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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]

Most of the dust models underestimate the load of the large dust particles, consider spherical shapes instead of irregular ones, and have to deal with a wide range of the dust refractive index (RI) to be used. This leads to an incomplete assessment of the dust radiative effects and dust-related impacts on climate and weather. The current work aims to provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, RI, and shape. We show that the main limitations stem from the size and RI, while using a more realistic shape plays only a minor role, with our results agreeing with recent findings in the literature. At the top of the atmosphere (TOA) close to dust sources, the underestimation of size issues an underestimation of the direct warming effect of dust of ∼ 18–25 W m -2 , for DOD = 1 (dust optical depth) at 0.5 µ m, depending on the solar zenith angle (SZA) and RI. The underestimation of the dust size in models is less above the ocean than above dust sources, resulting in an underestimation of the direct cooling effect of dust above the ocean by up to 3 W m -2 , for aerosol optical depth (AOD) of 1 at 0.5 µ m. We also show that the RI of dust may change its DRE by 80 W m -2 above the dust sources and by 50 W m -2 at downwind oceanic areas for DOD = 1 at 0.5 µ m at TOA. These results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust DRE, especially near the dust sources and mostly for studies dealing with local radiation effects of dust aerosols. [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]

The increasing resolution of numerical weather prediction models makes inter-column three-dimensional (3D) radiative transport more and more important. However, 3D radiative-transfer solvers are still computationally expensive, largely preventing their use in operational weather forecasting. To address this issue, developed the TenStream solver. It extends the well-established two-stream method to three dimensions by using 10 instead of 2 streams to describe the transport of radiative energy through Earth's atmosphere. Building upon this method, this paper presents the dynamic TenStream solver, which provides a further acceleration of the original TenStream model. Compared to traditional solvers, this speedup is achieved by utilizing two main concepts. First, radiation is not calculated from scratch every time the model is called. Instead, a time-stepping scheme is introduced to update the radiation field, based on the result from the previous radiation time step. Secondly, the model is based on incomplete solves, with just the first few steps of an iterative scheme towards convergence performed every time it is called. Essentially, the model thereby just uses the ingoing fluxes of a grid box to update its outgoing fluxes. Combined, these two approaches move radiative transfer much closer to the way advection is handled in the dynamical core of a numerical weather prediction (NWP) model, as both use previously calculated results to update their variables and thereby just require access to the neighboring values of an individual grid box, facilitating model parallelization. To demonstrate the feasibility of this new solver, we apply it to a precomputed shallow-cumulus-cloud time series and test its performance in terms of both speed and accuracy. In terms of speed, the dynamic TenStream solver is shown to be about 3 times slower than a traditional 1D δ -Eddington approximation but noticeably faster than currently available 3D solvers. To evaluate the accuracy of the dynamic TenStream solver, we compare its results as well as calculations carried out using a 1D δ -Eddington approximation and the original TenStream solver, to benchmark calculations performed with the 3D Monte Carlo solver MYSTIC. We demonstrate that at the grid box level, dynamic TenStream is able to calculate heating rates and net irradiances at domain boundaries that are very close to those obtained by the original TenStream solver, thus offering a much better representation of the MYSTIC benchmark than the 1D δ -Eddington results. By calling the dynamic TenStream solver less frequently than the δ -Eddington approximation, we furthermore show that our new solver produces significantly better results than a 1D δ -Eddington approximation carried out with a similar computational demand. At these lower calling frequencies, however, the incomplete solves in the dynamic TenStream solver also lead to a buildup of bias with time, which becomes larger the lower the calling frequency is. [ABSTRACT FROM AUTHOR]

Cloud radiative properties play a significant role in radiation and energy budgets and are influenced by both the cloud top height and the particle size distribution. Both cloud top heights and particle size distributions can be derived from 2-D intensity and polarization measurements by the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). The cloud top heights are determined using a stereographic method , and the particle size distributions are derived in terms of the cloud effective radius and the effective variance from multidirectional polarized measurements of the cloudbow. In this study, the accuracy of the two methods is evaluated using realistic 3-D radiative transfer simulations of specMACS measurements of a synthetic field of shallow cumulus clouds, and possible error sources are determined. The simulations are performed with the 3-D Monte Carlo radiative transport model MYSTIC using cloud data from highly resolved large-eddy simulations (LESs). Both retrieval methods are applied to the simulated data and compared to the respective properties of the underlying cloud field from the LESs. Moreover, the influence of the cloud development on both methods is evaluated by applying the algorithms to idealized simulated data where the clouds did not change during the simulated overflight of 1 min over the cloud field. For the cloud top height retrieval, an absolute mean difference of less than 70 m with a standard deviation of about 130 m compared to the expected heights from the model is found. The elimination of the cloud development as a possible error source results in mean differences of (46±140) m. For the effective radius, an absolute average difference of about (-0.2±1.30) µ m from the expected effective radius from the LES model input is derived for the realistic simulation and (-0.03±1.28) µ m for the simulation without cloud development. The difference between the effective variance derived from the cloudbow retrieval and the expected effective variance is (0.02±0.05) for both simulations. Additional studies concerning the correlations between larger errors in the effective radius or variance and the optical thickness of the observed clouds have revealed that low values in the optical thickness do not have an impact on the accuracy of the retrieval. [ABSTRACT FROM AUTHOR]

The spectrometer of the Munich Aerosol Cloud Scanner (specMACS) is a high-spatial-resolution hyperspectral and polarized imaging system. It is operated from a nadir-looking perspective aboard the German High Altitude and LOng range (HALO) research aircraft and is mainly used for the remote sensing of clouds. In 2019, its two hyperspectral line cameras, which are sensitive to the wavelength range between 400 and 2500 nm, were complemented by two 2D RGB polarization-resolving cameras. The polarization-resolving cameras have a large field of view and allow for multi-angle polarimetric imaging with high angular and spatial resolution. This paper introduces the polarization-resolving cameras and provides a full characterization and calibration of them. We performed a geometric calibration and georeferencing of the two cameras. In addition, a radiometric calibration using laboratory calibration measurements was carried out. The radiometric calibration includes the characterization of the dark signal, linearity, and noise as well as the measurement of the spectral response functions, a polarization calibration, vignetting correction, and absolute radiometric calibration. With the calibration, georeferenced, absolute calibrated Stokes vectors rotated into the scattering plane can be computed from raw data. We validated the calibration results by comparing observations of the sunglint, which is a known target, with radiative transfer simulations of the sunglint. [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]

Cloud geometry and in particular cloud top heights can be derived from 2-D camera measurements by applying a stereographic method to data from an overflight over a scene of clouds (see e.g. Koelling et al., 2019). Although airplane overpasses are relatively fast, cloud motion with the wind is important and can result in errors in the cloud localization. Here, the impact of the wind is investigated using the method from Koelling et al. (2019) for measurements of the airborne hyperspectral imaging system specMACS (spectrometer of the Munich Aerosol Cloud Scanner). Further, a method for the cloud motion correction using model winds from ECMWF is presented. It is shown that the update is important as the original algorithm without the cloud motion correction can over- or underestimate the cloud top heights by several hundred meters dependent on the wind speed and the relative wind direction. This is validated using data from the EUREC4A campaign as well as realistic 3-D radiative transfer simulations. From the comparison of the derived cloud top heights to the expected ones from the model input an average accuracy of the cloud top heights of less than (20 ± 140) m (mean deviation and one standard deviation) is estimated for the updated method. [ABSTRACT FROM AUTHOR]

