This is a very interesting paper that describes a unique set of instrumentation at a site in Antarctica that take data useful for studying ice clouds without supervision. The instrumentation consists of a Fourier transform spectroradiometer (REFIR-PAD), a depolarization lidar, and a micro-rain radar (MMR). From my own experience with satellite imager-based observations of ice clouds over polar regions, the retrievals are problematic and the issue is having some sort of ground truth with which to assess them. The methodology described in this article provides a very important step towards being able to provide a “truth” set for satellite-based comparisons, at least from my own limited perspective.

This paper is suitable for publication but could use some improvements on general readability. For me, the issue is that the manuscript is full of terms for both measured and simulated radiances and brightness temperatures, emissivities, beta indices for the microphysical retrievals, and also a large number of acronyms and abbreviations. This became quite difficult to track since many these parameters have multiple subscripts -

3. In the paragraph beginning on line 437, I am puzzled by the lack of cloud fraction information in the winter (dark) months for the combined Terra and Aqua MODIS cloud product. If the information is available for the MODIS product from each of the Terra and Aqua platforms, there must be a problem with the combined data product. This seems to be something that the MODIS cloud team has to resolve. Suggest leaving out the combined Terra and Aqua data product until it has been resolved.

An operational data product available for both the Suomi National Polar-orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration-20 (NOAA-20) platforms provides high-spatial-resolution infrared (IR) absorption band radiances for Visible Infrared Imaging Radiometer Suite (VIIRS) based on a VIIRS and Crosstrack Infrared Sounder (CrIS) data fusion method. This study investigates the use of these IR radiances, centered at 4.5, 6.7, 7.3, 9.7, 13.3, 13.6, 13.9, and 14.2 µm, to construct atmospheric moisture products (e.g., total precipitable water and upper tropospheric humidity) and to evaluate their accuracy. Total precipitable water (TPW) and upper tropospheric humidity (UTH) retrieved from hyperspectral sounder CrIS measurements are provided at the associated VIIRS sensor's high spatial resolution (750 m) and are compared subsequently to collocated operational Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and S-NPP VIIRS moisture products. This study suggests that the use of VIIRS IR absorption band radiances will provide continuity with Aqua MODIS moisture products.

We present an improved remote sensing technique to infer an optimal habit/shape model for ice particles in cirrus clouds using multi-angle polarimetric measurements at 865 nm made by the Airborne Multi-angle SpectroPolarimeter Imager (AirMSPI) instrument. The common method of ice model inference is based on intensity (total reflectivity) measurements, which is generally not applicable to optically thin ice clouds (i.e., cirrus clouds) where single scattering dominates. The new approach is able to infer an ice model in clouds with optical thicknesses smaller than 5. The improvement is made by first assuming the optical thickness retrieved using total reflectivity. Subsequently, the polarized reflectivity is calculated based on look-up tables generated from simulated polarized reflectances computed for cirrus clouds in conjunction with eight ice particle models. The ice particle model that leads to the closest fit to the measurements is regarded as the optimal ice particle model. Additionally, an alternative method is applied that does not consider polarized reflectivity. These two methods are applied to a data sample as a proof-of-concept study where AirMSPI observed a single cirrus layer. In this case study, the hexagonal column aggregate model works for most pixels both with and without considering polarized reflectivities. The retrieval cost function is high when the camera pairs with large zenith angles are included in the retrievals. This result suggests that further studies will be necessary to have a better understanding of all eight selected ice particle models at scattering angles smaller than 100°.

This is a very thorough article that discusses improvements and modifications to the cloud microphysical and optical property retrieval algorithms in Version 4 of the CALIPSO products from the Imaging Infrared Radiometer (IIR).This paper is suitable for publication but could use some improvements on general readability.For me, the issue is that the manuscript is full of terms for both measured and simulated radiances and brightness temperatures, emissivities, beta indices for the microphysical retrievals, and also a large number of acronyms and abbreviations.This became quite difficult to track since many these parameters have multiple subscripts -C1 Printer-friendly version Discussion paper change between V3 and V4? Additionally, does the range ever approach the boundaries of the LUT, either very low or very high values?How often does this happen?

