This paper develops a Business Resilience Index (BRI) that measures county-level resilience to natural disasters at a county-quarter frequency for the United States over 2014–2024. The index integrates high-frequency labor market outcomes from the Quarterly Census of Employment and Wages with flood insurance policy information from FEMA, disaster damages from the NOAA Storm Events Database, and social and health determinants from County Health Rankings. Starting from a broad candidate set, we apply an interpretable feature-screening pipeline to retain 79 variables and then use principal component analysis to extract four orthogonal structural dimensions of resilience: market scale, socioeconomic resilience, urban density risk, and industrial economy profile. We construct a domain-weighted strategic index and benchmark it against data-driven and equal-weight alternatives, showing that county rankings are highly stable across weighting schemes. To evaluate whether the BRI aligns with recovery behavior under acute shocks, we implement a matched difference-in-differences event study around two major flood episodes—Texas in 2015Q2 and North Carolina in 2018Q3. Conditional on exposure intensity and matched comparability, higher pre-event BRI counties exhibit earlier stabilization and a stronger post-event employment path relative to lower BRI counties, with differences in magnitude and timing across cases. Overall, the BRI provides an interpretable, high-frequency baseline for identifying capacity constraints that may slow recovery and for supporting preparedness targeting and post-disaster monitoring.

Ultra-short-term wind-power forecasting must handle both stable conditions and rapid ramp events, yet many architectures do not explicitly distinguish positive from negative ramp evidence. We evaluate whether that distinction improves multi-step predictions under a chronological wind-power forecasting protocol. RAA-Net combines a persistence-anchored baseline with a causal GRU and separate mappings for upward and downward ramps. A calibration-set blending rule substitutes persistence for selected low-volatility cases. We evaluated two turbines from the public SDWPF dataset at 40–160 min horizons, using chronological train, validation, calibration, and test partitions and three-seed neural-model comparisons against eight baselines. RAA-Net records the lowest reported RMSE at every horizon for both turbines. On the primary turbine, it also records the best reported MAE, $R^2$, nRMSE, and nMAE at each horizon. Component ablations quantify the effects of the directional module, anchor, and blending rule. These findings support direction-specific ramp representations for the evaluated turbines and protocol.