Cloud radiative properties play a significant role in radiation and energy budgets and are influenced by both the cloud top height and particle size distribution. Both cloud top heights and particle size distributions can be derived from two-dimensional intensity and polarization measurements by the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). The cloud top heights are determined using a stereographic method (Kölling et al., 2019) and the particle size distributions are derived in terms of the cloud effective radius and the effective variance from multidirectional polarized measurements of the cloudbow (Pörtge et al., 2023). In this study, the two methods are validated using realistic 3-D radiative transfer simulations of specMACS measurements of a synthetic field of shallow cumulus clouds to ensure the methods' accuracy and to determine possible error sources. The simulations are performed with the 3-D Monte Carlo radiative transport model MYSTIC (Mayer, 2009) using cloud data from highly resolved LES simulations. Both retrieval methods are applied to the simulated data and compared to the respective properties of the underlying cloud field from the LES simulations. Moreover, the influence of the cloud development on both methods is evaluated by applying the algorithms to idealized simulated data where the clouds did not change during the simulated overflight of one minute over the cloud field. For the cloud top height retrieval an absolute mean difference of less than 70 m with a standard deviation of about 130 m compared to the expected heights from the model is found. The elimination of the cloud development as a possible error source results in mean differences of (46 ± 140) m. For the effective radius, an absolute average difference of about (−0.2 ± 1.30) µm from the expected effective radius from the LES model input is derived for the realistic simulation and (−0.03 ± 1.27) µm for the simulation without cloud development. The difference between the effective variance derived from the cloudbow retrieval and the expected effective variance is (0.02 ± 0.05) for both simulations. [ABSTRACT FROM AUTHOR]

The spectrometer of the Munich Aerosol Cloud Scanner (specMACS) is a high spatial resolution hyperspectral and polarized imaging system. It is operated in a nadir-looking perspective aboard the German High Altitude and LOng range research aircraft (HALO) and is mainly used for the remote sensing of clouds. In 2019, its two hyperspectral line-cameras which are sensitive to the wavelength range between 400 and 2500 nm were complemented by two 2D RGB polarization resolving cameras. The polarization resolving cameras have a large field of view and allow for multi-angle polarimetric imaging with high angular and spatial resolution. This paper introduces the polarization resolving cameras and provides a full characterization and calibration of them. We performed a geometric calibration and georeferencing of the two cameras. In addition, a radiometric calibration using laboratory calibration measurements was carried out. The radiometric calibration includes the characterization of dark signal, linearity, and noise as well as the measurement of the spectral response functions, a polarization calibration, vignetting correction, and absolute radiometric calibration. With the calibration, georeferenced absolute calibrated Stokes vectors rotated into the scattering plane can be computed from raw data. We validated the calibration results by comparing observations of the sunglint, which is a known target, with radiative transfer simulations of the sunglint. [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]

The increasing resolution of numerical weather prediction models makes three-dimensional (3D) radiative effects more and more important. However, 3D radiative transfer solvers are still computationally expensive, largely preventing their use in operational weather forecasting. To address this issue, Jakub and Mayer (2015) developed the TenStream solver. It extends the well-established two-stream method to three dimensions by using ten instead of two streams to describe the transport of radiative energy through Earth's atmosphere. Building upon this method, this paper presents the dynamic TenStream solver, which provides a further acceleration of the original TenStream solver. Compared to traditional solvers, this speed-up is achieved by utilizing two main concepts: First, radiation is not calculated from scratch every time the model is called. Instead, a time-stepping scheme is introduced to update the radiative field based on the result from the previous radiation time step. Secondly, the model is based on incomplete solves, performing just the first few steps of an iterative scheme towards convergence every time it is called. At its core, the model thereby just uses the ingoing fluxes of a grid box to update its outgoing fluxes. Combined, these two approaches put radiative transfer much closer to the way advection in the dynamical core of an NWP model is handled, as both use previously calculated results to update their variables and thereby just require access to the neighboring values of an individual model grid box, facilitating model parallelization. To demonstrate the feasibility of this new solver, we apply it to a precomputed shallow cumulus cloud time series and test its performance both in terms of speed and accuracy. In terms of speed, our new solver is shown to be about three times slower than a traditional 1D δ -Eddington solver, but noticeably faster than currently available 3D solvers. To evaluate the accuracy of our new solver, we compare its results, as well as calculations carried out by a 1D δ -Eddington solver and the original TenStream solver, to benchmark calculations performed with the 3D Monte Carlo solver MYSTIC. We demonstrate that our new solver is able to calculate heating rates and net surface irradiances very close to those obtained by the original TenStream solver, thus offering a noticeable improvement compared to the 1D δ -Eddington results even when operated at lower calling frequencies. At these lower calling frequencies, the incomplete solves in the dynamic TenStream solver lead to the build-up of a bias with time, which becomes larger the lower the calling frequency is. However, this increase in bias flattens out after a while and remains smaller than the heating rate bias introduced by the 1D δ -Eddington solver at any point in time. Most importantly, our new solver is shown to produce significantly better results when compared to 1D δ -Eddington solves carried out with a similar computational demand. [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]

Most of the dust models underestimate the load of the large dust particles, consider spherical shapes instead of irregular ones, and have to deal with a wide range of dust refractive index (RI) to be used. This leads to an incomplete assessment of the dust radiative effects and dust-related impacts on climate and weather. The current work aims to provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, RI and shape. We show that the main limitations stem from the size and RI, while the shape plays only a minor role, with our results agreeing with recent findings in the literature. At the top of the atmosphere (TOA) close to dust sources, the underestimation of size issues an underestimation of the direct warming effect of dust of ~18–25 W/m2, for dust aerosol optical depth (DOD) of 1 at 0.5 μm, depending on the solar zenith angle (SZA) and RI. The underestimation of the dust size in models is less above the ocean than above dust sources, resulting in an underestimation of the direct cooling effect of dust above the ocean by up to 3 W/m2, for AOD of 1 at 0.5 μm. We also show that the RI of dust may change its DRE by 80 W/m2 above the dust sources, and by 50 W/m2 at downwind oceanic areas, for AOD of 1 at 0.5 μm at TOA. These results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust DRE, especially near the dust sources and mostly for studies dealing with local radiation effects of dust aerosols. [ABSTRACT FROM AUTHOR]

This work introduces a model for all-sky-image-based cloud and direct irradiance nowcasting (MACIN), which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASIs). With a synthetic setup based on simulated cloud scenes, the model and its components are validated in depth. We train a convolutional neural network on real ASI images to identify clouds. Cloud masks are generated for the synthetic ASI images with this network. Cloud height and motion are derived using sparse matching. In contrast to other studies, all derived cloud information, from both ASIs and multiple time steps, is combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times of up to 20 min. For the cloud masks derived from the ASI images, we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud-base heights is 4 % and cloud motion error is in the range of ±0.1ms-1. For the DNI nowcasts, we found an improvement over persistence for lead times larger than 1 min. Using the synthetic setup, we computed a DNI reference for a point and also an area of 500m×500m. Errors for area nowcasts as required, e.g., for photovoltaic plants, are smaller compared with errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup. [ABSTRACT FROM AUTHOR]

