| 04 Feb 2026
AgPaDS v1.0: A GPU-accelerated interactive Lagrangian atmospheric transport model with 3-D in situ visualization for simulating windborne dispersal of crop pathogens
Abstract. Lagrangian models are widely adopted to study atmospheric transport processes, with applications in various domains, including the investigation of windborne crop diseases and epidemic risks in agriculture. Widely used Atmospheric Transport Modelling frameworks (ATMs) do not exploit the potential for performance gains and advanced computer graphics that GPUs provide, and they impose limitations regarding customization for domain-specific applications in crop epidemiology and agrometeorology. Here we introduce AgPaDS, the Agricultural Pest and Disease Simulator, a GPU-accelerated stochastic Lagrangian model with an option for advanced interactive 3-D in-situ visualization of global-scale atmospheric transport simulations. The tool was developed with two main objectives: (i) accelerate compute times by means of an efficient GPU implementation that enables exploratory visual analyses by means of interactive simulation setup in a graphical user interface with embedded 3-D in situ visualization; (ii) build a new atmospheric transport model dedicated to applications in crop epidemiology with model components not available in widely used ATMs and with flexibility for future domain-specific customizations. It is based on an optimized massively parallelized CUDA C ++ and OpenGL implementation. We report on model formulation, technical implementation and testing, including a systematic comparison with HYSPLIT, one of the most widely used ATMs, and a case-evaluation with in-situ visualization of complex 3-D dynamics of simulated crop pathogen transport during the hurricane that has likely transmitted soybean rust into the USA in 2004. A set of supplementary videos illustrates interactive and in situ 3-D visualization methods. We show that AgPaDS maintains good agreement with HYSPLIT whilst providing substantial speedups for simulations with very large particle numbers (up to three orders of magnitude). The model can simulate the release of millions of Lagrangian particles from heterogeneous crop landscapes on global scales with live 3-D visualization of simulated windborne dispersal of crop pathogens. Examples of future use-cases include (i) exploratory 3-D visual analyses of atmospheric transport simulations, including interactions between meteorological and biological processes; (ii) assessment of global airborne connectivity of agricultural landscapes; (iii) efficient representation of wind dispersal in crop disease forecasting systems.
General comments
The authors present a new interesting tool, AgPaDS, developed to rapidly simulate Lagrangian transport in the atmosphere, coupled with advanced visualization and interactive simulation configurations, for agricultural applications. They compare its performance with HYSPLIT, one of the mostly used atmospheric transport models, demonstrating a significant increase in computational efficiency across different tests, thanks to the use of GPU.
This tool goes into the direction of solving persistent challenges faced by agricultural researchers, such as (a) the simulation time (which is dealt by directly in the model) and (b) the coupling of windborne pathogen transport with plant epidemiological dynamic models (which the authors propose as a possible advancement for this tool).
Specific comments
Undoubtedly, the improvement in computation time compared to the benchmark is outstanding, and the visualization tools are also remarkable. The article’s focus is more on the algorithmic aspects than on the Lagrangian implementations, for which the authors drew inspiration from NAME. I recognize that this is a very technical paper, and as someone not deeply familiar with Lagrangian modeling at this level of implementation, I admit it is not always easy to follow. For instance, (i) I would have preferred the model description (section 3) to appear first in the Materials and Methods; (ii) acronyms (GPU, ECMWF, CUDA) are used without explanation. On the other hand, despite the claims of a better compatibility of this tool with crop or epidemiological models, the coupling does not seem straightforward.
I commend the authors for their accurate model evaluation setups, both in terms of experimental design and in terms of indicators used to measure the comparisons. However, I believe that a couple of points require revision or at least further discussion. In the second experiment (Table 3), the authors state that differences between HYSPLIT simulations and AgPaDS are less than one order of magnitude, but the data actually show a one-order-of-magnitude difference; this is not inadequate per se, but needs to be better contextualized. Moreover, in the third set of experiments (~Line 735), when comparing the simulation of atmospheric transport of Phakopsora spores by hurricane Ivan if it would be feasible to compare the simulated and observed deposition/presence of the soybean rust infections in USA (and not on the severity), instead of leaving it qualitatively.
My last question is, given the strong inspiration drawn from NAME, is there a specific reason why the authors chose to benchmark against HYSPLIT rather than on NAME?
Minor corrections