Atmospheric processes

Abstract:

Abstract:

Abstract Next-generation high-resolution (km-scale) climate models promise unprecedented accuracy in climate projections, but realizing their potential requires robust methods to quantify how well simulations align with real-world observations. Average-based metrics conventionally used for climate model evaluation ignore the physics encoded in the fine-scale structures of km-scale simulations. To overcome this limitation, we propose a novel, statistically principled evaluation methodology based on the likelihood function of a generative image model. Our method provides a continuous similarity metric derived from the likelihood distribution of observation and simulation snapshots, which can redefine the evaluation, intercomparison, and parameter tuning of high-resolution climate models. We demonstrate the applicability and interpretability of this method by evaluating convective clouds simulated by two state-of-the-art global km-scale models, using their outgoing infrared radiation fields. This work establishes a scalable pathway toward observation-based evaluation of next-generation climate simulations.

Abstract:

Plain Language Summary: Understanding the environments that support spatial growth of storms is crucial for predicting their impacts. This study uses a 6.5‐month‐long high‐resolution model simulation to examine the environments of simulated storm systems separated based on their rate of spatial growth. Results show that environments with a faster growth rate have greater moisture and instability. While vertical wind shear, defined as the change in wind speed and/or direction with height, is a well‐known factor in storm organization, its magnitude does not vary strongly with growth rate when shear is averaged over a large area, likely due to the large spread in values present. However, the direction of wind shear is likely an important factor in the rate of growth, and faster spatial growth is found for storms growing near the mountains when a favorable shear direction (parallel to the mountain range) is present. A low‐level jet, an area of stronger wind speeds elevated off of the surface, is found more frequently in environments with a faster growth rate and is likely an important mechanism that facilitates differences in thermodynamics and wind shear.

Abstract:

Abstract:

To study and monitor the Sun and its atmosphere, various space missions have been launched in the past decades. With rapid improvement in technology and different mission requirements, the data products are subject to constant change. However, for such long-term studies as solar variability or multi-instrument investigations, uniform data series are required. In this study, we built on and expanded the instrument-to-instrument translation (ITI) framework, which provides unpaired image translations. We applied the tool to data from the Extreme Ultraviolet Imager (EUI), specifically the Full Sun Imager (FSI) on Solar Orbiter and the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). This approach allowed us to create a homogeneous dataset that combines the two extreme ultraviolet (EUV) imagers in the 174/171 Å and 304 Å channels. We demonstrate that ITI is able to provide image calibration between Solar Orbiter and SDO EUV imagers, independent of the varying orbital position of Solar Orbiter. The comparison of the intercalibrated light curves derived from 174/171 Å and 304 Å filtergrams from EUI and AIA shows that ITI can provide uniform data series that outperform a standard baseline calibration. We evaluate the perceptual similarity in terms of the Fréchet inception distance, which demonstrates that ITI achieves a significant improvement of perceptual similarity between EUI and AIA. The study provides intercalibrated observations from Solar Orbiter/EUI/FSI with SDO/AIA, enabling a homogeneous dataset suitable for solar cycle studies and multi-viewpoint investigations.