MODIS Cloud Optical Property Retrieval Uncertainties Derived from Pixel-Level VNIR/SWIR Radiometric Uncertainties

Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of optical thickness and effective particle radius for liquid water and ice phase clouds employ a well-known VNIR/ SWIR solar reflectance technique. For this type of algorithm, we evaluate the quantitative uncertainty in simultaneous retrievals of these two cloud parameters to pixel-level radiometric calibration estimates and other fundamental (and tractable) error sources

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 NASA MODIS, VIIRS, and the notional future PACE imager. The results show that two shortwave-infrared channels (2135 nm and 2250 nm) provide more information on cloud thermodynamic phase than either channel alone. 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

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

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<sup>−2</sup> for MODIS overpass times while the SWCRF bias for ice clouds is smaller in absolute terms by about 0.7 W m<sup>−2</sup>, 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<sup>−2</sup> 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<sup>−2</sup> 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

Over-utilization of Advanced Imaging in the Hospital Setting: An Educational Approach to Reduce Unnecessary Inpatient Studies

By several measures, health care spending continues to rise, forcing businesses and families to cut back on operations and household expenses. In 2008, health care spending in the United States reached $2.4 trillion dollars, and is projected to reach$3.1 trillion in 2012.During the past decades, there has been a steady increase in the utilization of expensive inpatient imaging studies, with an overall increase in health care costs. In particular, advanced imaging includes CT, MRI and Nuclear Medicine, used for the diagnosis and management of hospitalized patients. The reasons for unnecessary imaging examinations include indirect financial benefit to physicians, medico-legal considerations, lack of accepted guidelines or failure to follow established ones. In the United States alone, it is estimated that CT testing accounts for around 6,000 additional cancers per year, with about half of those proving fatal. Each radiologic study using gadolinium presented a 2.4% risk of developing nephrogenic systemic fibrosis, with significant morbidity and mortality. We have shown that education of the ordering physicians is a feasible and cost effective means to decrease the over-otilization of advanced imaging in the inpatient setting

Climate Data Records: A MODIS Perspective

No abstract availabl

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-Cloud Aerosols Using CALIOP and MODIS Data

This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement

Spectral Dependence of MODIS Cloud Droplet Effective Radius Retrievals for Marine Boundary Layer Clouds

Low-level warm marine boundary layer (MBL) clouds cover large regions of Earth's surface. They have a significant role in Earth's radiative energy balance and hydrological cycle. Despite the fundamental role of low-level warm water clouds in climate, our understanding of these clouds is still limited. In particular, connections between their properties (e.g. cloud fraction, cloud water path, and cloud droplet size) and environmental factors such as aerosol loading and meteorological conditions continue to be uncertain or unknown. Modeling these clouds in climate models remains a challenging problem. As a result, the influence of aerosols on these clouds in the past and future, and the potential impacts of these clouds on global warming remain open questions leading to substantial uncertainty in climate projections. To improve our understanding of these clouds, we need continuous observations of cloud properties on both a global scale and over a long enough timescale for climate studies. At present, satellite-based remote sensing is the only means of providing such observations

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

TOOLS SHAREAbstractRecently, Zhang et al. (2016) presented a mathematical framework based on a secondorder Taylor series expansion in order to quantify the planeparallel homogeneous bias (PPHB) in cloud optical thickness () and effective droplet radius (r(sub eff)) retrieved from the bispectral solar reflective method. This study provides observational validation of the aforementioned framework, using highresolution 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 and r(sub eff) 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 ) and r > 0.79 (for r(sub eff)). To test the feasibility of PPHB predictions for present and future satellite missions, a scale analysis with varying horizontal resolutions of the subpixel and pixellevel observations is performed, followed by tests of corrections with only limited observational highresolution data. It is shown that for reasonably thick clouds with a mean subpixel 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 r(sub eff) become less reliable, which can be attributed primarily to an increased retrieval uncertainty for r(sub eff)