Abstract. Retrieval of semitransparent ice cloud properties from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi-NPP and NOAA-20 platforms is challenging due to the absence of infrared (IR) water vapor and CO2 absorption channels. However, on these platforms, there is a companion sensor called the Crosstrack Infrared Sounder (CrIS) that provides these spectral measurements, but at a lower spatial resolution (~ 15 km at nadir). To mitigate the lack of VIIRS spectral measurements in these IR absorption channels, recent studies suggest an approach to supplement VIIRS measurements by fusion of the imager and sounder data. In particular, Weisz et al. (2017) demonstrate a method to construct IR water vapor and CO2 absorption channel radiances for VIIRS at 750 m spatial resolution. Based on these constructed channels for both Suomi-NPP and NOAA-20, this study evaluates three cloud properties – cloud mask, cloud thermodynamic phase, and cloud top height – through comparison to the CALIPSO/CALIOP V4-20 cloud layer products and MODIS Collection 6.1 cloud top products. Each of these cloud properties show improvement with the use of these constructed channel radiances. The major improvement for the cloud mask is found over polar regions, where the correct cloud detection percentage increases due to decrease in missed cloud and/or false detection. For cloud thermodynamic phase, the ice cloud fraction increases over non-polar regions and the combined liquid water and ice cloud discrimination improves in comparison with CALIPSO. The retrieved cloud top height for semitransparent ice clouds increases over non-polar regions and tends to be closer to the true CALIPSO/CALIOP cloud top height. Moreover, the uncertainty of cloud top height retrievals decreases globally for these clouds.

Measurements of mid- to far-infrared nadir radiances obtained from the UK Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 aircraft during the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) are used to assess the performance of various ice cloud bulk optical property models. Through use of a minimization approach, we find that the simulations can reproduce the observed spectra in the mid-infrared to within measurement uncertainty, but they are unable to simultaneously match the observations over the far-infrared frequency range. When both mid- and far-infrared observations are used to minimize residuals, first-order estimates of the spectral flux differences between the best-performing simulations and observations indicate a compensation effect between the mid- and far-infrared such that the absolute broadband difference is < 0.7 W m−2. However, simply matching the spectra using the mid-infrared (far-infrared) observations in isolation leads to substantially larger discrepancies, with absolute differences reaching ∼ 1.8 (3.1) W m−2. These results show that simulations using these microphysical models may give a broadly correct integrated longwave radiative impact but that this masks spectral errors, with implicit consequences for the vertical distribution of atmospheric heating. They also imply that retrievals using these models applied to mid-infrared radiances in isolation will select cirrus optical properties that are inconsistent with far-infrared radiances. As such, the results highlight the potential benefit of more extensive far-infrared observations for the assessment and, where necessary, the improvement of current ice bulk optical models.

• Regarding hyperspectral instruments as I said, in this work the authors use the spectrally resolved measurements of CrIS to simulate moderate resolution channels. In doing this the authors omitted to describe and acknowledge the great diagnostic power inside the spectral resolution and coverage of instruments like CrIS. For a reader who is not an expert in the field, it might appear that CrIS (and all the hyperspectral instruments) is a less accurate instrument than VIIRS because it has a worse spatial resolution. As an example, consider sentence at lines 15-21 of page 2 and lines 1-5 of page 3. It seems that CrIS has channels at 15 and 6.7 micron, missing in VIIRS instrument, but with degraded spatial resolution. I think that the authors should spend a sentence to indicate the peculiarities of hyperspectral instruments and add a figure showing a typical CrIS measurement in comparison with the spectral coverage of the channels used in the methodology described in the manuscript. Moreover I wish to recall that already 15 years ago it has been shown that with hyperspectral observation alone in the atmospheric window between 800-900 cm−1 is possible to detect and classify clouds. The authors can find an example in the following papers doi:10.1364/AO.41.000965 and doi:10.1016/S0022-4073(02)00083-3.

Satellite-imager-based operational cloud property retrievals generally assume that a cloudy pixel can be treated as being plane-parallel with horizontally homogeneous properties. This assumption can lead to high uncertainties in cloud heights, particularly for the case of optically thin, but geometrically thick, clouds composed of ice particles. This study demonstrates that ice cloud emissivity uncertainties can be used to provide a reasonable range of ice cloud layer boundaries, i.e., the minimum to maximum heights. Here ice cloud emissivity uncertainties are obtained for three IR channels centered at 11, 12, and 13.3 µm. The range of cloud emissivities is used to infer a range of ice cloud temperature and heights, rather than a single value per pixel as provided by operational cloud retrievals. Our methodology is tested using MODIS observations over the western North Pacific Ocean during August 2015. We estimate minimum–maximum heights for three cloud regimes, i.e., single-layered optically thin ice clouds, single-layered optically thick ice clouds, and multilayered clouds. Our results are assessed through comparison with CALIOP version 4 cloud products for a total of 11873 pixels. The cloud boundary heights for single-layered optically thin clouds show good agreement with those from CALIOP; biases for maximum (minimum) heights versus the cloud-top (base) heights of CALIOP are 0.13 km (−1.01 km). For optically thick and multilayered clouds, the biases of the estimated cloud heights from the cloud top or cloud base become larger (0.30/−1.71 km, 1.41/−4.64 km). The vertically resolved boundaries for ice clouds can contribute new information for data assimilation efforts for weather prediction and radiation budget studies. Our method is applicable to measurements provided by most geostationary weather satellites including the GK-2A advanced multichannel infrared imager.

