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

• Published in: Geoscientific Model Development
• Main Authors: Lamer, Katia, Fridlind, Ann M., Ackerman, Andrew S., Kollias, Pavlos, Clothiaux, Eugene E., Kelley, Maxwell
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2018
• Subjects: article; Verlagsveröffentlichung; north slope; Alaska
• Online Access: https://doi.org/10.5194/gmd-11-4195-2018; https://noa.gwlb.de/receive/cop_mods_00004366; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00004323/gmd-11-4195-2018.pdf; https://gmd.copernicus.org/articles/11/4195/2018/gmd-11-4195-2018.pdf

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.