The direct radiative forcing due to aerosols remains highly uncertain in the South-East Atlantic. During the fire season, absorbing aerosols from the African biomass burning are transported towards the ocean where they are frequently observed above the semi-permanent layer of stratocumulus. Biomass burning aerosols above an ocean surface will exert a negative radiative forcing, while over a highly reflectant cloud, will exert a positive radiative forcing. As a result, the direct forcing of aerosols is expected to be highly influenced by the strong diurnal cycle of the clouds in this region.Until recently, passive satellite observations of aerosols were limited to cloud-free scenes as aerosol and cloud retrievals used to be mutually exclusive. Also, cloud properties retrieved from shortwave passive sensors are expected to be biased in the South-East Atlantic because operational retrievals do not account for the impact of above-cloud aerosol absorption on the measured signal. In the last decade, methods have been developed to retrieve simultaneously above-cloud aerosol and corrected-cloud properties. However, these methods have been mostly applied to polar orbiting instruments, which provide one or two observations a day. Consequently, the study of aerosol-radiation interaction in the South-East Atlantic would benefit from the high-temporal-resolution observations provided by geostationary satellite platforms.In this study, we develop a novel and comprehensive approach to estimate the direct forcing of aerosols in the South-East Atlantic by combining cloudy and cloud-free retrievals at high temporal resolution from the geostationnary satellite MSG/SEVIRI. These observations allows to properly evaluate climate models participating in CMIP-6 and AeroCom phase III. The reasons for the differences between the observed and the modelled direct effects will be discussed.

To evaluate the SEVIRI retrieval for aerosols above clouds presented in Part 1 of the companion paper, the algorithm is applied over the south-east Atlantic Ocean during the CLARIFY-2017 field campaign period. The first step of our analysis compares the retrieved aerosol and cloud properties against equivalent products from the MODIS MOD06ACAERO retrieval (Meyer et al., 2015). While the correlation between the two satellite retrievals of the above-cloud aerosol optical thickness (AOT) is good (R = 0.78), the AOT retrieved by SEVIRI is 20.3 % smaller than that obtained from the MODIS retrieval. This difference in AOT is attributed mainly to the more absorbing aerosol model assumed for the SEVIRI retrieval compared to MODIS. The underlying cloud optical thickness (COT) derived from the two satellites is in good agreement (R = 0.90). The cloud droplet effective radius (CER) retrieved by SEVIRI is consistently smaller than MODIS by 2.2 µm, which is mainly caused by the use of different spectral bands of the satellite instruments. In the second part of our analysis, we compare the forecast water vapour profiles used for the SEVIRI atmospheric correction as well as the aforementioned aerosol and cloud products with in situ measurements made from the Facility for Airborne Atmospheric Measurements (FAAM) aircraft platform during the CLARIFY-2017 campaign. Around Ascension Island, the column water vapour used to correct the SEVIRI signal is overestimated by 3.1 mm in the forecast compared to that measured by dropsondes. However, the evidence suggests that the accuracy of the atmospheric correction improves closer to the African coast. Consistency is observed between the SEVIRI above-cloud AOT and in situ measurements (from cavity ring-down spectroscopy instruments) when the measured single-scattering albedo is close to that assumed in the retrieval algorithm. On the other hand, the satellite retrieval overestimates the AOT when the assumed aerosol model is not absorbing enough. Consistency is also found between the cloud properties retrieved by SEVIRI and the CER measured by a cloud droplet probe and the liquid water path derived from a microwave radiometer. Despite the instrumental limitations of the geostationary satellite, the consistency obtained between SEVIRI, MODIS and the aircraft measurements demonstrates the ability of the retrieval in providing additional information on the temporal evolution of the aerosol properties above clouds.