The cloud droplet size distribution is often described by a gamma distribution defined by the effective radius and the effective variance. The effective radius is directly related to the cloud's optical thickness, which influences the radiative properties of a cloud. The effective variance affects, among other things, the evolution of precipitation. Both parameters can be retrieved from measurements of the cloudbow. The cloudbow (or rainbow) is an optical phenomenon that forms due to the single scattering of radiation by liquid cloud droplets at the cloud edge. The polarized radiance of the cloudbow crucially depends on the cloud droplet size distribution. The effective radius and the effective variance can be retrieved by fitting model simulations (stored in a lookup table) to polarized cloudbow observations. This study uses measurements from the wide-field polarization-sensitive camera of the spectrometer of the Munich Aerosol Cloud Scanner (specMACS) aboard the German "High Altitude and LOng range research aircraft" (HALO). Along with precise cloud geometry data derived by a stereographic method, a geolocalization of the observed clouds is possible. Observations of the same cloud from consecutive images are combined into one radiance measurement from multiple angles. Two case studies of trade-wind cumulus clouds measured during the EUREC 4 A (ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte) field campaign are presented, and the cloudbow technique is demonstrated. The results are combined into maps of the effective radius and the effective variance with a 100m × 100m spatial resolution and large coverage (across-track swath width of 8km). The first case study shows a stratiform cloud deck with distinct patches of large effective radii up to 40µm and a median effective variance of 0.11. specMACS measures at a very high angular resolution (binned to 0.3∘) which is necessary when large droplets are present. The second case study consists of small cumulus clouds (diameters of approximately 2km). The retrieved effective radius is 7.0µm , and the effective variance is 0.08 (both median values). This study demonstrates that specMACS is able to determine the droplet size distribution of liquid water clouds even for small cumulus clouds, which are a problem for traditional droplet size retrievals based on total reflectances. [ABSTRACT FROM AUTHOR]

Extratropical cyclones drive midlatitude weather, including extreme events, and determine midlatitude climate. Their dynamics and predictability are strongly shaped by cloud diabatic processes. While the cloud impact due to latent heating is much studied, little is known about the impact of cloud radiative heating (CRH) on the dynamics and predictability of extratropical cyclones. Here, we address this question by means of baroclinic life cycle simulations performed at a convection-permitting resolution of 2.5 km with the ICON model. The simulations use a newly implemented channel setup with periodic boundary conditions in the zonal direction. Moreover, the simulations apply a new modeling technique for which only CRH interacts with the cyclone, which circumvents changes in the mean state due to clear-sky radiative cooling that has complicated the interpretation of previous work. We find that CRH increases the kinetic energy of the cyclone system. The impact is most prominent at upper levels. To understand the CRH impact on the upper-tropospheric circulation, we diagnose the evolution of differences in potential vorticity between a simulation with and without CRH, and we quantify through which processes these differences grow over the course of the cyclone's life cycle. According to this diagnostic, CRH affects the cyclone mostly via the intensification of latent heating from cloud microphysical processes. Near the tropopause, direct diabatic modification of potential vorticity by intensified latent heat release precedes further changes in the tropopause by the upper-tropospheric divergent flow, which represents an indirect impact of latent heat release. Subsequently, differences in the tropopause structure amplify with the rotational flow during the highly nonlinear stage of the baroclinic wave. Our results show that although CRH is comparably small in magnitude, it can affect extratropical cyclones by changing cloud microphysical heating and subsequently the large-scale flow. The CRH impact follows a previously identified mechanism of multi-stage upscale error growth. At the same time, simulations in which CRH is disabled after certain days show that the CRH impact operates throughout the entire intensification phase of the cyclone. This means that CRH does not merely provide an arbitrary initial perturbation to the cyclone, from which differences grow in a generic way. Instead, our results suggest that uncertainties associated with the representation of CRH in numerical models have a more systematic impact and may more fundamentally influence model predictions of extratropical cyclones. [ABSTRACT FROM AUTHOR]

Operational retrievals of tropospheric trace gases from space-borne spectrometers are based on one-dimensional radiative transfer models. To minimize cloud effects, trace gas retrievals generally implement a simple cloud model based on radiometric cloud fraction estimates and photon path length corrections. The latter relies on measurements of the oxygen collision pair (O 2 –O 2) absorption at 477 nm or on the oxygen A-band around 760 nm to determine an effective cloud height. In reality however, the impact of clouds is much more complex, involving unresolved sub-pixel clouds, scattering of clouds in neighbouring pixels, and cloud shadow effects, such that unresolved three-dimensional effects due to clouds may introduce significant biases in trace gas retrievals. Although clouds have significant effects on trace gas retrievals, the current cloud correction schemes are based on a simple cloud model, and the retrieved cloud parameters must be interpreted as effective values. Consequently, it is difficult to assess the accuracy of the cloud correction only based on analysis of the accuracy of the cloud retrievals, and this study focuses solely on the impact of the 3D cloud structures on the trace gas retrievals. In order to quantify this impact, we study NO 2 as a trace gas example and apply standard retrieval methods including approximate cloud corrections to synthetic data generated by the state-of-the-art three-dimensional Monte Carlo radiative transfer model MYSTIC. A sensitivity study is performed for simulations including a box cloud, and the dependency on various parameters is investigated. The most significant bias is found for cloud shadow effects under polluted conditions. Biases depend strongly on cloud shadow fraction, NO 2 profile, cloud optical thickness, solar zenith angle, and surface albedo. Several approaches to correct NO 2 retrievals under cloud shadow conditions are explored. We find that air mass factors calculated using fitted surface albedo or corrected using the O 2 –O 2 slant column density can partly mitigate cloud shadow effects. However, these approaches are limited to cloud-free pixels affected by surrounding clouds. A parameterization approach is presented based on relationships derived from the sensitivity study. This allows measurements to be identified for which the standard NO 2 retrieval produces a significant bias and therefore provides a way to improve the current data flagging approach. [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]

Three-dimensional (3D) cloud structures may impact atmospheric trace gas products from ultraviolet–visible (UV–Vis) sounders. We used synthetic and observational data to identify and quantify possible cloud-related bias in NO 2 tropospheric vertical column density (TVCD). The synthetic data were based on high-resolution large eddy simulations which were input to a 3D radiative transfer model. The simulated visible spectra for low-earth-orbiting and geostationary geometries were analysed with standard retrieval methods and cloud correction schemes that are employed in operational NO 2 satellite products. For the observational data, the NO 2 products from the TROPOspheric Monitoring Instrument (TROPOMI) were used, while the Visible Infrared Imaging Radiometer Suite (VIIRS) provided high-spatial-resolution cloud and radiance data. NO 2 profile shape, cloud shadow fraction, cloud top height, cloud optical depth, and solar zenith and viewing angles were identified as the metrics being the most important in identifying 3D cloud impacts on NO 2 TVCD retrievals. For a solar zenith angle less than about 40 ∘ the synthetic data show that the NO 2 TVCD bias is typically below 10 %, while for larger solar zenith angles the NO 2 TVCD is low-biased by tens of percent. The horizontal variability of NO 2 and differences in TROPOMI and VIIRS overpass times make it challenging to identify a similar bias in the observational data. However, for optically thick clouds above 3000 m, a low bias appears to be present in the observational data. [ABSTRACT FROM AUTHOR]

