685 research outputs found
Microphysical variability in southeast Pacific Stratocumulus clouds: synoptic conditions and radiative response
Synoptic and satellite-derived cloud property variations for the southeast Pacific stratocumulus region associated with changes in coastal satellite-derived cloud droplet number concentrations (<i>N</i><sub><i>d</i></sub>) are explored. MAX and MIN <i>N</i><sub><i>d</i></sub> composites are defined by the top and bottom terciles of daily area-mean <i>N</i><sub><i>d</i></sub> values over the Arica Bight, the region with the largest mean oceanic <i>N</i><sub><i>d</i></sub>, for the five October months of 2001, 2005, 2006, 2007 and 2008. The ability of the satellite retrievals to capture composite differences is assessed with ship-based data. <i>N</i><sub><i>d</i></sub> and ship-based accumulation mode aerosol concentrations (<i>N</i><sub><i>a</i></sub>) correlate well (<i>r</i> = 0.65), with a best-fit aerosol activation value <span style="border-bottom: 1px solid #000; vertical-align: 50%; font-size: .7em; color: #000;"><i>d</i>ln <i>N</i><sub><i>d</i></sub></span><span style="margin-left: -2.7em; margin-right: 0.5em; vertical-align: -45%; font-size: .7em; color: #000;"><i>d</i>ln <i>N</i><sub><i>a</i></sub></span> of 0.56 for pixels with <i>N</i><sub><i>d</i></sub>>50 cm<sup>−3</sup>. The adiabatically-derived MODIS cloud depths also correlate well with the ship-based cloud depths (<i>r</i>=0.7), though are consistently higher (mean bias of almost 60 m). The MAX-<i>N</i><sub>d</sub> composite is characterized by a weaker subtropical anticyclone and weaker winds both at the surface and the lower free troposphere than the MIN-<i>N</i><sub><i>d</i></sub> composite. The MAX-<i>N</i><sub>d</sub> composite clouds over the Arica Bight are thinner than the MIN-<i>N</i><sub>d</sub> composite clouds, have lower cloud tops, lower near-coastal cloud albedos, and occur below warmer and drier free tropospheres (as deduced from radiosondes and NCEP Reanalysis). CloudSat radar reflectivities indicate little near-coastal precipitation. The co-occurrence of more boundary-layer aerosol/higher <i>N</i><sub><i>d</i></sub> within a more stable atmosphere suggests a boundary layer source for the aerosol, rather than the free troposphere. <br><br> The MAX-<i>N</i><sub><i>d</i></sub> composite cloud thinning extends offshore to 80° W, with lower cloud top heights out to 95° W. At 85° W, the top-of-atmosphere shortwave fluxes are significantly higher (~50%) for the MAX-<i>N</i><sub>d</sub> composite, with thicker, lower clouds and higher cloud fractions than for the MIN-<i>N</i><sub>d</sub> composite. The change in <i>N</i><sub><i>d</i></sub> at this location is small (though positive), suggesting that the MAX-MIN <i>N</i><sub>d</sub> composite differences in radiative properties primarily reflects synoptic changes. Circulation anomalies and a one-point spatial correlation map reveal a weakening of the 850 hPa southerly winds decreases the free tropospheric cold temperature advection. The resulting increase in the static stability along 85° W is highly correlated to the increased cloud fraction, despite accompanying weaker free tropospheric subsidence
Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks
Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks
Visualisation and 'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure
Revisiting our roots
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30131/1/0000507.pd
Does precipitation susceptibility vary with increasing cloud thickness in marine stratocumulus?
The relationship between precipitation rate and accumulation mode aerosol concentration in marine stratocumulus-topped boundary layers is investigated by applying the precipitation susceptibility metric to aircraft data obtained during the VOCALS Regional Experiment. A new method to calculate the precipitation susceptibility that incorporates non-precipitating clouds is introduced. The mean precipitation rate <i>R</i> over a segment of the data is expressed as the product of a drizzle fraction <i>f</i> and a drizzle intensity <i>I</i> (mean rate for drizzling columns). The susceptibility <i>S</i><sub>x</sub> is then defined as the fractional decrease in precipitation variable <i>x</i> = {<i>R</i>, <i>f</i>, <i>I</i>} per fractional increase in the concentration of aerosols with dry diameter >0.1 μm, with cloud thickness <i>h</i> held fixed. The precipitation susceptibility <i>S</i><sub>R</sub> is calculated using data from both precipitating and non-precipitating cloudy columns to quantify how aerosol concentrations affect the mean precipitation rate of all clouds of a given <i>h</i> range and not just the mean precipitation of clouds that are precipitating. <i>S</i><sub>R</sub> systematically decreases with increasing <i>h</i>, and this is largely because <i>S</i><sub>f</sub> decreases with <i>h</i> while <i>S</i><sub>I</sub> is approximately independent of <i>h</i>. In a general sense, <i>S</i><i>f</i> can be thought of as the effect of aerosols on the probability of precipitation, while <i>S</i><sub>I</sub> can be thought of as the effect of aerosols on the intensity of precipitation. Since thicker clouds are likely to precipitate regardless of ambient aerosol concentration, we expect <i>S</i><sub>f</sub> to decrease with increasing <i>h</i>. The results are broadly insensitive to the choice of horizontal averaging scale. Similar susceptibilities are found for both cloud base and near-surface drizzle rates. The analysis is repeated with cloud liquid water path held fixed instead of cloud thickness. Simple power law relationships relating precipitation rate to aerosol concentration or cloud droplet concentration do not capture this observed behavior
Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information
Adsorption of Aromatics in MFI-Type Zeolites: Experiments and Framework Flexibility in Monte Carlo Simulations
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