Particle-resolved direct numerical simulations (PR-DNS) are crucial for unraveling the intricate interplay of aerosol-cloud-turbulence processes. However, such models are challenged by the huge computational cost due to the extremely high resolution. Our prior work showcased that leveraging machine learning emulators could slash computational expenses by two orders of magnitude while maintaining remarkable precision for dynamic fields, and exhibited generalizability across diverse initial conditions and at super-resolution scales without retraining the emulators. Building upon this foundation, this work extends the emulator’s application to thermodynamic in the two spatial dimensions and droplet fields in the three spatial dimensions. Furthermore, to enhance the robust generalizability of the emulator for different initial values and super resolution, we introduce a novel multi-initial learning approach for the neural operator method. For the droplet fields, we introduce a novel loss function tailored to assess distribution differences using the Mallows distance, focusing particularly on droplet size distributions. Our findings indicate that the machine learning emulators hold promising potential to effectively mimic numerical PR-DNS simulations, thereby significantly advancing our understanding of the complex interactions within aerosol-cloud-turbulence processes.

Particle-resolved direct numerical simulations (PR-DNS) play an increasing role in investigating aerosol-cloud-turbulence interactions at the most fundamental level of processes. However, the high computational cost associated with high resolution simulations poses considerable challenges for large domain or long duration simulation using PR-DNS. To address these issues, here we present a digital twin model of the complex physics-based PR-DNS developed by use of the data-driven Fourier Neural Operator (FNO) method. The results demonstrate high accuracy at various resolutions and the digital twin model is two orders of magnitude cheaper in terms of computational demand compared to the physics-based PR-DNS model. Furthermore, the FNO digital-twin model exhibits strong generalization capabilities for different initial conditions and ultra-high-resolution without the need to retrain models. These findings highlight the potential of the FNO method as a promising tool to simulate complex fluid dynamics problems with high accuracy, computational efficiency, and generalization capabilities, enhancing our understanding of the aerosol-cloud-precipitation system.

A document by Sisi Chen. Click on the document to view its contents.

A document by Robert L. McGraw. Click on the document to view its contents.

Measurements of in-cloud vertical air motion are key to quantitatively describe cloud dynamics and their role in cloud microphysics. Here, a retrieval technique for estimating the in-cloud vertical air motion using the upward edge of the radar Doppler spectrum is presented. An additional broadening correction factor that depends on the signal-to-noise ratio (SNR) is introduced. A variety of independent measurements are used to assess the performance of the new retrieval. The vertical air motion is unbiased with an uncertainty of 0.2 ms-1for SNR less than 30. The properties of in-cloud vertical air motion are investigated from one-year of ground-based observations of warm marine boundary layer clouds. Clouds with higher LWP are characterized by stronger vertical air motions compared to those having lower LWP values.