The cloud droplet size distribution is often described by a gamma distribution defined by the effective radius (reff) and the effective variance (veff). The effective radius is directly related to the cloud's optical thickness which influences the radiative properties of a cloud. The effective variance affects, among other things, the evolution of precipitation. Both parameters can retrieved from measurements of the cloudbow. The cloudbow or rainbow is an optical phenomenon, which forms by single scattering of radiation by liquid cloud droplets at the cloud edge. The polarized radiance of the cloudbow crucially depends the cloud droplet size distribution. reff and veff can be retrieved by fitting model simulations (stored in a look-up table) to polarized cloudbow observations. This study uses measurements from the wide-field polarization-sensitive camera of the LMU spectral imager system spec-MACS onboard the German research airplane HALO. Together with precise cloud geometry data derived by a stereographic method, a geolocalization of the observed clouds is possible. Observations of the same cloud from consecutive images are combined into one radiance measurement from multiple angles. Two case studies of trade wind cumulus clouds measured during the EUREC4A field campaign are presented and the cloudbow technique is demonstrated. The results are combined into maps of reff and veff with a spatial resolution of 100m by 100m and large coverage (across-track swath width: 8km). The first case study shows a stratiform cloud deck with distinct patches of large effective radii up to 40 µm and a median effective variance of 0.12. The second case study consists of small cumulus clouds (diameters of approximately 2km). The retrieved reff 6.92 µm and veff is 0.07 (both median values). [ABSTRACT FROM AUTHOR]

Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data, and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2 A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMFs) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: the first includes simple box clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical of central Europe such as shallow cumulus, convective cloud cells, cirrus and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset, the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analysed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behaviour of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure. [ABSTRACT FROM AUTHOR]

After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called "VADUGS" (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash-contaminated pixels with a probability of detection of 0.84 and a false-alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m -2 for concentrations smaller than 2.0 g m -2 and small overestimations in the range 5 %–50 % for moderate viewing angles 35–65 ∘ , but up to 300 % for satellite viewing zenith angles close to 90 or 0 ∘. Ash top heights are mainly underestimated, with the smallest underestimation of -9 % for viewing zenith angles between 40 and 50 ∘. Absolute errors are smaller than 70 % and with high correlation coefficients of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented. [ABSTRACT FROM AUTHOR]

Operational retrievals of tropospheric trace gases from space-borne spectrometers are based on one-dimensional radiative transfer models. To minimize cloud effects, trace gas retrievals generally implement Lambertian cloud models based on radiometric cloud fraction estimates and photon path length corrections. The latter relies on measurements of the oxygen collision pair (O2-O2) absorption at 477 nm or on the oxygen A-band around 760 nm. In reality however, the impact of clouds is much more complex, involving unresolved sub-pixel clouds, scattering of clouds in neighboring pixels and cloud shadow effects, such that unresolved three-dimensional effects due to clouds may introduce significant biases in trace gas retrievals. In order to quantify this impact, we study NO2 as a trace gas example, and apply standard retrieval methods including approximate cloud corrections to synthetic data generated by the state-of-the-art three-dimensional Monte Carlo radiative transfer model MYSTIC. A sensitivity study is performed for simulations including a box-cloud, and the dependency on various parameters is investigated. The most significant bias is found for cloud shadow effects under polluted conditions. Biases depend strongly on cloud shadow fraction, NO2 profile, cloud optical thickness, solar zenith angle, and surface albedo. Several approaches to correct NO2 retrievals under cloud shadow conditions are explored. We find that air mass factors calculated using fitted surface albedo or corrected using the O2-O2 slant column density can partly mitigate cloud shadow effects. However, these approaches are limited to cloud-free pixels affected by surrounding clouds. A parameterization approach is presented based on relationships derived from the sensitivity study. This allows identifying measurements for which the standard NO2 retrieval produces a significant bias, and therefore provides a way to improve the current data flagging approach. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) cloud structures may impact atmospheric trace gas products from ultraviolet-visible (UV-VIS) sounders. We used synthetic and observational data to identify and quantify possible cloud-related bias in NO2 tropospheric vertical column densities (TVCD). The synthetic data were based on high-resolution large eddy simulations which were input to a 3D radiative transfer model. The simulated visible spectra for low-earth orbiting and geostationary geometries were analysed with standard retrieval methods and cloud correction schemes that are employed in operational NO2 satellite products. For the observational data the NO2 products from the TROPOspheric Monitoring Instrument (TROPOMI) were used while the Visible Infrared Imaging Radiometer Suite (VIIRS) provided high spatial resolution cloud and radiance data. Cloud shadow fraction, cloud top height, cloud optical depth, solar zenith and viewing angles, were identified as the metrics being the most important in identifying 3D cloud impacts on NO2 TVCD retrievals. For a solar zenith angle less than about 40#176; the synthetic data show that the NO2 TVCD bias is typically below 10 %. For larger solar zenith angles both synthetic and observational data often show NO2 TVCD bias on the order of tens of %. Specifically, for clearly identified cloud shadow bands in the observational data, the NO2 TVCD appears low-biased when the cloud shadow fraction > 0.0 compared to when the cloud shadow fraction is zero. For solar zenith angles between 50-60#176;, about 16 % of TROPOMI pixels with high quality value NO2 TVCD retrievals, were found to be impacted by cloud effects larger than 20 %. [ABSTRACT FROM AUTHOR]

Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC. The dataset includes simulated spectra in two spectral ranges (400-500nm and the O2A-band from 755-775nm). Moreover it includes layer air mass factors (layer-AMF) calculated at 460nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: The first includes simple box-clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical for central Europe such as shallow cumulus, convective cloud cells, cirrus, and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analyzed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20%. The dataset is available for the scientific community to assess the behavior of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure. [ABSTRACT FROM AUTHOR]

After the eruption of volcanoes all over the world the monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called VADUGS (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash contaminated pixels with a probability of detection of 0.84 and a false alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m-2 for concentrations smaller than 2.0 g m-2 and small overestimations in the range 5-50 % for moderate viewing angles 35-65°, but up to 300 % for satellite viewing zenith angles close to 90° or 0°. Ash top heights are mainly underestimated, with the smallest underestimation of -9 % for viewing zenith angles between 40° and 50°. Absolute errors are smaller than 70 % and with high correlation coefficients up to 0.7 for ash clouds with high mass column concentrations. A comparison against spaceborne lidar observations by CALIPSO/CALIOPconfirms these results. VADUGS is run operationally at the German Weather Service and this application is presented as well. [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]

The representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years. Nevertheless, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud–radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of the global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud-regime-dependent methodologies in next-generation atmospheric models. This study, highlighting the Tripleclouds potential for three essential cloud types, signifies the need for more research examining a broader spectrum of cloud morphologies. [ABSTRACT FROM AUTHOR]

