Retrieval Of Cloud Optical Properties Research Articles

Derivation of Shortwave Radiometric Adjustments for SNPP and NOAA-20 VIIRS for the NASA MODIS-VIIRS Continuity Cloud Products

Climate studies, including trend detection and other time series analyses, necessarily require stable, well-characterized and long-term data records. For satellite-based geophysical retrieval datasets, such data records often involve merging the observational records of multiple similar, though not identical, instruments. The National Aeronautics and Space Administration (NASA) cloud mask (CLDMSK) and cloud-top and optical properties (CLDPROP) products are designed to bridge the observational records of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Aqua satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the joint NASA/National Oceanic and Atmospheric Administration (NOAA) Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA’s new generation of operational polar-orbiting weather platforms (NOAA-20+). Early implementations of the CLDPROP algorithms on Aqua MODIS and SNPP VIIRS suffered from large intersensor biases in cloud optical properties that were traced back to relative radiometric inconsistency in analogous shortwave channels on both imagers, with VIIRS generally observing brighter top-of-atmosphere spectral reflectance than MODIS (e.g., up to 5% brighter in the 0.67 µm channel). Radiometric adjustment factors for the SNPP and NOAA-20 VIIRS shortwave channels used in the cloud optical property retrievals are derived from an extensive analysis of the overlapping observational records with Aqua MODIS, specifically for homogenous maritime liquid water cloud scenes for which the viewing/solar geometry of MODIS and VIIRS match. Application of these adjustment factors to the VIIRS L1B prior to ingestion into the CLDMSK and CLDPROP algorithms yields improved intersensor agreement, particularly for cloud optical properties.

Extending XBAER Algorithm to Aerosol and Cloud Condition

The retrieval of cloud optical properties for aerosol contaminated water cloud is challenging because of the complexity of physical processes in such situations. Conventionally, cloud optical data products are typically derived, ignoring the aerosol impacts on radiative transfer in the retrieval process. This is potentially a significant source of error. In this paper, the eXtensible Bremen Aerosol Retrieval (XBAER) algorithm has been optimized for the retrieval of aerosol/cloud properties for the aerosol contaminated cloud (ACC) scenarios. This version of XBAER delivers cloud optical thickness (COT) and cloud effective radius (CER) for ACCs and simultaneously retrieves aerosol optical thickness (AOT). The surface parameterization and aerosol types used in the standard XBAER algorithm have been adapted in this retrieval to account for the ACC conditions. Aerosol types in XBAER for the retrieval of ACC scenarios have been parameterized to comprise weak and strong absorptions. The comparisons of COT, CER, and AOT retrieved using this adapted XBAER algorithm and two new NASA algorithms show good agreements, especially for biomass burning aerosol. The correlation coefficients are >0.9 for AOT, ~0.8 for CER, and ~0.7 for COT. There is also a good agreement for dust plume contaminated cloud scene. The XBAER derived AOT values for dust aerosols are systematically smaller than the NASA retrieval, but both products have the same spatial distribution patterns. The comparison of COT retrieved using the adapted XBAER algorithm and that retrieved from ground-based microwave radiometer (MWR) measurements shows much better agreement for ACC conditions with high AOT.

Improving Cloud Optical Property Retrievals for Partly Cloudy Pixels Using Coincident Higher‐Resolution Single Band Measurements: A Feasibility Study Using ASTER Observations

AbstractClear‐sky contamination is a challenging and long‐lasting problem for cloud optical thickness (τ) and effective droplet radius (reff) retrievals using passive satellite sensors. This study explores the feasibility of improving both τ and reff retrievals for partly cloudy (PCL) pixels by using available subpixel samples in a visible to near‐infrared band, which many satellite sensors offer. Data are provided by high‐resolution reflectance (R) observations and cloud property retrievals by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at horizontal resolutions between 30‐960m. For partly cloudy 960‐m observations, the clear‐sky component of the pixels induces significant underestimations of up to 58% for τ, while overestimations in reff can exceed 41%. This yields underestimations in the derived liquid water path and cloud droplet number concentration of up to 68% and 72%, respectively. By means of three different assumptions it is shown that subpixel R observations in the visible to near‐infrared band can be used to estimate higher‐resolution R for the second band in the retrieval scheme, as well as the subpixel cloud cover. The estimated values compare well to actually observed ASTER results and are used to retrieve cloud properties, which are unbiased by the clear‐sky component of PCL pixels. While the presented retrieval approach is only evaluated for marine boundary layer clouds, it is computationally efficient and can be easily applied to observations from different imagers. As an example, the PCL retrieval scheme is applied to data by the Moderate Resolution Imaging Spectroradiometer, where similar biases for PCL pixels are observed.

