Cloud Optical Thickness Retrievals Research Articles

Vertical Profiles of Ice Cloud Microphysical Properties and Their Impacts on Cloud Retrieval Using Thermal Infrared Measurements

AbstractDespite the vertically inhomogeneous (VIH) structure of ice clouds, current passive remote sensing methods assume plane‐parallel homogeneous (PPH) layers, which can lead to retrieval errors. An adequate VIH cloud model is required to improve retrieval performance. In this study, CloudSat and CALIPSO satellite measurements in a 1‐year period were analyzed to model cloud vertical inhomogeneity, and its impacts on cloud retrieval were assessed using thermal infrared (TIR) measurements. The satellite measurements revealed that the peak ice water content (IWC) located around the cloud vertical midpoint moved toward the cloud base as the ice water path (IWP) increased in clouds with small IWP values; thicker clouds exhibited a gradual shift in IWC peak location toward the cloud top as IWP increased. The vertical profiles of both the cloud‐particle effective radius (CER) and a proxy of cloud‐particle number concentration showed close associations with the vertical IWC profile. An empirical model linking cloud geometrical thickness to columnar optical properties (IWP and column‐mean CER) as a function of cloud‐top temperature was also proposed. Compared with a model assuming PPH clouds, the VIH cloud model improved retrieval performance by reducing the retrieval error of the TIR‐based passive remote sensing algorithm. Further, by increasing the retrieved values of cloud‐top height noticeably for high‐level clouds and column‐mean CER considerably, with minimal effects on cloud optical thickness retrieval, the VIH cloud model yielded results that were in better agreement with radar/lidar ice cloud products than those obtained under the assumption of PPH cloud layers.

Quantifying the Impacts of Subpixel Reflectance Variability on Cloud Optical Thickness and Effective Radius Retrievals Based On High‐Resolution ASTER Observations

AbstractRecently, Zhang et al. (2016, https://doi.org/10.1002/2016JD024837) presented a mathematical framework based on a second‐order Taylor series expansion in order to quantify the plane‐parallel homogeneous bias (PPHB) in cloud optical thickness (τ) and effective droplet radius (reff) retrieved from the bispectral solar reflective method. This study provides observational validation of the aforementioned framework, using high‐resolution reflectance observations from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) over 48 marine boundary layer cloud scenes. ASTER reflectances at a horizontal resolution of 30 m are aggregated up to a scale of 1,920 m, providing retrievals of τ and reff at different spatial resolutions. A comparison between the PPHB derived from these retrievals and the predicted PPHB from the mathematical framework reveals a good agreement with correlation coefficients of r > 0.97 (for Δτ) and r > 0.79 (for Δreff). To test the feasibility of PPHB predictions for present and future satellite missions, a scale analysis with varying horizontal resolutions of the subpixel and pixel‐level observations is performed, followed by tests of corrections with only limited observational high‐resolution data. It is shown that for reasonably thick clouds with a mean subpixel τ larger than 5, correlations between observed and predicted PPHB remain high, even if the number of available subpixels decreases or just a single band provides the information about subpixel reflectance variability. Only for thin clouds the predicted Δreff become less reliable, which can be attributed primarily to an increased retrieval uncertainty for reff.

Comparing airborne and satellite retrievals of cloud optical thickness and particle effective radius using a spectral radiance ratio technique: two case studies for cirrus and deep convective clouds

