The forecasting skill of numerical weather prediction (NWP) models critically depends on the accurate initial conditions, also known as analysis, provided by data assimilation (DA). Traditional DA methods often face a trade-off between computational cost and accuracy due to complex linear algebra computations and the high dimensionality of the model, especially in nonlinear systems. Moreover, processing massive data in real-time requires substantial computational resources. To address this, we introduce an artificial intelligence-based data assimilation framework (ADAF) to generate high-quality kilometer-scale analysis. This study is the pioneering work using real-world observations from varied locations and multiple sources to verify the AI method's efficacy in DA, including sparse surface weather observations and satellite imagery. We implemented ADAF for four near-surface variables in the Contiguous United States (CONUS). The results indicate that ADAF surpasses the High Resolution Rapid Refresh Data Assimilation System (HRRRDAS) in accuracy by 16% to 33% for near-surface atmospheric conditions, aligning more closely with actual observations, and can effectively reconstruct extreme events, such as tropical cyclone wind fields. Sensitivity experiments reveal that ADAF can generate high-quality analysis even with low-accuracy backgrounds and extremely sparse surface observations. ADAF can assimilate massive observations within a three-hour window at low computational cost, taking about two seconds on an AMD MI200 graphics processing unit (GPU). ADAF has been shown to be efficient and effective in real-world DA, underscoring its potential role in operational weather forecasting.

Nitrogen dioxide (NO2) is an important air pollutant formed during the combustion of fossil fuels. The reduced human activities in the wake of the COVID-19 pandemic led to sharp reductions in surface NO2 around the globe. Near-real-time NASA computer simulations, combined with surface observations, can be used to track the decline and subsequent recovery of NO2 related to COVID-19 stay-at-home orders around the globe. Since NO2 concentrations are a good proxy for industrial, transportation, and domestic activities, the changes in NO2 derived from NASA models during mandatory quarantine correlate well with officially reported changes in economic output. Observations from 4,778 air quality monitoring sites in 46 countries are used to monitor the change in NO2 as a result of COVID-19 containment measures.