Cirrus Horizontal Heterogeneity and 3‐D Radiative Effects on Cloud Optical Property Retrievals From MODIS Near to Thermal Infrared Channels as a Function of Spatial Resolution

AbstractTo retrieve cloud optical properties, current satellite operational imager algorithms simplify the forward radiative transfer problem by assuming that cloudy pixels are horizontally homogeneous and radiatively independent. This study investigates the effects of cirrus horizontal heterogeneity and 3‐D radiative effects on cloud optical thickness (COT) and ice crystal effective radius (CER) retrievals obtained using simulated nadir near‐infrared/shortwave‐infrared (NIR/SWIR) reflectances at 0.86 and 2.13 μm and thermal infrared (TIR) radiances at 8.5, 11.0, and 12.0 μm, first separately and next using the five wavelengths together. Synthetic cirrus radiation fields are generated using a cirrus 3‐D cloud generator and a 3‐D radiative transfer code. When both cloud 3‐D and heterogeneity effects are considered, the solar reflectance‐based retrievals have the largest errors (up to 10% for COT and 80% for CER, depending on solar angles) for spatial resolutions less than 500–1,000 m, while the TIR‐based retrievals have the largest errors (up to 30% for COT and 50% for CER) above this resolution due to parallel homogeneous approximation bias. Therefore, TIR radiance‐based retrievals are preferable for spatial resolutions equal or higher than ~500 m to 1 km, while NIR/SWIR reflectance‐based retrievals are preferable for coarser spatial resolutions. The combination of NIR/SWIR and TIR measurements performed better together than individually for CER retrieval only for resolutions coarser than 2.5 km because 3‐D effects are negligible at this scale. Thus, the spectral dependence of subpixel cloud horizontal heterogeneity and 3‐D radiative effects has strong consequences when simultaneously using different channels for retrieving cirrus properties.

Characterizing the information content of cloud thermodynamic phase retrievals from the notional PACE OCI shortwave reflectance measurements.

We rigorously quantify the probability of liquid or ice thermodynamic phase using only shortwave spectral channels specific to the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and the notional future Plankton, Aerosol, Cloud, ocean Ecosystem imager. The results show that two shortwave-infrared channels (2135 and 2250 nm) provide more information on cloud thermodynamic phase than either channel alone; in one case, the probability of ice phase retrieval increases from 65 to 82% by combining 2135 and 2250 nm channels. The analysis is performed with a nonlinear statistical estimation approach, the GEneralized Nonlinear Retrieval Analysis (GENRA). The GENRA technique has previously been used to quantify the retrieval of cloud optical properties from passive shortwave observations, for an assumed thermodynamic phase. Here we present the methodology needed to extend the utility of GENRA to a binary thermodynamic phase space (i.e., liquid or ice). We apply formal information content metrics to quantify our results; two of these (mutual and conditional information) have not previously been used in the field of cloud studies.

Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS Collections 5.1 and 6 over global oceans.

Differences in cloud droplet effective radius and cloud droplet number concentration (CDNC) estimates inferred from the Aqua MODIS Collections 5.1 and 6 cloud products (MYD06) are examined for warm clouds over global oceans for the year 2008. Individual pixel level retrievals for both collections are aggregated to 1° × 1° and compared globally and regionally for the three main spectral channel pairs used for MODIS cloud optical property retrievals. Comparisons between both collections are performed for cases in which all three effective radii retrievals are classified by the MODIS Cloud Product as valid. The contribution to the observed differences of several key MYD06 Collection 6 algorithm updates are also explored, with a focus on changes to the surface reflectance model, assumed solar irradiance, above cloud emission, cloud top pressure, and pixel registration. Global results show a neutral to positive (> 50 cm-3) change for C6-derived CDNC relative to C5.1 for the 1.6 µm and 2.1 µm channel retrievals, corresponding to a neutral to -2 µm difference in droplet effective radius. For 3.7 µm retrievals, CDNC results show a negative change in the tropics, with differences transitioning toward positive values with increasing latitude spanning -25 to +50 cm-3 related to a +2.5 to -1 µm transition in effective radius. Cloud optical thickness differences were small relative to effective radius, and found to not significantly impact CDNC estimates. Regionally, the magnitude and behavior of the annual CDNC cycle are compared for each effective radius retrieval. Results from this study indicate significant intercollection differences in aggregated values of effective radius due to changes to the pre-computed retrieval lookup tables for ocean scenes, changes to retrieved cloud top pressure, solar irradiance, or above cloud thermal emission, depending upon spectral channel. The observed differences between collections may have implications for existing MODIS derived climatologies and validation studies of effective radius and CDNC.

