Statistical Analyses on the Seasonal Rainfall Trend and Annual Rainfall Variability in Bhutan

10.2151/sola.2021-035Scientific Online Letters on the Atmosphere17202-20

Investigating drought over the Central Highland, Vietnam, using regional climate models

10.1016/j.jhydrol.2014.11.006Journal of Hydrology526265-27

Evaluation of High-resolution Satellite Rainfall Data over Singapore

AbstractThe 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

Investigating the relationship between Aerosol Optical Depth and Precipitation over Southeast Asia with Relative Humidity as an influencing factor

Abstract Atmospheric aerosols influence precipitation by changing the earth’s energy budget and cloud properties. A number of studies have reported correlations between aerosol properties and precipitation data. Despite previous research, it is still hard to quantify the overall effects that aerosols have on precipitation as multiple influencing factors such as relative humidity (RH) can distort the observed relationship between aerosols and precipitation. Thus, in this study, both satellite-retrieved and reanalysis data were used to investigate the relationship between aerosols and precipitation in the Southeast Asia region from 2001 to 2015, with RH considered as a possible influencing factor. Different analyses in the study indicate that a positive correlation was present between Aerosol Optical Depth (AOD) and precipitation over northern Southeast Asia region during the autumn and the winter seasons, while a negative correlation was identified over the Maritime Continent during the autumn season. Subsequently, a partial correlation analysis revealed that while RH influences the long-term negative correlations between AOD and precipitation, it did not significantly affect the positive correlations seen in the winter season. The result of this study provides additional evidence with respect to the critical role of RH as an influencing factor in AOD-precipitation relationship over Southeast Asia

Application of Multi-Channel Convolutional Neural Network to Improve DEM Data in Urban Cities

A digital elevation model (DEM) represents the topographic surface of the Earth and is an indispensable source of data in many applications, such as flood modeling, infrastructure design and land management. DEM data at high spatial resolution and high accuracy of elevation data are not only costly and time-consuming to acquire but also often confidential. In this paper, we explore a cost-effective approach to derive good quality DEM data by applying a multi-channel convolutional neural network (CNN) to enhance free resources of available DEM data. Shuttle Radar Topography Mission (SRTM) data, multi-spectral imaging Sentinel-2, as well as Google satellite imagery were used as inputs to the CNN model. The CNN model was first trained using high-quality reference DEM data in a dense urban city—Nice, France—then validated on another site in Nice and finally tested in the Orchard Road area (Singapore), which is also an equally dense urban area in Singapore. The CNN model not only shows an impressive reduction in the root mean square error (RMSE) of 50% at validation site in Nice and 30% at the test site in Singapore, but also results in much clearer profiles of the land surface than input SRTM data. A comparison between CNN performance and that of an earlier conducted study using artificial neural networks (ANN) was conducted as well. The comparison within this limited study shows that CNN yields a more accurate DEM

Distributional Impacts of Climate Change and Food Security in Southeast Asia

Climate Change Floods Droughts Risk Management Food Security Polic

Distributional Impacts of Climate Change and Food Security in Southeast Asia

Climate Change Floods Droughts Risk Management Food Security Polic

Large-scale reforestation can increase water yield and reduce drought risk for water-insecure regions in the Asia-Pacific

10.1111/gcb.16404Global Change Biology28216385-640