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This study investigates the sensitivity of two brightness temperature differences (BTDs) in the infrared (IR) window of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to various cloud parameters in order to understand their information content, with a focus on cloud thermodynamic phase. To this end, this study presents radiative transfer calculations, providing an overview of the relative importance of all radiatively relevant cloud parameters, including thermodynamic phase, cloud-top temperature (CTT), optical thickness (τ), effective radius (Reff), and ice crystal habit. By disentangling the roles of cloud absorption and scattering, we are able to explain the relationships of the BTDs to the cloud parameters through spectral differences in the cloud optical properties. In addition, an effect due to the nonlinear transformation from radiances to brightness temperatures contributes to the specific characteristics of the BTDs and their dependence on τ and CTT. We find that the dependence of the BTDs on phase is more complex than sometimes assumed. Although both BTDs are directly sensitive to phase, this sensitivity is comparatively small in contrast to other cloud parameters. Instead, the primary link between phase and the BTDs lies in their sensitivity to CTT (or more generally the surface–cloud temperature contrast), which is associated with phase. One consequence is that distinguishing high ice clouds from low liquid clouds is straightforward, but distinguishing mid-level ice clouds from mid-level liquid clouds is challenging. These findings help to better understand and improve the working principles of phase retrieval algorithms. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the cloud thermodynamic phase is crucial for assessing the cloud radiative effect and is a prerequisite for remote sensing retrievals of microphysical cloud properties. While previous algorithms mainly detected ice and liquid phases, there is now a growing awareness for the need to further distinguish between warm liquid, supercooled and mixed-phase clouds. To address this need, we introduce a novel method named ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI), which enables cloud detection and the determination of cloud-top phase using SEVIRI (Spinning Enhanced Visible and Infrared Imager), the geostationary passive imager aboard Meteosat Second Generation. ProPS discriminates between clear sky, optically thin ice (TI) cloud, optically thick ice (IC) cloud, mixed-phase (MP) cloud, supercooled liquid (SC) cloud and warm liquid (LQ) cloud. Our method uses a Bayesian approach based on the cloud mask and cloud phase from the lidar–radar cloud product DARDAR (liDAR/raDAR). The validation of ProPS using 6 months of independent DARDAR data shows promising results: the daytime algorithm successfully detects 93 % of clouds and 86 % of clear-sky pixels. In addition, for phase determination, ProPS accurately classifies 91 % of IC, 78 % of TI, 52 % of MP, 58 % of SC and 86 % of LQ clouds, providing a significant improvement in accurate cloud-top phase discrimination compared to traditional retrieval methods. [ABSTRACT FROM AUTHOR]

Cloud-radiative heating (CRH) within the atmosphere affects the dynamics and predictability of extratropical cyclones. However, CRH is uncertain in numerical weather prediction and climate models, and this could affect model predictions of extratropical cyclones. In this paper, we present a systematic quantification of CRH uncertainties. To this end, we study an idealized extratropical cyclone simulated at a convection-permitting resolution of 2.5 km and combine large-eddy-model simulations at a 300 m resolution with offline radiative transfer calculations. We quantify four factors contributing to the CRH uncertainty in different regions of the cyclone: 3D cloud-radiative effects, parameterization of ice optical properties, cloud horizontal heterogeneity, and cloud vertical overlap. The last two factors can be considered essentially resolved at 300 m but need to be parameterized at a 2.5 km resolution. Our results indicate that parameterization of ice optical properties and cloud horizontal heterogeneity are the two factors contributing most to the mean uncertainty in CRH at larger spatial scales and can be more relevant for the large-scale dynamics of the cyclone. On the other hand, 3D cloud-radiative effects are much smaller on average, especially for stratiform clouds within the warm conveyor belt of the cyclone. Our analysis in particular highlights the potential to improve the simulation of CRH by better representing ice optical properties. Future work should, in particular, address how uncertainty in ice optical properties affects the dynamics and predictability of extratropical cyclones. [ABSTRACT FROM AUTHOR]

This study investigates the sensitivity of two brightness temperature differences (BTDs) in the infrared (IR) window of the SEVIRI imager to various cloud parameters in order to better understand their information content, with a focus on cloud thermodynamic phase. To this end, this study presents radiative transfer calculations, providing an overview of the relative importance of all radiatively relevant cloud parameters, including thermodynamic phase, cloud top temperature (CTT), optical thickness (τ), effective radius (Reff) and ice crystal habit. By disentangling the roles of cloud absorption and scattering, we are able to explain the relationships of the BTDs to the cloud parameters on the one hand by spectral differences in the cloud optical properties. In addition, an effect due to the nonlinear transformation from radiances to brightness temperatures contributes to the specific characteristics of the BTDs and their dependence on τ and CTT. We find that the dependence of the BTDs on phase is more complex than sometimes assumed. Although both BTDs are directly sensitive to phase, this sensitivity is comparatively small in contrast to other cloud parameters. Instead, the primary link between phase and the BTDs lies in their sensitivity to CTT, which is associated with phase. One consequence is that distinguishing high ice clouds from low liquid clouds is straightforward, but distinguishing mid-level ice clouds from mid-level liquid clouds is challenging. These findings help to better understand and improve the working principles of phase retrieval algorithms. [ABSTRACT FROM AUTHOR]

