In this study, we propose a simple method to derive vertically resolved aerosol particle number concentration (Na) using combined polarimetric and lidar remote sensing observations. This method ...relies on accurate polarimeter retrievals of the fine-mode column-averaged aerosol particle extinction cross section and accurate lidar measurements of vertically resolved aerosol particle extinction coefficient such as those provided by multiwavelength high spectral resolution lidar. We compare the resulting lidar + polarimeter vertically resolved N(a) product to in situ N(a) data collected by airborne instruments during the NASA aerosol cloud meteorology interactions over the western Atlantic experiment (ACTIVATE). Based on all 35 joint ACTIVATE flights in 2020, we find a total of 32 collocated in situ and remote sensing profiles that occur on 11separate days, which contain a total of 322 cloud-free vertically resolved altitude bins of 150 m resolution. We demonstrate that the lidar + polarimeter N(a) agrees to within 106% for 90% of the 322 vertically resolved points. We also demonstrate similar agreement to within 121% for the polarimeter-derived column-averaged N(a). We find that the range-normalized mean absolute deviation (NMAD) for the polarimeter-derived column-averaged Na is 21%, and the NMAD for the lidar + polarimeter-derived vertically resolved Na is 16%. Taken together, these findings suggest that the error in the polarimeter-only column-averaged N(a) and the lidar + polarimeter vertically resolved N(a) are of similar magnitude and represent a significant improvement upon current remote sensing estimates of N(a).
3D effects cause substantial underestimation of cloud optical thickness (COT) in airborne and satellite retrievals based on 1D radiative transfer computations (such as in the case of widely used ...bispectral technique). For a single-layer isolated cloud we propose a simple linear correction of the retrieved COT with the renormalization factor dependent on the cloud’s aspect ratio (the ratio between vertical and horizontal dimensions of the cloud). This is an empirical assumption which we successfully test using synthetic 3D RT data. We introduce a heuristic “block model” of 3D radiative effects and show that the functional form of the renormalization factor is consistent with the process of radiation escape from cloud sides in an essentially 3D geometry. We also extend the block model to the case of single-layer broken cloud field with radiative interaction between the neighboring clouds. In this case the renormalization factor depends also on the distance between clouds.
A modeling study of a low‐lying mixed‐phase cloud layer observed on 8 April 2008 during the Indirect and Semi‐Direct Aerosol Campaign is presented. Large‐eddy simulations with size‐resolved ...microphysics were used to test the hypothesis that heterogeneous ice nucleus (IN) concentrations measured above cloud top can account for observed ice concentrations, while also matching ice size distributions, radar reflectivities, and mean Doppler velocities. The conditions for the case are favorable for the hypothesis: springtime IN concentrations are high in the Arctic, the predominant ice habit falls slowly, and overlying IN concentrations were greater than ice particle number concentrations. Based on particle imagery, we considered two dendrite types, broad armed (high density) and stellar (low density), in addition to high and low density aggregates. Two simulations with low‐density aggregates reproduced observations best overall: one in which IN concentrations aloft were increased fourfold (as could have been present above water saturation) and another in which initial IN concentrations were vertically uniform. A key aspect of the latter was an IN reservoir under the well‐mixed cloud layer: as the simulations progressed, the reservoir IN slowly mixed upward, helping to maintain ice concentrations close to those observed. Given the uncertainties of the measurements and parameterizations of the microphysical processes embedded in the model, we found agreement between simulated and measured ice number concentrations in most of the simulations, in contrast with previous modeling studies of Arctic mixed‐phase clouds, which typically show a large discrepancy when IN are treated prognostically and constrained by measurements.
Key Points
We found agreement between simulated and measured ice number concentrations
IN concentrations need to be sufficiently higher than ice concentrations
Properties of the observed ice crystals not well constrained by measurements
This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color ...Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.