Nitrate pollution of freshwaters and methane emissions into the atmosphere are crucial factors in deteriorating the quality of drinking water and in contributing to global climate change. The n-damo (nitrite-dependent anaerobic methane oxidation), nitrate-dependent anaerobic methane oxidation and the anaerobic oxidation of ammonium (anammox) represent two microbially mediated processes that can reduce nitrogen loading of aquatic ecosystems and associated methane emissions to the atmosphere. Here, we report vertical concentration and stable-isotope profiles of CH4 , NO3- , NO2- , and NH4+ in the water column of Fohnsee (lake in southern Bavaria, Germany) that may indicate linkages between denitrification, anaerobic oxidation of methane (AOM), and anammox. At a water depth from 12 to 20 m , a methane–nitrate transition zone (NMTZ) was observed, where δ13C values of methane and δ15N and δ18O of dissolved nitrate markedly increased in concert with decreasing concentrations of methane and nitrate. These data patterns, together with the results of a simple 1-D diffusion model linked with a degradation term, show that the nonlinear methane concentration profile cannot be explained by diffusion and that microbial oxidation of methane coupled with denitrification under anaerobic conditions is the most parsimonious explanation for these data trends. In the methane zone at the bottom of the NMTZ (20 to 22 m) δ15N of ammonium increased by 4 ‰ , while ammonium concentrations decreased. In addition, a strong 15N enrichment of dissolved nitrate was observed at a water depth of 20 m , suggesting that anammox is occurring together with denitrification. The conversion of nitrite to N2 and nitrate during anammox is associated with an inverse N isotope fractionation and may explain the observed increasing offset (Δδ15 N) of 26 ‰ between δ15N values of dissolved nitrate and nitrite at a water depth of 20 m compared to the Δδ15Nnitrate-nitrite of 11 ‰ obtained in the NMTZ at a water depth between 16 and 18 m. The associated methane concentration and stable-isotope profiles indicate that some of the denitrification may be coupled to AOM, an observation supported by an increased concentration of bacteria known to be involved in n-damo/denitrification with AOM (NC10 and Crenothrix) and anammox (" Candidatus Anammoximicrobium") whose concentrations were highest in the methane and ammonium oxidation zones, respectively. This study shows the potential for a coupling of microbially mediated nitrate-dependent methane oxidation with anammox in stratified freshwater ecosystems, which may be important for affecting both methane emissions and nitrogen concentrations in lakes. [ABSTRACT FROM AUTHOR]

The radiative effect of long-range-transported Saharan air layers is investigated on the basis of simultaneous airborne high-spectral-resolution and differential-absorption lidar measurements in the vicinity of Barbados. Within the observed Saharan air layers, increased water vapor concentrations compared to the dry trade wind atmosphere are found. The measured profiles of aerosol optical properties and water vapor mixing ratios are used to characterize the atmospheric composition in radiative transfer calculations, to calculate radiative effects of moist Saharan air layers and to determine radiative heating rate profiles. An analysis based on three case studies reveals that the observed enhanced amounts of water vapor within Saharan air layers have a much stronger impact on heating rate calculations than mineral dust aerosol. Maximum mineral dust short-wave heating and long-wave cooling rates are found at altitudes of highest dust concentration (short wave: +0.5 K d -1 ; long wave: -0.2 K d -1 ; net: +0.3 K d -1). However, when considering both aerosol concentrations and measured water vapor mixing ratios in radiative transfer calculations, the maximum heating/cooling rates shift to the top of the dust layer (short wave: +2.2 K d -1 ; long wave: -6.0 to -7.0 K d -1 ; net: -4.0 to -5.0 K d -1). Additionally, the net heating rates decrease with height – indicating a destabilizing effect in the dust layers. Long-wave counter-radiation of Saharan air layers is found to reduce cooling at the tops of the subjacent marine boundary layers and might lead to less convective mixing in these layers. The overall short-wave radiative effect of mineral dust particles in Saharan air layers indicates a maximum magnitude of -40 W m -2 at surface level and a maximum of -25 W m -2 at the top of the atmosphere. [ABSTRACT FROM AUTHOR]

The treatment of unresolved cloud–radiation interactions in weather and climate models has considerably improved over the recent years, compared to conventional plane-parallel radiation schemes, which previously persisted in these models for multiple decades. One such improvement is the state-of-the-art Tripleclouds radiative solver, which has one cloud-free and two cloudy regions in each vertical model layer and is thereby capable of representing cloud horizontal inhomogeneity. Inspired by the Tripleclouds concept, primarily introduced by , we incorporated a second cloudy region into the widely employed δ -Eddington two-stream method with the maximum-random overlap assumption for partial cloudiness. The inclusion of another cloudy region in the two-stream framework required an extension of vertical overlap rules. While retaining the maximum-random overlap for the entire layer cloudiness, we additionally assumed the maximum overlap of optically thicker cloudy regions in pairs of adjacent layers. This extended overlap formulation implicitly places the optically thicker region towards the interior of the cloud, which is in agreement with the core–shell model for convective clouds. The method was initially applied on a shallow cumulus cloud field, evaluated against a three-dimensional benchmark radiation computation. Different approaches were used to generate a pair of cloud condensates characterizing the two cloudy regions, testing various condensate distribution assumptions along with global cloud variability estimate. Regardless of the exact condensate setup, the radiative bias in the vast majority of Tripleclouds configurations was considerably reduced compared to the conventional plane-parallel calculation. Whereas previous studies employing the Tripleclouds concept focused on researching the top-of-the-atmosphere radiation budget, the present work applies Tripleclouds to atmospheric heating rate and net surface flux. The Tripleclouds scheme was implemented in the comprehensive libRadtran radiative transfer package and can be utilized to further address key scientific issues related to unresolved cloud–radiation interplay in coarse-resolution atmospheric models. [ABSTRACT FROM AUTHOR]

We present a procedure for geometric, spectral, and absolute radiometric characterization of the weather-proof RGB camera HaloCam RAW and demonstrate its application in a case study. This characterization procedure can be generalized to other RGB camera systems with similar field of view. HaloCam RAW is part of the automated halo observation system HaloCam and designed for the quantitative analysis of halo displays. The geometric calibration was performed using a chessboard pattern to estimate camera matrix and distortion coefficients. For the radiometric characterization of HaloCam RAW , the dark signal and vignetting effect were determined to correct the measured signal. Furthermore, the spectral response of the RGB sensor and the linearity of its radiometric response were characterized. The absolute radiometric response was estimated by cross calibrating HaloCam RAW against the completely characterized spectrometer of the Munich Aerosol Cloud Scanner (specMACS). For a typical measurement signal the relative (absolute) radiometric uncertainty amounts to 2.8 % (5.0 %), 2.4 % (5.8 %), and 3.3 % (11.8 %) for the red, green, and blue channel, respectively. The absolute radiometric uncertainty estimate is larger mainly due to the inhomogeneity of the scene used for cross calibration and the absolute radiometric uncertainty of specMACS. Geometric and radiometric characterization of HaloCam RAW were applied to a scene with a 22 ∘ halo observed on 21 April 2016. The observed radiance distribution and 22 ∘ halo ratio compared well with radiative transfer simulations assuming a range of ice crystal habits and surface roughness values. This application demonstrates the potential of developing a retrieval method for ice crystal properties, such as ice crystal size, shape, and surface roughness using calibrated HaloCam RAW observations together with radiative transfer simulations. [ABSTRACT FROM AUTHOR]

