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In recent years, the remarkable progress in exoplanet science has unveiled a stunning diversity of exoplanets. Technological advancements have significantly expanded our capacity to investigate planetary objects beyond our solar system, which exhibit diverse properties including their sizes, compositions, and orbital characteristics. Although understanding the complex chemical-climate responses occurring in planetary-body atmospheres plays a pivotal role in determining potential habitability, much work is still required in this field. To interpret forthcoming spectral measurements obtained from exoplanetary atmospheres, there is a concerted effort to model these worlds. However, model parameters are often inferred from limited data sets. Consequently, laboratory-based atmospheric simulations are necessary for understanding complex gas-phase interactions and to complement atmospheric models through experimental examination of input parameters. In this paper we introduce the new Berlin Atmospheric Simulation Experimental chamber (BASE), present its current capabilities and provide an overview of experimental scenarios with applications ranging from planetary objects in the solar system (e.g. Mars, Venus, Titan) to Earth-like exoplanets. • Exoplanetary science demands experimental validation of atmospheric processes. • Laboratory simulations are fine tuned to a specific set of atmospheric parameters. • UV photons and electrons initiate important chemical reactions in the gas phase. • BASE simulates pressure, composition and radiation levels of planetary atmospheres. • BASE will provide important laboratory data to improve current atmospheric models. [ABSTRACT FROM AUTHOR]

Various fire emission estimates for southern Africa during 2019, derived with multiple burned area data sets with resolutions ranging from 500 to 20 m, are evaluated using satellite carbon monoxide (CO) observations. Southern African emissions derived from burned area generated by 20 m Sentinel‐2 satellite imagery are up to 120% higher than other estimates because small fires are better detected with a higher‐resolution satellite instrument. A comprehensive comparison between simulated and observed atmospheric CO indicates that the Sentinel‐2 burned area data significantly improves emission estimates, with up to 15% reduction in CO concentration biases in comparison to emissions based on coarser resolution burned area data. We also found that the temporal lag between emissions and atmospheric CO concentrations during the peak fire month was related to atmospheric transport. These findings emphasize the importance of utilizing higher‐resolution satellite instruments in accurately estimating emissions and understanding the impact of small fires on global climate. Plain Language Summary: We studied how much carbon monoxide (CO) was released into the air in southern Africa due to fires. To derive CO emissions we used different remotely sensed data of area burned, with varying levels of detail, from 500 to 20 m, and used these emissions in a model to mimic atmospheric transport and chemistry of CO. We found that emissions derived from the very detailed 20‐m satellite images from Sentinel‐2 produced a similar large amount of CO in the atmosphere over southern Africa as seen by CO satellite observations. This is because the smaller fires, which are harder to spot, were detected with the 20‐m burned area satellite observations. We also found that the air movement and inflow of polluted air from outside Africa impacted the peak of air pollution. Key Points: Sentinel‐2 burned area detects small fires, resulting in up to 120% higher fire emissions compared to conventional resolution estimatesImproved fuel load modeling and new emission factors yield more trustworthy fire emissions that can be evaluated with top‐down constraintsAtmospheric modeling and TROPOMI CO observations indicate a 15% bias reduction using Sentinel‐2 burned area compared with coarser fire data [ABSTRACT FROM AUTHOR]

Fine-scale models for the transport of marine aerosols are of great interest for the study of micro-climates and air quality in areas of complex topography, such as in urbanized coastal areas. To this end, the MIO laboratory implemented the Meso-NH model in its LES version over the northwest Mediterranean coastal zone using a recent sea-spray source function. Simulated meteorological parameters and aerosol concentrations are compared to experimental data acquired in the Mediterranean coastal zone in spring 2008 on board the R/V Atalante. Key findings indicate that the large eddy simulation (LES) mode closely matches with the experimental data, enabling an in-depth analysis of the numerical model ability to predict variations in aerosol concentrations. These variations are influenced by different wind directions, which lead to various fetch distances typical of coastal zones. [ABSTRACT FROM AUTHOR]

