The performance of Regional Climate Model 4 (RegCM4) in simulating rainfall is evaluated through a comparison of several observations using Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) and Climate Research Unit (CRU) in 19-year simulation period of 1982-2000 in Kalimantan. The analysis includes the perormance of RegCM4 in capturing the rainfall type, the rainfall characteristics when ENSO, the error and the diversity of data, and dominant cycles in Kalimantan. The analysis is continued with a calculation of Maximum Dry Spell Length (MDSL) and Probability Distribution Function (PDF). We found that RegCM4 produces overestimated value, but it is capable in capturing rainfall type, rainfall characteristics during ENSO, and dominant cycles in Kalimantan.
Abstract. Spatially, climate condition is vary within a region and considered as essential information for planning activities such as agro-climate zonation. An approach to understand the spatial climate variability is the utilization of climate regionalization that is applied to rainfall data to distinguish differences in the pattern and magnitude (characteristics) of spatial rainfall variability over a region. Unfortunately, the application of climate regionalization poses a challenging issue in Indonesia, considering the availability of climate data. Recent advances in satellite and reanalysis data measuring climate variability over a large area provided an opportunity for the application of climate regionalization in the country. Using the West Java, one of main crop production regions in Indonesia, climate regionalization techniques were applied to map spatial variability of climate types based on rainfall data recorded by climate stations (point based analysis) and estimated by modeled/reanalysis data and satellite observations (gridded data). The regionalization derived from gridded rainfall data have reasonably better in capturing the zonal pattern of differences in climate types within the study region than the regionalization applied to insufficient numbers of site-based rainfall observation. This indicates that the gridded data offers an alternative for climate regionalization, when site-based observations are unavailable or limited.
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