This paper presents a method to retrieve three-dimensional cumulus cloud macro- and microphysics measured by remote sensing instruments on the German research aircraft HALO. This is achieved by combining our hyper-spectral pushbroom spectrometer specMACS with active and passive remote sensing instruments, such as a lidar, a microwave radiometer, a radar and dropsondes. Two-dimensional cloud information such as cloud size, optical thickness, effective radius and thermodynamic phase are retrieved by specMACS with established remote sensing methods. Information of the other active and passive remote sensing instruments with a smaller field-of-view are mapped to the wider specMACS swath following Barker et al. (2011). The combination of specMACS with passive and active remote sensing quantities, for example, the Cloud Top Height from lidar measurements, allows new possibilities: three-dimensional cloud macrophysics can be reconstructed. Applying a sub-adiabatic microphysical model constrained with measurements allows to extend the measured quantities to a three-dimensional representation of microphysics. A consistency check by means of a three-dimensional radiative transfer simulation of the specMACS observations of these derived three-dimensional cloud fields shows good agreement. [ABSTRACT FROM AUTHOR]

We calculate the variation of spectral solar irradiance in the umbral shadow of the total solar eclipse of 21 August 2017 and compare it to observations. Starting from the Sun's and Moon's positions, we derive a realistic profile of the lunar shadow at the top of the atmosphere, including the effect of solar limb darkening. Subsequently, the Monte Carlo model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres) is used to simulate the transfer of solar radiation through the Earth's atmosphere. Among the effects taken into account are the atmospheric state (pressure, temperature), concentrations of major gas constituents and the curvature of the Earth, as well as the reflectance and elevation of the surrounding area. We apply the model to the total solar eclipse on 21 August 2017 at a position located in Oregon, USA, where irradiance observations were performed for wavelengths between 306 and 1020 nm. The influence of the surface reflectance, the ozone profile, the mountains surrounding the observer and aerosol is investigated. An increased sensitivity during totality is found for the reflectance, aerosol and topography, compared to non-eclipse conditions. During the eclipse, the irradiance at the surface not only depends on the total ozone column (TOC) but also on the vertical ozone distribution, which in general complicates derivations of the TOC from spectral surface irradiance. The findings are related to an analysis of the prevailing photon path and its difference compared to non-eclipse conditions. Using the most realistic estimate for each parameter, the model is compared to the irradiance observations. During totality, the relative difference between model and observations is less than 10% in the spectral range from 400 to 1020 nm. Slightly larger deviations occur in the ultraviolet range below 400 and at 665 nm. [ABSTRACT FROM AUTHOR]

The realistic representation of aerosol–cloud interactions is of primary importance for accurate climate model projections. The investigation of these interactions in strongly contrasting clean and polluted atmospheric conditions in the Amazon region has been one of the motivations for several field campaigns, including the airborne "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) (ACRIDICON-CHUVA)" campaign based in Manaus, Brazil, in September 2014. In this work we combine in situ and remotely sensed aerosol, cloud, and atmospheric radiation data collected during ACRIDICON-CHUVA with regional, online-coupled chemistry-transport simulations to evaluate the model's ability to represent the indirect effects of biomass burning aerosol on cloud microphysical and optical properties (droplet number concentration and effective radius). We found agreement between the modeled and observed median cloud droplet number concentration (CDNC) for low values of CDNC, i.e., low levels of pollution. In general, a linear relationship between modeled and observed CDNC with a slope of 0.3 was found, which implies a systematic underestimation of modeled CDNC when compared to measurements. Variability in cloud condensation nuclei (CCN) number concentrations was also underestimated, and cloud droplet effective radii (reff) were overestimated by the model. Modeled effective radius profiles began to saturate around 500 CCN cm -3 at cloud base, indicating an upper limit for the model sensitivity well below CCN concentrations reached during the burning season in the Amazon Basin. Additional CCN emitted from local fires did not cause a notable change in modeled cloud droplet effective radii. Finally, we also evaluate a parameterization of CDNC at cloud base using more readily available cloud microphysical properties, showing that we are able to derive CDNC at cloud base from cloud-side remote-sensing observations. [ABSTRACT FROM AUTHOR]

Recently, polarimetry has been used to enhance classical photometry to infer aerosol optical properties, as polarized radiation contains additional information about the particles. Therefore, we have equipped the Sun–sky automatic radiometer (SSARA) with polarizer filters to measure linearly polarized light at 501.5 nm. We describe an improved radiometric and polarimetric calibration method, which allows us to simultaneously determine the linear polarizers' diattenuation and relative orientation with high accuracy (0.002 and 0.1 ∘ , respectively). Furthermore, we employed a new calibration method for the alt-azimuthal mount capable of correcting the instrument's pointing to within 32 arcmin. So far, this is limited by the accuracy of the Sun tracker. Both these methods are applicable to other Sun and sky radiometers, such as the Cimel CE318-DP instruments used in the AErosol RObotic NETwork (AERONET). During the A-LIFE (Absorbing aerosol layers in a changing climate: aging, LIFEtime and dynamics) field campaign in April 2017, SSARA collected 22 d of data. Here, we present two case studies. The first demonstrates the performance of an aerosol retrieval from SSARA observations under partially cloudy conditions. In the other case, a high aerosol load due to a Saharan dust layer was present during otherwise clear-sky conditions. [ABSTRACT FROM AUTHOR]

We calculate the variation of spectral solar irradiance in the umbral shadow of the total solar eclipse of August 21, 2017 and compare it to observations. Starting from the sun's and moon's positions, we derive a realistic profile of the lunar shadow at the top of the atmosphere, including the effect of solar limb darkening. Subsequently, the Monte-Carlo model MYSTIC is used to simulate the transfer of solar radiation through the earth's atmosphere. Among the effects taken into account are the atmospheric state (pressure, temperature), concentrations of major gas constituents and the curvature of the earth, as well as the reflectance and elevation of the surrounding area. We apply the model to the total solar eclipse on August 21, 2017 at a position located in Oregon, where irradiance observations were performed for wavelengths between 306 nm and 1020 nm. The influence of the surface reflectance, the ozone profile and mountains surrounding the observer is investigated. An increased sensitivity during totality is found for the reflectance and topography, compared to non-eclipse conditions. During the eclipse, the irradiance at the surface does not only depend on the total ozone column (TOC) but also the vertical ozone distribution, which in general complicates derivations of the TOC from spectral surface irradiance. The findings are related to an analysis of the prevailing photon path and its difference compared to non-eclipse conditions. Using the most realistic estimate for each parameter, the model is compared to the irradiance observations. During totality, the relative difference between model and observations is less than 10 % in the spectral range from 400 nm to 1020 nm. Slightly larger deviations occur in the ultraviolet range below 400 nm and at 665 nm. [ABSTRACT FROM AUTHOR]