This paper describes a neural network algorithm for the estimation of liquid water cloud optical properties from the Polarization and Directionality of Earth's Reflectances-3 (POLDER-3) instrument ...aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The algorithm has been trained on synthetic multi-angle, multi-wavelength measurements of reflectance and polarization and has been applied to the processing of 1 year of POLDER-3 data. Comparisons of the retrieved cloud properties with Moderate Resolution Imaging Spectroradiometer (MODIS) products show that the neural network algorithm has a low bias of around 2 in cloud optical thickness (COT) and between 1 and 2µm in the cloud effective radius. Comparisons with existing POLDER-3 datasets suggest that the proposed scheme may have enhanced capabilities for cloud effective radius retrieval, at least over land. An additional feature of the presented algorithm is that it provides COT and effective radius retrievals at the native POLDER-3 Level 1B pixel level.
In this paper, an algorithm for the retrieval of aerosol and land surface properties from airborne spectropolarimetric measurements – combining neural networks and an iterative scheme based on ...Phillips–Tikhonov regularization – is described. The algorithm – which is an extension of a scheme previously designed for ground-based retrievals – is applied to measurements from the Research Scanning Polarimeter (RSP) on board the NASA ER-2 aircraft. A neural network, trained on a large data set of synthetic measurements, is applied to perform aerosol retrievals from real RSP data, and the neural network retrievals are subsequently used as a first guess for the Phillips–Tikhonov retrieval. The resulting algorithm appears capable of accurately retrieving aerosol optical thickness, fine-mode effective radius and aerosol layer height from RSP data. Among the advantages of using a neural network as initial guess for an iterative algorithm are a decrease in processing time and an increase in the number of converging retrievals.
Abstract
A parameterization is presented that provides extinction cross section σe, single-scattering albedo ω, and asymmetry parameter g of ice crystals for any combination of volume, projected ...area, aspect ratio, and crystal distortion at any wavelength in the shortwave. Similar to previous parameterizations, the scheme makes use of geometric optics approximations and the observation that optical properties of complex, aggregated ice crystals can be well approximated by those of single hexagonal crystals with varying size, aspect ratio, and distortion levels. In the standard geometric optics implementation used here, σe is always twice the particle projected area. It is shown that ω is largely determined by the newly defined absorption size parameter and the particle aspect ratio. These dependences are parameterized using a combination of exponential, lognormal, and polynomial functions. The variation of g with aspect ratio and crystal distortion is parameterized for one reference wavelength using a combination of several polynomials. The dependences of g on refractive index and ω are investigated and factors are determined to scale the parameterized g to provide values appropriate for other wavelengths. The parameterization scheme consists of only 88 coefficients. The scheme is tested for a large variety of hexagonal crystals in several wavelength bands from 0.2 to 4 μm, revealing absolute differences with reference calculations of ω and g that are both generally below 0.015. Over a large variety of cloud conditions, the resulting root-mean-squared differences with reference calculations of cloud reflectance, transmittance, and absorptance are 1.4%, 1.1%, and 3.4%, respectively. Some practical applications of the parameterization in atmospheric models are highlighted.
Observations of long-lived mixed-phase Arctic boundary layer clouds on 7 May 1998 during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)-Arctic Cloud ...Experiment (ACE)/Surface Heat Budget of the Arctic Ocean (SHEBA) campaign provide a unique opportunity to test understanding of cloud ice formation. Under the microphysically simple conditions observed (apparently negligible ice aggregation, sublimation, and multiplication), the only expected source of new ice crystals is activation of heterogeneous ice nuclei (IN) and the only sink is sedimentation. Large-eddy simulations with size-resolved microphysics are initialized with IN number concentration N sub(IN) measured above cloud top, but details of IN activation behavior are unknown. If activated rapidly (in deposition, condensation, or immersion modes), as commonly assumed, IN are depleted from the well-mixed boundary layer within minutes. Quasi-equilibrium ice number concentration N sub(i) is then limited to a small fraction of overlying N sub(IN) that is determined by the cloud-top entrainment rate w sub(e) divided by the number-weighted ice fall speed at the surface upsilon sub(f). Because w sub(c) < 1 cm s super(-1) and upsilon sub(f) > 10 cm s super(-1), N sub(i)/N sub(IN) 1. Such conditions may be common for this cloud type, which has implications for modeling IN diagnostically, interpreting measurements, and quantifying sensitivity to increasing N sub(IN) (when w sub(e)/ upsilon sub(f) < 1, entrainment rate limitations serve to buffer cloud system response). To reproduce observed ice crystal size distributions and cloud radar reflectivities with rapidly consumed IN in this case, the measured above-cloud N sub(IN) must be multiplied by approximately 30. However, results are sensitive to assumed ice crystal properties not constrained by measurements. In addition, simulations do not reproduce the pronounced mesoscale heterogeneity in radar reflectivity that is observed.
