Lekha Patel

Aerosol effects on clouds and radiation depend on the distribution of particle size, chemical composition, and mixing state, although this detail is difficult to retain at the spatial scales and computational costs of Earth system models (ESMs). This project addresses this challenge through coordinated advances in machine learning, performance-portable atmospheric chemistry, laboratory evaluation, and particle transport.

1. Atmospheric aerosols play a critical role in the Earth’s climate system, influencing the planet’s radiative balance and cloud properties, yet they remain one of the largest sources of uncertaint...

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high-performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. In a proof-of-concept integration with the particle-resolved model PartMC, TChem-atm reproduces the existing PartMC–CAMP implementation within solver tolerances and delivers substantial GPU speedups, especially for large particle populations. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. TChem-atm enables performance-portable execution across CPUs and GPUs, though optimal efficiency may require modest architecture-specific tuning (e.g., team and vector sizes), with up to a twofold improvement on the NVIDIA H100. It directly supports sectional and particle-resolved host models, while modal aerosol schemes require minor adaptation to provide particle-scale quantities such as representative diameters. By enabling chemically detailed, multiphase simulations with performance portability and host-model flexibility, TChem-atm facilitates the incorporation of advanced chemistry into atmospheric models.

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. We demonstrate TChem-atm's integration into the particle-resolved aerosol model PartMC and validate its accuracy against the existing PartMC–CAMP implementation, showing agreement within solver tolerances. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. By enabling chemically detailed, multiphase simulations with true performance portability and host-model flexibility, TChem-atm provides a new foundation for next-generation atmospheric models.

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. We demonstrate TChem-atm's integration into the particle-resolved aerosol model PartMC and validate its accuracy against the existing PartMC–CAMP implementation, showing agreement within solver tolerances. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. By enabling chemically detailed, multiphase simulations with true performance portability and host-model flexibility, TChem-atm provides a new foundation for next-generation atmospheric models.

The estimation of explosive yield from heterogeneous observational data presents fundamental challenges in inverse problems, particularly when combining traditional physical measurements with modern artificial intelligence-interpreted modalities. We present a novel Bayesian fractional posterior framework that fuses seismic waves, crater dimensions, synthetic aperture radar imagery, and vision-language model interpreted ground-level images to estimate the yield of the 2020 Beirut explosion. Unlike conventional approaches that may treat data sources equally, our method learns trust weights for each modality through a Dirichlet prior, automatically calibrating the relative information content of disparate observations. Applied to the Beirut explosion, the framework yields an estimate of 0.34--0.48 kt TNT equivalent, representing 12 to 17 percent detonation efficiency relative to the 2.75 kt theoretical maximum from the blast's stored ammonium nitrate. The fractional posterior approach demonstrates superior uncertainty quantification compared to single-modality estimates while providing robustness against systematic biases. This work establishes a principled framework for integrating qualitative assessments with quantitative physical measurements, with applications to explosion monitoring, disaster response, and forensic analysis.

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. We demonstrate TChem-atm's integration into the particle-resolved aerosol model PartMC and validate its accuracy against the existing PartMC–CAMP implementation, showing agreement within solver tolerances. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. By enabling chemically detailed, multiphase simulations with true performance portability and host-model flexibility, TChem-atm provides a new foundation for next-generation atmospheric models.

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. We demonstrate TChem-atm's integration into the particle-resolved aerosol model PartMC and validate its accuracy against the existing PartMC–CAMP implementation, showing agreement within solver tolerances. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. By enabling chemically detailed, multiphase simulations with true performance portability and host-model flexibility, TChem-atm provides a new foundation for next-generation atmospheric models.

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. We demonstrate TChem-atm's integration into the particle-resolved aerosol model PartMC and validate its accuracy against the existing PartMC–CAMP implementation, showing agreement within solver tolerances. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. By enabling chemically detailed, multiphase simulations with true performance portability and host-model flexibility, TChem-atm provides a new foundation for next-generation atmospheric models.

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. We demonstrate TChem-atm's integration into the particle-resolved aerosol model PartMC and validate its accuracy against the existing PartMC–CAMP implementation, showing agreement within solver tolerances. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. By enabling chemically detailed, multiphase simulations with true performance portability and host-model flexibility, TChem-atm provides a new foundation for next-generation atmospheric models.