The realistic representation of cloud-aerosol interactions is of primary importance for accurate climate model projections. The investigation of these interactions in strongly contrasting clean and polluted atmospheric conditions in the Amazon area has been one of the motivations for several field observations, including the airborne 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) (ACRIDICON-CHUVA) campaign based in Manaus, Brazil in September 2014. In this work we combine in situ and remotely sensed aerosol, cloud, and atmospheric radiation data collected during ACRIDICON-CHUVA with regional, online-coupled chemistry-transport simulations to evaluate the model's ability to represent the indirect effects of biomass burning aerosol on cloud microphysical properties (droplet number concentration and effective radius). We found agreement between modeled and observed median cloud droplet number concentrations (CDNC) for low values of CDNC, i.e., low levels of pollution. In general, a linear relationship between modeled and observed CDNC with a slope of two was found, which means a systematic underestimation of modeled CDNC as compared to measurements. Variability in cloud condensation nuclei (CCN) number concentrations and cloud droplet effective radii (reff) was also underestimated by the model. Modeled effective radius profiles began to saturate around 500 CCN per cm³ at cloud base, indicating an upper limit for the model sensitivity well below CCN concentrations reached during the burning season in the Amazon Basin. Regional background aerosol concentrations were sufficiently high such that the additional CCN emitted from local fires did not cause a notable change in modelled cloud microphysical properties. In addition, we evaluate a parameterization of CDNC at cloud base using more readily available cloud microphysical properties, aimed at in situ observations and satellite retrievals. Our study casts doubt on the validity of regional scale modeling studies of the cloud albedo effect in convective situations for polluted situations where the number concentration of CCN is greater than 500 cm-3. [ABSTRACT FROM AUTHOR]

The interaction between radiation and clouds represents a source of uncertainty in numerical weather prediction (NWP) due to both intrinsic problems of one-dimensional radiation schemes and poor representation of clouds. The underlying question addressed in this study is how large the NWP radiative bias is for shallow cumulus clouds and how it scales with various input parameters of radiation schemes, such as solar zenith angle, surface albedo, cloud cover and liquid water path. A set of radiative transfer calculations was carried out for a realistically evolving shallow cumulus cloud field stemming from a large-eddy simulation (LES). The benchmark experiments were performed on the highly resolved LES cloud scenes (25 m grid spacing) using a three-dimensional Monte Carlo radiation model. An absence of middle and high clouds is assumed above the shallow cumulus cloud layer. In order to imitate the poor representation of shallow cumulus in NWP models, cloud optical properties were horizontally averaged over the cloudy part of the boxes with dimensions comparable to NWP horizontal grid spacing (several kilometers), and the common δ -Eddington two-stream method with maximum-random overlap assumption for partial cloudiness was applied (denoted as the "1-D" experiment). The bias of the 1-D experiment relative to the benchmark was investigated in the solar and thermal parts of the spectrum, examining the vertical profile of heating rate within the cloud layer and the net surface flux. It is found that, during daytime and nighttime, the destabilization of the cloud layer in the benchmark experiment is artificially enhanced by an overestimation of the cooling at cloud top and an overestimation of the warming at cloud bottom in the 1-D experiment (a bias of about -15 K d -1 is observed locally for stratocumulus scenarios). This destabilization, driven by the thermal radiation, is maximized during nighttime, since during daytime the solar radiation has a stabilizing tendency. The daytime bias at the surface is governed by the solar fluxes, where the 1-D solar net flux overestimates (underestimates) the corresponding benchmark at low (high) Sun. The overestimation at low Sun (bias up to 80 % over land and ocean) is largest at intermediate cloud cover, while the underestimation at high Sun (bias up to -40 % over land and ocean) peaks at larger cloud cover (80 % and beyond). At nighttime, the 1-D experiment overestimates the amount of benchmark surface cooling with the maximal bias of about 50 % peaked at intermediate cloud cover. Moreover, an additional experiment was carried out by running the Monte Carlo radiation model in the independent column mode on cloud scenes preserving their LES structure (denoted as the "ICA" experiment). The ICA is clearly more accurate than the 1-D experiment (with respect to the same benchmark). This highlights the importance of an improved representation of clouds even at the resolution of today's regional (limited-area) numerical models, which needs to be considered if NWP radiative biases are to be efficiently reduced. All in all, this paper provides a systematic documentation of NWP radiative biases, which is a necessary first step towards an improved treatment of radiation–cloud interaction in atmospheric models. [ABSTRACT FROM AUTHOR]

Recently, polarimetry has been used to enhance classical photometry to infer aerosol optical properties, as polarized radiation contains additional information about the particles. Therefore, we have equipped the SSARA sun and sky photometer with polarizer filters to measure linearly polarized light at 501.5 nm. We have developed a novel radiometric and polarimetric calibration method, which allows us to simultaneously determine the linear polarizers' diattenuation and relative orientation with high accuracy (0.002 and 0.1°, respectively). Furthermore, we employed a new calibration method for the azimuthal mount capable of correcting the instrument's pointing to within 32 arcmin. So far, this is limited by the accuracy of the sun-tracker. Both these methods are applicable to other sun and sky radiometers, such as Cimel CE318-DP instruments used in AERONET. During the A-LIFE field campaign in April 2017, SSARA collected 22 days of data. Here, we present two case studies: The first demonstrates the performance of an aerosol retrieval from SSARA observations under partially cloudy conditions. In the other case, a high aerosol load due to a Saharan dust layer was present during otherwise perfect clear sky conditions. [ABSTRACT FROM AUTHOR]

Convective clouds play an essential role for Earth's climate as well as for regional weather events since they have a large influence on the radiation budget and the water cycle. In particular, cloud albedo and the formation of precipitation are influenced by aerosol particles within clouds. In order to improve the understanding of processes from aerosol activation, from cloud droplet growth to changes in cloud radiative properties, remote sensing techniques become more and more important. While passive retrievals for spaceborne observations have become sophisticated and commonplace for inferring cloud optical thickness and droplet size from cloud tops, profiles of droplet size have remained largely uncharted territory for passive remote sensing. In principle they could be derived from observations of cloud sides, but faced with the small-scale heterogeneity of cloud sides, "classical" passive remote sensing techniques are rendered inappropriate. In this work the feasibility is demonstrated to gain new insights into the vertical evolution of cloud droplet effective radius by using reflected solar radiation from cloud sides. Central aspect of this work on its path to a working cloud side retrieval is the analysis of the impact unknown cloud surface geometry has on effective radius retrievals. This study examines the sensitivity of reflected solar radiation to cloud droplet size, using extensive 3-D radiative transfer calculations on the basis of realistic droplet size resolving cloud simulations. Furthermore, it explores a further technique to resolve ambiguities caused by illumination and cloud geometry by considering the surroundings of each pixel. Based on these findings, a statistical approach is used to provide an effective radius retrieval. This statistical effective radius retrieval is focused on the liquid part of convective water clouds, e.g., cumulus mediocris, cumulus congestus, and trade-wind cumulus, which exhibit well-developed cloud sides. Finally, the developed retrieval is tested using known and unknown cloud side scenes to analyze its performance. [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]

