Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation
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Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

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  • Journal Title:
    Geoscientific Model Development
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    The Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) is used to diagnose model performance and physical processes via an apple-to-apple comparison to satellite measurements. Although the COSP provides useful information about clouds and their climatic impact, outputs that have a subcolumn dimension require large amounts of data. This can cause a bottleneck when conducting sets of sensitivity experiments or multiple model intercomparisons. Here, we incorporate two diagnostics for warm rain microphysical processes into the latest version of the simulator (COSP2). The first one is the occurrence frequency of warm rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to CloudSat radar reflectivity, putting the warm rain process diagnostics into the context of the geographical distributions of precipitation. The second diagnostic is the probability density function of radar reflectivity profiles normalized by the in-cloud optical depth, the so-called contoured frequency by optical depth diagram (CFODD), which illustrates how the warm rain processes occur in the vertical dimension using statistics constructed from CloudSat and MODIS simulators. The new diagnostics are designed to produce statistics online along with subcolumn information during the COSP execution, eliminating the need to output subcolumn variables. Users can also readily conduct regional analysis tailored to their particular research interest (e.g., land-ocean differences) using an auxiliary post-process package after the COSP calculation. The inline diagnostics are applied to the MIROC6 general circulation model (GCM) to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multi-sensor diagnostics are intended to serve as a tool that facilitates process-oriented model evaluations in a manner that reduces the burden on modelers for their diagnostics effort.
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  • Source:
    Geosci. Model Dev., 12, 4297–4307, 2019 https://doi.org/10.5194/gmd-12-4297-2019
  • DOI:
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA15OAR4310153
  • Rights Information:
    CC BY
  • Compliance:
    Submitted
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