Odele Coddington

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

[1] 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 mm) 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 mm, 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.

A Solar Irradiance Climate Data Record

AbstractWe present a new climate data record for total solar irradiance and solar spectral irradiance between 1610 and the present day with associated wavelength and time-dependent uncertainties and quarterly updates. The data record, which is part of the National Oceanic and Atmospheric Administration’s (NOAA) Climate Data Record (CDR) program, provides a robust, sustainable, and scientifically defensible record of solar irradiance that is of sufficient length, consistency, and continuity for use in studies of climate variability and climate change on multiple time scales and for user groups spanning climate modeling, remote sensing, and natural resource and renewable energy industries. The data record, jointly developed by the University of Colorado’s Laboratory for Atmospheric and Space Physics (LASP) and the Naval Research Laboratory (NRL), is constructed from solar irradiance models that determine the changes with respect to quiet sun conditions when facular brightening and sunspot darkening features...

The Shannon information content of hyperspectral shortwave cloud albedo measurements: Quantification and practical applications

[1] The Shannon information content provides an objective measure of the information in a data set. In this paper, we quantify the information content of hyperspectral liquid water cloud measurements over a spectral range (300–2500 nm) representing approximately 95% of the total energy in the solar spectrum. We also use the Shannon information content to analyze the cloud retrieval wavelengths and weightings used by the Solar Spectral Flux Radiometer (SSFR) and to determine the cumulative information in the SSFR retrieval. These applications illustrate the utility of the Shannon information content in guiding the effective processing of hyperspectral data. Such efficiency is of growing importance considering the push toward spectrally resolved satellite measurements of reflected solar irradiance used to study climate.

Characterizing the retrieval of cloud properties from optical remote sensing

[1] This paper presents a new approach to the formal characterization of the optical retrieval of cloud optical thickness and effective droplet radius based on a nonlinear methodology that is derived from a general stochastic inverse problem formulation similar to standard Bayesian estimation theory. The methodology includes efficient use of the precomputed radiative transfer model simulations which are already available in standard retrieval algorithms. Another important property of the methodology is that it does not require performing the retrieval with actual measurements in order to characterize the retrieval results. One utility of this analysis is the quantification of information content in the standard retrieval problem, and the increase of information through adding channels (radiances at different wavelengths) to the inversion. This was demonstrated for the five‐wavelength retrieval using airborne hyperspectral shortwave irradiance measurements. The ability of the method to evaluate the impact of observation and radiative transfer model uncertainties on the retrieved cloud properties is also demonstrated. Further benefits from this study will be in its application to the cloud retrieval algorithms to be developed for future space‐ and airborne instruments. The present study puts forth the framework necessary to quantify that increase in information and to optimize new retrieval algorithms that efficiently accommodate the enhanced measurement space.

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 Administrations 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.

Attribution of Earth-reflected Hyperspectral Data using Bayesian Positive Source Separation

We treat Earth-reflected radiation as mixtures of spectra unique to sources of scattering and absorption in Earth’s atmosphere and surface. We identify the signals and quantify their mixtures using source separation in a Bayesian framework.

Quantifying the Information Content of Hyperspectral Cloud Data

We quantify the information content of hyperspectral cloud measurements at over 300 narrow spectral bands from the near-ultraviolet to the near-infrared. We use this to evaluate the retrieval wavelengths and their impact on cloud retrievals.