Abstract. Solar radiation reflected by cirrus and deep convective clouds (DCCs) was measured by the Spectral Modular Airborne Radiation Measurement System (SMART) installed on the German High Altitude and Long Range Research Aircraft (HALO) during the Mid-Latitude Cirrus (ML-CIRRUS) and the Aerosol, Cloud, Precipitation, and Radiation Interaction and Dynamic of Convective Clouds System – Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modelling and to the Global Precipitation Measurement (ACRIDICON-CHUVA) campaigns. On particular flights, HALO performed measurements closely collocated with overpasses of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. A cirrus cloud located above liquid water clouds and a DCC topped by an anvil cirrus are analyzed in this paper. Based on the nadir spectral upward radiance measured above the two clouds, the optical thickness τ and particle effective radius reff of the cirrus and DCC are retrieved using a radiance ratio technique, which considers the cloud thermodynamic phase, the vertical profile of cloud microphysical properties, the presence of multilayer clouds, and the heterogeneity of the surface albedo. For the cirrus case, the comparison of τ and reff retrieved on the basis of SMART and MODIS measurements yields a normalized mean absolute deviation of up to 1.2 % for τ and 2.1 % for reff. For the DCC case, deviations of up to 3.6 % for τ and 6.2 % for reff are obtained. The larger deviations in the DCC case are mainly attributed to the fast cloud evolution and three-dimensional (3-D) radiative effects. Measurements of spectral upward radiance at near-infrared wavelengths are employed to investigate the vertical profile of reff in the cirrus. The retrieved values of reff are compared with corresponding in situ measurements using a vertical weighting method. Compared to the MODIS observations, measurements of SMART provide more information on the vertical distribution of particle sizes, which allow reconstructing the profile of reff close to the cloud top. The comparison between retrieved and in situ reff yields a normalized mean absolute deviation, which ranges between 1.5 and 10.3 %, and a robust correlation coefficient of 0.82.

April 2008 Saharan dust event: Its contribution to PM10 concentrations over the Anatolian Peninsula and relation with synoptic conditions

An online-coupled regional Weather Research and Forecasting model with chemistry (WRF-Chem) is utilized incorporating 0.1°×0.1° spatial resolution HTAP (Hemispheric Transport of Air Pollution) anthropogenic emissions to investigate the spatial and temporal distribution of a Saharan dust outbreak, which contributed to high levels (>50μg/m3) of daily PM10 concentrations over Turkey in April 2008. Aerosol optical depth and cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board of Aqua satellite are used to better analyze the synoptic conditions that generated the dust outbreak in April 2008. A “Sharav” low pressure system, which transports the dust from Saharan source region over Turkey along the cold front, tends to move faster in WRF-Chem simulations than observed. This causes the predicted dust event to arrive earlier than observed leading to an overestimation of surface PM10 concentrations in WRF-Chem simulation at the beginning of the event.

Cirrus Cloud Optical Thickness and Effective Diameter Retrieved by MODIS: Impacts of Single Habit Assumption, 3‐D Radiative Effects, and Cloud Inhomogeneity

AbstractFor inhomogeneous cirrus clouds, cloud optical thickness (COT) and effective diameter (De) provided by the Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 cloud products are associated with errors due to the single habit assumption (SHA), independent pixel assumption (IPA), photon absorption effect (PAE), and plane‐parallel assumption (PPA). SHA means that every cirrus cloud is assumed to have the same shape habit of ice crystals. IPA errors are caused by three‐dimensional (3D) radiative effects. PPA and PAE errors are caused by cloud inhomogeneity. We proposed a method to single out these different errors. These errors were examined using the Spherical Harmonics Discrete Ordinate Method simulations done for the MODIS 0.86 μm and 2.13 μm bands. Four midlatitude and tropical cirrus cases were studied. For the COT retrieval, the impacts of SHA and IPA were especially large for optically thick cirrus cases. SHA errors in COT varied distinctly with scattering angles. For the De retrieval, SHA decreased De under most circumstances. PAE decreased De for optically thick cirrus cases. For the COT and De retrievals, the dominant error source was SHA for overhead sun whereas for oblique sun, it could be any of SHA, IPA, and PAE, varying with cirrus cases and sun‐satellite viewing geometries. On the domain average, the SHA errors in COT (De) were within −16.1%–42.6% (−38.7%–2.0%), whereas the 3‐D radiative effects‐ and cloud inhomogeneity‐induced errors in COT (De) were within −5.6%–19.6% (−2.9%–8.0%) and −2.6%–0% (−3.7%–9.8%), respectively.

Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

Validation of quasi-invariant ice cloud radiative quantities with MODIS satellite-based cloud property retrievals

Similarity relations applied to ice cloud radiance calculations are theoretically analyzed and numerically validated. If τ(1–ϖ) and τ(1–ϖg) are conserved where τ is optical thickness, ϖ the single-scattering albedo, and g the asymmetry factor, it is possible that substantially different phase functions may give rise to similar radiances in both conservative and non-conservative scattering cases, particularly in the case of large optical thicknesses. In addition to theoretical analysis, this study uses operational ice cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Collection 5 (C5) and Collection 6 (C6) cloud property products to verify radiative similarity relations. It is found that, if the MODIS C5 and C6 ice cloud optical thickness values are multiplied by their respective (1–ϖg) factors, the resultant products referred to as the effective optical thicknesses become similar with their ratio values around unity. Furthermore, the ratios of the C5 and C6 ice cloud effective optical thicknesses display an angular variation pattern similar to that of the corresponding ice cloud phase function ratios. The MODIS C5 and C6 values of ice cloud similarity parameter, defined as [(1–ϖ)/(1–ϖg)]1/2, also tend to be similar.

