Probabilistic Evaluations of a Cloud System Resolving Model Using ARM Observations
| Pete Henderson | University of Colorado |
| Robert Pincus | NOAA-CIRES Climate Diagnostics Center |
Category: Modeling
We use long (3-year) time-series of profiles, observed at ARM Climate Research Facility (ACRF) Southern Great Plains (SGP) site, to evaluate the performance of a cloud system resolving model (CSRM) using probabilistic techniques. This approach makes direct use of instantaneous observations, thereby avoiding temporal averaging. The model is driven by ARM's observationally constrained forcing data, and modeled thermodynamic fields are kept close to those observed by nudging them toward the sounding values. Snapshots of the modeled cloud and thermodynamic fields are saved every hour. To compare the model cloud to the observed cloud, we simulate the reflectivities seen by the active instruments used by active remote sensing cloud layer (ARSCL), and use their sensitivities to define cloud occurrence in the model. The probability of cloud (PoC) in the CSRM is then evaluated against ARSCL, as a function of height, using methods from ensemble forecast verification that compare statistics of subsets sorted by the model forecasts, which is possible in a single realization of the CSRM by treating each column of its domain as a member of an ensemble. Techniques such as reliability diagrams and Brier scores are then used to quantify performance. Results will be shown for a range of CSRM configurations, from high-resolution (500-m) 3D and lower-resolution 2D, typical of the CSRM's implementation in the Multiscale Modeling Framework, including those employing an intermediate prognostic higher-order turbulence closure (IPHOC).
This poster will be displayed at ARM Science Team Meeting.
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