In-flight icing constitutes a major hazard to aviation, and so it is vital to be able to forecast the risk of icing accurately. The Met Office is one of two World Area Forecast Centres (WAFC) to routinely produce and validate icing forecasts. The existing verification methodology of Bowyer and Gill (2018) evaluates each WAFC forecast against satellite-derived icing potential. However, the methodology currently evaluates the full Global forecast data, whereas satellite-derived icing potential is only available during daytime. At night, the presence of cloud is reported, and so only correct rejections and false alarms are possible during nocturnal hours. We first present an extension to the existing verification methodology by restricting the analysis to only include daytime data and demonstrate that this significantly reduces the degree of over-forecasting previously reported. In addition, we examine the performance of the new WAFC icing severity forecasts and compare against the routine product, during Winter 2020/2021. There are two major challenges when comparing forecast icing severity and forecast icing potential. The first is that we are comparing the potential for an icing event to occur with its predicted intensity. The second challenge is that the new severity forecasts are on a 0.25º grid, compared with 1.25º for icing potential. We present results both under the assumption that moderate icing potential is the same as moderate icing severity, and as an independent comparison with a new satellite-derived icing severity product. We also test the sensitivity to the choice of verification grid by re-gridding to both 0.25º and 1.25º. The results show that WAFC icing severity is over-predicted when compared with satellite-derived severity, especially over the tropics. However, icing events are likely to be too infrequent in the observations, and so the magnitude of the over-prediction is over-estimated. All forecasts show regional skill at predicting icing severity and the results are not sensitive to the choice of verification grid. However, the performance of the higher resolution icing severity forecast is likely to influenced by the double penalty problem.

Satellite sounder infrared radiances are among the most important contributions to the global observing system and have been assimilated into global numerical weather prediction (NWP) analyses for many years. They are also used as fundamental climate data records for climate monitoring. Prior to assimilation or producing climate records, the radiances should have all residual instrument biases removed. One way of estimating the mean biases is to continuously monitor the measured radiances against the NWP model equivalent radiances. This article is an extension of one published in 2012 which documented these biases for three years but now the time span of the monitoring has extended to beyond ten years, allowing the long-term stability of the instruments to be assessed. Data from high-resolution infrared sounder (HIRS), Advanced Along Track Scanning Radiometer (AATSR), and Spinning Enhanced Visible and Infrared Imager (SEVIRI), radiometers; atmospheric infrared sounder (AIRS), a spectrometer; and infrared atmospheric sounding interferometer (IASI), an interferometer, were included. Changes in mean biases and standard deviations were used to investigate the temporal stability of the bias and radiometric noise of the instruments over ten years. A double difference technique was employed to remove the effect of changes or deficiencies in the NWP system and radiative transfer (RT) model, which can contribute to the biases. The IASI and AIRS radiances were stable but with a different bias between the two instruments due to different versions of the RT model used. The SEVIRI radiometers were stable in most channels with the exception of the 13.4 mu m channel. The HIRS instruments were subject to sudden changes in bias and increases in standard deviation compared with NWP simulations during the past decade.

Uncertainty estimates are important when retrieving properties of clouds and aerosols from satellites measurements. These measurements must be interpreted using a form of inverse theory, such as optimal estimation. In atmospheric remote sensing these inverse methods often assume that the forward model is linear in the region of uncertainty. This assumption is not necessarily valid. This paper presents an exact confidence procedure in contrast to the linear approximation using a maximum likelihood estimator. Two simple examples of retrieving the effective radius and optical depth of a volcanic ash cloud and water cloud show a discrepancy between the linear approximation and the exact procedure. The exact procedure is especially useful for inference where the entire parameter space has been forward modelled prior to or during the retrieval, such as using look up tables. When the inference method calculates the likelihood over the whole parameter space, it is less computationally expensive than a linear approximation.

High-temporal-resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the south-east Atlantic Ocean (SEAO). This paper presents a method developed to simultaneously retrieve aerosol and cloud properties in aerosol above-cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT) and the cloud droplet effective radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10 % error on the humidity profile leads to an 18.5 % bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00 and 15:00 UTC, an uncertainty of 40 % is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single-scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17 % between 09:00 and 15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6 % on the COT and 3 % on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above-cloud events over the SEAO.

A new generation of L‐band sensors, such as the European Space Agency (ESA)'s Soil Moisture Ocean Salinity (SMOS) mission, have the capability to provide information on the ocean surface wind speed under high wind and rain conditions. In this study we evaluate the use of SMOS wind speeds within Met Office numerical weather prediction (NWP). Observation minus model background (O−B) departure statistics are used to investigate SMOS error characteristics, quality flags, and develop a quality‐control method. Observation errors and spatial correlation distances are estimated using a statistical method. Observing system experiments are performed to diagnose the impact of SMOS on NWP forecasts and analyses, including tropical cyclone (TC) predictions.The quality of SMOS retrievals appears reduced in the presence of sea ice, strong river plumes, and radio‐frequency interference (RFI) contamination. SMOS wind retrievals have reduced sensitivity at low–moderate winds speeds. Above 15 m s−1, SMOS winds tend to be faster than the model and have higher O−B variance compared to scatterometer winds from ASCAT. Above 30 m s−1, root‐mean‐square errors from SMOS are smaller than ASCAT. The impact of SMOS on TC predictions is sensitive to the use of the Met Office TC Central Pressure Initialisation Scheme (TCCPIS) which is confirmed to have a large, beneficial impact on intensity predictions. The assimilation of SMOS results in a small increase in TC intensity leading to a reduction in pressure/wind errors in the analysis and short‐range forecasts, but cannot replicate the impact from the TCCPIS. The spatial resolution of SMOS is a clear limitation for analysing TC structure. In the case of Hurricane Kilo, the analysed and short‐range forecast central pressures are closer to best‐track when the storm radius is large and the eye is resolved. The challenge is to extract the useful information on intensity whilst preserving storm structure.

