To cite this abstract / Pentru a cita rezumatul:
Cheval S, Petrisor AI (2003), Predicting Average Monthly Temperatures and Precipitations Using the Geostatistical Analyst. Abstract, 33rd Annual Meeting of the South Carolina Chapter of the American Statistical Association, Russell House, Columbia, SC, USA, April 18, 2003

Predicting Average Monthly Temperatures and Precipitations Using the Geostatistical Analyst

Sorin Cheval, PhD Candidate, Institute of Geography, Romanian Academy, Bucharest
Alexandru Petrisor, PhD Candidate, Department of Environmental Health Sciences, Norman J. Arnold School of Public Health, USC

Climatology strives to explain temporal and spatial distributions, and the dynamics of data that describe the state of the atmosphere. Identifying and assessing the complex relationships between as many as possible variables fosters a better understanding of the processes driving climatic patterns. The temporal dimension of phenomena is one of the most important factors that bias the scientific approach, therefore the integration, analysis, and visualization of spatial data that has a significant temporal component has become a priority in environmental sciences. Geographical Information Systems (GIS) can represent, correlate and assess the relationships between large amounts of data, referenced to a known spatial and temporal framework. Hence, besides being a powerful cartographic tool, a GIS can store, retrieve and combine data to create new representation of geographic space, provides tools for spatial analysis and performs.

Monthly precipitations and temperature data were collected in Romania at five stations during 1961-2000. Data were grouped in arrays having the year on the vertical axis and the month on the horizontal one. Several interpolation methods have been used: spline fitting, ordinary kriging, inverse distance weighting, and filtering. Out of all interpolation methods, given its mathematical foundation, kriging yielded the most significant and sound results. Our study indicated that temperatures follow an easily predictable pattern, with slight variations corresponding to lengths of the cold or the warm season. Precipitation clusters appeared more relevant. In our setting, they pointed out dry and wet spells, suggesting the occurrence of hazard events such as floods or droughts. This was particularly obvious in naturally dry/wet regions. Our method emphasized the temporal variability of the phenomena. Thus, climate changes, one of the most debated issues, might be identified and qualitatively predicted.