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Abstract Understanding the spatial structure of extreme floods is critical both for reliable design flood estimation and for coordinated development of regional response and flood mitigation strategies. Yet, analysis of rare, high‐magnitude floods is challenged by the limited sample size. This study investigates the spatial covariability of extreme floods across the coterminous United States (CONUS) for large return periods (2–100 years) by proposing three distinct co‐dependency measures: (a) annual co‐occurrence probability (), (b) 7‐day co‐occurrence probability (), and (c) Measures of Co‐occurrence within a 500 km radius. The proposed measures are developed to associate flood spatial dependence with the underlying physical drivers and are evaluated against null distributions of spatially independent floods that preserve their seasonality. Results show that floods co‐occur far more often than expected under independence, with stronger dependence for larger return periods (e.g., for 100‐year floods ≈19%, vs. 1% under independence). The analysis indicates that snowmelt‐driven basins exhibit high dependence for smaller floods (2–25 years), but rainfall‐driven regions (particularly coasts) dominate for extreme events (50–100 years). MOC hotspots confirm that summer tropical storms (East Coast) and winter atmospheric rivers (West Coast) are the primary drivers of widespread extremes. Given the proposed co‐dependency measures' effectiveness flood processes at various spatial and temporal scales, we suggest they could be leveraged for regionally tailored, season‐specific flood mitigation and emergency‐response strategies. 

Abstract Geostationary weather satellites collect high‐resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half‐integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates us to statistically model wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. We conduct extensive simulation studies to determine the situations where our method performs well. The proposed method is applied to the GOES‐15 brightness temperature data over Colorado and reduces prediction error of brightness temperature compared to the DMW Algorithm. 

Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework’s backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain. Statistical and computational properties of our distributed approach are investigated theoretically and in simulations. The proposed approach is used to extract new insights into autism spectrum disorder from the autism brain imaging data exchange.