Station2Radar: query conditioned gaussian splatting for precipitation field

TL;DR

Query-Conditioned Gaussian Splatting (QCGS), the first framework to fuse automatic weather station (AWS) observations with satellite imagery for generating precipitation fields, enables efficient, resolution-flexible precipitation field generation in real time.

Abstract

Precipitation forecasting relies on heterogeneous data. Weather radar is accurate, but coverage is geographically limited and costly to maintain. Weather stations provide accurate but sparse point measurements, while satellites offer dense, high-resolution coverage without direct rainfall retrieval. To overcome these limitations, we propose Query-Conditioned Gaussian Splatting (QCGS), the first framework to fuse automatic weather station (AWS) observations with satellite imagery for generating precipitation fields. Unlike conventional 2D Gaussian splatting, which renders the entire image plane, QCGS selectively renders only queried precipitation regions, avoiding unnecessary computation in non-precipitating areas while preserving sharp precipitation structures. The framework combines a radar point proposal network that identifies rainfall-support locations with an implicit neural representation (INR) network that predicts Gaussian parameters for each point. QCGS enables efficient, resolution-flexible precipitation field generation in real time. Through extensive evaluation with benchmark precipitation products, QCGS demonstrates over 50\% improvement in RMSE compared to conventional gridded precipitation products, and consistently maintains high performance across multiple spatiotemporal scales.

Figures (13)

• Figure 1: Construction of precipitation fields from (a) sparse AWS observations. (b) Empirical kernel-based interpolation oversmooths and blurs rainfall boundaries. (c) QCGS leverages satellite context and gauge anchors to selectively place Gaussians, producing resolution-flexible and structurally consistent fields. (d) Ground-truth radar at 2 km resolution for reference.
• Figure 2: Overview of the proposed QCGS pipeline. AWS observations and satellite BT imagery are fused to produce a coarse surrogate field and candidate rainfall-support points. A rainfall-aware sampling strategy and an INR-based Gaussian estimator then predict splatting parameters, yielding resolution-flexible precipitation fields through selective Gaussian rendering.
• Figure 3: Visualization of different point sampling strategies for precipitation fields. (a) Ground truth radar field, (b) uniform sampling, (c) edge-based sampling, (d) heavy-rain sampling, and (e) our mixed strategy. Uniform sampling provides overall coverage but lacks details in heavy rainfall regions. Edge-based sampling emphasizes boundaries but overlooks fine-scale structure. Heavy-rain sampling concentrates points on intense precipitation, leaving light-rain areas underrepresented. In contrast, our mixed strategy balances intensity-aware sampling with spatial coverage across the field.
• Figure 4: Panels (a)–(e) show the comparison of daily accumulated rainfall (mm, day) between radar and four rainfall products: IMERG, GSMaP, MSWEP, and QCGS. The “Total diff.” values (mm, day) denote the spatially integrated difference from radar for each product. Panel (f) presents the PSD across spatial scales (since $1^\circ \approx 111$ km, a wavenumber of 1(1/degree) corresponds approximately to a spatial scale of $\sim$110 km.)
• Figure 5: Examples of consecutive one-hour QCGS-generated frames. The last three frames exhibit rainfall patterns that were absent in the first three, indicating temporal inconsistencies. Such frame-to-frame mismatch can hinder the performance of video prediction models that rely on coherent temporal dynamics.