This work describes a method to retrieve the location and geometry of clouds using RGB images from a video camera on an aircraft and data from the aircraft's navigation system. Opposed to ordinary stereo methods for which two cameras with fixed relative position at a certain distance are used to match images taken at the exact same moment, this method uses only a single camera and the aircraft's movement to provide the needed parallax. Advantages of this approach include a relatively simple installation on a (research) aircraft and the possibility to use different image offsets that are even larger than the size of the aircraft. Detrimental effects are the evolution of observed clouds during the time offset between two images as well as the background wind. However we will show that some wind information can also be recovered and subsequently used for the physics-based filtering of outliers. Our method allows the derivation of cloud top geometry which can be used, e.g., to provide location and distance information for other passive cloud remote sensing products. In addition it can also improve retrieval methods by providing cloud geometry information useful for the correction of 3-D illumination effects. We show that this method works as intended through comparison to data from a simultaneously operated lidar system. The stereo method provides lower heights than the lidar method; the median difference is 126 m. This behavior is expected as the lidar method has a lower detection limit (leading to greater cloud top heights for the downward view), while the stereo method also retrieves data points on cloud sides and lower cloud layers (leading to lower cloud heights). Systematic errors across the measurement swath are less than 50 m. [ABSTRACT FROM AUTHOR]

The interaction between radiation and clouds represents a source of uncertainty in numerical weather prediction (NWP) due to both intrinsic problems of one-dimensional radiation schemes and poor representation of clouds. The underlying question addressed in this study is how large is the NWP radiative bias for shallow cumulus clouds and how does it scale with various input parameters of radiation schemes, such as solar zenith angle, surface albedo, cloud cover and liquid water path. A set of radiative transfer calculations was carried out for a realistically evolving shallow cumulus cloud field stemming from a large-eddy simulation (LES). The benchmark experiments were performed on the highly-resolved LES cloud scenes using a three-dimensional Monte Carlo radiation model. An absence of middle and high cloud is assumed above the shallow cumulus cloud layer. In order to imitate poor representation of shallow cumulus in NWP models, cloud optical properties were horizontally averaged over the cloudy part of the boxes with dimensions comparable to NWP horizontal grid spacing (several km) and the common δ-Eddington two-stream method with maximum-random overlap assumption for partial cloudiness was applied (denoted as 1-D experiment). The bias of the 1-D experiment relative to the benchmark was investigated in the solar and thermal part of the spectrum, examining the vertical profile of heating rate within the cloud layer and net surface flux. It is found that during daytime and nighttime, the destabilization of the cloud layer in the benchmark experiment is artifically enhanced by an overestimation of the cooling at cloud top and an overestimation of the warming at cloud bottom in the 1-D experiment (bias of about -15Kday-1 is observed locally for stratocumulus scenarios). This destabilization, driven by the thermal radiation, is maximized during nighttime, since during daytime the solar radiation has a stabilizing tendency. The daytime bias at the surface is governed by the solar fluxes, where the 1-D solar net flux overestimates (underestimates) the corresponding benchmark at low (high) sun. The overestimation at low sun (bias up to 80% over land and ocean) is largest at intermediate cloud cover, while underestimation at high sun (bias up to -40% over land and ocean) is peaked at larger cloud cover (80% and beyond). At nighttime, the 1-D experiment overestimates the amount of benchmark surface cooling with the maximal bias of about 50% peaked at intermediate cloud cover. Moreover, an additional experiment was carried out by running the Monte Carlo radiation model in the independent column mode on cloud scenes preserving their LES structure (denoted as ICA experiment). The ICA is predominantly more accurate than the 1-D experiment (with respect to the same benchmark). This highlights the importance of an improved representation of clouds even at the resolution of today's regional (limited-area) numerical models, which needs to be considered if NWP radiative biases are to be efficiently reduced. All in all, this paper provides a systematic documentation of NWP radiative biases, which is a necessary first step towards an improved treatment of radiation-cloud interaction in atmospheric models. [ABSTRACT FROM AUTHOR]

During the ACRIDICON-CHUVA 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 by the method. Main sources of uncertainty are unknown in-cloud absorption path contributions due to complex 3D geometry or unknown microphysical properties, variable surface albedo and aerosol distribution. A systematic difference of 3.8 km between stereo and spectral method is found which can be attributed to 3D geometry effects not considered in the methods 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 tpyical distance around 20 km between sensor and convective cloud elements of typically 1-10 km horizontal and vertical extent. [ABSTRACT FROM AUTHOR]

This work describes a method to retrieve location and geometry of clouds using RGB images from a video camera on an aircraft and data from the aircraft's navigation system. Opposed to ordinary stereo methods where two cameras with fixed relative position at a certain distance are used to match images taken at the exact same moment, this method uses only a single camera and the aircrafts movement to provide the needed parallax. Advantages of this approach include a relatively simple installation on an aircraft and the possibility to use different image offsets, even larger than the size of the aircraft. Detrimental effects are the evolution of observed clouds during the time offset between two images as well as the background wind. However we will show that to some extent usable wind information can also be recovered. Our method allows the derivation of cloud top geometry which can be used, e.g., to provide location and distance information for other passive cloud remote sensing products. In addition it can also improve retrieval methods by providing cloud geometry information useful for the correction of 3D illumination effects. We show that this method works as intended by comparson to data from a simultaneously operated lidar system. The stereo method provides lower heights than the lidar method, on average by 126 m. This behaviour is expected as the lidar method has a lower detection limit (leading to greater cloud top heights for the downward view) while the stereo method also retrieves data points on cloud sides and lower cloud layers (leading to lower cloud heights). Systematic errors across the measurement swath contribute less than 50 m. [ABSTRACT FROM AUTHOR]

Convective clouds play an essential role for Earth's climate as well as for regional weather events since they have a large influence on the radiation budget and the water cycle. In particular, cloud albedo and the formation of precipitation are influenced by aerosol particles within clouds. In order to improve the understanding of processes from aerosol activation, over cloud droplet growth to changes in cloud radiative properties, remote sensing techniques become more and more important. While passive retrievals for spaceborne observations have become sophisticated and commonplace to infer cloud optical thickness and droplet size from cloud tops, profiles of droplet size have remained largely uncharted territory for passive remote sensing. In principle they could be derived from observations of cloud sides, but faced with with the small-scale structure of cloud sides, classical passive remote sensing techniques are rendered inappropriate. In this work the feasibility is demonstrated to gain new insights into the vertical evolution of cloud droplet effective radius by using reflected solar radiation from cloud sides. Central aspect of this work on its path to a working cloud side retrieval is the analysis of the impact unknown cloud surface geometry has on effective radius retrievals. Using extensive 3D radiative transfer calculations on the basis of realistic droplet size resolving cloud simulations, the sensitivity of reflected solar radiation to cloud droplet size is examined. Sensitivity is enhanced by considering the pixel surrounding to resolve ambiguities caused by illumination and cloud geometry. Based on these findings, a statistical approach is used to provide an effective radius retrieval. An in-depth sensitivity study of the presented approach on the basis of a wide range of radiative transfer test cases demonstrates the feasibility to retrieve cloud particle size profiles from cloud sides. [ABSTRACT FROM AUTHOR]