Kristian Holten Møller

Wind farm parameterizations (WFPs) are used to include the effects of operating wind farms on near-surface weather variables simulated by weather forecasting. In the first part of this series of papers, we implemented and evaluated two WPFs in the HARMONIE-AROME numerical weather prediction model (Fischereit et al., 2024). In this second part, we apply them in HARMONIE-AROME to perform sequential weather forecasts for Northern Europe with a lead time of 48 h every 12 h for two separate months. Combined, the selected summer and winter months are shown to represent the 30-year wind climate (wind speed, wind direction, and stability) in the forecast area reasonably well. A European wind turbine database is constructed as an input for the forecasts by combining eight different sources, harmonizing and filling gaps in the combined data set, filling missing data using random forest-based models, and associating wind farm information with individual turbines using a developed wind farm splitting algorithm. The final product and the algorithms are published for open access. We included scenarios for the forecasts with both on- and offshore turbines, as well as only offshore turbines, and analyzed the impact of wind farms on the hub height wind and near-surface temperature forecast. The forecasts using the WFPs show strong reductions in hub height wind speed near the wind farms. Wind forecast using the WFP of Fitch et al. (2012) compares best with observations for all sites, especially for mast and lidar sites close to wind farms. Onshore turbines have a non-negligible wake effect both in terms of strength and area, and must, therefore, be included for accurate wind forecasting. The differences in wind speed between forecasts ignoring wind farms and with a WFP included are statistically significant for many on- and offshore farms. The impact of wind farms on near-surface temperature is small on average over the 2 months, but can be considerable during certain periods. The two WFPs cause opposing signs of impact, i.e., warming versus cooling during nighttime.

The rapid expansion of solar power capacity necessitates the development of advanced, high-resolution meteorological tools to ensure national grid stability and efficient energy market integration. This poster presents a high-visibility collaboration between the Danish Meteorological Institute (DMI) and Energinet, the Danish Transmission System Operator (TSO), focused on the end-to-end development and operationalization of a satellite-based solar nowcasting system. The project is strategically structured into three distinct development tracks designed to modernize grid planning capabilities through improved short-term forecasts. In the first track, we have successfully transitioned a current optical-flow model based on Meteosat Second Generation (MSG) data into a live production environment. This system currently operates at a 15-minute temporal resolution and update frequency. As of early 2026, the first operational version of this system is live within a containerized Kubernetes environment orchestrated by AirFlow, which triggers automated updates every fifteen minutes. This infrastructure utilizes stable S3-storage for data handling and is transitioning from temporary researcher-led server solutions to permanent, integrated data flows. Validation of this system against current state-of-the-art operational numerical weather prediction output will be presented. The second development track involves adapting these models for the newly available Meteosat Third Generation (MTG) data, which provides significantly improved spatial resolution at 10-minute intervals. This transition includes establishing routines for skill comparison to quantify improvement over the first nowcasting system. Finally, the third track explores the development of an AI-based nowcasting model designed to learn realistic cloud development from historical MTG satellite imagery to further reduce nowcast uncertainty. This project serves as a technical blueprint for the integration of meteorological research into operational IT infrastructure to support the ongoing green energy transition.

Wind farm parameterizations (WFPs) are used to include the effects of operating wind farms on near-surface weather variables simulated by weather forecasting. In the first part of this series of papers, we implemented and evaluated two WPFs in the HARMONIE-AROME numerical weather prediction model (Fischereit et al., 2024). In this second part, we apply them in HARMONIE-AROME to perform sequential weather forecasts for Northern Europe with a lead time of 48 hours every 12 hours for two separate months. Combined, the selected summer and winter months are shown to represent the 30-year wind climate (wind speed, wind direction, and stability) in the forecast area reasonably well. A European wind turbine database is constructed as an input for the forecasts by combining eight different sources, harmonizing and filling gaps in the combined data set, filling missing data using random forest-based models, and associating wind farm information with individual turbines using a developed wind farm splitting algorithm. The final product and the algorithms are published for open access. We included scenarios for the forecasts with both on- and offshore turbines, as well as only offshore turbines, and analyzed the impact of wind farms on the hub height wind and near-surface temperature forecast. The forecasts using the WFPs show strong reductions in hub height wind speed near the wind farms. Wind forecast using the WFP of Fitch et al. (2012) compares best with observations for all sites, especially for mast and lidar sites close to wind farms. Onshore turbines have a nonnegligible wake effect both in terms of strength and area, and must, therefore, be included for accurate wind forecasting. The differences in wind speed between forecasts ignoring wind farms and with a WFP included are statistically significant for many on- and offshore farms. The impact of wind farms on near-surface temperature is small on average over the 2 months, but can be considerable during certain periods. The two WFPs cause opposing signs of impact, i.e., warming versus cooling during nighttime.

