Updated paper based on JOSS feedback

Author: AgentOxygen

docs/joss/paper.bib

@@ -1,3 +1,31 @@
+@software{ehfheatwaves,
+  author       = {Loughran, Tammas},
+  title        = {{tammasloughran/ehfheatwaves}: First Zenodo release},
+  year         = {2021},
+  version      = {v1.2},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.5637520},
+  url          = {https://doi.org/10.5281/zenodo.5637520}
+}
+
+@article{xclim,
+	doi = {10.21105/joss.05415},
+	url = {https://doi.org/10.21105/joss.05415},
+	year = {2023}, publisher = {The Open Journal},
+	volume = {8}, number = {85},
+	pages = {5415},
+	author = {Bourgault, Pascal and Huard, David and Smith, Trevor James and Logan, Travis and Aoun, Abel and Lavoie, Juliette and Dupuis, Éric and Rondeau-Genesse, Gabriel and Alegre, Raquel and Barnes, Clair and Laperrière, Alexis Beaupré and Biner, Sébastien and Caron, David and Ehbrecht, Carsten and Fyke, Jeremy and Keel, Tom and Labonté, Marie-Pier and Lierhammer, Ludwig and Low, Jwen-Fai and Quinn, Jamie and Roy, Philippe and Squire, Dougie and Stephens, Ag and Tanguy, Maliko and Whelan, Christopher},
+	title = {xclim: xarray-based climate data analytics},
+	journal = {Journal of Open Source Software}
+} 
+
+@Manual{hw,
+  title = {heatwave3: Detect Heatwaves and Cold-Spells within cubed data},
+  author = {Robert W. Schlegel and Guilherme Pinto and Lily Genevier and Jacob Cohen and Panini Dasgupta and Evan Meeker and Elise Beaudin and Albertus J. Smit},
+  year = {2024},
+  note = {R package version 0.0.3, https://github.com/robwschlegel/heatwave3},
+  url = {https://robwschlegel.github.io/heatwave3/index.html},
+}
 
 @article{perkins_review_2015,
 	title = {A review on the scientific understanding of heatwaves—{Their} measurement, driving mechanisms, and changes at the global scale},

docs/joss/paper.md

@@ -28,13 +28,13 @@ The heatwave diagnostics package (`HDP`) is a Python package that provides the c
 # Statement of Need
 
 Accurate quantification of the evolution of heatwave trends in climate model output is critical for evaluating future changes in hazard. The framework for indexing heatwaves by comparing a time-evolving measure of heat against some seasonally-varying percentile threshold is well-established in the literature (@baldwin_temporally_2019; @schoetter_changes_2015; @acero_changes_2024; @argueso_seasonal_2016).
-Metrics such as heatwave frequency and duration are commonly used in hazard assessments, but there are few centralized tools and no universal heatwave criteria for computing them. This has resulted in parameter heterogeneity across the literature and has prompted some studies to adopt multiple definitions to build robustness (@perkins_review_2015). However, many studies rely on only a handful of metrics and definitions due to the excessive data management and computational burden of sampling a greater number of parameters (@perkins_measurement_2013). The introduction of large ensembles has further complicated the development of software tools, which have remained mostly specific to individual studies. Some generalized tools have been developed to address this problem, but do not contain explicit methods for evaluating the potential sensitivities of heatwave hazard to the choices of heat measure, extreme heat threshold, and heatwave definition.
+Metrics such as heatwave frequency and duration are commonly used in hazard assessments, but there are few centralized tools and no universal heatwave criteria for computing them. This has resulted in parameter heterogeneity across the literature and has prompted some studies to adopt multiple definitions to build robustness (@perkins_review_2015). However, many studies rely on only a handful of metrics and definitions due to the excessive data management and computational burden of sampling a greater number of parameters (@perkins_measurement_2013). The introduction of large ensembles has further complicated the development of software tools, which have remained mostly specific to individual studies. Some generalized tools have been developed to address this problem, but they are often not explicitly optimized for evaluating the potential sensitivities of heatwave hazard to the choices of heat measure, extreme heat threshold, and heatwave definition.
 
-Development of the `HDP` was started in 2023 primarily to address the computational obstacles around handling terabyte-scale large ensembles, but quickly evolved to investigate new scientific questions around how the selection of characteristic heatwave parameters may impact hazard analysis. The `HDP` can provide insight into how the spatial-temporal response of heatwaves to climate perturbations depends on the choice of heatwave parameters. Although software does exist for calculating heatwave metrics (e.g. [heatwave3](https://robwschlegel.github.io/heatwave3/index.html), [xclim](https://xclim.readthedocs.io/en/stable/indices.html), [ehfheatwaves](https://tammasloughran.github.io/ehfheatwaves/)), these tools are not optimized to analyze more than a few definitions and thresholds at a time nor do they offer diagnostic plots.
+Development of the `HDP` was started in 2023 primarily to address the computational obstacles around handling terabyte-scale large ensembles, but quickly evolved to investigate new scientific questions around how the selection of characteristic heatwave parameters may impact hazard analysis. The `HDP` can provide insight into how the spatial-temporal response of heatwaves to climate perturbations depends on the choice of heatwave parameters. While other software packages primarly focus on calculating heatwave metrics for individual parameter selections (e.g. `heatwave3`[@hw], `xclim`[@xclim], `ehfheatwaves`[@ehfheatwaves]), the `HDP` builds on these tools by optimizing the computation to evaluate metrics across large ranges of the parameter space.
 
 # Key Features
 
-## Extension of XArray with Implementations of Dask and Numba
+## Extension of Xarray with Implementations of Dask and Numba
 
 `xarray` is a popular Python package used for geospatial analysis and for working with the netCDF files produced by climate models. The `HDP` workflow is based around `xarray` and seamlessly integrates with the `xarray.DataArray` data structure. Parallelization of `HDP` functions is achieved through the integration of `dask` with automated chunking and task graph construction features built into the `xarray` library.