Near-real time cloud retrievals from operational and research meteorological satellites

Minnis, Patrick; Nguyen, Louis; Palikonda, Rabindra; Heck, Patrick W.; Spangenberg, Douglas A.; Doelling, David R.; Ayers, J. Kirk; Smith, William L.; Khaiyer, Mandana M.; Trepte, Qing Z.; Avey, L.; Chang, Fu‐Lung; Yost, Christopher R.; Chee, T.; Sun-Mack, S.

doi:10.1117/12.800344

Cited by 112 publications

(109 citation statements)

References 18 publications

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“…We predict meteorology and aerosol mass (M) and number (N) distributions at the regional scale with the WRF-Chem model (24,25) configured for this area (6). Cloud optical depth and effective droplet radii retrieved from Terra MODerate-resolution Imaging Spectroradiometer (MODIS) and Geostationary Operational Environmental Satellite (GOES) imager data (26,27) are used to compute observed N d (28). We perform experiments utilizing these retrievals (see SI Text, Assimilation experiments).…”

Section: Resultsmentioning

confidence: 99%

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Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Saide

Carmichael

Scott

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Limitations in current capabilities to constrain aerosols adversely impact atmospheric simulations. Typically, aerosol burdens within models are constrained employing satellite aerosol optical properties, which are not available under cloudy conditions. Here we set the first steps to overcome the long-standing limitation that aerosols cannot be constrained using satellite remote sensing under cloudy conditions. We introduce a unique data assimilation method that uses cloud droplet number (N d ) retrievals to improve predicted below-cloud aerosol mass and number concentrations. The assimilation, which uses an adjoint aerosol activation parameterization, improves agreement with independent N d observations and with in situ aerosol measurements below shallow cumulus clouds. The impacts of a single assimilation on aerosol and cloud forecasts extend beyond 24 h. Unlike previous methods, this technique can directly improve predictions of near-surface fine mode aerosols responsible for human health impacts and low-cloud radiative forcing. Better constrained aerosol distributions will help improve health effects studies, atmospheric emissions estimates, and airquality, weather, and climate predictions.air quality | indirect effect | weather prediction | stratiform cloud | microphysics A mbient aerosols are important air pollutants with direct impacts on human health (1). They also play important roles in Earth's weather and climate systems through their direct (2), semi-direct (3), and indirect effects (4) on radiative transfer and clouds. Their role is dependent on their size, number, phase, and composition distributions, which vary significantly in space and time. There remain large uncertainties in predictions of aerosol distributions due to uncertainties in emission estimates and in chemical and physical processes associated with their formation and removal (5-9). These uncertainties in aerosol distributions lead to large uncertainties in weather and air-quality predictions and in estimates of health and climate-change impacts (10).Constraining ambient aerosol distributions with current Earthobserving systems is a difficult task. The most common approach is to assimilate satellite retrievals of aerosol optical depth (AOD) (11, 12), a quantity that represents total aerosol mass and composition in the atmospheric column. The requirement of cloudfree conditions for a successful retrieval and the remaining challenges of relating AOD to aerosol mass, size, and composition limit the utility of AOD retrievals (by themselves) in constraining aerosol mass and composition (13,14). These shortcomings are particularly true in regions of persistent marine stratocumulus, such as the southeast Pacific off the coast of Chile and Peru, where aerosol-cloud interactions are important to the energy balance (15), and limitations in current observing and modeling capabilities adversely impact regional and global weather and climate predictions (16). A typical MODIS AOD scene in this region ( Fig.

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Section: Resultsmentioning

confidence: 99%

“…Also, there is no limitation on aerosol composition distribution (sulfate dominates the case studied) as long as the aerosol properties participating in the activation process (e.g., hygroscopicity, solubility) are specified correctly. These applications are currently feasible given the availability of near realtime cloud retrievals (26).…”

Section: Discussionmentioning

confidence: 99%

Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Saide

Carmichael

Scott

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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“…The radiative heating rate Q rad in each grid is calculated using the Rapid Radiative Transfer Model for GCMs under clear-sky condition, and only the heating rate above the observed cloud top is used. The cloud top pressure data come from GOES 8 satellite-observed cloud products using Visible Infrared Solar-Infrared Split-Window Technique [Minnis et al, 2008]. The impact of cloud properties on the radiative cooling rate above it is not considered due to the lack of needed cloud information, but this can be improved as vertical profiles of cloud data become available.…”

Section: Methodsmentioning

confidence: 99%

Three‐dimensional constrained variational analysis: Approach and application to analysis of atmospheric diabatic heating and derivative fields during an ARM SGP intensive observational period

Tang

Zhang

2015

JGR Atmospheres

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Atmospheric vertical velocities and advective tendencies are essential large-scale forcing data to drive single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulations (LESs). However, they cannot be directly measured from field measurements or easily calculated with great accuracy. In the Atmospheric Radiation Measurement Program (ARM), a constrained variational algorithm (1-D constrained variational analysis (1DCVA)) has been used to derive large-scale forcing data over a sounding network domain with the aid of flux measurements at the surface and top of the atmosphere (TOA). The 1DCVA algorithm is now extended into three dimensions (3DCVA) along with other improvements to calculate gridded large-scale forcing data, diabatic heating sources (Q 1 ), and moisture sinks (Q 2 ). Results are presented for a midlatitude cyclone case study on 3 March 2000 at the ARM Southern Great Plains site. These results are used to evaluate the diabatic heating fields in the available products such as Rapid Update Cycle, ERA-Interim, National Centers for Environmental Prediction Climate Forecast System Reanalysis, Modern-Era Retrospective Analysis for Research and Applications, Japanese 55-year Reanalysis, and North American Regional Reanalysis. We show that although the analysis/reanalysis generally captures the atmospheric state of the cyclone, their biases in the derivative terms (Q 1 and Q 2 ) at regional scale of a few hundred kilometers are large and all analyses/reanalyses tend to underestimate the subgrid-scale upward transport of moist static energy in the lower troposphere. The 3DCVA-gridded large-scale forcing data are physically consistent with the spatial distribution of surface and TOA measurements of radiation, precipitation, latent and sensible heat fluxes, and clouds that are better suited to force SCMs, CRMs, and LESs. Possible applications of the 3DCVA are discussed.

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“…While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (Z) are https://ntrs.nasa.gov/search.jsp?R=20110012804 2018-05-10T23:38:00+00:00Z standard products being derived routinely from operational satellite data [6,7]. These and other cloud products have been validated under a variety of conditions [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

4-D cloud water content fields derived from operational satellite data

Smith¹,

Minnis²,

Benjamin³

et al. 2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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Near-real time cloud retrievals from operational and research meteorological satellites

Cited by 112 publications

References 18 publications

Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number

Three‐dimensional constrained variational analysis: Approach and application to analysis of atmospheric diabatic heating and derivative fields during an ARM SGP intensive observational period

4-D cloud water content fields derived from operational satellite data

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