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.

Wind radii estimates in tropical cyclones (TCs) are crucial to helping determine the TC wind structure for the production of effective warnings and to constrain initial conditions for a number of applications. In that context, we report on the capabilities of a new generation of satellite microwave radiometers operating at L-band frequency (approximate to 1.4 GHz) and dual C band (approximate to 6.9 and 7.3 GHz). These radiometers provide wide-swath (>1,000 km) coverage at a spatial resolution of approximate to 40 km and revisit of approximate to 3 days. The L-band measurements are almost unaffected by rain and atmospheric effects, while dual C-band data offer an efficient way to significantly minimize these impacts. During storm conditions, increasing foam coverage and thickness at the ocean surface sufficiently modify the surface emissivity at these frequencies and, in turn, the brightness temperature (Tb) measurements. Based on aircraft measurements, new geophysical model functions have been derived to infer reliable ocean surface wind speeds from measured Tb variations. Data from these sensors collected over 2010-15 are shown to provide reliable estimates of the gale-force (34 kt), damaging (50 kt), and destructive winds (64 kt) within the best track wind radii uncertainty. Combined, and further associated with other available observations, these measurements can now provide regular quantitative and complementary surface wind information of interest for operational TC forecasting operations.

To investigate the characteristics of Spinning Enhanced Visible and Infrared Imager (SEVIRI)-derived products from the UK Met Office algorithm, one year of cloud occurrence frequency (COF) and cloud-top height (CTH) data from May 2013 to April 2014 was analysed in comparison with Cloud Profiling Radar (CPR) and Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) cloud products observed from the A-Train constellation. Because CPR operated in daylight-only data collection mode, daytime products were validated in this study. It is important to note that the different sensor characteristics cause differences in CTH retrievals. The CTH of active instruments, CPR and CALIOP, is derived from the return time of the backscattered radar or LiDAR signal, while the infrared sensor, SEVIRI, measures a radiatively effective CTH. Therefore, some systematic differences in comparison results are expected. However, similarities in spatial distribution and seasonal variability of COFs were noted among SEVIRI, CALIOP, and CPR products, although COF derived by the SEVIRI algorithm showed biases of 14.35% and −3.90% compared with those from CPR and CALIOP measurements, respectively. We found that the SEVIRI algorithm estimated larger COF values than the CPR product, especially over oceans, whereas smaller COF was detected by SEVIRI measurements over land and in the tropics than by CALIOP, where multi-layer clouds and thin cirrus clouds are dominant. CTHs derived from SEVIRI showed better agreement with CPR than with CALIOP. Further comparison with CPR showed that SEVIRI CTH was highly sensitive to the CO2 bias correction used in the Minimum Residual method. Compared with CPR CTHs, SEVIRI has produced stable CTHs since the bias correction update in November 2013, with a correlation coefficient of 0.93, bias of −0.27 km, and standard deviation of 1.61 km.

The eruption of Eyjafjallajokull in 2010 has triggered the rapid development of volcanic ash remote sensing activities at the Met Office. Volcanic ash qualitative and quantitative mapping have been achieved using lidar on board the Facility for Airborne Atmospheric Measurements (FAAM) research aircraft, and using improved satellite retrieval algorithms. After the eruption, a new aircraft facility, the Met Office Civil Contingencies Aircraft (MOCCA), has been set up to enable a rapid response, and a network of ground-based remote sensing sites with lidars and sunphotometers is currently being developed. Thanks to these efforts, the United Kingdom (UK) will be much better equipped to deal with such a crisis, should it happen in the future.

