Estimation of Atmospheric Water Vapor from ANFIS Technique and Its Validation with GPS Data

Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence of GPS data.Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence of GPS data

Isoprene hotspots at the Western Coast of Antarctic Peninsula during MASEC′16

Isoprene (C⁠5H⁠8) plays an important role in the formation of surface ozone (O3) and the secondary organic aerosol (SOA) which contributed to the climate change. This study aims to determine hourly distribution of tropospheric isoprene over the Western Coast of Antarctic Peninsula (WCAP) during the Malaysian Antarctic Scientific Expedition Cruise 2016 (MASEC′16). In-situ measurements of isoprene were taken using a custom-built gas chromatography with photoionization detector, known as iDirac. Biological parameters such as chlorophyll a (chl-a) and particulate organic carbon (POC) were compared to the in-situ isoprene measurements. Significant positive correlation was observed between isoprene and POC concentrations (r2=0.67, p<0.001), but not between isoprene and chl-a. The hotspots of isoprene over maritime Antarctic were then were investigated using NAME dispersion model reanalysis. Measurements showed that isoprene mixing ratio were the highest over region of King George Island, Deception Island and Booth Island with values of ∼5.0, ∼0.9 and ∼5.2ppb, respectively. Backward trajectory analysis showed that air masses may have lifted the isoprene emitted by marine algae. We believe our findings provide valuable data set of isoprene estimation over the under sampled WCAP

Calibration model of a low-cost air quality sensor using an adaptive neuro-fuzzy inference system

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor

Isoprene hotspots at the Western Coast of Antarctic Peninsula during MASEC′16

Isoprene (C5H8) plays an important role in the formation of surface ozone (O3) and the secondary organic aerosol (SOA) which contributed to the climate change. This study aims to determine hourly distribution of tropospheric isoprene over the Western Coast of Antarctic Peninsula (WCAP) during the Malaysian Antarctic Scientific Expedition Cruise 2016 (MASEC′16). In-situ measurements of isoprene were taken using a custom-built gas chromatography with photoionization detector, known as iDirac. Biological parameters such as chlorophyll a (chl-a) and particulate organic carbon (POC) were compared to the in-situ isoprene measurements. Significant positive correlation was observed between isoprene and POC concentrations (r2 = 0.67, p < 0.001), but not between isoprene and chl-a. The hotspots of isoprene over maritime Antarctic were then were investigated using NAME dispersion model reanalysis. Measurements showed that isoprene mixing ratio were the highest over region of King George Island, Deception Island and Booth Island with values of ∼5.0, ∼0.9 and ∼5.2 ppb, respectively. Backward trajectory analysis showed that air masses may have lifted the isoprene emitted by marine algae. We believe our findings provide valuable data set of isoprene estimation over the under sampled WCAP

Cloud-to-Ground lightning observations over the Western Antarctic region

This paper presents the observations and characterization of Cloud-to-Ground (CG) lightning activity in Western Antarctica in a region that covers the Amundsen/Bellingshausen Sea (ABS), the Antarctic Peninsula (AP) and the Weddell Sea (WS). Lightning data have been collected by a lightning detector (Boltek LD-350) and an atmospheric electric field mill (EFM-100) sensors deployed at the Carlini Base on the Antarctic Peninsula (CARL: 62.23oS, 58.63oW). The flash rate and flash multiplicity were analysed for three different seasons within a 1,000 km range, starting at the end of summer (February 2017) and ending in winter (July 2017). Three storm days for each month (within the 1,000 km radius from the LD sensor) with three composite active thunderstorms (labelled as Storm region A, B, and C) for each day have been selected from a collection of storm days between February and July 2017. A total of 355,899 flashes have been recorded with 156,190 Positive CG and 199,709 Negative CG flashes from these 54 thunderstorms. In total, Positive CG flash counts made up around 43.9% of the total detected CG flashes. Most of the Positive CG flashes (>80%) had only 1 or 2 strokes with a maximum number of 5. For Negative CG flashes, the average multiplicity and the maximum multiple stroke were 1.2 and 16 respectively. Most CG flashes were detected during the summer and fall months. Positive CG flashes were prevalent in Western Antarctic storms even during the winter. The mean, median and range of the ratio of Positive CG to Negative CG flashes were 0.7, 0.718 and 0.217–1.279, respectively