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

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

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.

An ANN application for water quality forecasting.

Rapid urban and coastal developments often witness deterioration of regional seawater quality. As part of the management process, it is important to assess the baseline characteristics of the marine environment so that sustainable development can be pursued. In this study, artificial neural networks (ANNs) were used to predict and forecast quantitative characteristics of water bodies. The true power and advantage of this method lie in its ability to (1) represent both linear and non-linear relationships and (2) learn these relationships directly from the data being modeled. The study focuses on Singapore coastal waters. The ANN model is built for quick assessment and forecasting of selected water quality variables at any location in the domain of interest. Respective variables measured at other locations serve as the input parameters. The variables of interest are salinity, temperature, dissolved oxygen, and chlorophyll-alpha. A time lag up to 2Delta(t) appeared to suffice to yield good simulation results. To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The results show the ANN's great potential to simulate water quality variables. Simulation accuracy, measured in the Nash-Sutcliffe coefficient of efficiency (R(2)), ranged from 0.8 to 0.9 for the training and overfitting test data. Thus, a trained ANN model may potentially provide simulated values for desired locations at which measured data are unavailable yet required for water quality models.