Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contributions bring to the network remains an open problem. Existing approaches address data quality but not data valuation; in operational meteorology, adjoint-based methods derive value from the forecast model itself but require full data assimilation infrastructure. We propose to utilise differentiable AI weather models to fill this gap and characterise gradient-based attribution on gridded GFS analysis inputs as a candidate value signal, evaluating fidelity, calibration, cost, and gaming vulnerability across more than 400 configurations. Attribution captures near-optimal sensor placement utility with monotonically faithful payments, but can be inflated by adversarial inputs, with detection requiring external baseline data. These findings establish gradient attribution as a computationally validated signal for model-informed reward allocation in participatory weather sensing.

翻译：大规模物联网气象感知网络需要激励机制来维持参与度，但如何量化个体数据贡献对网络的价值仍是一个未解难题。现有方法关注数据质量而非数据估值；在业务气象学中，伴随方法可从预报模型本身推导价值，但需要完整的数据同化基础设施。我们提出利用可微分AI气象模型填补这一空白，将基于梯度的网格化GFS分析输入归因表征为候选价值信号，在超过400种配置中评估其保真度、校准性、成本及博弈脆弱性。归因能以单调保真的支付方式捕获近最优传感器布设效用，但可能被对抗性输入放大，且检测需依赖外部基线数据。这些结果确立了梯度归因作为参与式气象感知中模型导向奖赏分配的一种计算验证信号。