144 research outputs found

    A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations

    Full text link
    An object‐based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small‐scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.Key PointsThe complexity and the bias introduced in small‐scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core are prominentThe classification tree algorithm with objective thresholding is successful in detecting different types of precipitation features with high spatial complexityAn efficient and informative study about the biases produced by GCMs should involve daily (or hourly) output (rather than monthly mean) analysis over local scalesPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/136040/1/jame20331.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/136040/2/jame20331_am.pd

    The Role of the Model in the Data Assimilation System

    Get PDF

    Fundamentals of Modeling, Data Assimilation, and High-performance Computing

    Get PDF
    This lecture will introduce the concepts of modeling, data assimilation and high- performance computing as it relates to the study of atmospheric composition. The lecture will work from basic definitions and will strive to provide a framework for thinking about development and application of models and data assimilation systems. It will not provide technical or algorithmic information, leaving that to textbooks, technical reports, and ultimately scientific journals. References to a number of textbooks and papers will be provided as a gateway to the literature

    Stratospheric Data Analysis System (STRATAN)

    Get PDF
    A state of the art stratospheric analyses using a coupled stratosphere/troposphere data assimilation system is produced. These analyses can be applied to stratospheric studies of all types. Of importance to this effort is the application of the Stratospheric Data Analysis System (STRATAN) to constituent transport and chemistry problems

    Stratospheric General Circulation with Chemistry Model (SGCCM)

    Get PDF
    In the past two years constituent transport and chemistry experiments have been performed using both simple single constituent models and more complex reservoir species models. Winds for these experiments have been taken from the data assimilation effort, Stratospheric Data Analysis System (STRATAN)

    Satellite observation and mapping of wintertime ozone variability in the lower stratosphere

    Get PDF
    Comparison is made between 30 mbar ozone fields that are generated by a transport chemistry model utilizing the winds from the Goddard Space Flight Center stratospheric data assimilation system (STRATAN), observations from the LIMS instrument on Nimbus-7, and the ozone fields that result from 'flying a mathematical simulation of LIMS observations through the transport chemistry model ozone fields. The modeled ozone fields were found to resemble the LIMS observations, but the model fields show much more temporal and spatial structure than do the LIMS observations. The 'satellite mapped' model results resemble the LIMS observations much more closely. These results are very consistent with the earlier discussions of satellite space-time sampling by Salby

    Gibberellins and Gravitropism in Maize Shoots

    Full text link
    corecore