# Improvements in Wave Age-Based Sea and Swell Separation for Offshore Industry Applications

S Kistner, D Caichac, R Cariou, P M Jensen, R B Sanchez, P D Grode

<figure><img src="image_1.png" /></figure>

## To cite this version:

S Kistner, D Caichac, R Cariou, P M Jensen, R B Sanchez, et al.. Improvements in Wave Age-Based Sea and Swell Separation for Offshore Industry Applications. Journal of Physics: Conference Series, 2024, 2875 (1), pp.012012. 10.1088/1742-6596/2875/1/012012. [hal-05154310](https://HAL Id: hal-05154310)

HAL Id: hal-05154310

https://nantes-universite.hal.science/hal-05154310v1

Submitted on 9 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

PAPER • OPEN ACCESS

# Improvements in Wave Age-Based Sea and Swell Separation for Offshore Industry Applications

To cite this article: S Kistner et al 2024 *J. Phys.: Conf. Ser.* **2875** 012012

View the [article online](#) for updates and enhancements.

You may also like

- Flow observations using nacelle lidars: A study on the University of Stavanger campus
L Vogt, J B Jakobsen, J T Snæbjørnsson et al.

- Controller influence on the fatigue of a floating wind turbine and load case impact assessment
Alberto Sánchez, Mikel Vicinay, Miren Sánchez et al.

- Using Large Language Models to Recommend Repair Actions for Offshore Wind Maintenance
C Walker, C Rothon, K Aslansefat et al.

UNITED THROUGH SCIENCE & TECHNOLOGY

<figure><img src="image_4.png" /></figure>

The Electrochemical Society
Advancing solid state & electrochemical science & technology

248th
ECS Meeting
Chicago, IL
October 12-16, 2025
Hilton Chicago

<figure><img src="image_5.png" /></figure>

Science +
Technology +
YOU!

Register by
September 22
to save $$

REGISTER NOW

<figure><img src="image_6.png" /></figure>
# Improvements in Wave Age-Based Sea and Swell Separation for Offshore Industry Applications

S Kistner<fnref n="1" />, D Caichac<fnref n="1" />, R Cariou<fnref n="2" />, P M Jensen<fnref n="1" />, R B Sanchez<fnref n="1" />,<fnref n="3" />, P D Grode<fnref n="1" />

E-mail: stki@dhigroup.com

**Abstract.** For offshore wind turbines, an accurate description of the wave conditions is crucial for their design, construction, installation, maintenance, and operation. Typical sea states in the open ocean are often a combination of wind-sea and swell systems which cannot be fully described by a single set of parameters. In such cases the wave conditions are better defined by separating and quantifying the individual wave systems. In this paper, the wave age method has been revisited, and a modification has been proposed to add more functionality to better describe the wind/wave alignment of the wind-sea. A qualitative analysis was performed using offshore spectral energy data from a global wave model. The analysis was carried out for four different geographical locations: the Philippine Sea, the North Sea, the Atlantic Ocean, and the Pacific Ocean. The analysis compares the wind-sea and swell partitions separated by the standard and modified wave age methods. It shows an improved description of the wind-sea compared to the standard equation, especially in the case of high wind speed events and where the wind-sea partition is expected to dominate most of the spectral energy.

## 1. Introduction

An ocean wave spectrum describes the distribution of the total wave variance over frequency and direction for a given sea state. The dominant part of the wave energy spectrum is associated with gravity waves with periods ranging between 1s and 30s and can be classified as wind-sea or swell. Typical sea states in the open ocean are often a combination of a wind-sea system and one or more swell systems. In general, wind-sea refers to waves that are near the wave generation area and are developing (young sea state) or in equilibrium with the local wind, whereas swell refers to waves that are generated by distant storms and propagated over long distances, typically with a phase velocity higher than a referenced wind speed [1]. Therefore, the direction of swell is therefore not necessarily correlated with the direction of the wind, whereas wind-sea waves are typically aligned with the wind.