Solar photovoltaic power output is modulated by atmospheric aerosols and clouds and thus contains valuable information on the optical properties of the atmosphere. As a ground-based data source with high spatiotemporal resolution it has great potential to complement other ground-based solar irradiance measurements as well as those of weather models and satellites, thus leading to an improved characterisation of global horizontal irradiance. In this work several algorithms are presented that can retrieve global tilted and horizontal irradiance and atmospheric optical properties from solar photovoltaic data and/or pyranometer measurements. The method is tested on data from two measurement campaigns that took place in the Allgäu region in Germany in autumn 2018 and summer 2019, and the results are compared with local pyranometer measurements as well as satellite and weather model data. Using power data measured at 1 Hz and averaged to 1 min resolution along with a non-linear photovoltaic module temperature model, global horizontal irradiance is extracted with a mean bias error compared to concurrent pyranometer measurements of 5.79 W m -2 (7.35 W m -2) under clear (cloudy) skies, averaged over the two campaigns, whereas for the retrieval using coarser 15 min power data with a linear temperature model the mean bias error is 5.88 and 41.87 W m -2 under clear and cloudy skies, respectively. During completely overcast periods the cloud optical depth is extracted from photovoltaic power using a lookup table method based on a 1D radiative transfer simulation, and the results are compared to both satellite retrievals and data from the Consortium for Small-scale Modelling (COSMO) weather model. Potential applications of this approach for extracting cloud optical properties are discussed, as well as certain limitations, such as the representation of 3D radiative effects that occur under broken-cloud conditions. In principle this method could provide an unprecedented amount of ground-based data on both irradiance and optical properties of the atmosphere, as long as the required photovoltaic power data are available and properly pre-screened to remove unwanted artefacts in the signal. Possible solutions to this problem are discussed in the context of future work. [ABSTRACT FROM AUTHOR]

Cloud radiative heating (CRH) within the atmosphere affects the dynamics and predictability of extratropical cyclones. However, CRH is uncertain in numerical weather prediction and climate models, and this could affect model predictions of extratropical cyclones. In this paper, we present a systematic quantification of CRH uncertainties. To this end, we study an idealized extratropical cyclone simulated at a convection-permitting resolution of 2.5 km, and combine large-eddy simulations at 300 m resolution with offline radiative transfer calculations. We quantify four factors contributing to the CRH uncertainty in different regions of the cyclone: 3D cloud radiative effects, parameterization of ice-optical properties, cloud horizontal heterogeneity, and cloud vertical overlap. The two last factors can be considered to be essentially resolved at 300 m but need to be parametrized at 2.5 km resolution. Our results indicate that the parameterization of ice-optical properties and the cloud horizontal heterogeneity are the two factors contributing most to the uncertainty in CRH. On the other hand, 3D cloud radiative effects are much smaller, especially for stratiform clouds within the warm conveyor belt of the cyclone. Our analysis in particular highlights the potential to improve the simulation of CRH by better representing ice-optical properties. Future work should in particular address how uncertainty in ice-optical properties affects the dynamics and predictability of extratropical cyclones. [ABSTRACT FROM AUTHOR]

In this study, which is the third part of the HaloCam series after , we present a novel technique to retrieve quantitative information about ice crystal optical and microphysical properties using ground-based imaging observations of halo displays. Comparing HaloCam's calibrated RGB images of 22 and 46 ∘ halo observations against a lookup table of simulated radiances, this technique allows the retrieval of the sizes and shapes of randomly oriented crystals as well as the fraction of smooth and rough ice crystals for cirrus clouds. We analyzed 4400 HaloCam images between September 2015 and November 2016 showing a visible 22 ∘ halo. The optical properties of hexagonal 8-element aggregates of columns with a mean ice crystal effective radius of about 20 µ m and a mixture of 37 % smooth and 63 % rough crystals on average best match the HaloCam observations. Implemented on different sites, HaloCam in combination with the machine-learning-based halo detection algorithm HaloForest can provide a consistent dataset for climatological studies of ice crystal properties representing typical cirrus clouds. Representative ice crystal optical properties are required for remote sensing of cirrus clouds as well as climate modeling. Since ground-based passive imaging observations provide information about the forward scattering part of the ice crystal optical properties, the results of this work ideally complement the results of satellite-based and airborne studies. [ABSTRACT FROM AUTHOR]

