Wayan Suparta

Using a global positioning system to estimate precipitable water vapor in Antarctica

Abstract The Global Positioning System (GPS) technology has become an essential tool in retrieving atmospheric precipitable water vapor (PWV) around the globe. This paper addressed the determination of GPS PWV in Antarctica and its response to atmospheric events. The GPS PWV result is then compared to the PWV determined from radiosonde (RS) and NCEP/NCAR Reanalysis. Time series comparisons of PWV between GPS and RS show strong linear correlation (R 2=0.70, p<1%) with their different mean values bounded at ±3 mm and with an RMS of 1.41 mm. Moreover, the total annual GPS PWV content at Scott Base, Casey, and Syowa stations averaged over the period from 2006 to 2008 has been quantified below 30 mm during summer polar days (wet summer) and of about 15 mm in winter polar night (dry winter), and their variability has shown a seasonal cycle dependence. One of the most remarkable features of the PWV variability from these regions carried by transient weather systems was that they closely followed the temperature patterns and revealed U-distribution. The temperature and PWV patterns characterize a coreless winter phenomenon.

Modeling of zenith path delay over Antarctica using an adaptive neuro fuzzy inference system technique

ZPD value has been estimated using ANFIS with three inputs of meteorological data.ZPD ANFIS was validated with GPS ZPD from CDDIS NASA, which found agreed very well.ZPD can be determined without GPS data and has beneficial for meteorologist. Accessibility and accurate estimation of the tropospheric delay plays a crucial role in meteorological studies and weather forecasts as well as improving positioning accuracy. We propose to employ an adaptive neuro fuzzy inference system (ANFIS) to build estimation and prediction models for zenith path delay (ZPD). Five selected stations over Antarctica were used to examine the applicability of ANFIS. GPS ZPD data of 2010 with five-minute resolution was used as the target output. A fuzzy clustering algorithm is adopted to enhance the performance of the models, which is able to minimize the number of membership functions and rules for better efficiency in the models. To investigate the accuracy of models developed, a combination of the surface pressure (P), temperature (T) and relative humidity (H) is performed to obtain the best estimation of ZPD. The results demonstrated that ANFIS models with three inputs network (P, T and H) agreed very well with ZPD obtained from GPS than separated input only coming from P or T, or P and T, or P and H. Finally, the input network (P, T and H) is selected in developing the ZPD predictive models. The prediction resulted from one-step to eight-step ahead development, demonstrated that the high-resolution of data used in training process will increase the accuracy of the predictive model.

GPS water vapor monitoring and TroWav updated for ENSO studies

The GPS receiver and the meteorology sensors have been installed at Universiti Malaysia Sabah (UMS), Kota Kinabalu (UMSK: 6.03°N, 116.12°E and 63.49 m) for study a climate condition that has associated with El Niño Southern Oscillation (ENSO). The atmospheric precipitable water vapor (PWV) derived from GPS technique was used as an indicator of ENSO detection. In the processing, the Niell Mapping Function (NMF) used to map the atmospheric delay from zenith to the line-of-site was updated using the Vienna Mapping Function (VMF). With GPS antenna co-located with meteorological sensors, the comparison of GPS PWV with time-dependent surface pressure and sea surface temperature (SST) during the first ten weeks observation found that surface pressure is opposed to the PWV changes.

Estimation of precipitable water vapor using an adaptive neuro-fuzzy inference system technique

Water vapor has an important role in the global climate change development. Because it is essential to human life, many researchers proposed the estimation of atmospheric water vapor values such as for meteorological applications. Lacking of water vapor data in a certain area will a problem in the prediction of current climate change. Here, we reported a novel precipitable water vapor (PWV) estimation using an adaptive neuro-fuzzy inference system (ANFIS) model that has powerful accuracy and higher level. Observation of the surface temperature, barometric pressure and relative humidity from 4 to 10 April 2011 has been used as training and the PWV derived from GPS as a testing of these models. The results showed that the model has demonstrated its ability to learn well in events that are trained to recognize. It has been found a good skill in estimating the PWV value, where strongest correlation was observed for UMSK station (r = 0.95) and the modest correlation was for NTUS station (r = 0.73). In general, the resulting error is very small (less than 5%). Thus, this model approach can be proposed as an alternative method in estimating the value of PWV for the location where the GPS data is inaccessible.

Monitoring the variability of precipitable water vapor over the Klang Valley, Malaysia during flash flood

Klang Valley is a focal area of Malaysian economic and business activities where the local weather condition is very important to maintain its reputation. Heavy rainfalls for more than an hour were reported up to 40 mm in September 2013 and 35 mm in October 2013. Both events are monitored as the first and second cases of flash flood, respectively. Based on these cases, we investigate the water vapor, rainfall, surface meteorological data (surface pressure, relative humidity, and temperature) and river water level. The precipitable water vapor (PWV) derived from Global Positioning System (GPS) is used to indicate the impact of flash flood on the rainfall. We found that PWV was dropped 4 mm in 2 hours before rainfall reached to 40 mm and dropped 3 mm in 3 hours before 35 mm of rainfall in respective cases. Variation of PWV was higher in September case compared to October case of about 2 mm. We suggest the rainfall phenomena can disturb the GPS propagation and therefore, the impact of PWV before, during and after the flash flood event at three selected GPS stations in Klang Valley is investigated for possible mitigation in the future.

