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The uncertainties in two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations over Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of seasonal, monthly and daily variations, the diurnal cycle, and extreme precipitation over a 10-year period (2000-2010). Results indicate that the uncertainties in extreme precipitation is higher in GSMaP than in TRMM, possibly due to the issues such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and the swath time of satellite. Such discrepancies between satellite-borne and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results. Overall, both satellite products are unable to capture the observed extremes and provide a good agreement with observations only at coarse time scales. Also, the satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the Intertropical Convergence Zone (ITCZ) is located over Singapore. However, they do not reproduce the gauge-observed diurnal cycle in summer. The disagreement in summer could be attributed to the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. From the analyses of extreme precipitation indices, it is inferred that both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spells. This study on quantification of their uncertainty is useful in many aspects especially that these satellite products stand scrutiny over places where there are no good ground data to be compared against. This has serious implications on climate studies as in model evaluations and in particular, climate model simulated future projections, when information on precipitation extremes need to be reliable as they are highly crucial for adaptation and mitigation. [ABSTRACT FROM AUTHOR]

We present preliminary analyses of the historical (1986-2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models’ performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better. [ABSTRACT FROM AUTHOR]

The uncertainties of two high-resolution satellite precipitation products (TRMM 3B42 v7.0 and GSMaP v5.222) were investigated by comparing them against rain gauge observations in Singapore on sub-daily scales. The satellite-borne precipitation products are assessed in terms of diurnal cycle and extreme precipitation for 10 years from Dec. 2000 to Nov. 2010. The satellite products agree well on the late afternoon maximum and heavier rainfall of gauge-based data in winter season when the ITCZ is located in Singapore. However, they fail in estimating diurnal cycle in summer. The disagreement in summer can be accounted for by the dominant satellite overpass time (about 14:00 SGT) later than the diurnal peak time (about 09:00 SGT) of gauge precipitation. According to analysis of extreme precipitation indices, both satellite datasets tend to overestimate the light rain and frequency but underestimate high intensity precipitation and the length of dry spell over all stations. In particular, the uncertainty of extreme precipitation is higher in GSMaP than in TRMM, possibly due to the several effects such as satellite merging algorithm, the finer spatio-temporal scale of high intensity precipitation, and swath time of satellite. Such discrepancies between satellite-born and gauge-based precipitations at sub-daily scale can possibly lead to distorting analysis of precipitation characteristics and/or application model results, this research on quantification of their uncertainty is useful in many respects, especially that the satellite products can stand scrutiny overplaces/stations where there are no good data to be compared against. [ABSTRACT FROM AUTHOR]

Summary The Standardized Precipitation Index (SPI) has been computed based on the monthly precipitation for different observed and modelled datasets over the Central Highland, Vietnam during the period 1990–2005. Station data from a total of 13 stations were collected from the study region and used for benchmarking to compare gridded observation data and two regional climate models (RCMs). Various characteristics of drought across the study region were analyzed using spatial and temporal distributions, number of drought events, their frequency and their deficit. The RCMs were able to capture the SPI temporal distributions of station data fairly well. The analysis from RCMs showed close estimation for the number of drought events to station data and gridded observations. In terms of Drought Deficit and frequency, the RCMs matched the station data better than gridded observations. The drought trend was carried out using a Modified Mann–Kendall trend test which yielded no clear trends that suggested the need for longer records of data. The results also highlight uncertainties in gridded data and the need for robust station data quality and record lengths. The regional climate models proved to be appropriate tools in assessing drought over the study area as they can serve as good proxies over data sparse regions, especially in developing countries, for studying detailed climate features at sub regional and local scales. [ABSTRACT FROM AUTHOR]