We present a machine learning based emulator of a microphysics scheme for condensation and precipitation processes (Zhao‐Carr) used operationally in a global atmospheric forecast model (FV3GFS). Our tailored emulator architecture achieves high skill (≥94%) in predicting condensate and precipitation amounts and maintains low global‐average bias (≤4%) for 1 year of continuous simulation when replacing the Fortran scheme. The stability and success of this emulator stems from key design decisions. By separating the emulation of condensation and precipitation processes, we can better enforce physical priors such as mass conservation and locality of condensation, and the vertical dependence of precipitation falling downward, using specific network architectures. An activity classifier for condensation imitates the discrete‐continuous nature of the Fortran microphysics outputs (i.e., tendencies are identically zero where the scheme is inactive, and condensate is zero where clouds are fully evaporated). A temperature‐scaled conditional loss function ensures accurate condensate adjustments for a high dynamic range of cloud types (e.g., cold, low‐condensate cirrus clouds or warm, condensate‐rich clouds). Despite excellent overall performance, the emulator exhibits some deficiencies in the uppermost model levels, leading to biases in the stratosphere. The emulator also has short episodic skill dropouts in isolated grid columns and is computationally slower than the original Fortran scheme. Nonetheless, our challenges and strategies should be applicable to the emulation of other microphysical schemes. More broadly, our work demonstrates that with suitable physically motivated architectural choices, ML techniques can accurately emulate complex human‐designed parameterizations of fast physical processes central to weather and climate models. Plain Language Summary: In this study, we create computer code that uses machine learning to mimic a weather model's algorithm for handling how clouds form and rain falls. When used in the weather model to replace this algorithm, our machine learning code is highly accurate in simulations for a whole year. We achieve this by making smart code design choices. We split the code into two parts: one for cloud formation and one for rain and snow. This allows us to better build important aspects of these processes into the machine learning approach. For instance, clouds form where it is moist and evaporate when it gets dry, and rain and snow fall downward. Our code learns cloud behavior based on temperature to ensure it works both for cold, thin clouds high up in the sky and warm, thick clouds closer to the ground. Our work shows a path for suitably designed machine learning code to eventually replace important parts of weather and climate models, but also that this path still requires careful human design respecting known physical principles. Key Points: We build an emulator to replace the Zhao‐Carr Fortran microphysics scheme in FV3GFSThe integrated emulator sustains high skill throughout a 1‐year simulationTailoring the ML architecture to the structure of the underlying scheme greatly improves the online behavior of the emulator [ABSTRACT FROM AUTHOR]

Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting. Plain Language Summary: Numerical models used for projecting climate change impacts must use ad‐hoc assumptions about the effects of unresolved small‐scale processes. These assumptions contribute to uncertainty in predicting how rainfall and temperature will change in the future. Expensive fine‐grid simulations which eliminate the need for some of these assumptions are possible to run for shorter (month‐to year‐long) duration. We use such a simulation to train a data‐driven representation of the effects of processes, like clouds, which are poorly simulated by a cheaper coarse‐grid model. The data‐driven representation (a neural network) predicts rates of temperature and moisture change in each column using inputs from that grid column. This approach has been previously shown to work for models with idealized boundary conditions, but not for the realistic setting we use. When this neural network is used in a coarse‐resolution model, the realism of many global skill metrics is as good or better than a baseline model with traditional representation of small‐scale processes. However, some features are degraded, such as the time‐evolving pattern of rainfall in the tropics and humidity in the upper atmosphere. This work is a first step toward the use of data‐driven representations of unresolved processes in realistic global atmospheric models. Key Points: Effective sources of heat and moisture are computed from a global storm‐resolving simulation accounting for semi‐resolved dynamicsA neural network is trained to predict columns of the effective sources using profiles of temperature and specific humidityWhen used online, stable month‐long simulations are possible although skill is not yet comparable to a previous corrective approach [ABSTRACT FROM AUTHOR]

Ensemble weather forecasting involves the integration of multiple simulations to improve the accuracy of predictions by introducing a probabilistic approach. It is difficult to accurately predict heavy rainfall events that cause flash floods and, thus, ensemble forecasting could be useful to reduce uncertainty in the forecast, thus improving emergency response. In this framework, this study presents the efforts to develop and assess a flash flood forecasting system that combines meteorological, hydrological, and hydraulic modeling, adopting an ensemble approach. The integration of ensemble weather forecasting and, subsequently, ensemble hydrological-hydraulic modeling can improve the accuracy of flash flood predictions, providing useful probabilistic information. The flash flood that occurred on 26 January 2023 in the Evrotas river basin (Greece) is used as a case study. The meteorological model, using 33 different initial and boundary condition datasets, simulated heavy rainfall, the hydrological model, using weather inputs, simulated discharge, and the hydraulic model, using discharge data, estimated water level at a bridge. The results show that the ensemble modeling system results in timely forecasts, while also providing valuable flooding probability information for 1 to 5 days prior, thus facilitating bridge flood warning. The continued refinement of such ensemble multi-model systems will further enhance the effectiveness of flash flood predictions and ultimately save lives and property. [ABSTRACT FROM AUTHOR]