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+---
+title: "xCDAT: A Python Package for Simple and Robust Analysis of Climate Data"
+tags:
+    - Python
+    - xarray
+    - climate science
+    - climate research
+    - climate data
+    - climate data analysis
+authors:
+    - name: Tom Vo
+      orcid: 0000-0002-2461-0191
+      affiliation: 1
+    - name: Stephen Po-Chedley
+      orcid: 0000-0002-0390-238X
+      affiliation: 1
+    - name: Jason Boutte
+      affiliation: 1
+      # TODO: Does Jason have an ORCID?
+    - name: Jiwoo Lee
+      orcid: 0000-0002-0016-7199
+      affiliation: 1
+    - name: Chengzhu Zhang
+      orcid: 0000-0002-9632-0716
+      affiliation: 1
+affiliations:
+    - name: Lawrence Livermore National Lab, Livermore, USA
+      index: 1
+date: 24 April 2023
+bibliography: paper.bib
+---
+
+# Summary
+
+xCDAT (Xarray Climate Data Analysis Tools) is an extension of xarray for climate data
+analysis on structured grids. It serves as a modern successor to the Community Data
+Analysis Tools (CDAT) library. The goal of xCDAT is to provide generalizable climate domain features and utilities with xarray for simple and robust analysis of climate data.
+
+xCDAT's key features includes spatial averaging, temporal averaging, horizontal
+regridding, and vertical regridding. Some features are inspired by CDAT, while others
+leverage powerful libraries in the xarray ecosystem (e.g., xESMF, cf_xarray) to deliver
+simple and robust APIs.
+
+# Statement of need
+
+CDAT Driving Need
+
+-   The CDAT library has provided over 20 years of robust and comprehensive climate data
+    analysis and visualization packages for the open-source community. Many scientists
+    and software libraries continue to utilize CDAT as a major dependency in their
+    workflows. As software technologies advance and the size of data grows, there is a
+    driving need for a modern successor to CDAT that is simple to use, performant, and
+    robust.
+
+Origins of xCDAT
+
+    In early 2021, a team of scientists and software engineers at LLNL spent several
+    months researching for a viable successor to CDAT. They found that libraries such as
+    xarray and Iris offered some similar features to CDAT, but these features were not
+    exactly comparable to those in CDAT. For example, CDAT implements unique logic for
+    handling axis coordinate metadata such as generating coordinate bounds which are
+    used in weighted averaging operations.
+
+    The team decided that it was sensible to develop xCDAT as CDAT's successor.
+    Xarray was chosen as the core technology because of its stability, maturity,
+    extensibility, and interoperability with the SciPy stack (e.g., dask, matplotlib,
+    pandas). xCDAT focuses on streamlining the user experience of developing analysis
+    code to reduce the complexity in achieving of certain domain-specific features in
+    xarray. Below is a code example for calculating the spatial average of tas
+    using xarray vs. xcdat. # TODO: Show figure of code example for spatial averaging
+
+    xCDAT aims to be a generalizable package that is compatible with structured grids
+    that are CF-compliant (e.g,. CMIP6).
+
+Xarray design
+
+-   https://docs.xarray.dev/en/stable/internals/extending-xarray.html
+    "Xarray is designed as a general purpose library and hence tries to avoid including overly domain specific functionality. But inevitably, the need for more domain specific logic arises."
+
+Leveraging Xarray with Dask
+
+# Features
+
+# Documentation
+
+# Figures
+
+Figures can be included like this:
+![Caption for example figure.\label{fig:example}](figure.png)
+and referenced from text using \autoref{fig:example}.
+
+Figure sizes can be customized by adding an optional second parameter:
+![Caption for example figure.](figure.png){ width=20% }
+
+# Acknowledgements
+
+We acknowledge contributions from Karl Taylor, Peter Gleckler, Paul Durack, and
+Chris Golaz who all have provided insightful knowledge and guidance in the planning
+and implementation of this software.
+
+xCDAT is jointly developed by scientists and developers from the Energy Exascale Earth
+System Model (E3SM) Project and Program for Climate Model Diagnosis and Intercomparison
+(PCMDI). The work is performed for the E3SM project, which is sponsored by Earth System
+Model Development (ESMD) program, and the Simplifying ESM Analysis Through Standards
+(SEATS) project, which is sponsored by the Regional and Global Model Analysis (RGMA)
+program. ESMD and RGMA are programs for the Earth and Environmental Systems Sciences
+Division (EESSD) in the Office of Biological and Environmental Research (BER) within the
+Department of Energy's Office of Science.
+
+# References
+
+Xarray
+CDAT