The Multi‐angle Imaging SpectroRadiometer (MISR) provides measurements over a wider scattering‐angle range for a given location than a cross‐track scanning sensor such as the MODerate resolution Imaging Spectroradiometer (MODIS). Based on a full year (2013) of fused MISR‐MODIS datasets, we develop a variable surface roughness model for ice particles with the goal of identifying the optimal degree of roughness in the ice model for a given pixel containing single‐layer ice clouds. For the MISR‐MODIS observations over oceans, severe roughness values are often selected for a pixel when optical thickness (τ) and particle effective radius (Reff) are large in conjunction with larger cloud heterogeneity index (Hσ) or a warmer cloud top temperature. Furthermore, τ, Reff, and ice water path are retrieved with the optimal model and compared to operational MODIS Collection 6 (MC6) products that assume a constant roughness. In general, the retrievals based on the present optimal model lead to greater consistency with MISR measurements, and result in larger median τ by 10.1% and smaller median Reff by 6.5% but almost identical ice water path in comparison with the MC6 counterparts. The higher average τ value is caused by a slightly larger number of large τ cases, but the smaller average Reff value is due to the shifting of the retrieved Reff value toward smaller values by approximately 2–4 μm in comparison to the MC6 distribution over all seasons. Both τ retrievals have similar regional and monthly variations, but a larger annual cycle of Reff is associated with the optimal model.

The current wealth of spaceborne passive and active measurements from ultraviolet to the infrared wavelengths provides an unprecedented opportunity to construct ice cloud bulk optical property models that lead to consistent ice cloud property retrievals across multiple sensors and platforms. To infer the microphysical and radiative properties of ice clouds from these satellite measurements, the general approach is to assume an ice cloud optical property model that implicitly assumes the habit (shape) and size distributions of the ice particles in these clouds. The assumption is that this ice optical property model will be adequate for global retrievals. In this review paper, we first summarize the key optical properties of individual particles and then the bulk radiative properties of their ensemble, followed by a review of the ice cloud models developed for application to satellite remote sensing. We illustrate that the random orientation condition assumed for ice particles is arguably justified for passive remote sensing applications based on radiometric measurements. The focus of the present discussion is on the ice models used by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Clouds and Earth’s Radiant Energy System (CERES) science teams. In addition, we briefly review the ice cloud models adopted by the Polarization and Directionality of the Earth’s Reflectance (POLDER) and the Himawari-8 Advanced Himawari Imager (AHI) for ice cloud retrievals. We find that both the MODIS Collection 6 ice model and the CERES two-habit model result in spectrally consistent retrievals.

Cloud longwave scattering is generally neglected in general circulation models (GCMs), but it plays a significant and highly uncertain role in the atmospheric energy budget as demonstrated in recent studies. To reduce the errors caused by neglecting cloud longwave scattering, two new radiance adjustment methods are developed that retain the computational efficiency of broadband radiative transfer simulations. In particular, two existing scaling methods and the two new adjustment methods are implemented in the Rapid Radiative Transfer Model (RRTM). The results are then compared with those based on the Discrete Ordinate Radiative Transfer model (DISORT) that explicitly accounts for multiple scattering by clouds. The two scaling methods are shown to improve the accuracy of radiative transfer simulations for optically thin clouds but not effectively for optically thick clouds. However, the adjustment methods reduce computational errors over a wide range, from optically thin to thick clouds. With the adjustment methods, the errors resulting from neglecting cloud longwave scattering are reduced to less than 2 W m(-2) for the upward irradiance at the top of the atmosphere and less than 0.5 W m(-2) for the surface downward irradiance. The adjustment schemes prove to be more accurate and efficient than a four-stream approximation that explicitly accounts for multiple scattering. The neglect of cloud longwave scattering results in an underestimate of the surface downward irradiance (cooling effect), but the errors are almost eliminated by the adjustment methods (warming effect).