Inter-comparisons of marine boundary layer cloud properties from the ARM CAP-MBL campaign and two MODIS cloud products.

From April 2009 to December 2010, the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program carried out an observational field campaign on Graciosa Island, targeting the marine boundary layer (MBL) clouds over the Azores region. In this paper, we present an inter-comparison of the MBL cloud properties, namely, cloud liquid water path (LWP), cloud optical thickness (COT) and cloud-droplet effective radius (CER), among retrievals from the ARM mobile facility (AMF) and two Moderate Resolution Spectroradiometer (MODIS) cloud products (GSFC-MODIS and CERES-MODIS). A total of 63 daytime single-layer MBL cloud cases are selected for inter-comparison. Comparison of collocated retrievals indicates that the two MODIS cloud products agree well on both COT and CER retrievals, with the correlation coefficient R>0.95. despite their significant difference in spatial sampling. In both MODIS products, the CER retrievals based on the 2.1 µm band (CER2.1) is significantly smaller than that based on the 3.7 µm band (CER3.7). The GSFC-MODIS cloud product is collocated and compared with ground-based ARM observations at several temporal-spatial scales. In general, the correlation increases with more precise collocation. For the 63 selected MBL cloud cases, the GSFC-MODIS LWP and COT retrievals agree reasonably well with the ground-based observations with no apparent bias and correlation coefficient R around 0.85 and 0.70, respectively. However, GSFC-MODIS CER3.7 and CER2.1 retrievals have a lower correlation (R~0.5) with the ground-based retrievals. For the 63 selected cases, they are on average larger than ground observations by about 1.5 µm and 3.0 µm, respectively. Taking into account that the MODIS CER retrievals are only sensitive to cloud top reduces the bias only by 0.5 µm.

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

ABSTRACTA method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (re) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and re with uncertainties around 10% for small values of COT and re; for water clouds these uncertainties can be greater than 10% for small values of re. The uncertainties found related with geometries are around 3%. The COT and re are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (re) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (re) and root mean square error values lower than 10 (8 μm) for COT (re). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRESW) are related for high fractional sky covers (Fsc >0.8), showing that water clouds produce more negative CRESW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high Fsc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.

Effects of ice crystal surface roughness and air bubble inclusions on cirrus cloud radiative properties from remote sensing perspective

We study the combined effects of surface roughness and inhomogeneity on the optical scattering properties of ice crystals and explore the consequent implications to remote sensing of cirrus cloud properties. Specifically, surface roughness and inhomogeneity are added to the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 (MC6) cirrus cloud particle habit model. Light scattering properties of the new habit model are simulated using a modified version of the Improved Geometric Optics Method (IGOM). Both inhomogeneity and surface roughness affect the single scattering properties significantly. In visible bands, inhomogeneity and surface roughness both tend to smooth the phase function and eliminate halos and the backscattering peak. The asymmetry parameter varies with the degree of surface roughness following a U shape - decreases and then increases - with a minimum at around 0.15, whereas it decreases monotonically with the air bubble volume fraction. Air bubble inclusions significantly increase phase matrix element -P12 for scattering angles between 20°–120°, whereas surface roughness has a much weaker effect, increasing -P12 slightly from 60°–120°. Radiative transfer simulations and cirrus cloud property retrievals are conducted by including both the factors. In terms of surface roughness and air bubble volume fraction, retrievals of cirrus cloud optical thickness or the asymmetry parameter using solar bands show similar patterns of variation. Polarimetric simulations using the MC6 cirrus cloud particle habit model are shown to be more consistent with observations when both surface roughness and inhomogeneity are simultaneously considered.