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.

Characterizing a New Surface-Based Shortwave Cloud Retrieval Technique, Based on Transmitted Radiance for Soil and Vegetated Surface Types

This paper presents an approach using the GEneralized Nonlinear Retrieval Analysis (GENRA) tool and general inverse theory diagnostics including the maximum likelihood solution and the Shannon information content to investigate the performance of a new spectral technique for the retrieval of cloud optical properties from surface based transmittance measurements. The cumulative retrieval information over broad ranges in cloud optical thickness (τ), droplet effective radius (re), and overhead sun angles is quantified under two conditions known to impact transmitted radiation; the variability in land surface albedo and atmospheric water vapor content. Our conclusions are: (1) the retrieved cloud properties are more sensitive to the natural variability in land surface albedo than to water vapor content; (2) the new spectral technique is more accurate (but still imprecise) than a standard approach, in particular for τ between 5 and 60 and re less than approximately 20 μm; and (3) the retrieved cloud properties are dependent on sun angle for clouds of from 5 to 10 and re < 10 μm, with maximum sensitivity obtained for an overhead sun.

Examining the impact of overlying aerosols on the retrieval of cloud optical properties from passive remote sensing

Haywood et al. (2004) show that an aerosol layer above a cloud can cause a bias in the retrieved cloud optical thickness and effective radius. Monitoring for this potential bias is difficult because space‐based passive remote sensing cannot unambiguously detect or characterize aerosol above cloud. We show that cloud retrievals from aircraft measurements above cloud and below an overlying aerosol layer are a means to test this bias. The data were collected during the Intercontinental Chemical Transport Experiment (INTEX‐A) study based out of Portsmouth, New Hampshire, United States, above extensive, marine stratus cloud banks affected by industrial outflow. Solar Spectral Flux Radiometer (SSFR) irradiance measurements taken along a lower level flight leg above cloud and below aerosol were unaffected by the overlying aerosol. Along upper level flight legs, the irradiance reflected from cloud top was transmitted through an aerosol layer. We compare SSFR cloud retrievals from below‐aerosol legs to satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to detect an aerosol‐induced bias. In regions of small variation in cloud properties, we find that SSFR and MODIS‐retrieved cloud optical thickness compares within the uncertainty range for each instrument while SSFR effective radius tend to be smaller than MODIS values (by 1–2 μm) and at the low end of MODIS uncertainty estimates. In regions of large variation in cloud properties, differences in SSFR and MODIS‐retrieved cloud optical thickness and effective radius can reach values of 10 and 10 μm, respectively. We include aerosols in forward modeling to test the sensitivity of SSFR cloud retrievals to overlying aerosol layers. We find an overlying absorbing aerosol layer biases SSFR cloud retrievals to smaller effective radii and optical thickness while nonabsorbing aerosols had no impact.

The shortwave radiative forcing bias of liquid and ice clouds from MODIS observations

Abstract. We present an assessment of the plane-parallel bias of the shortwave cloud radiative forcing (SWCRF) of liquid and ice clouds at 1 deg scales using global MODIS (Terra and Aqua) cloud optical property retrievals for four months of the year 2005 representative of the meteorological seasons. The (negative) bias is estimated as the difference of SWCRF calculated using the Plane-Parallel Homogeneous (PPH) approximation and the Independent Column Approximation (ICA). PPH calculations use MODIS-derived gridpoint means while ICA calculations use distributions of cloud optical thickness and effective radius. Assisted by a broadband solar radiative transfer algorithm, we find that the absolute value of global SWCRF bias of liquid clouds at the top of the atmosphere is about 6 W m−2 for MODIS overpass times while the SWCRF bias for ice clouds is smaller in absolute terms by about 0.7 W m−2, but with stronger spatial variability. If effective radius variability is neglected and only optical thickness horizontal variations are accounted for, the absolute SWCRF biases increase by about 0.3–0.4 W m−2 on average. Marine clouds of both phases exhibit greater (more negative) SWCRF biases than continental clouds. Finally, morning (Terra)–afternoon (Aqua) differences in SWCRF bias are much more pronounced for ice clouds, up to about 15% (Aqua producing stronger negative bias) on global scales, with virtually all contribution to the difference coming from land areas. The substantial magnitude of the global SWCRF bias, which for clouds of both phases is collectively about 4 W m−2 for diurnal averages, should be considered a strong motivation for global climate modelers to accelerate efforts linking cloud schemes capable of subgrid condensate variability with appropriate radiative transfer schemes.

