategies are recommended for mitigating retrieval errors (when possible, and without obstructing the line of sight): expand the horizontal extent of the LMA network, maximize the vertical sensor baseline by using mountainous terrain if available, and improve TOA measurement timing accuracy. Adding sensors to the network is relatively ineffective, unless of course the addition of sensors expands the horizontal extent and/or vertical baseline of the network. It is also shown how the standard retrieval method can be generalized by considering, in addition to the regular (unpolarized) point VHF source, the polarized transient very low frequency/low frequency (VLF/LF) electric point dipole source. Multiple observations (i.e., VHF arrival time and power, and VLF/LF arrival time and electric field amplitude) are simultaneously implemented into the new generalized mathematical framework, and the potential benefits are indicated.

Clouds over the summertime Sahara: an evaluation of Met Office retrievals from Meteosat Second Generation using airborne remote sensing

Novel methods of cloud detection are applied to airborne remote sensing observations from the unique Fennec aircraft dataset, to evaluate the Met Office-derived products on cloud properties over the Sahara based on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) satellite. Two cloud mask configurations are considered, as well as the retrievals of cloud-top height (CTH), and these products are compared to airborne cloud remote sensing products acquired during the Fennec campaign in June 2011 and June 2012. Most detected clouds (67 % of the total) have a horizontal extent that is smaller than a SEVIRI pixel (3 km × 3 km). We show that, when partially cloud-contaminated pixels are included, a match between the SEVIRI and aircraft datasets is found in 80 ± 8 % of the pixels. Moreover, under clear skies the datasets are shown to agree for more than 90 % of the pixels. The mean cloud field, derived from the satellite cloud mask acquired during the Fennec flights, shows that areas of high surface albedo and orography are preferred sites for Saharan cloud cover, consistent with published theories. Cloud-top height retrievals however show large discrepancies over the region, which are ascribed to limiting factors such as the cloud horizontal extent, the derived effective cloud amount, and the absorption by mineral dust. The results of the CTH analysis presented here may also have further-reaching implications for the techniques employed by other satellite applications facilities across the world.

Clouds over the summertime Sahara: An evaluation of Met Office Meteosat retrievals using airborne remote sensing

Novel methods of cloud detection are applied to the unique Fennec aircraft dataset, to evaluate the Met Office derived products on cloud properties over the Sahara based on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on-board Meteosat. Two cloud mask configurations are considered, as well as the retrievals of cloud-top height, and these products are compared to airborne cloud remote sensing products acquired during the Fennec campaign in June 2011 and June 2012. Most detected clouds (67 % of the total) have a horizontal extent which is smaller than a SEVIRI pixel (3 km × 3 km). We show that, when partially cloud contaminated pixels are included, a match between the SEVIRI and aircraft datasets is found in 80 ± 8 % of the pixels. Moreover, under clear skies the datasets are shown to agree for more than 90 % of the pixels. Cloud-top height retrievals however show large discrepancies over the region, which are ascribed to limiting factors such as the cloud horizontal extent, the derived effective cloud amount, and the absorption by mineral dust.