Wind farm parameterizations (WFPs) are used to include the effects of operating wind farms on near-surface weather variables simulated by weather forecasting. In the first part of this series of papers, we implemented and evaluated two WPFs in the HARMONIE-AROME numerical weather prediction model (Fischereit et al., 2024). In this second part, we apply them in HARMONIE-AROME to perform sequential weather forecasts for Northern Europe with a lead time of 48 hours every 12 hours for two separate months. Combined, the selected summer and winter months are shown to represent the 30-year wind climate (wind speed, wind direction, and stability) in the forecast area reasonably well. A European wind turbine database is constructed as an input for the forecasts by combining eight different sources, harmonizing and filling gaps in the combined data set, filling missing data using random forest-based models, and associating wind farm information with individual turbines using a developed wind farm splitting algorithm. The final product and the algorithms are published for open access. We included scenarios for the forecasts with both on- and offshore turbines, as well as only offshore turbines, and analyzed the impact of wind farms on the hub height wind and near-surface temperature forecast. The forecasts using the WFPs show strong reductions in hub height wind speed near the wind farms. Wind forecast using the WFP of Fitch et al. (2012) compares best with observations for all sites, especially for mast and lidar sites close to wind farms. Onshore turbines have a nonnegligible wake effect both in terms of strength and area, and must, therefore, be included for accurate wind forecasting. The differences in wind speed between forecasts ignoring wind farms and with a WFP included are statistically significant for many on- and offshore farms. The impact of wind farms on near-surface temperature is small on average over the 2 months, but can be considerable during certain periods. The two WFPs cause opposing signs of impact, i.e., warming versus cooling during nighttime.

Wind farm parameterizations (WFPs) are used to include the effects of operating wind farms on near-surface weather variables simulated by weather forecasting. In the first part of this series of papers, we implemented and evaluated two WPFs in the HARMONIE-AROME numerical weather prediction model (Fischereit et al., 2024). In this second part, we apply them in HARMONIE-AROME to perform sequential weather forecasts for Northern Europe with a lead time of 48 hours every 12 hours for two separate months. Combined, the selected summer and winter months are shown to represent the 30-year wind climate (wind speed, wind direction, and stability) in the forecast area reasonably well. A European wind turbine database is constructed as an input for the forecasts by combining eight different sources, harmonizing and filling gaps in the combined data set, filling missing data using random forest-based models, and associating wind farm information with individual turbines using a developed wind farm splitting algorithm. The final product and the algorithms are published for open access. We included scenarios for the forecasts with both on- and offshore turbines, as well as only offshore turbines, and analyzed the impact of wind farms on the hub height wind and near-surface temperature forecast. The forecasts using the WFPs show strong reductions in hub height wind speed near the wind farms. Wind forecast using the WFP of Fitch et al. (2012) compares best with observations for all sites, especially for mast and lidar sites close to wind farms. Onshore turbines have a nonnegligible wake effect both in terms of strength and area, and must, therefore, be included for accurate wind forecasting. The differences in wind speed between forecasts ignoring wind farms and with a WFP included are statistically significant for many on- and offshore farms. The impact of wind farms on near-surface temperature is small on average over the 2 months, but can be considerable during certain periods. The two WFPs cause opposing signs of impact, i.e., warming versus cooling during nighttime.

