Comparison of Weighted/Unweighted and Interpolated Grid Data at Regional and Global Scales

Abstract

Uniform grid data are widely used in climate science and related interdisciplinary fields. Such data usually describe the hydrometeorological states averaged over uniform latitude–longitude grids. While these data have larger grid areas in the tropics than other high-latitude regions, less attention has been paid to the areal weights of these grid data. Here, we revisited two methods available for processing these uniform grid data, including weighted sample statistics and grid interpolation. The former directly considers the grid area differences using geodetic weights; the latter converts the uniform grids to equal-area grids for conventional data analysis. When applied to global temperature and precipitation data, we found larger differences between weighted and unweighted samples and smaller differences between weighted and interpolated samples, highlighting the importance of areal weights in grid data analysis. Given the different results from various methods, we call for explicit clarification of the grid data processing methods to improve reproducibility in climate research.

Keywords

• gridded datasets
• weighted sample
• weighted histogram
• weighted sample distribution
• climate data