Investigation of ice particle habits to be used for ice cloud remote sensing for the GCOM-C satellite mission

Abstract. In this study, various ice particle habits are investigated in conjunction with inferring the optical properties of ice clouds for use in the Global Change Observation Mission-Climate (GCOM-C) satellite programme. We develop a database of the single-scattering properties of five ice habit models: plates, columns, droxtals, bullet rosettes, and Voronoi. The database is based on the specification of the Second Generation Global Imager (SGLI) sensor on board the GCOM-C satellite, which is scheduled to be launched in 2017 by the Japan Aerospace Exploration Agency. A combination of the finite-difference time-domain method, the geometric optics integral equation technique, and the geometric optics method is applied to compute the single-scattering properties of the selected ice particle habits at 36 wavelengths, from the visible to the infrared spectral regions. This covers the SGLI channels for the size parameter, which is defined as a single-particle radius of an equivalent volume sphere, ranging between 6 and 9000 µm. The database includes the extinction efficiency, absorption efficiency, average geometrical cross section, single-scattering albedo, asymmetry factor, size parameter of a volume-equivalent sphere, maximum distance from the centre of mass, particle volume, and six nonzero elements of the scattering phase matrix. The characteristics of calculated extinction efficiency, single-scattering albedo, and asymmetry factor of the five ice particle habits are compared. Furthermore, size-integrated bulk scattering properties for the five ice particle habit models are calculated from the single-scattering database and microphysical data. Using the five ice particle habit models, the optical thickness and spherical albedo of ice clouds are retrieved from the Polarization and Directionality of the Earth's Reflectances-3 (POLDER-3) measurements, recorded on board the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The optimal ice particle habit for retrieving the SGLI ice cloud properties is investigated by adopting the spherical albedo difference (SAD) method. It is found that the SAD is distributed stably due to the scattering angle increases for bullet rosettes with an effective diameter (Deff) of 10 µm and Voronoi particles with Deff values of 10, 60, and 100 µm. It is confirmed that the SAD of small bullet-rosette particles and all sizes of Voronoi particles has a low angular dependence, indicating that a combination of the bullet-rosette and Voronoi models is sufficient for retrieval of the ice cloud's spherical albedo and optical thickness as effective habit models for the SGLI sensor. Finally, SAD analysis based on the Voronoi habit model with moderate particle size (Deff = 60 µm) is compared with the conventional general habit mixture model, inhomogeneous hexagonal monocrystal model, five-plate aggregate model, and ensemble ice particle model. The Voronoi habit model is found to have an effect similar to that found in some conventional models for the retrieval of ice cloud properties from space-borne radiometric observations.

Influences of cloud heterogeneity on cirrus optical properties retrieved from the visible and near-infrared channels of MODIS/SEVIRI for flat and optically thick cirrus clouds

11Nonstandard abbreviations: Cloud Optical Thickness (COT), Effective Diameter (De), Independent Pixel Approximation (IPA), Ice Water Content (IWC), Near-infrared (NIR), Plane Parallel Approximation (PPA), Severely Roughened Aggregated Columns (SRAC), Thermal infrared (TIR), Three-dimensional (3-D) ,Visible (VIS). The influences of three-dimensional radiative effects and horizontal heterogeneity effects on the retrieval of cloud optical thickness (COT) and effective diameter (De) for cirrus clouds are explored by the SHDOM radiative transfer model. The stochastic cirrus clouds are generated by the Cloudgen model based on the Atmospheric Radiation Measurement program data. Incorporating a new ice cloud spectral model, we evaluate the retrieval errors for two solar zenith angles (SZAs) (30° and 60°), four solar azimuth angles (0°, 45°, 90°, and 180°), and two sensor settings (Moderate Resolution Imaging Spectrometer (MODIS) onboard Aqua and Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard METEOSAT-8). The domain-averaged relative error of COT (μ) ranges from −24.1 % to -1.0 % (SZA = 30°) and from −11.6 % to 3.3 % (SZA = 60°), with the uncertainty within 7.5 % to –12.5 % (SZA = 30°) and 20.0 % - 27.5 % (SZA = 60°). For the SZA of 60° only, the relative error and uncertainty are parameterized by the retrieved COT by linear functions, providing bases to correct the retrieved COT and estimate their uncertainties. Besides, De is overestimated by 0.7–15.0μm on the domain average, with the corresponding uncertainty within 6.7–26.5μm. The retrieval errors show no discernible dependence on solar azimuth angle due to the flat tops and full coverage of the cirrus samples. The results are valid only for the two samples and for the specific spatial resolution of the radiative transfer simulations.

