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allen-dumas-etal_2021_urbclim

Sensitivity of Mesoscale Modelling to the Resolution of Urban Morphological Feature Inputs

Melissa R. Allen-Dumas^1*, Levi T. Sweet-Breua¹, Christa M. Brelsfordb²,Joshua R. New³

¹ Computational Sciences and Engineering Division, Oak Ridge National Laboratory, OneBethel Valley Road, Oak Ridge, TN. 37831

² Geospatial Science and Human Security Division, Oak Ridge National Laboratory, OneBethel Valley Road, Oak Ridge, TN 37831

³ Electrification and Energy Infrastructure Division, Oak Ridge National Laboratory, OneBethel Valley Road, Oak Ridge, TN 37831

* corresponding author: allenmr (at) ornl.gov

Abstract

As the numerical weather prediction community seeks deeper understanding of multi-scale interactions among the atmosphere, human systems and the overall earth system, more explicit representation of surface terrain in these models has become necessary. While a great body of work has examined the differences in error and uncertainty of simulations at various horizontal grid resolutions, no studies have been performed that compare the results of running the models at the same horizontal grid resolution but with different resolutions of embedded urban neighborhood morphology. We examine the differences in meteorological output from the Weather Research and Forecasting (WRF) model run at 270m horizontal resolution using 10m resolution neighborhood morphological inputs and 100m resolution inputs. We find that that horizontal resolution differences in urban morphological inputs to numerical weather models result in model output differences, especially in the spatial variability of micrometeorological parameters.

Journal reference

TBD

Code reference

In NatureF2 branch: https://code.ornl.gov/mrp/im3_ornl

Ingests shapefiles and turns them into inputs for WRF. Generated both 10 and 100-meter morphologies.

Data reference

Input data

Reference for each minted data source for your input data. For example:

For morphologies, inputs are only shapefiles.

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NAR (North American Reanalysis Dataset) - Input to WRF.

• Need citations

Output data

Reference for each minted data source for your output data. For example:

Morphologies from NATUREF as inputs to WRF. Swapped J. Ching inputs with NATUREF inputs.

Human, I.M. (2021). My output dataset name [Data set]. DataHub. https://doi.org/some-doi-number

Contributing modeling software

Model	Version	Repository Link	DOI
NATUREF	version	link to code repository	link to DOI dataset
WRF	v4.1	https://www2.mmm.ucar.edu/wrf/users/download/get_source.html	link to DOI dataset

Reproduce my experiment

Fill in detailed info here or link to other documentation that is a thorough walkthrough of how to use what is in this repository to reproduce your experiment.

1. Install the software components required to conduct the experiement from Contributing modeling software
2. Download and install the supporting input data required to conduct the experiement from Input data
3. Run the following scripts in the workflow directory to re-create this experiment:

Script Name	Description	How to Run
step_one.py	Script to run the first part of my experiment	python3 step_one.py -f /path/to/inputdata/file_one.csv
step_two.py	Script to run the last part of my experiment	python3 step_two.py -o /path/to/my/outputdir

4. Download and unzip the output data from my experiment Output data
5. Run the following scripts in the workflow directory to compare my outputs to those from the publication

Script Name	Description	How to Run
compare.py	Script to compare my outputs to the original	python3 compare.py --orig /path/to/original/data.csv --new /path/to/new/data.csv

Reproduce my figures

Use the scripts found in the figures directory to reproduce the figures used in this publication.

Script Name	Description	How to Run
r_code.R	Script to generate my figures	python3 generate_figures.py -i /path/to/inputs -o /path/to/outuptdir