SEASONAL FORECAST OF AVERAGE MONTHLY TEMPERATURE FIELDS AT TERRITORIES OF WESTERN SIBERIA

The article is devoted to the problems of seasonal changes of temperature in the warm and cold periods of the year. To describe the development of the process over time, a spatial statistical model has been created that allows predicting physical fields over the entire specified information space. To predict seasonal changes in temperature, we used a two-dimensional model with an interval of the process history within a three-year period. The results of testing the spatial model for the territory of Western Siberia on large statistical material are compared with the forecast results obtained using the one-dimensional model. The obtained results are also compared with the forecast results using the climate model and the inertial model. The possibilities of modifying the model in order to increase its efficiency are discussed.

Spatial modeling for radon concentrations in subway stations in Seoul, Korea

This study examined environmental and geological determinants of radon concentration in subway stations by applying spatial statistical model to the integrated GIS database. The data were collected for 254 underground...

Environmental Covariate Representation of Seasonal U.S. Tornado Frequency

The significant tornado parameter is a widely used meteorological composite index that combines several variables known to favor tornadic supercell thunderstorms. This research examines the spatial relationship between U.S. tornado frequency and the significant tornado parameter (the predictor covariate) across four seasons in order to establish a spatial–statistical model that explains significant amounts of variance in tornado occurrence (the predictand). U.S. tornadoes are highly dependent on the significant tornado parameter in a climatological sense. The strength of this dependence is seasonal, with greatest dependence found during December–February and least dependence during June–August. Additionally, the strength of this dependence has not changed significantly through the 39-yr study period (1979–2017). Results herein represent an important step forward for the creation of a predictive spatial–statistical model to aid in tornado prediction at seasonal time scales.

The Relationship between Elevation Roughness and Tornado Activity: A Spatial Statistical Model Fit to Data from the Central Great Plains

The statistical relationship between elevation roughness and tornado activity is quantified using a spatial model that controls for the effect of population on the availability of reports. Across a large portion of the central Great Plains the model shows that areas with uniform elevation tend to have more tornadoes on average than areas with variable elevation. The effect amounts to a 2.3% [(1.6%, 3.0%) = 95% credible interval] increase in the rate of a tornado occurrence per meter of decrease in elevation roughness, defined as the highest minus the lowest elevation locally. The effect remains unchanged if the model is fit to the data starting with the year 1995. The effect strengthens for the set of intense tornadoes and is stronger using an alternative definition of roughness. The elevation-roughness effect appears to be strongest over Kansas, but it is statistically significant over a broad domain that extends from Texas to South Dakota. The research is important for developing a local climatological description of tornado occurrence rates across the tornado-prone region of the Great Plains.