Abstract. This study demonstrates a method of retrieving the mass column loading and cloud-top pressure of a volcanic ash cloud, together with the effective radius and spread of the ash particle size distribution, as well as the cloud top pressure of any underlying water cloud, using an optimal estimation technique applied to Infrared Atmospheric Sounding Interferometer data. Two shapes of particle size distribution are considered, a log-normal and a gamma distribution. Results show that it is viable to retrieve a measure of the size distribution spread, namely the geometric standard deviation, when a log-normal distribution is assumed, whereas this is not the case for an assumed gamma distribution in terms of its effective variance. The volcanic conditions under which the method works well are discussed, as are its shortcomings. The method is applied to two volcanic eruptions: Eyjafjallajökull, Iceland using data from 6th May 2010 and Kasatochi, Alaska using data from 8th August 2008. The results show that the retrieved geometric standard deviation of these ash clouds is spatially variable, and is generally similar to what is assumed in many passive infrared remote sensing techniques. An abrupt change in the retrieved geometric standard deviation has been observed for the Eyjafjallajökull eruption along the trajectory of the ash cloud, and possible explanations for this are discussed.

ABSTRACTVolcanic eruptions are natural hazards with dire consequences to life and economy. As most volcanoes are in remote areas, satellites play a vital role in providing observations and input to models used for forecasting volcanic plume evolution. Radiances from the Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp polar orbiting meteorological satellites were used to detect sulphur dioxide (SO2) from three volcanic eruptions in 2014, in different meteorological situations. Two of these eruption cases, Mount Sinabung in January and Kelut in February, are in the tropics, whereas the case in October from Bárðarbunga, is at a higher latitude. The SO2 plumes from these volcanic eruptions were identified easily and tracked using established methods from the literature, that are based on the principle that areas of increased SO2 from the volcano produce a reduction in the spectrum of the observed radiances (in the relevant absorption bands of IASI), whereas areas outside the volcanic plume do not. An estimate of the plume height was obtained in the first two cases by examination of the winds from an NWP model at different heights, and in the third, where the meteorological pattern is more complicated, by using trajectories from a Lagrangian model and matching their position with satellite observations of the plume at different times. The importance of meteorology in the detection and evolution of volcanic plumes, especially at low levels, is especially well demonstrated by the Bárðarbunga eruption. Estimates of the plume concentrations were obtained from explicit line‐by‐line calculations.

The Holuhraun fissure eruption (Bárðarbunga volcanic system, central Iceland) has been ongoing since 31 August 2014 and is now the largest in Europe since the 1783-84 Laki event. For the first time in the modern age we have the opportunity to study at first hand the environmental impact of a flood basalt fissure eruption (> 1 km3 lava). Flood basalt eruptions are one of the most hazardous volcanic scenarios in Iceland and have had enormous societal and economic consequences across the northern hemisphere in the past. The Laki eruption caused the deaths of> 20% of the Icelandic population by environmental pollution and famine and potentially also increased European levels of mortality through air pollution by sulphur-bearing gas and aerosol. A flood basalt eruption was included in the UK National Risk Register in 2012 as one of the highest priority risks. The gas emissions from Holuhraun have been …

This work provides a sensitivity study of a two-channel passive infrared remote sensing retrieval of effective radius and optical depth using the Spinning Enhanced Visible and Infrared Imager with channels centred at 10.8 and 12.0 μm and a look-up table approach to calculate mass column loading. The retrieval is applied to images of two ash clouds from the 2010 Eyjafjallajökull eruption on 6 and 13 May 2010. The 2010 eruption of Eyjafjallajökull is well characterised, especially in terms of the airborne volcanic ash, which allows the relative uncertainties to be investigated within the realms of observation and reasonable approximation. The parameters investigated are as follows: refractive index, surface temperature, cloud top temperature, ash bulk density and, in particular, the uncertainties related to the spread of the ash particle size distribution—in terms of the geometric standard deviation of a lognormal particle size distribution. The lack of constraint on particle size distribution is shown to cause the largest uncertainty in retrieved mass column loading for the 6 May and 13 May ash cloud. A review of measured in situ size distributions of airborne particles is presented with justification for the choice of a lognormal size distribution for the 2010 Eyjafjallajökull eruption.

Infrared satellite measurements were used to track the volcanic plume from Grímsvötn, Iceland in 2011. Following the explosive eruption, the plume was sheared by the atmosphere into two distinct volcanic clouds, one was ash‐rich and the other was mainly composed of sulphur dioxide (SO2) gas. Satellite retrievals of the volcanic ash were found to be consistent with an andesitic/basaltic composition, providing a framework for composition evaluation immediately following an eruption. SO2 was detectable for several weeks following the eruption highlighting the importance of monitoring volcanic clouds for a prolonged period to manage the associated risk of air traffic.