An algorithm for retrieving the macroscopic, physical, and optical properties of clouds from thermal infrared measurements is applied to the Himawari-8 multiband observations. A sensitivity study demonstrates that the addition of the single CO2 band of Himawari-8 is effective for the estimation of cloud top height. For validation, retrieved cloud properties are compared systematically with collocated active remote sensing counterparts with small time lags. While retrievals agree reasonably for single-layer clouds, multilayer cloud systems with optically thin upper clouds overlying lower clouds are the major source of error in the present algorithm. Validation of cloud products is critical for identifying the characteristics, advantages, and limitation of each product and should be continued in the future. As an application example, data are analyzed for eight days in the vicinity of the New Guinea to study the diurnal cycle of the cloud system. The present cloud property analysis investigates cloud evolution through separation of different cloud types and reveals typical features of diurnal cycles related to the topography. Over land, middle clouds increase from 0900 to 1200 local solar time (LST), deep convective clouds develop rapidly during 1200 - 1700 LST with a subsequent increase in cirrus and cirrostratus cloud amounts. Over the ocean near coastlines, a broad peak of convective cloud fraction is seen in the early morning. The present study demonstrates the utility of frequent observations by Himawari-8 for life cycle study of cloud systems, owing to the ability to capture their continuous temporal variations.

The inference of ice cloud properties from remote sensing data depends on the assumed forward ice particle model, as they are used in the radiative transfer simulations that are part of the retrieval process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (MC6) ice cloud property retrievals are produced in conjunction with a single-habit ice particle model with a fixed degree of ice particle surface roughness (the MC6 model). In this study, we examine the MC6 model and five other ice models with either smoother or rougher surface textures to determine an optimal model to reproduce the angular variation of the radiation field sampled by the Multi-angle Imaging Spectroradiometer (MISR) as a function of latitude. The spherical albedo difference (SAD) method is used to infer an optimal ice particle model. The method is applied to collocated MISR and MODIS data over ocean for clouds with temperatures ≤233 K during December solstice from 2012–2015. The range of solar zenith angles covered by the MISR cameras is broader at the solstices than at other times of the year, with fewer scattering angles associated with sun glint during the December solstice than the June solstice. The results suggest a latitudinal dependence in an optimal ice particle model, and an additional dependence on the solar zenith angle (SZA) at the time of the observations. The MC6 model is one of the most optimal models on the global scale. In further analysis, the results are filtered by a cloud heterogeneity index to investigate cloudy scenarios that are less susceptible to potential 3D effects. Compared to results for global data, the consistency between measurements and a given model can be distinguished in both the tropics and extra-tropics. The SAD analysis suggests that the optimal model for thick homogeneous clouds corresponds to more roughened ice particles in the tropics than in the extra-tropics. While the MC6 model is one of the models most consistent with the global data, it may not be the most optimal model for the tropics.

In our companion study, we show how cloud property products change from MODIS (Moderate Resolution Imaging Spectroradiometer) collection 5.1 (C51) to collection 6 (C6) for both ice and liquid water clouds through a pixel‐to‐pixel comparison. However, the question remains as to the full impacts of these cloud property differences between collections on the inference of cloud radiative effects (CREs). In this study, we address this question from a modeling perspective using one year (2012) of MODIS gridded annual‐averaged ice/liquid water cloud properties at 0.5° × 0.5° spatial resolution. The rapid radiative transfer model for general circulation model applications is used to simulate the broadband radiative fluxes at the top of the atmosphere under clear‐sky and cloudy‐sky conditions. The shortwave, longwave, and net radiative effects of ice, liquid water, and total clouds are derived individually assuming different cloud optical property parameterization schemes. The results provide quantifications of ice and liquid water CRE contributions to the total CRE. We find significant differences in the simulated CREs between C6 and C51 for ice clouds (up to 23 W m −2 for the shortwave CRE) and liquid water clouds (approximately −4.5 W m −2 for the shortwave CRE). The C6 total CRE provides the closest match with the Clouds and the Earth's Radiant Energy System Energy Balanced And Filled product. Sensitivity studies are performed to estimate the impacts of different ice optical parameterization schemes and multilayer cloud overlap assumptions. Results show that the C6–C51 CRE differences are larger than the CRE variations caused by the other factors.

Polar-orbiting weather satellite platforms generally include an imager and a sounder. With a data fusion method that uses these sensors, we demonstrate the ability to construct infrared (IR) absorption narrowband radiances at imager resolution. While a sensor such as MODIS has multiple IR absorption bands, the current visible infrared imaging radiometer suite (VIIRS) imager has only IR window bands. We show fusion results for IR radiances at 4.52 mu m (CO2), 6.72 mu m (H2O), and 13.94 mu m (CO2) by comparing MODIS observed and constructed radiances for these bands. Both regional and global results are analyzed, with radiance differences tending to be fairly low and unbiased. Similar bands are constructed from VIIRS and CrIS data, with regional and global results shown. With this approach, it will be possible to improve continuity in derived cloud products over the generations of polar-orbiting weather satellite sensors and continue applications that require IR absorption bands. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.