Evaluating the principles of cloud remote sensing with AVHRR and MAS imagery over SHEBA

A rigorous discrete ordinates radiative transfer formulation has been applied to two Advanced Very High Resolution Radiometer (AVHRR) images extracted from telemetry collected by the CCGS Des Groseilliers satellite tracking system during SHEBA to estimate cloud optical depth and effective radius of the cloud droplet size distribution. The two cases, from 2 and 3 June 1998, were chosen for analysis because (1) the images contained mostly liquid water clouds and (2) contemporaneous MODIS Airborne Simulator (MAS) overflight imagery was available for these AVHRR overpasses. The objective is to apply the same detailed radiative transfer formulation to both the MAS and AVHRR data so that the quality of the retrievals from the latter can be evaluated. Retrievals of cloud optical properties from MAS are assumed to be more reliable, because (1) all MAS channels have direct radiometric calibration, (2) the higher spatial resolution of MAS (50 m nadir versus 1.1 km nadir with AVHRR) should yield smaller uncertainties related to partially cloudy pixels in a given study area, and (3) effective droplet radius can be retrieved directly from the MAS 1.62‐μm channel without additional uncertainties involved with subtracting a thermal radiance component. Examination of the retrievals from both sensors in these two cases reveals considerable spatial variability (more than a factor of 2) in cloud optical depth, on a variety of scales ranging from tens of meters to tens of kilometers, even for relatively uniform liquid water clouds. Retrievals of cloud effective droplet radius from AVHRR are generally consistent with those from MAS, suggesting that AVHRR can be reliably used to estimate this quantity. However, AVHRR‐based retrievals of cloud optical depth are subject to large errors that result from small uncertainties in the absolute radiometric calibration of AVHRR channel 2. Using recalibration coefficients from one of the more robust AVHRR postlaunch calibration efforts, the cloud optical depths that we retrieved from NOAA 14 AVHRR channel 2 are consistently 30–50% larger than those obtained from MAS. The intercomparison of MAS and AVHRR retrievals of cloud optical depth also revealed errors with AVHRR due to partial cloud cover, and these errors are not immediately apparent when examining the AVHRR retrievals alone. If the AVHRR retrievals are averaged to spatial resolutions of order 10–30 km, they appear to become more stable for use in applications such as atmospheric energy budget calculations.

GOES 10 cloud optical property retrievals in the context of vertically varying microphysics

An optimal estimation approach is applied to the physical retrieval of single‐layer cloud optical properties (optical depth and effective radius) using multispectral imager channels on the western Geostationary Operational Environmental Satellite (GOES 10). The retrieval includes diagnostic information pertaining to uncertainty and dependence on a priori assumptions required by the forward model. Satellite retrievals of 0.65‐μm cloud optical depth and effective radius (micrometers) for marine stratocumulus (in both drizzle and drizzle‐free conditions) during the CloudSat Antecedent Validation Experiment and tropical cirrus during the Atmospheric Radiation Measurement (ARM)‐Unmanned Aerospace Vehicle spring flight series are examined together with data from the NASA/Jet Propulsion Laboratory Airborne Cloud Radar (ACR), the ARM Cloud Detection Lidar, and the Colorado State University Scanning Spectral Polarimeter (SSP) instruments. Optical depths are found to be consistent between GOES and SSP after taking into account the degradation of responsivity in GOES channel 1. Colocated ACR/GOES observations support past evidence that passive satellite detection of drizzle size droplets (in terms of a significant positive bias in the retrieved effective radius) may be possible under certain conditions. The ability of a dual lidar/radar active observing system to provide independent information over different cloud particle size regimes is illustrated and speaks to the vertical variability of cloud microphysics. The GOES estimate of effective radius was found to be more representative of the upper 0.5 km of the cirrus clouds examined.