We developed EcoTWIN v1.0, a new fully distributed tracer-aided ecohydrological model that tracks water, isotopes, and nutrients fluxes. The model was successfully tested in 17 large European catchments across diverse geological and climatic backgrounds. As a tracer-aided model, EcoTWIN not only captures flow paths but also estimates water ages/travel times, thus bridging hydrology with water quality. This opens new possibilities for understanding the synergy between water and nitrogen cycles.

The Singular Value Decomposition-three Dimensional Ensemble Variational data assimilation scheme is applied for the first time in the Tropical Regional Atmospheric Model System. With optimized three-dimensional perturbation generation and parallel strategies, computational costs were greatly reduced. Results indicate that the optimized scheme maintains reasonable accuracy while achieving much higher efficiency, suggesting good potential for practical forecasting use.

Sediment transport refers to the displacement of granular materials, such as sand and gravel, under the combined action of gravity and fluid flow. This study presents an open-source numerical model developed to investigate this process, with particular emphasis on the migration of an isolated dune. Beyond this specific application, the model has broad potential, including the analysis of erosion around engineered structures, ripple formation, and the morphological evolution of river systems.

This paper leverages the Free Surface Stabilization Algorithm of Kaus et al. (2010) to construct a fully implicit discretization of viscous free surface flows. It also presents the first second order accurate time-stepping scheme applicable to ice sheet models. We test the new method on an idealized problem and on a 2D glacier simulation. The results indicates that the method has great potential to speedup ice sheet models.

We describe an improved method and the associated free licensed package ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events) for estimating the statistics of temperature extremes. This method uses climate model simulations (including multiple scenarios simultaneously) to provide a prior of the real-world changes, constrained by the observations. The method and the tool are illustrated via an application to temperature over Europe until 2100, for four scenarios.

Colored noise processes can be used to imitate processes that are two small to include fully in a model. The naïve introduction of a colored noise process to a numerical algorithm can lead to unrealistic outputs. This is remedied by the introduction of the recently introduced the Generalized Ito Correction (GIC). We demonstrate the effectiveness of GIC to improve results at a low cost on two models from the atmosphere modeling literature for a range of colored noise processes.

Our tool is an easy-to-use, interpretable causal inference software. It can be applied in any scientific discipline exploring time series. The tool uses heterogeneous Granger causality. It can be used on time-series data to infer causal relationships between multiple variables and a target time-series. The tool is demonstrated on different types of applications related to meteorological events in a renewable energy, air pollution, and postprocessing benchmark data.

Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.

Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size is challenging due to computational and observational limits. We developed a machine learning model to predict aerosol size distributions, which accurately replicates advanced models and field measurements.

The Microbial Community Model (MCoM) is a flexible biogeochemical modeling framework which resolves a rich set of interactions between photosynthetic and heterotrophic microbes, including cross-feeding, metabolite exchange, and nutrient recycling. As such, it allows to assess community-level effects on elemental turnover emerging from microbial interactions. Its scalability allows to represent both simple pairwise interactions and large, diverse communities.

This paper describes efforts to develop aerosol data assimilation capabilities for NOAA’s regional air quality modeling system by assimilating PM_2.5 observations within the Joint Effort for Data Assimilation Integration framework. Results from the September 2020 Western U.S. wildfires show that assimilating AirNow and/or PurpleAir PM_2.5 data reduces mean absolute errors of 1–24 h PM_2.5 forecasts over the continental United States on average relative to a control without data assimilation.

A new software for the calculation of quality controlled wind profiles from heterogeneous Doppler lidar measurements is presented. The processing is designed modularly. A provided standard processing chain is validated using radiosondes for three common Doppler lidar types at different locations.

This study evaluated the PM_2.5 simulation by the latest Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) mechanism coupled with the Community Multiscale Air Quality (CMAQ) model, covering different seasons and specific regions over China. Modelling results derived by CRACMM are compared with two well-established chemical mechanisms. The research findings provide a solid foundation for the further application of CRACMM in understanding and regulating air pollution globally.

The AquaCrop model has been incorporated into the NASA Land Information System, to advance regional crop growth simulations at any spatial resolution, with a range of different input sources for meteorology, soil and crop parameters. This system also facilitates the assimilation of satellite data to update the crop and water conditions during model simulations. We present three exploratory applications to highlight pathways for future research on regional-scale crop estimation.

Volatile organic compounds (VOCs) affect air quality and climate, but their behavior in the atmosphere is still uncertain. We launched a global research effort to compare how different models represent these compounds and to improve their accuracy. By analyzing model results alongside observations and satellite data, we aim to better understand the atmospheric composition of these compounds.