A framework based on 2-D Taylor expansion for quantifying the impacts of sub-pixel reflectance variance and covariance on cloud optical thickness and effective radius retrievals based on the bi-spectral method.

The bi-spectral method retrieves cloud optical thickness (τ) and cloud droplet effective radius (r e ) simultaneously from a pair of cloud reflectance observations, one in a visible or near infrared (VIS/NIR) band and the other in a shortwave-infrared (SWIR) band. A cloudy pixel is usually assumed to be horizontally homogeneous in the retrieval. Ignoring sub-pixel variations of cloud reflectances can lead to a significant bias in the retrieved τ and r e . In the literature, the retrievals of τ and r e are often assumed to be independent and considered separately when investigating the impact of sub-pixel cloud reflectance variations on the bi-spectral method. As a result, the impact on τ is contributed only by the sub-pixel variation of VIS/NIR band reflectance and the impact on r e only by the sub-pixel variation of SWIR band reflectance. In our new framework, we use the Taylor expansion of a two-variable function to understand and quantify the impacts of sub-pixel variances of VIS/NIR and SWIR cloud reflectances and their covariance on the τ and r e retrievals. This framework takes into account the fact that the retrievals are determined by both VIS/NIR and SWIR band observations in a mutually dependent way. In comparison with previous studies, it provides a more comprehensive understanding of how sub-pixel cloud reflectance variations impact the τ and r e retrievals based on the bi-spectral method. In particular, our framework provides a mathematical explanation of how the sub-pixel variation in VIS/NIR band influences the r e retrieval and why it can sometimes outweigh the influence of variations in the SWIR band and dominate the error in r e retrievals, leading to a potential contribution of positive bias to the r e retrieval. We test our framework using synthetic cloud fields from a large-eddy simulation and real observations from MODIS. The predicted results based on our framework agree very well with the numerical simulations. Our framework can be used to estimate the retrieval uncertainty from sub-pixel reflectance variations in operational satellite cloud products and to help understand the differences in τ and r e retrievals between two instruments.

OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

Abstract. This paper describes an approach for cloud parameter retrieval (radiometric cloud-fraction estimation) using the polarization measurements of the Global Ozone Monitoring Experiment-2 (GOME-2) onboard the MetOp-A/B satellites. The core component of the Optical Cloud Recognition Algorithm (OCRA) is the calculation of monthly cloud-free reflectances for a global grid (resolution of 0.2° in longitude and 0.2° in latitude) to derive radiometric cloud fractions. These cloud fractions will serve as a priori information for the retrieval of cloud-top height (CTH), cloud-top pressure (CTP), cloud-top albedo (CTA) and cloud optical thickness (COT) with the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. This approach is already being implemented operationally for the GOME/ERS-2 and SCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRA algorithm applied to the GOME-2 sensors. Based on more than five years of GOME-2A data (April 2008 to June 2013), reflectances are calculated for ≈ 35 000 orbits. For each measurement a degradation correction as well as a viewing-angle-dependent and latitude-dependent correction is applied. In addition, an empirical correction scheme is introduced in order to remove the effect of oceanic sun glint. A comparison of the GOME-2A/B OCRA cloud fractions with colocated AVHRR (Advanced Very High Resolution Radiometer) geometrical cloud fractions shows a general good agreement with a mean difference of −0.15 ± 0.20. From an operational point of view, an advantage of the OCRA algorithm is its very fast computational time and its straightforward transferability to similar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 and Sentinel 5. In conclusion, it is shown that a robust, accurate and fast radiometric cloud-fraction estimation for GOME-2 can be achieved with OCRA using polarization measurement devices (PMDs).

