Introduction to xwrf

Overview

This notebook will introduce the basics of gridded, labeled data with Xarray. Since Xarray introduces additional abstractions on top of plain arrays of data, our goal is to show why these abstractions are useful and how they frequently lead to simpler, more robust code.

We’ll cover these topics:

Create a DataArray, one of the core object types in Xarray
Understand how to use named coordinates and metadata in a DataArray
Combine individual DataArrays into a Dataset, the other core object type in Xarray
Subset, slice, and interpolate the data using named coordinates
Open netCDF data using XArray
Basic subsetting and aggregation of a Dataset
Brief introduction to plotting with Xarray

Prerequisites

Concepts	Importance	Notes
NumPy Basics	Necessary
Introduction to Xarray	Neccessary	Understanding of data structures
Intermediate NumPy	Helpful	Familiarity with indexing and slicing arrays
Datetime	Helpful	Familiarity with time formats and the `timedelta` object
Understanding of NetCDF	Helpful	Familiarity with metadata structure

Time to learn: 30 minutes

Imports

Simmilar to numpy, np; pandas, pd; you may often encounter xarray imported within a shortened namespace as xr.

from datetime import timedelta

import cmweather
import xarray as xr
import xwrf
import glob

import matplotlib.pyplot as plt

Introducing xwrf

xWRF is a package designed to make the post-processing of WRF output data more pythonic. It’s aim is to smooth the rough edges around the unique, non CF-compliant WRF output data format and make the data accessible to utilities like dask and the wider Pangeo universe.

It is built as an Accessor on top of xarray, providing a very simple user interface.

Finding a Dataset

Our dataset of interest is a WRF dataset from the SAIL domain, near Gothic, Colorado, available as an ARM PI dataset - https://iop.archive.arm.gov/arm-iop-file/2021/guc/sail/xu-wrf/README.html.

More information on the dataset:

These are the Weather Research and Forecasting (WRF) regional climate model simulations for supporting the analysis of temperature, precipitation, and other hydroclimate variables and evaluating SAIL data. The WRF model has three nested domains centered at the SAIL location (East River, Colorado) for the SAIL period from Oct 01, 2021 to Dec 31, 2022. We used the BSU subgrid-scale physics schemes, CFSR meteorological forcing datasets, and the topographic shading radiation schemes in our WRF simulation. Detailed information on the model configuration can be found at https://doi.org/10.5194/egusphere-2022-437

Use `.postprocess()` to clean the dataset

While all variables are present, e.g. the information about the projection is still in the metadata and also for some fields, there are non-metpy compliant units attributes.

So let’s try to use the standard xWRF.postprocess() function in order to make this information useable.

As you see, xWRF added some coordinate data, reassigned some dimensions and generally increased the amount of information available in the dataset.

Plot the Data!

Now that we have our data in an easier to analyze format, let’s plot one of the fields.

ds.SNOW.plot(x='XLONG',
             y='XLAT',
             cmap='Blues')

<matplotlib.collections.QuadMesh at 0x3116e9ed0>

../../_images/0f10631fa7403fea588e367eadb48b044d36eb0a1a5aa19bce0299181f1d4251.png

Challenge: Why is there more snow in some areas, and less in others?

Investigate other fields in the datasets
Look at other time steps - where are our precipitation fields?
Find possible scientific explanations here!

files = sorted(glob.glob("../data/sail/wrf/*"))
files

['../data/sail/wrf/wrfout_d03_2023-03-10_00_00_00',
 '../data/sail/wrf/wrfout_d03_2023-03-10_06_00_00',
 '../data/sail/wrf/wrfout_d03_2023-03-10_12_00_00',
 '../data/sail/wrf/wrfout_d03_2023-03-10_18_00_00',
 '../data/sail/wrf/wrfout_d03_2023-03-11_00_00_00']

Summary

xwrf can be a helpful tool when working with WRF data in Python! In this tutorial, we investigated WRF data from the SAIL campaign, digging into the datasets and using visualization techniques to analyze our results.

What’s next?

How do we scale up this analysis beyond a 24 hour run? Or higher resolution data? In future notebooks, we explore tools to help with increasing our ability to analyze high resolution datasets with xwrf.

Resources and references

This notebook was adapated from material in xwrf Documentation.

The dataset used here is an ARM PI dataset, courtesy of Zexuan Xu (zexuanxu@lbl.gov). If you use this dataset, please be sure to cite:

https://doi.org/10.5439/1971597
More information can be found in the related publication - https://doi.org/10.5194/egusphere-2022-437

Introduction to xwrf

Contents

Introduction to xwrf

Overview

Prerequisites

Imports

Introducing xwrf

Finding a Dataset

Examining the data

Use `.postprocess()` to clean the dataset

Plot the Data!

Investigate the change in SWE over time

Calculate the Change in SWE

Challenge: Why is there more snow in some areas, and less in others?

Summary

What’s next?

Resources and references

Introduction to xwrf

Contents

Introduction to xwrf

Overview

Prerequisites

Imports

Introducing xwrf

Finding a Dataset

Examining the data

Use .postprocess() to clean the dataset

Plot the Data!

Investigate the change in SWE over time

Calculate the Change in SWE

Challenge: Why is there more snow in some areas, and less in others?

Summary

What’s next?

Resources and references

Use `.postprocess()` to clean the dataset