Geo-spatial grid-based transformations of precipitation estimates using spatial interpolation methods

Geo-spatial interpolation methods are often necessary in instances where the precipitation estimates available from multisensor source data on a specific spatial grid need to be transformed to another grid with a different spatial grid or orientation. The study involves development and evaluation of spatial interpolation or weighting methods for transforming hourly multisensor precipitation estimates (MPE) available in the form of 4×4 km2 HRAP (hydrologic rainfall analysis project) grid to a Cartesian 2×2 km2 radar (NEXt generation RADar:NEXRAD) grid. Six spatial interpolation weighting methods are developed and evaluated to assess their suitability for transformation of precipitation estimates in space and time. The methods use distances and areal extents of intersection segments of the grids as weights in the interpolation schemes. These methods were applied to transform precipitation estimates from HRAP to NEXRAD grids in the South Florida Water Management District (SFWMD) region in South Florida, United States. A total of 192 rain gauges are used as ground truth to assess the quality of precipitation estimates obtained from these interpolation methods. The rain gauge data in the SFWMD region were also used for radar data bias correction procedures. To help in the assessment, several error measures are calculated and appropriate weighting functions are developed to select the most accurate method for the transformation. Three local interpolation methods out of six methods were found to be competitive and inverse distance based on four nearest neighbors (grids) was found to be the best for the transformation of data.

► Models for geo-spatial grid-based transformations of precipitation data.
► Deterministic local interpolation methods use multi-sensor precipitation data.
► Methods deal with large spatial size and number of grids, extent and orientation.
► Assessment of the methods using several performance measures.