Context. In this paper we present a grid of self-consistent 1D model atmospheres of cool stars, sub-stellar objects, and exoplanets in the effective temperature range 300-3000 K, including cloud formation, chemical non-equilibrium effects, and stellar irradiation. Aims. The new grid extends the classical MARCS model atmosphere grid from 2008 towards lower effective temperatures and a broader range of object types. Methods. The new model atmosphere computations, MSG, are based on a combination of three well-tested codes, the classical MARCS 1D atmospheres, the StaticWeather cloud formation code, and the GGchem chemical equilibrium code. The combined code has been updated with new and more complete molecular and atomic opacities, cloud formation, and advanced chemical equilibrium calculations, and we also added new numerical methods at low temperatures to allow for a more robust convergence. Results. The coupling between the MARCS radiative transfer and GGchem chemical equilibrium computations has effectively made it possible to reach convergence based on the electron pressure for warmer models and gas pressure for cooler models, enabling self-consistent modelling of stellar, sub-stellar, and exoplanetary objects in a very wide range of effective temperatures. We will make new cloud-free and non-irradiated models for solar metallicity and a selected variety of other chemical compositions immediately available from our home page (https://cels.nbi.ku.dk). Illustrative examples of cloudy and irradiated models as well as models based on non-equilibrium chemistry are also presented, and we will describe these in more detail and make them available upon completion at the same place for a larger range of parameter space. Conclusions. For solar metallicity models, the new additional molecular opacities only affect the structure of models cooler than T_eff = 2500 K, and the effect becomes substantial for models below T_eff ~1500 K. Atomic line opacities are important for models warmer than ~3000 K. The line profile of the molecular opacities may have a larger effect on the model structure than previously anticipated, particularly in the uppermost layers at low gas pressure. The qualitative changes in the relative abundances of TiO, H₂O, CH₄, NH₃, and other molecules in our models follow the observationally defined M, L, T (and Y) sequences, but they also reveal more complex and depth-dependent abundance changes and therefore a spectral classification depending on more parameters. The self-consistent coupling to StaticWeather cloud computations allows for detailed comparison between nucleation and observed relative dimming of different spectral bands, with advanced applications for new identification methods of potential exoplanetary biology.

We investigate the gas-phase photo-oxidation of 2-ethoxyethanol (2-EE) initiated by the OH radical with a focus on its autoxidation pathways. Gas-phase autoxidation─intramolecular H-shifts followed by O₂ addition─has recently been recognized as a major atmospheric chemical pathway that leads to the formation of highly oxygenated organic molecules (HOMs), which are important precursors for secondary organic aerosols (SOAs). Here, we examine the gas-phase oxidation pathways of 2-EE, a model compound for glycol ethers, an important class of volatile organic compounds (VOCs) used in volatile chemical products (VCPs). Both experimental and computational techniques are applied to analyze the photochemistry of the compound. We identify oxidation products from both bimolecular and autoxidation reactions from chamber experiments at varied HO₂ levels and provide estimations of rate coefficients and product branching ratios for key reaction pathways. The H-shift processes of 2-EE peroxy radicals (RO₂) are found to be sufficiently fast to compete with bimolecular reactions under modest NO/HO₂ conditions. More than 30% of the produced RO₂ are expected to undergo at least one H-shift for conditions typical of modern summer urban atmosphere, where RO₂ bimolecular lifetime is becoming >10 s, which implies the potential for glycol ether oxidation to produce considerable amounts of HOMs at reduced NO_x levels and elevated temperature. Understanding the gas-phase autoxidation of glycol ethers can help fill the knowledge gap in the formation of SOA derived from oxygenated VOCs emitted from VCP sources.

Recently, hydroperoxy amides were identified as major products of OH-initiated autoxidation of tertiary amines in the atmosphere. The formation mechanism is analogous to that found for ethers and sulfides but substantially faster. However, the atmospheric fate of the hydroperoxy amides remains unknown. Using high-level theoretical methods, we study the most likely OH-initiated oxidation pathways of the hydroperoxy and dihydroperoxy amides derived from trimethylamine autoxidation. Overall, we find that the OH-initiated oxidation of the hydroperoxy amides predominantly leads to the formation of imides under NO-dominated conditions and more highly oxidized hydroperoxy amides under HO₂-dominated conditions. Unimolecular reactions are found to be surprisingly slow, likely due to the restricting, planar structure of the amide moiety.

Autoxidation has been acknowledged as a major oxidation pathway in a broad range of atmospherically important compounds including isoprene and monoterpenes. More recently, autoxidation has also been identified as central and even dominant in the atmospheric oxidation of the rather small nonhydrocarbons dimethyl sulfide (DMS) and trimethylamine (TMA). Here, we find even faster autoxidation in the aliphatic amine triethylamine (TEA). The atmospherically dominating autoxidation leads to highly oxygenated and functionalized compounds. Products with as many as three hydroperoxy (OOH) groups and an O:C ratio larger than 1 are formed. We present theoretical multiconformer transition-state theory (MC-TST) calculations of the unimolecular reactions in the autoxidation following the OH + TEA reaction and calculate peroxy radical H-shift rate coefficients >20 s^-1 for the first two generations of H-shifts. The efficient autoxidation in TEA is verified by the observation of the proposed highly oxidized products and radicals in flow-tube experiments. We find that the initial OH hydrogen abstraction at the α-carbon is strongly favored, with the β-carbon abstraction yield being less than a few percent.