Model EvaLuation using Observations, DIagnostics and Experiments Software (MELODIES)

MELODIES MONET has the goal to facilitate:

• Reading observational datasets with various spatial and temporal resolutions
• Reading model output from regional or global models with structured or unstructured grids
• Matching variable names and units between observations and model output
• Matching observations and model results in space and time, applying observation operators and algorithms for quantitative comparison, as well as matching meteorological situations and other conditions
• Plotting and analyzing comparisons: computing statistics with uncertainties, producing a performance matrix indicating limitations of comparisons 

Please see the MELODIES MONET Read the Docs site for more details on the framework, how to install and run the preliminary version now available, and -- most importantly! -- How to get involved and contribute to the development. 

Publications describing or using MELODIES MONET

• Earthcube Newsletter article Jan 2022
• Poster presented at the Earthcube Annual Meeting: "MELODIES for MUSICA" (doi:10.6084/m9.figshare.14773524).
• Zhu, Q., R. Schwantes, et al., Atmos. Chem. Phys., 24, 5265–5286, https://doi.org/10.5194/acp-24-5265-2024, 2024. 
• He, J., et al., PNAS Nexus, 3, https://doi.org/10.1093/pnasnexus/pgad483, 2024.

Tutorial

• Slides and recorded video from March 2022: https://www2.acom.ucar.edu/event/workshop/musica-tutorial-2021 
• In-person tutorial at NCAR Oct 15-16, 2024 - Registration now open

MELODIES MONET developers: 
David Fillmore, Rebecca Buchholz, Pablo Lichtig, Shima Shams, Benjamin Gaubert, Louisa Emmons (NCAR/ACOM) 
Becky Schwantes, Colin Harkins, Quazi Rasool, Meng Li, Colby Francoeur, & Jian He (NOAA-CSL)
Barry Baker, Zachary Moon, Beiming Tang (NOAA/ARL)
Jordan Schnell (NOAA/GSL)
Maggie Bruckner (U. Wisconsin)
Cheng Hsuan (Sarah) Lu (U. Albany)