first draft of a paper for JOSS is done

joss/paper.bib

@@ -10,6 +10,59 @@ @article{PelaezZapata_2024a
 }
 
 
+@inproceedings{Benoit_1993,
+  title = {Extensive Comparison of Directional Wave Analysis Methods from Gauge Array Data},
+  booktitle = {Proc. {{Intern}}. {{Symp}}. on {{Ocean Wave Measurement}} and {{Analysis}}},
+  author = {Benoit, Michel},
+  year = {1993},
+  pages = {740--754}
+}
+
+@inproceedings{Benoit_1993a,
+  title = {Practical {{Comparative Performance Survey}} of {{Methods Used}} for {{Estimating Directional Wave Spectra}} from {{Heave-Pitch-Roll Data}}},
+  booktitle = {Coastal {{Engineering}} 1992},
+  author = {Benoit, Michel},
+  year = {1993},
+  month = jun,
+  pages = {62--75},
+  publisher = {American Society of Civil Engineers},
+  address = {Venice, Italy},
+  doi = {10.1061/9780872629332.005},
+  urldate = {2023-07-04},
+  isbn = {978-0-87262-933-2 978-0-7844-7953-7}
+}
+
+@inproceedings{Benoit_and_Teisson_1995,
+  title = {Laboratory {{Comparison}} of {{Directional Wave Measurement Systems}} and {{Analysis Techniques}}},
+  booktitle = {Coastal {{Engineering}} 1994},
+  author = {Benoit, Michel and Teisson, Charles},
+  year = {1995},
+  month = aug,
+  pages = {42--56},
+  publisher = {American Society of Civil Engineers},
+  address = {Kobe, Japan},
+  doi = {10.1061/9780784400890.004},
+  urldate = {2023-07-04},
+  isbn = {978-0-7844-0089-0 978-0-7844-7952-0}
+}
+
+@inproceedings{Benoit_etal_1997,
+  title = {Analyzing Multidirectional Wave Spectra: A Tentative Classification of Available Methods},
+  booktitle = {Proceedings of the 27th {{IAHR Congress}}},
+  author = {Benoit, Michel and Frigaard, Peter and Sh{\"a}ffer, Hemming A.},
+  year = {1997},
+  publisher = {Canadian Government Publishing},
+  address = {San Francisco, CA, USA}
+}
+
+@techreport{Barstow_etal_2005,
+  type = {{{COST}} Office},
+  title = {Measuring and {{Analysing}} the Directional Spectrum of Ocean Waves},
+  author = {Barstow, Stephen F and Bidlot, Jean-Raymond and Caires, Sofia and Donelan, Mark A and Drennan, William M and Dupuis, H{\'e}l{\`e}ne and Graber, Hans C and Green, J Jim and Gronlie, Oistein and Gu{\'e}rin, Christine and Gurgel, Klaus-Werner and G{\"u}nther, Heinz and Hauser, Dani{\`e}le and Hayes, Kenneth and Hessner, Katrin and Hoja, Danielle and Icard, Delphine and Kahma, Kimmo K and Keller, William C and Krogstad, Harald E and Lefevre, Jean-Michel and Lehner, Susanne and Magnusson, Anne Karin and Monbaliu, Jaak and Borge, Jose Carlos Nieto and Pettersson, Heidi and Plant, William J and Quentin, C{\'e}line Gwena{\"e}lle and Reichert, Konstanze and Reistad, Magnar and Rosenthal, Wolfgang and Saetra, Oyvind and {Schulz-Stellenfleth}, Johannes and Walsh, Edward J and Weill, Alain and Wolf, Judith and Wright, C Wayne and Wyatt, Lucy R},
+  year = {2005},
+  institution = {{European cooperation in the field of scientific and technical research}}
+}
+
 @inproceedings{Krogstad_etal_2006,
   title = {Wavelet and Local Directional Analysis of Ocean Waves},
   booktitle = {The Sixteenth International Offshore and Polar Engineering Conference},
@@ -47,3 +100,15 @@ @article{Donelan_etal_2015
   doi = {10.1002/2015JC010808},
   urldate = {2023-07-04}
 }
+
+@article{Hoyer_2017,
+  title     = {xarray: {N-D} labeled arrays and datasets in {Python}},
+  author    = {Hoyer, S. and J. Hamman},
+  journal   = {Journal of Open Research Software},
+  volume    = {5},
+  number    = {1},
+  year      = {2017},
+  publisher = {Ubiquity Press},
+  doi       = {10.5334/jors.148},
+  url       = {https://doi.org/10.5334/jors.148}
+}

joss/paper.md

@@ -27,70 +27,21 @@ bibliography: paper.bib
 
 # Summary
 
-The research purpose of the Extended Wavelet Directional Method (EWDM) software
-is to address the limitations of conventional Fourier-based techniques in
-accurately capturing directional wave information. It aims to provide a robust
-estimation of the directional wave spectrum from diverse sources of data such as
-GPS buoys, pitch-roll buoys, arrays of wave staffs, and ADCPs. The software
-implements wavelet-based algorithms, Kernel Density Estimation (KDE), and tools
-for processing and visualizing directional wave data, making it suitable for
-researchers, students, and engineers in physical oceanography. In the context of
-related work, the EWDM builds upon the traditional Wavelet Directional Method
-(WDM) by extending its capabilities to incorporate a wide range of data sources
-and configurations, thus offering an alternative and improved methodology for
-estimating directional wave spectra. Furthermore, the collaborative and open
-approach of the EWDM welcomes contributions, feedback, and collaboration from
-the community, aligning with the principles of transparency, reproducibility,
-and accessibility within the physical oceanography research community.
+The EWDM (Extended Wavelet Directional Method) is a Python toolkit developed to accurately estimate the directional spectra of ocean waves using wavelet-based techniques. It aims to provide a robust estimation of the directional wave spectrum from diverse sources of data, including GPS buoys, pitch-roll buoys, arrays of wave staffs, and ADCPs. This research tool addresses the limitations of conventional techniques in capturing accurate directional wave information. Furthermore, it extends the capabilities of the original WDM [@Donelan_etal_1996], which was previously only able to resolve spatial arrays of wave staffs, to include other common sources of single-point data such as ADCPs and wave buoys [@PelaezZapata_2024a; @Krogstad_etal_2006]. Key features of the EWDM include the implementation of wavelet-based algorithms for extracting directional information from wave time series, improved estimation of wave directional distribution using Kernel Density Estimation (KDE), tools for processing and visualizing directional wave data, and compatibility with popular data sources such as SOFAR Spotter buoys and the CDIP database. The package is powered by `xarray` [@Hoyer_2017] labelled multi-dimensional arrays, enhancing its efficiency and scalability.
 
 
 # Statement of need
 
-Various aspects of physical oceanography, including air-sea interactions,
-wave-induced mixing and upper layer dynamics, as well as several applications in
-coastal engineering, such as, wave forecasting, prediction of rogue waves,
-quantification of coastal erosion, and the design of offshore structures and
-renewable energy devices, require accurate and precise knowledge of the
-directional distribution of ocean waves. The directional wave spectrum provide
-essential information about the distribution of wave energy with respect to
-frequency and direction. However, the dynamic and unpredictable nature of ocean
-waves, coupled with the constraints of current measurement technologies and
-analysis methods, pose challenges in attaining accurate directional information
+Various aspects of physical oceanography, including air-sea interactions, wave-induced mixing and upper layer dynamics, as well as several applications in coastal engineering, such as, wave forecasting, prediction of rogue waves, quantification of coastal erosion, and the design of offshore structures and renewable energy devices, require accurate and precise knowledge of the directional distribution of ocean waves [@Barstow_etal_2005]. The directional wave spectrum provide essential information about the distribution of wave energy with respect to frequency and direction. However, the dynamic and unpredictable nature of ocean waves, coupled with the constraints of current measurement technologies and analysis methods, pose challenges in attaining accurate directional information.
 