In support of the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, this study evaluates the performance of the Remote sensing of Trace gas and Aerosol Product (RemoTAP) algorithm ...based on synthetic orbit measurements of realistic atmospheric and geophysical scenes over land. To make use of the added value of the multi-angle polarimeter (MAP) aboard the CO2M mission, the RemoTAP algorithm is developed to perform simultaneous retrieval of trace gas and aerosol properties from both MAP and CO
2
imager (CO2I) measurements. At the same time, it has the capability to perform the retrieval of trace gas from only CO2I measurements. To set up the baseline tests, we apply a simple filter based on non-scattering retrievals in different CO2I bands which is able to filter out 80% of the cirrus-contaminated pixels, and after posterior filtering based on goodness of fit, 95% of the cirrus-contaminated cases are screened out. The MAP-CO2I retrieval method is able to reduce the aerosol-induced retrieval error in column-averaged dry-air mole fraction of CO
2
(XCO
2
) in terms of RMSE and bias by more than a factor of 2, compared to CO2I-only retrievals on the filtered pixels. A strong correlation between XCO
2
error and surface albedo in CO2I-only retrievals is significantly reduced for MAP-CO2I retrievals. Moreover, XCO
2
biases in CO2I-only retrievals exhibit a significant spatiotemporal variability caused by a strong dependence on aerosol load. The biases can be up to 2 ppm over some regions, which are much larger than for the global case. It shows that only by the inclusion of MAP measurements, the large aerosol-induced biases can be mitigated, resulting in the retrievals that meet the mission requirement (precision
<
0.7 ppm and bias
<
0.5 ppm). The error estimates for XCO2 retrievals cover the uncertainties related to the instrument, aerosol, and cirrus, although other error sources, for example, in temperature and pressure profiles, may increase the overall error somewhat. The impact of cirrus on the retrieval, which can be significant, is also investigated. When not accounted for in the retrieval, the presence of a thin layer of cirrus with an optical thickness at 550 nm smaller than 0.3 can increase XCO
2
errors by a factor of about 3 for MAP-CO2I retrievals, leading to an RMSE of 2.3 ppm for cirrus-contaminated scenes. When fitting cirrus properties, this can be reduced to 1.27 ppm for cirrus-contaminated cases. For CO
2
retrievals using the proxy method, in a highly idealized situation where it is assumed that a perfect CH
4
prior is available, an RMSE of 0.93 ppm and a bias of 0.3 ppm are achieved. These retrievals are hardly influenced by cirrus but depend linearly on the accuracy of the CH
4
prior.
Lidar measurements obtained during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment under a mixed-phase stratus cloud that was lightly precipitating ice show a range of surprisingly low ...depolarization ratios (4%–23%), despite an absence of cloud droplets there. These depolarization ratios are much lower than the range of theoretical values obtained for various ice habits. The depolarization ratios correlate well with radar reflectivity, suggesting that the variation in depolarization ratios results from variations in ice water content, rather than variation in ice habits or orientation. By calculating lidar depolarization based on (i) large-eddy simulations and (ii) in situ ice size distribution measurements, it is shown that the presence of humidified aerosol particles in addition to the ice precipitation can explain the distribution and vertical profile of the observed depolarization ratios, although uncertainties related to the aerosol size distributions are substantial. These calculations show that humidified aerosol must be taken into account when interpreting lidar depolarization measurements for cloud and precipitation phase discrimination or ice habit classification, at least under conditions similar to those observed during SHEBA.
PDF
