Productive agricultural areas in Kebumen, Central Java, Indonesia are potentially vulnerable to the adverse impacts of climate change. We surveyed small-scale farmers to assess climate change-related perceptions and adaptations in the agriculture sector. The majority of the respondents agreed that there were changes in climatological variables affecting their farming activities, especially in terms of precipitation and temperature. However, the results also revealed that only 13% of respondents believed that human activities play a significant role. Three forms of adaptations have been developed by the farmers, namely: (1) agricultural diversification, (2) agricultural intensification, and (3) socioeconomic adaptation. Changing crops to more climate-tolerant varieties was one of the most common agricultural diversification practices (implemented by 88% of farmers). Most of the farmers also tried to maintain agricultural productivity by adjusting a local planting calendar (implemented by 94% of farmers). The use of machinery to intensify farming practice was an uncommon strategy (implemented by only 30% of farmers) because of expensive maintenance and small cultivation areas. The results suggested the importance of increasing farmers’ knowledge and technological know-how related to climate change and its implications, developing effective adaptation and mitigation efforts, and constructing climate-resilient infrastructure in the agricultural sector.

This study aims to characterize the background physical processes in the development of those heavy precipitation clouds that contribute to the Tropical Rainfall Measuring Mission (TRMM) active and passive sensor differences. The combined global observation data from TRMM, CloudSat, and European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) from 2006 to 2014 were utilized to address this issue. Heavy rainfall events were extracted from the top 10% of the rain events from the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rain-rate climatology. Composite analyses of CloudSat and ERA-Interim were conducted to identify the detailed cloud structures and the background environmental conditions. Over tropical land, TMI tends to preferentially detect deep isolated precipitation clouds for relatively drier and unstable environments, while PR identifies more organized systems. Over the tropical ocean, TMI identifies heavy rainfall events with notable convective organization and clear regional gradients between the western and eastern Pacific Ocean, while PR fails to capture the eastward shallowing of convective systems. The PR–TMI differences for the moist and stable environments are reversed over tropical land.

This research aims to compare precipitation data derived from satellite observation and ground measurements through a dense station network over Central Java, Indonesia. A precipitation estimate from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Version 7 are compared with precipitation data from interpolated rain gauge stations. Correlation analysis, mean bias error (MBE), and root mean square error (RMSE) were utilized in the analysis for each thee-monthly seasonal statistics. The result shows that the 3B42 products often estimate lower rainfall than observed from weather stations in the peak of the rainy season (DJF). Further, it is revealed that the 3B42 product are less robust in estimating rainfall at high elevation, especially when humid environment, which is typical during the rainy season peak, are involved.

Properties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and 25 mm h⁻¹), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals, which TMI relies on for estimating precipitation over land and coast.