Adaptive Neuro-Fuzzy Interference System

This chapter explains in detail the theoretical background of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The detailed explanation of this method will highlight its importance in the estimation of ZTD model.

Modeling of Tropospheric Delays Using ANFIS

This book investigates tropospheric delays, one of the main error sources in Global Navigation Satellite Systems (GNSS), and its impact plays a crucial role in near real-time weather forecasting. Accessibility and accurate estimation of this parameter are essential for weather and climate research. Advances in GNNS application has allowed the measurements of Zenith Tropospheric Delay (ZTD) in all weather conditions and on a global scale with fine temporal and spatial resolution. However, GPS data are not always available for a full 24-hour period. Using a soft computing technique such as Adaptive Neuro-Fuzzy Inference System (ANFIS) as a new alternative, the ZTD can be determined by using the surface meteorological data as inputs. The estimation and prediction of ZTD value are presented in this book.

A study of El Niño-Southern oscillation impacts to the South China Sea region using ground-based GPS receiver

We observe an ENSO activity by using ground-based GPS receiver as an effort to study the effects of global warming and climate change in the tropical region. The precipitable water vapor (PWV) derived from Global Positioning System (GPS) meteorology in line with the sea surface temperature anomaly (SSTa) is used to indicate their response on ENSO activities. The PWV data used in this study was taken from the station at Universiti Malaysia Sabah, Kota Kinabalu (UMSK) over 2011, together with NTUS station (in the Singapore), PIMO (in Philippines) and BAKO (in Indonesia) are also compared. The relationship between PWV and SSTa at all stations on weekly basis exhibited modest with correlation coefficients between −0.30 and −0.78 significantly at the 99% confidence level. The negative correlation indicates that during a La Nina phase, the PWV is increased when the sea surface temperatures getting cold causes warm air mass in the central Pacific moved to west Pacific. The increased of PWV causes the GPS signals will be getting slower.

Modeling of Precipitable Water Vapor Using an Adaptive Neuro-Fuzzy Inference System in the Absence of the GPS Network

AbstractThis paper constructs an adaptive neuro-fuzzy inference system (ANFIS) model to estimate precipitable water vapor (PWV) in Southeast Asia, particularly in the Peninsular Malaysia, Sabah, and Singapore region. The input to the model is developed using the surface pressure, temperature, and relative humidity. The models are trained and tested using PWV values derived from the global positioning system (GPS). The data used are for May 2012 taken at the Nanyang Technology University of Singapore (NTUS) and Universiti Malaysia Sabah, Kinabalu (UMSK); and for February 2009 taken at the Universiti Kebangsaan Malaysia Bangi (UKMB). The validation process is conducted using June 2012 data for NTUS and UMSK and March 2009 data for UKMB. The performance the ANFIS model is compared with a multilayer perceptron (MLP), Elman neural networks, and multiple linear regression (MLR) models. Results from validations at the three stations showed that the ANFIS model performed well as compared with MLP, Elman neural ne...

The application of a hierarchical Bayesian spatiotemporal model for forecasting the SAA trapped particle flux distribution

We implement a hierarchical Bayesian spatiotemporal (HBST) model to forecast the daily trapped particle flux distribution over the South Atlantic Anomaly (SAA) region. The National Oceanic and Atmospheric Administration (NOAA)-15 data from 1–30 March 2008 with particle energies as >30 keV (mep0e1) and >300 keV (mep0e3) for electrons and 80–240 keV (mep0p2) and > 6900 keV (mep0p6) for protons were used as the model input to forecast the flux values on 31 March 2008. Data were transformed into logarithmic values and gridded in a 5∘×5∘ longitude and latitude size to fulfill the modelling precondition. A Monte Carlo Markov chain (MCMC) was then performed to solve the HBST Gaussian Process (GP) model by using the Gibbs sampling method. The result for this model was interpolated by a Kriging technique to achieve the whole distribution figure over the SAA region. Statistical results of the root mean square error (RMSE), mean absolute percentage error (MAPE), and bias (BIAS) showed a good indicator of the HBST method. The statistical validation also indicated the high variability of particle flux values in the SAA core area. The visual validation showed a powerful combination of HBST-GP model with Kriging interpolation technique. The Kriging also produced a good quality of the distribution map of particle flux over the SAA region as indicated by its small variance value. This suggests that the model can be applied in the development of a Low Earth Orbit (LEO)-Equatorial satellite for monitoring trapped particle radiation hazard.