For offshore wind turbines (OWTs), a good description of the sea state is essential for design, construction, installation, maintenance, and operation. Common design criteria for wave loads on OWTs are typically concerned with dynamic response and fatigue damage, which requires a sufficiently detailed description of the waves in the directional and frequency space. For combined wind and wave loads the effect of wind and wave misalignment can also affect the dynamic response and fatigue of the OWT. Ideally, a 2D (frequency and directional) wave spectrum would be used for the design calculations, but in practice, designers often rely on integrated wave parameters to reconstitute a theoretical frequency spectrum (e.g., Pierson-Moskowitz, JONSWAP).

The parameterisation can only provide a limited description of the total sea state. For multimodal wave systems, a single set of wave parameters is unlikely to provide an accurate description that captures

<figure><img src="image_7.png" /></figure>

<footnote n="1">¹ DHI A/S, 5 Agern Alle, Hørsholm, Denmark</footnote>

<footnote n="2">² École Centrale de Nantes, 1 Rue de la Noë, 44300 Nantes, France</footnote>

<footnote n="3">³ Vattenfall, 114 Ørestads Blvd, Copenhagen, Denmark</footnote>
the individual systems' unique frequency, direction and energy distribution. The description of the total sea state is typically improved by separating the different simultaneous wave systems before parameterising each system.

Despite the need to have a good understanding and methodology for distinguishing and quantifying the different wave systems in OWT design, defining the separation criteria remains a challenge. This task is largely subjective to the designer due to the open interpretation of what constitutes a sufficiently accurate description of the sea and swell parameters.

## 2. Common separation methods

Several techniques have been developed to separate and categorise the different simultaneous wave systems in a sea state. To avoid ambiguity, the methods can be categorised as partitioning or sea-swell separation.

### 2.1. Partitioning

Partitioning can be defined as a technique for identifying the different number of wave systems occurring simultaneously in a sea state, usually involving discrete separation of sea states in frequency and directional space. One of the earliest partitioning algorithms was devised by Gerling (1992) [2] to divide a directional spectral estimate from any source (e.g. radar image spectra, directional wave buoys, numerical models, etc.) into a discrete number of wave systems. Each system could be described by wave statistics, which provide a better high-level description of the wave field compared to parameters calculated from the total sea state.

Subsequently, Brüning et al. (1994) [3] and Hasselmann et al. (1996) [4] proposed a partitioning algorithm where the wave systems are considered as inverted "catchment areas", with spectral points whose "runoff" flows into a local (inverted) peak, hence the commonly used reference to the "watershed algorithm". These methods are based on a spectrum's geometric characteristic and do not consider the geophysical properties of the sea state or wave-wind interactions. As such, the standard watershed implementations do not explicitly distinguish between the wind-sea and swell partitions. Another limitation of the watershed algorithm is that there is no continuity in the order or number of partitions, which can impose certain limitations on design applications.

### 2.2. Sea-swell separation

Sea-swell separation refers to the characterisation or quantification of wave systems into either wind-sea or swell waves. One of the earliest sea-swell separation methods was defined by Komen et al. (1984) [5], known as the wave age method, which identifies the wind-sea fraction by correlating wave direction and wave phase velocity with the wind that generates them.

The wave energy statistical method from Chen et al. (2002) [1] estimates the fraction of the wind-wave energy partition from the product of a wind-sea probability and predicted parameters from a fully developed spectrum for a given wind forcing.

Another method by Portilla et al. (2009) [6], later modified by Chen et al. (2015) [7], is based on the concept of the overshoot phenomenon, which occurs with the development of a wind-sea system where the energy increases until it reaches a certain maximum value at a specified fetch, then gradually decreases to an equilibrium state for a fully developed sea state. By comparing the spectral peaks of the developing sea state to a theoretical spectrum of fully developed sea state (e.g. Pierson-Moskowitz) a wave system could be identified as either wind-sea or swell.