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 2. Retrieval evaluation

AbstractAn infrared‐based optimal estimation (OE‐IR) algorithm for retrieving ice cloud properties is evaluated. Specifically, the implementation of the algorithm with MODerate resolution Imaging Spectroradiometer (MODIS) observations is assessed in comparison with the operational retrieval products from MODIS on the Aqua satellite (MYD06), Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP), and the Imaging Infrared Radiometer (IIR); the latter two instruments fly on the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite in the Afternoon Constellation (A‐Train) with Aqua. The results show that OE‐IR cloud optical thickness (τ) and effective radius (reff) retrievals perform best for ice clouds having 0.5 < τ < 7 and reff < 50 µm. For global ice clouds, the averaged retrieval uncertainties of τ and reff are 19% and 33%, respectively. For optically thick ice clouds with τ larger than 10, however, the τ and reff retrieval uncertainties can exceed 30% and 50%, respectively. For ice cloud top height (h), the averaged global uncertainty is 0.48 km. Relatively large h uncertainty (e.g., > 1 km) occurs for τ < 0.5. Analysis of 1 month of the OE‐IR retrievals shows large τ and reff uncertainties in storm track regions and the southern oceans where convective clouds are frequently observed, as well as in high‐latitude regions where temperature differences between the surface and cloud top are more ambiguous. Generally, comparisons between the OE‐IR and the operational products show consistent τ and h retrievals. However, obvious differences between the OE‐IR and the MODIS Collection 6 reff are found.

Uncertainties in cloud phase and optical thickness retrievals from the Earth Polychromatic Imaging Camera (EPIC).

This paper presents an investigation of the expected uncertainties of a single channel cloud optical thickness (COT) retrieval technique, as well as a simple cloud temperature threshold based thermodynamic phase approach, in support of the Deep Space Climate Observatory (DSCOVR) mission. DSCOVR cloud products will be derived from Earth Polychromatic Imaging Camera (EPIC) observations in the ultraviolet and visible spectra. Since EPIC is not equipped with a spectral channel in the shortwave or mid-wave infrared that is sensitive to cloud effective radius (CER), COT will be inferred from a single visible channel with the assumption of appropriate CER values for liquid and ice phase clouds. One month of Aqua MODIS daytime granules from April 2005 is selected for investigating cloud phase sensitivity, and a subset of these granules that has similar EPIC sun-view geometry is selected for investigating COT uncertainties. EPIC COT retrievals are simulated with the same algorithm as the operational MODIS cloud products (MOD06), except using fixed phase-dependent CER values. Uncertainty estimates are derived by comparing the single channel COT retrievals with the baseline bi-spectral MODIS retrievals. Results show that a single channel COT retrieval is feasible for EPIC. For ice clouds, single channel retrieval errors are minimal (< 2%) due to the particle size insensitivity of the assumed ice crystal (i.e., severely roughened aggregate of hexagonal columns) scattering properties at visible wavelengths, while for liquid clouds the error is mostly limited to within 10%, although for thin clouds (COT < 2) the error can be higher. Potential uncertainties in EPIC cloud masking and cloud temperature retrievals are not considered in this study.

Cirrus cloud optical and microphysical property retrievals from eMAS during SEAC4RS using bi-spectral reflectance measurements within the 1.88 μm water vapor absorption band.

Previous bi-spectral imager retrievals of cloud optical thickness (COT) and effective particle radius (CER) based on the Nakajima and King (1990) approach, such as those of the operational MODIS cloud optical property retrieval product (MOD06), have typically paired a non-absorbing visible or near-infrared wavelength, sensitive to COT, with an absorbing shortwave or midwave infrared wavelength sensitive to CER. However, in practice it is only necessary to select two spectral channels that exhibit a strong contrast in cloud particle absorption. Here it is shown, using eMAS observations obtained during NASA's SEAC4RS field campaign, that selecting two absorbing wavelength channels within the broader 1.88 μm water vapor absorption band, namely the 1.83 and 1.93 μm channels that have sufficient differences in ice crystal single scattering albedo, can yield COT and CER retrievals for thin to moderately thick single-layer cirrus that are reasonably consistent with other solar and IR imager-based and lidar-based retrievals. A distinct advantage of this channel selection for cirrus cloud retrievals is that the below-cloud water vapor absorption minimizes the surface contribution to measured cloudy TOA reflectance, in particular compared to the solar window channels used in heritage retrievals such as MOD06. This reduces retrieval uncertainty resulting from errors in the surface reflectance assumption, as well as reduces the frequency of retrieval failures for thin cirrus clouds.

Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection

Operational nowcasting techniques for sea fog over the Yellow Sea rely on data from weather satellites because ground-based observations are hardly available. While there are several algorithms for detecting low stratus (LST) that are applicable to geostationary weather satellite data, sea fog retrieval is more complicated. These schemes mostly need ancillary data such as Cloud Optical Thickness (COT) and Droplet Effective Radius (DER). To retrieve the necessary parameters for sea fog detection over the Yellow Sea, the Comprehensive Analysis Program for Cloud Optical Measurement (CAPCOM) scheme developed by Kawamoto et al. (2001) was adapted to the Japanese Multifunctional Transport Satellites (MTSAT) system-Japanese Advanced Meteorological Imager (JAMI). COT and DER values were then retrieved for 64 cases over the Yellow Sea (= 85,000 LST pixels) and compared with the COT and DER products from the MYD06/MOD06, CAPCOM-MODIS (Moderate Resolution Imaging Spectroradiometer) and CloudSat (cloud radar). Results showed that the COT and DER values retrieved from JAMI were satisfactory. The MTSAT-2 JAMI data delivered better COT values than the MTSAT-1R JAMI data, due to the re-calibration of MTSAT-2 JAMI’s visible (VIS) band in 2011. Similarly, improvements were seen in DER retrieval, even though the VIS re-calibration primarily affects COT retrieval. By comparing the difference in stratus thickness calculated by MTSAT-1R and MTSAT-2, the COT and DER retrieved from MTSAT-2 JAMI can be used in ground fog retrieval schemes. These values exhibit less bias, especially in cases involving high cloud top and thin cloud thickness. Both the COT and DER retrievals from MTSAT-2 JAMI offer potential as reliable parameters for Yellow Sea fog detection.

Pixel‐scale assessment and uncertainty analysis of AIRS and MODIS ice cloud optical thickness and effective radius

AbstractComparisons of collocated Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) ice cloud optical thickness (τ) and effective radius (re) retrievals and their uncertainty estimates are described at the pixel scale. While an estimated 27% of all AIRS fields of view contain ice cloud, only 7% contain spatially uniform ice according to the MODIS 1 km optical property phase mask. The ice cloud comparisons are partitioned by horizontal variability in cloud amount, cloud top thermodynamic phase, vertical layering of clouds, and other parameters. The magnitudes of τ and re and their relative uncertainties are compared for a wide variety of pixel‐scale cloud complexity. The correlations of τ and re between the two instruments are strong functions of horizontal cloud heterogeneity and vertical cloud structure, with the highest correlations found in single‐layer, horizontally homogeneous clouds over the low‐latitude tropical oceans. While the τ comparisons are essentially unbiased for homogeneous ice cloud with variability that depends on scene complexity, a bias of 5–10 µm remains in re within the most homogeneous scenes identified, consistent with known radiative transfer differences in the visible and infrared bands. The AIRS and MODIS uncertainty estimates reflect the wide variety of cloud complexity, with greater magnitudes in scenes with larger horizontal variability. The AIRS averaging kernels suggest scene‐dependent information content that is consistent with infrared sensitivity to ice clouds. The AIRS‐normalized χ2 radiance fits suggest that accounting for horizontal cloud variability is likely to improve the AIRS ice cloud retrievals.

A spectral method for discriminating thermodynamic phase and retrieving cloud optical thickness and effective radius using transmitted solar radiance spectra

Abstract. A new retrieval scheme for cloud optical thickness, effective radius, and thermodynamic phase was developed for ground-based measurements of cloud shortwave solar spectral transmittance. Fifteen parameters were derived to quantify spectral variations in shortwave transmittance due to absorption and scattering of liquid water and ice clouds, manifested by shifts in spectral slopes, curvatures, maxima, and minima. To retrieve cloud optical thickness and effective particle radius, a weighted least square fit that matched the modeled parameters was applied. The measurements for this analysis were made with the ground-based Solar Spectral Flux Radiometer in Boulder, Colorado, between May 2012 and January 2013. We compared the cloud optical thickness and effective radius from the new retrieval to two other retrieval methods. By using multiple spectral features, we find a closer fit (with a root mean square difference over the entire spectra of 3.1% for a liquid water cloud and 5.9% for an ice cloud) between measured and modeled spectra compared to two other retrieval methods which diverge by a root mean square of up to 6.4% for a liquid water cloud and 22.5% for an ice cloud. The new retrieval introduced here has an average uncertainty in effective radius (± 1.2 μm) smaller by factor of at least 2.5 than two other methods when applied to an ice cloud.