-The directional distribution of ocean waves is essential for understanding air-sea
-interactions, but current measurement and analysis limitations hinder precise
-directional information. This impacts our understanding of this crucial quantity. The
-directional wave spectrum is vital for numerical wave modeling and has applications in
-air-sea interactions, wave climate, sea-state forecasting, microseism prediction,
-coastal erosion, and wave energy harvesting.
+The directional wave spectra can be estimated from measurements using either spatial arrays of wave gauges or single-point triplets, where three perpendicular wave quantities are measured at the same point [@Barstow_etal_2005]. This latter is for instance the case of wave buoys, which are the most wide-spread means for obtaining _in-situ_ wave data. However, current methods rely either on truncated Fourier series -that are unable to capture complex sea states- or on statistical fitting of the distribution of wave directions and often involve assumptions that may not hold in real-world conditions. See @PelaezZapata_2024a, @Benoit_1993, @Benoit_1993a, @Benoit_and_Teisson_1995 and @Benoit_etal_1997 for a comprehensive review of various methods.
 
-The estimation of directional wave spectra is paramount in the field of physical
-oceanography for understanding wave dynamics and coastal processes. Conventional
-Fourier-based techniques have inherent limitations in accurately capturing
-directional wave information, prompting the need for alternative methodologies.
-The wavelet-based approach has emerged as a promising alternative, offering
-improved directional wave spectrum estimation.
-
-The Extended Wavelet Directional Method (EWDM) is a Python toolkit designed to
-address the shortcomings of conventional Fourier-based techniques and provide a
-robust estimation of the directional wave spectrum from diverse sources of data,
-including GPS buoys, pitch-roll buoys, arrays of wave staffs, and ADCPs. With
-specific implementations for spatial arrays of wave staffs inspired by WDM
-proposed by [@Donelan_etal_1996], as well as methods for single-point triplet
-data drawn from [@PelaezZapata_2024a] and [@Krogstad_etal_2006], the EWDM
-extends the capabilities of the original WDM to incorporate a wide range of data
-sources and configurations.
-
-Key features of the EWDM include the implementation of wavelet-based algorithms
-for extracting directional information from wave time series, improved
-estimation of wave directional distribution using Kernel Density Estimation
-(KDE), tools for processing and visualizing directional wave data, and
-compatibility with popular data sources such as SOFAR Spotter buoys and the CDIP
-database. The package is powered by xarray labelled multi-dimensional arrays,
-enhancing its efficiency and scalability.
+@Donelan_etal_1996 introduced an alternative method based on the continuous wavelet transform (CWT) to overcome some of the limitations of classic methods, though specifically applicable to spatial arrays of wave gauges. Building on this, @PelaezZapata_2024a and @Krogstad_etal_2006 demonstrated the extension of this wavelet-based approach to single-point measurements, offering a promising advancement in directional wave spectrum estimation. This package is therefore an implementation of these algorithms. It has been successfully employed in the recent research by @PelaezZapata_2024a, delivering robust and accurate results. This application further demonstrates its effectiveness in addressing the challenges posed by real-world complex sea-state conditions. It is expected to be a valuable tool for oceanographers, engineers, students, and practitioners working in the field of ocean wave analysis.
 
 
 # Acknowledgements
 
+This work was funded by the European Research Council (ERC) under the EU Horizon 2020 research and innovation program (Grant Agreement 833125- HIGHWAVE) and by the Centre National d'Études Spatiales (CNES) under the project ARANSAT. DPZ would like to thank the maintainers and developers of open-source scientific Python ecosystem for making data processing and visualization more efficient. DPZ is grateful to the Navier (ENS Paris-Saclay) and Stokes (University College Dublin) teams for the fruitful discussions during the analysis of the data.
+
 
 # References