## 3. The wave age method

In the context of OWT designs, the wave age method [5] is a useful method for the sea-swell separation as it shares the advantages of the partitioning and sea-swell methods in that the wind-sea system is separated in the frequency and directional space, allowing the wave systems to be described both spectrally and parametrically.
The wave age method defines the region in the 2D wave spectrum as wind-sea if it satisfies the following:

$$ \frac{U_{10}}{c} \cos(\theta_{waves} - \theta_{wind}) > \beta \qquad (1) $$

Where $U_{10}$ is the wind velocity at 10 m above the water surface, c is the wave phase speed, $\theta_{waves}$ and $\theta_{wind}$ are the wave propagation and wind direction, respectively, and $\beta$ is a calibration factor, with values that typically range between 0.50 [6] and 0.83 [[8],[9]].

One of the limitations of the wave age method is that selection of the “optimal” factor $\beta$ is not irrelevant and directly affects the extent of the wind-sea area in the spectrum [[7],[6]]. The typical parameters of the wave age method were found to assign too much energy to the swell, which in turn underestimates the energy assigned to the wind-sea system, and thus underestimates the wave energy expected to be associated with wind-sea conditions. A modification of equation (1) with a second calibration coefficient, $\alpha$, is therefore proposed:

$$ \frac{U_{10}}{c} \cos(\theta_{waves} - \theta_{wind})^{\alpha} > \beta \qquad (2) $$

By adding a second calibration coefficient, $\alpha$, to the direction term, the sensitivity of wind-wave alignment could be adjusted, allowing more control over the separating criteria. The modified wave age separation method is visualised in Figure 1. For $\alpha = 1$ equation (2) gives the same results as equation (1).

<figure><img src="image_8.png" /><figcaption>Figure 1. Wave age method sea-swell separation for different values of calibration coefficients $\alpha$ and $\beta$. Areas above and below the curves are classified as wind-sea and swell respectively.</figcaption></figure>

Areas above and below the curves are classified as wind-sea and swell respectively. For decreasing values of $\beta$, the curves spread out along both the speed ($U_{10}/c$) and direction ($\cos(\theta_{waves} - \theta_{wind})$) axes, i.e., more energy is allocated to wind/sea for larger differences in wind and wave speed and direction. For decreasing values of $\alpha$, the curves are extended along the directional axis, assigning more energy to the wind-sea part for larger differences in the wind-wave alignment. As $\alpha$ approach 0 the
directional term approach unity for all directions, and thus the wave age equation becomes a function of the speed term only.

Cariou (2020) [10] undertook a series of spectral wave model tests to identify a combination of coefficients $\alpha$ and $\beta$ to better describe the wind-sea/swell separation. The tests considered stationary configurations of two swells combined with a constant and varying wind forcing (speed and direction). The fully spectral model outputs were separated into sea and swell using equation (2) for different combinations of calibration coefficients, ranging from $\alpha$ = 0.15 to 1.00 and $\beta$ = 0.70 to 0.83. The outputs were compared with the model inputs to assess the accuracy of the wind-sea/swell separation. The study proposed that $\alpha$ = 0.2 and $\beta$ = 0.78 provide an improved separation of wind-sea and swell compared to the standard coefficients of $\alpha$ = 1.0 and $\beta$ = 0.78.

The present work aims to corroborate the findings of Cariou (2020) [10] in offshore locations.

# 4. Testing the modified wave age method in a global wave model

## 4.1. *Implementation of the modified wave age equation coefficients*

In the third-generation spectral wave model by DHI (MIKE 21 SW), the modified wave age method is used for wave identification and separation of sea states into wind-sea and swell [11].

The MIKE 21 SW model simulates the growth, decay and transformation of wind-generated waves and swells in offshore and coastal areas. It is based on an unstructured, cell-centred finite volume method and use an unstructured mesh in geographic space. The model includes the Arduuin et al. [12] formulation for source terms and white capping. The wave model also includes dissipation due to bottom friction and depth-induced wave breaking, non-linear wave-wave interactions, refraction and shoaling due to depth variations.

## 4.2. *Application of modified wave age equation using a global wave model*

The standard and proposed values of $\alpha$ and $\beta$ are used to separate sea and swell in offshore locations, during highly energetic storm events when the wind-sea is expected to dominate the sea state. For this work, DHI's Global Wave Model (GWM) is used. This GWM has the following features:

* Boundary conditions defined as cyclic boundaries (i.e., continuous model domain)

* Unstructured (triangular) numerical mesh, with a resolution that varies between 50 km in offshore regions down to 15 km near the coastline (cell centre-to-centre distance)

* Bathymetric data from GEBCO (General Bathymetric Chart of the Oceans, www.gebco.net) interpolated into the unstructured mesh

* Wind field forcing (varying both in space and time) from the ERA5 dataset [13] (with some modifications on the larger wind speeds to improve the extreme wave height results).

* Wind speed stability correction by means of the COARE algorithm ([14],[15] and [16]), considering inputs from the ERA5 dataset (air temperature, relative humidity, sea surface temperature, salinity, and air pressure)

* Spectral discretisation of 36 directions (10° resolution) and logarithmic frequency axis of 36 frequencies, with a minimum frequency of $f_{min}$ = 0.035 Hz and a frequency factor of $c$ = 1.1

The GWM interpolated bathymetry and computational mesh is shown in Figure 2. Further details on DHI's Global Wave Model including the wind validation are presented in [17]<fnref n="4" />.

<footnote n="4">⁴ Report available on https://www.metocean-on-demand.com/</footnote>
<figure><img src="image_9.png" /><figcaption>**Figure 2.** Global Wave Model (GWM) bathymetry [mMSL] and computational mesh (Top panel), with a zoom in of the European area (bottom left), and Taiwan / South Korea / Japan area (bottom right). Also shown are the coordinates and depths of the spectral output points from DHI's GWM.</figcaption></figure>

The model was run for the year 2018 and spectral energy data were obtained at four (4) different geographical locations, namely the Philippine Sea (Taiwan), the North Sea (UK EEZ), the Atlantic Ocean (US East Coast) and the Pacific Ocean (US West Coast) as shown in Figure 2. Each of the fully spectral output points was separated into wind-sea and swell components using the standard coefficients of the wave age method (i.e., $\alpha = 1$ and $\beta = 0.83$), and the proposed coefficients ($\alpha = 0.2$ and $\beta = 0.78$) in equation (2).

The results for each location are shown in Figure 3 to Figure 6. For each location, a spectrogram (directional integrated energy density over time) and associated time series of significant wave height ($H_{m0}$) and wind speed 10 m above the surface ($U_{10}$) is shown for a high-energy event. In addition, the 1D wave spectrum and a 2D wave spectrum at the peak of the storm (based on $H_{m0}$) are presented. Orange (standard coefficients) and green (proposed coefficients) contours are used to identify the wind-sea/swell separation in the spectra or the wind-sea $H_{m0}$ in the time series. The wind-sea/swell separation in the directionally integrated space is not discrete, and as such, the separation contours in the spectrogram only indicate the frequency at which the energy density of the wind-sea spectrum is greater than the energy density of the swell spectrum.
<figure><img src="image_10.png" /><figcaption>**Figure 3.** Results for the Philippine Sea (Taiwan). (a) Spectrogram (black contours) of total energy density. The orange and green contours indicate the wind-sea/swell separation; (b) time series of $H_{m0}$ (total and wind-sea) and $U_{10}$; (c) 1D wave spectrum and (d) 2D wave spectrum at the peak of the storm.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 4.** Results for the North Sea (UK EEZ). (a) Spectrogram (black contours) of total energy density. The orange and green contours indicate the wind-sea/swell separation; (b) time series of $H_{m0}$ (total and wind-sea) and $U_{10}$; (c) 1D wave spectrum and (d) 2D wave spectrum at the peak of the storm.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure 5.** Results for the Atlantic Ocean (US East Coast). (a) Spectrogram (black contours) of total energy density. The orange and green contours indicate the wind-sea/swell separation; (b) time series of $H_{m0}$ (total and wind-sea) and $U_{10}$; (c) 1D wave spectrum and (d) 2D wave spectrum at the peak of the storm.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 6.** Results for the Pacific Ocean (US West Coast). (a) Spectrogram (black contours) of total energy density. The orange and green contours indicate the wind-sea/swell separation; (b) time series of $H_{m0}$ (total and wind-sea) and $U_{10}$; (c) 1D wave spectrum and (d) 2D wave spectrum at the peak of the storm.</figcaption></figure>
## 5. Discussion

In the spectrogram and time series, the development of young sea states can be observed by the growing wind speeds along with increased peakedness in the frequency spectra. Simultaneously, the wind-sea/swell separation contours (orange and green) shift from higher frequencies to lower frequencies and the wind-sea $H_{m0}$ increases both in magnitude and proportion relative to the total $H_{m0}$, indicating the development of a dominant wind-sea due to the increased correlation between wind speed, wave velocity and wind-wave orientation. At the peak of the storm, which is determined by the total $H_{m0}$, the sea states are either single peaked or weakly bi-modal. Where secondary peaks are evident, they are either weak (Taiwan and Pacific Ocean) or blend in (Atlantic Ocean). Given the time history of the sea state and wind speed, as well as visual observations from the spectra, the storm peaks for these four locations and events are expected to be strongly, if not completely, wind-sea dominated.

Visual inspection of the 1D and 2D spectra during the peak of the storm suggests that the frequency and directional range of the wind-sea associated with the standard coefficients ($\alpha = 1.0, \beta = 0.83$) is too narrow and that the wind-sea energy is underestimated. With the proposed coefficients, more of the low-frequency tail and a wider directional distribution is assigned to the wind-sea spectrum, and consequently, more energy is assigned to the wind-sea. This is partly due to the lowering of the calibration coefficient $\beta$, but mostly due to the relaxation of the wind/wave alignment threshold by the calibration coefficient $\alpha = 0.2$. The proposed coefficients arguably provide an improved representation of the wind-sea for these storms, which are likely to be wind-sea dominated.

For the design of an OWT, the risk of underestimating the wind/sea may be inadequate design specifications, which may, for example, lead to greater dynamic responses or potentially compromise structural integrity. At the same time, overestimating the swell may be conservative in terms of magnitude, but the wave direction may be more biased towards the wind-sea, which in turn may underestimate the wind-wave misalignment. Ultimately, it is up to the designer to decide on the separation criteria. With the modified wave age method, the additional calibration coefficient benefits the designer by providing more control over the wind-sea/swell separation to obtain parameters that better describe the design sea state.

The modified wave age method has some limitations, similar to the original wave age method, which are not addressed in this study. It is primarily applicable to offshore scenarios, as near-shore locations such as bays or harbours introduce wave refraction or diffraction, leading to discrepancies in wind-sea and wind directions, invalidating the directional term of the equation. For offshore wind farms, this is usually not a problem, but alternative separation methods may need to be considered. Rapid changes in wind speed and direction will likely lead to inaccurate estimates of the wind-wave/swell separation, as the wave age method does not consider past values. This could possibly be addressed by adding time dependence to the method, e.g. using an autoregressive equation. As the wave age method does not account for multiple swell systems, the parameterisation of the swell energy will have reduced accuracy in situations with two or more distinct swells. This could potentially be improved by combining the wave age method with a partitioning algorithm.

Although the modified equation with the proposed coefficients showed an improved representation of the wind-sea/swell separation at all four sites, further validation over a wider range of conditions is recommended. Further improvements are expected for site-specific adjustment of the coefficients.
**6. Conclusions and future work**

In this study, a modification of the wave age method for the partitioning of the total wave spectrum has been implemented and evaluated by adding a power coefficient $\alpha$ to the directional term of the wave age method (equation (2)). The modification allows more control over the separation criteria by adjusting the sensitivity of the wind-wave alignment.

Early numerical model tests by Cariou [10] proposed coefficients of $\alpha = 0.2$ and $\beta = 0.78$ which provided improved descriptions of the wind-sea and swell systems compared to the standard formulation using $\beta = 0.83$. Following this study, the modified wave age method is tested using a global wave model using the MIKE21 spectral wave model forced with wind from ECMWF's high accuracy global ERA5 wind dataset. The model was run for the year 2018 and spectral energy data were obtained at four different geographical locations. Each of the 2D spectral output points was separated into wind-sea and swell components using the standard coefficients and proposed coefficients with the modified wave age method.

In the examples presented, the modified wave age method better represents the wind-sea compared to the standard equation, especially in cases where there are high wind speed events (wind-driven storms) and the wind-sea partition is expected to dominate most of the spectral energy.

This modification is particularly important in the offshore wind industry where the wind-wave alignment is relevant and different wave systems contribute differently to the fatigue and dynamic response of OWTs. The additional calibration coefficient benefits the designer by providing more control over the wind-sea/swell separation to obtain parameters that better describe the design sea state.

While the reported coefficients showed promising results at four different sites, further refinement and validation of these coefficients is recommended to better suit local site conditions and meet the designer's expectations.

**Acknowledgements**

The authors would like to thank C2Wind for the original suggestion of the modified wave age method.

**References**

[1] Chen G, Chapron B, Ezraty R, Vandemark D A Global View of Swell and Wind Sea Climate in the Ocean by Satellite Altimeter and Scatterometer *J. Atmos. Oc. Tech.* 2002 **19** 11 p 1849-59

[2] Gerling TW Partitioning Sequences and Arrays of Directional Ocean Wave Spectra into Component Wave Systems *J. Atmos. Oc. Tech.* 1992 **9** 4 p 444-58

[3] Brüning C, Hasselmann S, Hasselmann K, Lehner S, Gerling TW FIrst evaluation of ERS-1 synthetic aperture radar wave mode data *Glob. Atmos. Ocean Syst.* 1994 **2** p 61-98

[4] Hasselmann S, Brüning C, Hasselmann K, Heimbach P An improved algorithm for the retrieval of ocean wave spectra from synthetic aperture radar image spectra *J. Geophys. Res. Oceans.* 1996 **101** C7 p 16615-29

[5] Komen GJ, Hasselmann S, Hasselmann K On the Existence of a Fully Developed Wind-Sea Spectrum *J. Phys. Oceanogr.* 1984 **14** 8 p 1271-85

[6] Portilla J, Ocampo-Torres FJ, Monbaliu J Spectral Partitioning and Identification of Wind Sea and Swell *J. Atmos. Oc. Tech.* 2009 **26** 1 p 107-22

[7] Chen Z, Zhang L, Zhao C, Chen X, Zhong J A Practical Method of Extracting Wind Sea and Swell from Directional Wave Spectrum *J. Atmos. Oc. Tech.* 2015 **32** 11 p 2147-59

[8] Donelan MA, Hamilton J, Hui WH Directional Spectra of Wind-Generated Waves *Philos. Trans. R. Soc. A* 1985 **315** 1534 p 509-62

[9] Komen GJ, Cavaleri L, Donelan M, Hasselmann K, Hasselmann S, Janssen PAEM *Dynamics and modelling of ocean waves* 1996

[10] Cariou R. Wave-age Criterion and Directional Analysis. *DHI Intern Research Project* 2020.

[11] Sørensen OR, Kofoed-Hansen H, Rugbjerg M, Sørensen LS *A Third-Generation Spectral Wave Model Using an Unstructured Finite Volume Technique* Coastal Engineering 2004 p. 894-906.

[12] Arduuin F, et al. Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation *J. Phys. Oceanogr.* 2010 **40** 9 p 1917-41
[13] Hersbach H, et al. ERA5 hourly data on single levels from 1940 to present In: (CDS) CCCSCSCDS, editor 2023

[14] Fairall CW, Bradley EF, Godfrey JS, Wick GA, Edson JB, Young GS Cool-skin and warm-layer effects on sea surface temperature *J. Geophys. Res. Oceans*. 1996 **101** C1 p 1295-308

[15] Fairall CW, Bradley EF, Rogers DP, Edson JB, Young GS Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment *J. Geophys. Res. Oceans*. 1996 **101** C2 p 3747-64

[16] Fairall CW, White AB, Edson JB, Hare JE Integrated Shipboard Measurements of the Marine Boundary Layer *J. Atmos. Oc. Tech*. 1997 **14** 3 p 338-59

[17] DHI EUDP Global Atlas of Siting Parameters Offshore. D5.3 Automated modelling report/manual (part 1). M5.1 Global model data for automated modelling in portal. DHI Global Wave Model: Set-up, Calibration and Validation 2023