Tree-ring data and national forest inventories are complementary data streams that can help resolve uncertainty about the future behavior of forests, including their response to climate change. Forest inventory data are spatially comprehensive and representative, but censuses in the western United States occur on a 10-year cycle; in contrast, tree-ring data have annual resolution. We present an analysis assimilating these two sources of data collected in interior western Forest Inventory and Analysis (FIA) plots. We use a hierarchical Bayesian hidden process model to draw upon both radial increment (from tree-rings) and diameter data (from inventories) to quantify the influences of tree size, climate, stand density index, and site index on individual tree growth. Our analysis focuses on Pinus ponderosa in the state of Arizona, using a set of ~700 trees with both tree-ring and diameter data, along with another ~10,000 trees with repeat diameter data. Of particular interest are (1) the effects of climate, which can be used to forecast future tree growth as a function of future expected climate (i.e., CMIP5 projections), and (2) interactions between stand density and climate and between tree size and other factors, because stand density and tree size are characteristics that managers can influence (in contrast to climate). Current challenges with this project are 1) poor MCMC behavior for a model using maximum temperatures as a predictor of tree growth and 2) the computational burden associated with a latent process model of the growth of thousands of trees over the course of four decades.