Multi-resolution Ensemble Assessment of Source Uncertainties in atmospheric River Evolution (MEASURE)
Abstract
Warm Conveyor Belts (WCBs), carrying air from the oceanic boundary layer to build and amplify ridges at the tropopause level, are collocated with the majority of the precipitation and cloud diabatic processes in extratropical cyclones. For some WCBs, the inflow region likewise coincides with Atmospheric River genesis, propagating significant downstream impacts on midlatitude precipitation. Our proposed research program improves the understanding of the shape, intensity, and predictability of ARs by providing a detailed statistical examination of their interaction with source disturbances to traveling waveguides. We will test the hypothesis that modeling physically resolved air-sea flux, combined with assimilating direct observations of model fields within WCBs, will improve the forecast skill of high-intensity AR events as compared with either convection-parameterized models or simulations that do not assimilate observations of moist diabatic processes in air-sea flux. Our methodology includes: i) utilizing the state-of-the-science MPAS-JEDI modeling and data assimilation (DA) system, ii) studying the response of the modeling system to the assimilation of multi-modalobservation strategies developed in the Atmospheric River Reconnaissance (AR Recon) campaign, and iii) assessing the impacts of waveguide disturbances on downstream predictability in ensemble-based DA and forecasts experiments using the Model Evaluation Tools (MET) framework to produce probabilistic ensemble forecast skill metrics. We utilize a combined approach of ensemble-based: i) forecasts, ii) DA, iii) skill verification and iv) observation sensitivity analyses in order to produce a rigorous statistical analysis of the behavior of rapidly growing perturbations in the ensemble forecast, as well as the best AR Recon sampling strategies for constraining model and initial errors in the interactions of the AR and WCBs to enhance extended forecast skill. The team will utilize theMPAS-JEDI-MET stackto evaluate key spatial regions and observation fields that drive downstream forecast sensitivity, in order to assess the evidence for a causal relationship between misrepresentation of moist diabatic processes in WCBs and downstream forecast error. This causal relationship will be evaluated by: i) ensemble-based regression, to measure the strength and statistical significance of effects; ii) determining collocation of sensitivity areas temporally and spatially with WCBs; and iii) by DA data denial experiments. Approved for public release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N000142412732
Entities
People
- Colin Grudzien
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego