This paper documents the atmospheric-moisture-tracking model WAM2layers v3 (Water Accounting Model – 2 layers, version 3). WAM2layers may be used to gain an understanding of atmospheric dynamics and to study rainfall patterns and extremes by mapping their sources or sinks, often in the context of climate and land-use changes. To this end, WAM2layers solves a prognostic equation for tagged moisture in gridded atmospheric datasets such as reanalysis data or climate model output. WAM2layers can be used in forward mode, to determine where evaporated water eventually precipitates, or in backward mode, to determine where precipitation originally evaporated.

WAM2layers v3 represents a complete rewrite of the WAM2layers model originally introduced in 2010 and subsequently used in more than 60 academic studies. This latest version incorporates performance optimizations to cope with the increased resolution of input data and introduces various best practices aimed at improved user-friendliness and software sustainability. As an increasing number of researchers are using the code, this paper is intended as an updated description and reference in the academic literature. After describing the history, model formulation, and numerical implementation, we present and evaluate two example cases to illustrate the use and skill of WAM2layers v3. We then discuss best practices, some important assumptions, and directions for future development.

This manuscript documents the atmospheric moisture tracking model WAM2layers v3 (Water Accounting Model – 2 layers – version 3). WAM2layers may be used to gain understanding of atmospheric dynamics and to study rainfall patterns and extremes by mapping their sources or sinks, often in the context of climate and land-use changes. To this end, WAM2layers solves a prognostic equation for tagged moisture in gridded atmospheric datasets such as reanalysis data or climate model output. WAM2layers can be used in forward mode to determine where evaporated water eventually precipitates, or in backward mode to determine where precipitation originally evaporated.

WAM2layers v3 represents a complete rewrite of the WAM2layers model originally introduced in 2010 and subsequently used in more than sixty academic studies. This latest version incorporates performance optimisations to cope with the increased resolution of input data, and introduces various best practices aimed at improved user-friendliness and software sustainability. Since an increasing number of researchers is using the code, this manuscript is intended as an updated description and reference in the academic literature. After describing the history, model formulation, and numerical implementation, we present and evaluate two example cases to illustrate the use and skill of WAM2layers v3. We then discuss best practices, some important assumptions, and directions for future development.

Airborne wind energy (AWE) systems harness energy at heights beyond the reach of tower-based wind turbines. To estimate the annual energy production (AEP), measured or modelled wind speed statistics close to the ground are commonly extrapolated to higher altitudes, introducing substantial uncertainties. This study proposes a clustering procedure for obtaining wind statistics for an extended height range from modelled datasets that include the variation in the wind speed and direction with height. K-means clustering is used to identify a set of wind profile shapes that characterise the wind resource. The methodology is demonstrated using the Dutch Offshore Wind Atlas for the locations of the met masts IJmuiden and Cabauw, 85 km off the Dutch coast in the North Sea and in the centre of the Netherlands, respectively. The cluster-mean wind profile shapes and the corresponding temporal cycles, wind properties, and atmospheric stability are in good agreement with the literature. Finally, it is demonstrated how a set of wind profile shapes is used to estimate the AEP of a small-scale pumping AWE system located at Cabauw, which requires the derivation of a separate power curve for each wind profile shape. Studying the relationship between the estimated AEP and the number of site-specific clusters used for the calculation shows that the difference in AEP relative to the converged value is less than 3 % for four or more clusters.