Kulyakorn Khuanmar

Determination of the dates of the southwest monsoon in northeastern Thailand from the data on precipitable water vapor obtained by GPS

This paper outlines a new method for determining the onset and withdrawal of the southwest monsoon using Precipitable Water Vapor (PWV) estimated from GPS data: GPS-PWV. In this study, PWV is computed from GPS data collected from four ground-based GPS receivers in northeastern Thailand. Initially, we study the temporal and geographical distributions of the GPS-PWV data. Their statistical characteristics show that GPS-PWV can be used as a tool for determining the onset and withdrawal of the southwest monsoon in northeastern Thailand. The GPS-PWV data were processed using a decision tree analysis to obtain a new set of criteria for determining the onset and withdrawal of the monsoon. The new criterion specifies the onset as a period when the daily averaged GPS-PWV exceeds 52.1 mm for eight consecutive days. The withdrawal is a period when the daily averaged GPS-PWV is 52.1 mm or less for six consecutive days. The dates of onset and withdrawal of monsoons calculated from this method was compared with those obtained with other methods. The difference equaled 1–2 day except years 2010 and 2012 when the errors were more than two days.

The development of Atmospheric Crop Moisture Index for irrigated agriculture

This paper develops the Atmospheric Crop Moisture Index (ACMI), an indicator of atmospheric drought. Levels in the index represent the possibility of rainfall or the lack of moisture needed at various stages of crop growth. The lack of moisture is determined with the ratio between water supply and demand, where the supply indicator is Precipitable Water Vapor (PWV). The ACMI was calculated from data collected between February 2010 and September 2014. The example of calculation of 10-day values of ACMI10 for rice is provided. The comparison of ACMI and other indices shows poor correlation with the SPI, the SPEI, and the scPDSI; however, it displays high correlation with precipitation, the PE, and the MAI. The ACMI is a parameter affected by surface temperature, relative humidity, wind speed, atmospheric pressure, and solar irradiation; all these parameters are included in the study of drought. The ACMI is an effective tool for agricultural water management.

Estimation of Missing GPS Precipitable Water Vapor Data by Zenith Wet Delay and Meteorological Data

This article presents a statistical correlation between GPS precipitable water vapor and meteorological data, i.e., surface temperature, air pressure, relative humidity, dew point temperature, and water vapor pressure by using linear regression. The data, recorded over a 4-year period, was used as an estimation of missing GPS precipitable water vapor data from discontinuous recordings. A multiple linear regression equation showed a correlation among zenith wet delay (ZWD), water vapor pressure (e) and surface temperature (T) was ZWD(e,T) = 17.4952e-0.8281T-93.164, with a coefficient of determination (R2) of 0.725, a mean absolute error of 8.71 mm, a root mean square error of 10.39 mm, and a mean absolute percentage error of 18.63%. The equation obtained can be used to estimate GPS precipitable water vapor data which is missing from recordings due to accident or technological error.