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Remotely sensed data products are now routinely used to study various aspects of the Earth's atmosphere. These remote sensing datasets are typically very high dimensional, have near global coverage and exhibit nonstationary spatial correlation structures. Proper statistical analysis of these datasets should be sufficiently flexible to account for all these aspects. To this end, we develop a kernel convolution construction of spatial processes on a sphere. As is the case with kernel convolution constructions on the plane, we establish a link between stationary kernels and a stationary covariance function on the sphere via the spherical harmonic decomposition of the kernel. We also introduce the Kent distribution as an appropriate kernel with interpretable parameters to be used in the kernel convolution construction. We demonstrate the discrete kernel convolution model using a dataset of remotely sensed CO 2 concentrations over the globe.

in order to provide contemporaneous measurements of soil organic carbon (SOC) across the US. Despite the broad extent of the RaCA data collection effort, direct observations of SOC are not available at the high spatial resolution needed for studying carbon storage in soil and its implications for important problems in climate science and agriculture. As a result, there is a need for predicting SOC at spatial locations not included as part of the RaCA project. In this paper, we compare spatial prediction of SOC using a subset of the RaCA data for a variety of statistical methods. We investigate the performance of methods with off-the-shelf software available (both stationary and nonstationary) as well as a novel nonstationary approach based on partitioning relevant spatially-varying covariate processes. Our new method addresses open questions regarding (1) how to partition the spatial domain for segmentation-based nonstationary methods, (2) incorporating partially observed covariates into a spatial model, and (3) accounting for uncertainty in the partitioning. In applying the various statistical methods we find that there are minimal differences in out-of-sample criteria for this particular data set, however, there are major differences in maps of uncertainty in SOC predictions. We argue that the spatially-varying measures of prediction uncertainty produced by our new approach are valuable to decision makers, as they can be used to better benchmark mechanistic models, identify target areas for soil restoration projects, and inform carbon sequestration projects.

Infant bronchiolitis is primarily due to infection by respiratory syncytial virus (RSV), which is highly seasonal. The goal of the study is to understand how circulation of RSV is impacted by fluctuations in temperature and humidity in order to inform prevention efforts. Using data from the Military Health System (MHS) Data Repository (MDR), we calculated rates of infant bronchiolitis for the contiguous US from July 2004 to June 2013. Monthly temperature and relative humidity were extracted from the National Climate Data Center. Using a spatiotemporal generalized linear model for binomial data, we estimated bronchiolitis rates and the effects of temperature and relative humidity while allowing them to vary over location and time. Our results indicate a seasonal pattern that begins in the Southeast during November or December, then spreading in a Northwest direction. The relationships of temperature and humidity were spatially heterogeneous, and we find that climate can partially account for early onset or longer epidemic duration. Small changes in climate may be associated with larger fluctuations in epidemic duration.