Revisiting the Execution Model of Ensemble Uncertainty Analysis in the Browser

Ensemble prediction systems are central to modern numerical weather forecasting, providing distributions of plausible atmospheric outcomes rather than single deterministic trajectories. While these ensembles are essential for assessing uncertainty, interactive exploration of ensemble structure, extremes, and spatio-temporal variability remains challenging in practice. Existing workflows rely predominantly on server-centric pipelines—typically Python/Xarray/Dask stacks or VTK-based backends—where computation and rendering occur remotely and the browser functions primarily as a thin client. These architectures introduce latency, require substantial data staging, and often collapse ensembles into low-order summaries that obscure multimodality and extremes.

We present NextSembles, a browser-native system for interactive ensemble uncertainty analysis that relocates data access, statistical computation, and visualization entirely to the client. NextSembles compiles the NetCDF-C library to WebAssembly, enabling standards-compliant NetCDF ingestion directly in the browser. Ensemble variables are decoded into contiguous slabs within WebAssembly linear memory and exposed as typed array views. Statistical reductions—including mean, variance, standard deviation, and probability-of-exceedance—are computed using C/WASM kernels operating directly on this memory, avoiding server round-trips and intermediate data representations.

To maintain responsiveness on large ensemble fields, NextSembles employs a tile-based execution model that subdivides spatial slices into latency-bounded units of work. Tile updates are propagated incrementally to the renderer, enabling progressive visual feedback while preserving full-resolution views. Visualization is performed using VTK-WASM (with WebGPU when available and WebGL fallback), supporting interactive exploration of spatial slices alongside coordinated temporal, distributional, and member-comparison views. A multitrack uncertainty timeline facilitates rapid identification of forecast periods exhibiting elevated ensemble spread.

We evaluate NextSembles on COSMO-1e/2e ensemble datasets, measuring kernel-level performance, end-to-end interaction latency, and data staging costs. Results show that browser-resident C/WASM reducers sustain sub-200 ms interaction latency for common analysis tasks on commodity hardware, enabling responsive, distribution-aware ensemble exploration without reliance on HPC backends or Python services.

NextSembles demonstrates that revisiting the execution model of ensemble uncertainty analysis enables transparent, low-latency workflows directly in the browser, complementing existing server-centric approaches.