J1.3 Interacting with WRF-LES Data Using Python

Tuesday, 24 January 2017: 2:00 PM
Conference Center: Chelan 5 (Washington State Convention Center )
Gökhan Sever, ANL, Argonne, IL; and J. Helmus, V. Ghate, Y. Feng, and S. Collis

The Weather Research and Forecasting model run in Large Eddy Simulation mode (WRF-LES) produces a very large amount of data and requires a unique set of tools and interfaces to analyze the model output. The task is further complicated when attempting to compare the model output with data collected by variety of ground-based instruments. This presentation will demonstrate an ongoing study in which WRF-LES model is used to understand the impact of an island in the Eastern Northern Atlantic (ENA) on the sampling of an otherwise oceanic airflow. Observations collected at the Atmospheric Radiation Measurement (ARM) site on Graciosa Island during marine stratocumulus cloud conditions are used in this study. This work involves configuring, understanding and visualizing WRF-LES output and performing forward modelling to place virtual instruments within the model domain. Python, specifically the scientific Python stack including xarray [1], PyTMatrix [2], MetPy [3] and Py-ART [4] were used to construct data models for LES output and the target instrument data streams as well as to perform scattering calculations and explore radiative transfer calculations. This design will assist the study of formation, temporal/spatial evolution of clouds, and the physics and dynamics associated with airflow over complex topography. We will showcase the successes, discuss difficulties and highlight lessons learned.

[1] Hoyer, S., Hamman, J. (In preparation). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software.

[2] Leinonen, J., High-level interface to T-matrix scattering calculations: architecture, capabilities and limitations, Opt. Express, vol. 22, issue 2, 1655-1660 (2014), doi: 10.1364/OE.22.001655.

[3] https://github.com/metpy/MetPy

[4] Helmus, J.J. & Collis, S.M., (2016). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software. 4(1), p.e25. DOI: http://doi.org/10.5334/jors.119

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