Marine cloud brightening (MCB) is a geoengineering approach to counteract climate change by the deliberate seeding of sea salt aerosol particles that, once they activated to cloud droplets, directly increase cloud reflectance and hence global albedo. However, a large fraction of the seeded aerosol may remain interstitial, i.e., unactivated particles among cloud droplets. Because the consideration of interstitial aerosol optical properties usually requires computationally expensive simulations of the entire particle spectrum and direct Mie calculations, we develop a simple parameterization to be used with computationally efficient bulk and even bin cloud microphysical schemes that do not treat the unactivated aerosol explicitly. Using parcel and large-eddy simulations with highly detailed Lagrangian cloud microphysics and direct Mie calculations as a reference, we show that the parameterization captures the variability in the interstitial aerosol extinction successfully. By applying the parameterization to typical MCB cases, we find that the consideration of interstitial aerosol extinction is important for the assessment of MCB in shallow clouds with weak updrafts, in which only a small fraction of aerosol particles is activated to cloud droplets. Significance Statement: The optical properties of clouds are not only determined by the number and size of cloud droplets. Unactivated aerosol particles, so-called interstitial aerosol, can contribute substantially to the optical thickness of shallow clouds with weak updrafts in aerosol-laden conditions. The consideration of interstitial aerosol optical thickness has been computationally challenging, but the new parameterization presented here allows for an efficient representation in various types of cloud models. The parameterization is shown to be an important addition for the assessment of marine cloud brightening (MCB), a potential geoengineering technique to counteract global warming by increasing the cloud albedo through the deliberate seeding of aerosol. [ABSTRACT FROM AUTHOR]

Ice clouds and their effect on earth's radiation budget are one of the largest sources of uncertainty in climate change predictions. The uncertainty in predicting ice cloud feedbacks in a warming climate arises due to uncertainties in measuring and explaining their current optical and microphysical properties as well as from insufficient knowledge about their spatial and temporal distribution. This knowledge can be significantly improved by active remote sensing, which can help to explore the vertical profile of ice cloud microphysics, such as ice particle size and ice water content. This study focuses on the well-established variational approach VarCloud to retrieve ice cloud microphysics from radar–lidar measurements. While active backscatter retrieval techniques surpass the information content of most passive, vertically integrated retrieval techniques, their accuracy is limited by essential assumptions about the ice crystal shape. Since most radar–lidar retrieval algorithms rely heavily on universal mass–size relationships to parameterize the prevalent ice particle shape, biases in ice water content and ice water path can be expected in individual cloud regimes. In turn, these biases can lead to an erroneous estimation of the radiative effect of ice clouds. In many cases, these biases could be spotted and corrected by the simultaneous exploitation of measured solar radiances. The agreement with measured solar radiances is a logical prerequisite for an accurate estimation of the radiative effect of ice clouds. To this end, this study exploits simultaneous radar, lidar, and passive measurements made on board the German High Altitude and Long Range Research Aircraft. By using the ice clouds derived with VarCloud as an input to radiative transfer calculations, simulated solar radiances are compared to measured solar radiances made above the actual clouds. This radiative closure study is done using different ice crystal models to improve the knowledge of the prevalent ice crystal shape. While in one case aggregates were capable of reconciling radar, lidar, and solar radiance measurements, this study also analyses a more problematic case for which no radiative closure could be achieved. In this case, collocated in situ measurements indicate that the lack of closure may be linked to unexpectedly high values of the ice crystal number density. [ABSTRACT FROM AUTHOR]

During the ACRIDICON-CHUVA (Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (Global Precipitation Measurement)) aircraft campaign in September 2014 over the Amazon, among other topics, aerosol effects on the development of cloud microphysical profiles during the burning season were studied. Hyperspectral remote sensing with the imaging spectrometer specMACS provided cloud microphysical information for sun-illuminated cloud sides. In order to derive profiles of phase or effective radius from cloud side observations, vertical location information is indispensable. For this purpose, spectral measurements of cloud-side-reflected radiation in the oxygen A absorption band collected by specMACS were used to determine absorption path length between cloud sides and the instrument aboard the aircraft. From these data, horizontal distance and eventually vertical height were derived. It is shown that, depending on aircraft altitude and sensor viewing direction, an unambiguous relationship of absorption and distance exists and can be used to retrieve cloud geometrical parameters. A comparison to distance and height information from stereo image analysis (using data of an independent camera) demonstrates the efficiency of the approach. Uncertainty estimates due to method, instrument and environmental factors are provided. The main sources of uncertainty are unknown in cloud absorption path contributions due to complex 3-D geometry or unknown microphysical properties, variable surface albedo and aerosol distribution. A systematic difference of 3.8 km between the stereo and spectral method is found which can be attributed to 3-D geometry effects not considered in the method's simplified cloud model. If this offset is considered, typical differences found are 1.6 km for distance and 230 m for vertical position at a typical distance around 20 km between sensor and convective cloud elements of typically 1–10 km horizontal and vertical extent. [ABSTRACT FROM AUTHOR]