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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 10
Geosci. Model Dev., 11, 4195-4214, 2018
https://doi.org/10.5194/gmd-11-4195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 4195-4214, 2018
https://doi.org/10.5194/gmd-11-4195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 16 Oct 2018

Methods for assessment of models | 16 Oct 2018

(GO)2-SIM: a GCM-oriented ground-observation forward-simulator framework for objective evaluation of cloud and precipitation phase

Katia Lamer1, Ann M. Fridlind2, Andrew S. Ackerman2, Pavlos Kollias3,4,5, Eugene E. Clothiaux1, and Maxwell Kelley2 Katia Lamer et al.
  • 1Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA 16802, USA
  • 2NASA Goddard Institute for Space Studies, New York, New York 10025, USA
  • 3Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, New York 11973, USA
  • 4School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York 11794, USA
  • 5University of Cologne, 50937 Cologne, Germany

Abstract. General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise.

Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidar–radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8% of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40%. So, while hydrometeor-phase maps determined in forward space are very different from model reality they capture the information sensors can provide and thereby enable objective model evaluation.

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Weather and climate predictions of cloud, rain, and snow occurrence remain uncertain, in part because guidance from observation is incomplete. We present a tool that transforms predictions into observations from ground-based remote sensors. Liquid water and ice occurrence errors associated with the transformation are below 8 %, with ~ 3 % uncertainty. This (GO)2-SIM forward-simulator tool enables better evaluation of cloud, rain, and snow occurrence predictions using available observations.
Weather and climate predictions of cloud, rain, and snow occurrence remain uncertain, in part...
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