GPS-Based Water Vapour Variability in Declining Phase of Solar Cycle 23: A Possibility Coupling?

Wayan Suparta

Institute of Space Science (ANGKASA) Universiti Kebangsaan Malaysia (UKM)

43600 Bangi, Selangor Darul Ehsan, Malaysia wayan@ukm.my

Abstract— Sun-Earth coupling studies can explain some physical mechanisms of how solar activity exerts their influences on weather/climate changes. Using the GPS-derived precipitable water vapour (PWV) measurements as a tool to study water vapour variability, it is fundamental to calculation of a reliable weather forecast for advancement space weather prediction and such methods provide suitable platforms for the studies of solar-climate relationship. This paper presents an analysis of the PWV variability and its responses to solar activity during the declining phase of solar cycle 23.

Keywords: GPS PWV, Solar activity, Climate, Coupling

I. INTRODUCTION It is now well established that the Global Positioning System

(GPS) has powerful tool for use in space weather prediction and improved numerical weather prediction. Determination of integrated precipitable water vapour (PWV) of the neutral atmosphere and the total electron content (TEC) of the upper atmosphere by exploitation the propagation delays of the electromagnetic signals is now being a hot subject for scientific research and is one goal achievement in the application of space-geodetic techniques. Networks of GPS receivers can measure the spatio-temporal both PWV and TEC fields on a near-real-time and continues basis in all weather conditions and on a global scale. The source errors in GPS positioning and their effects in particular on weather prediction is ongoing improved by modeling and or improved by using the equipments that to have high accuracy. The capabilities of GPS techniques and their impacts to various technologies, including the operation of low-Earth orbiting satellites, electric power transmission grids, high frequency radio communications and radars, geophysical exploration, and atmospheric studies is one point of view of the growth rate and the success of GPS technology development.

Since the Sun is the source of the energy that causes the motion of the atmosphere and their energy clearly drives the Earth’s climate and controls both physical conditions of the Earth’s upper atmosphere and the space environment around the Earth. Practical experience reveals that there should be some causal links between solar activity and atmospheric events, such as large-scale and long term meteorological and hydrological changes [1, 2, 3, 4]. Sun-Earth coupling due to its transport energy that occurs throughout the Sun-Earth system is fundamental science to space physics, aerospace and satellite communication, meteorology and terrestrial climate cycle. It is

therefore crucial understand the application of GPS-based PWV for improving climate data records as well as to a better improving our understanding of the coupling mechanisms between the sun activity and the global climate.

Water vapour, as a climate variable in the lower atmosphere is one of the most fundamental of all climate variables due to its importance as a natural resource and its role in atmospheric dynamics. High or low natural frequency of water vapour variability is an important factor in global change issues because it may obscure human influence on hydrologic variations. Therefore, based on the advanced of GPS technique and a clear influence of solar activity on upper atmosphere, as represented by GPS-derived TEC during geomagnetic storms, we are able to relate the influence of solar events on terrestrial climate through PWV measurements [5]. In this basis, the PWV variability was applied for first time to study the coupling between solar activity and weather/climate. The use of GPS signals to study the coupling between the solar activity and the weather/climate is practically feasible due to its acts as a wire to transfer electromagnetic energy from space to the ground.

The objective of the work described herein is to analyze the connection between solar activity and Earth’s climate during declining phase of the solar cycle 23. The study of this relationship is of great practical significance for life on Earth, which makes a better understanding of physical processes of a forthcoming water vapour cycle is important towards improved weather prediction as well as improved global climate models.

II. DATA AND METHOD OF ANALYSIS

The techniques makes use of multiple satellite observation using a dual-frequency GPS receiver. Typically at our site, 8-12 GPS satellites were visible above 13 degrees elevation at any one time. The Trimble Zephyr Geodetic antenna and a Trimble TS5700 GPS receiver was chosen due to its improved accuracy, enhanced multipath resistance and superior satellite tracking at all elevation angles. The site of observation is at Scott Base station Antarctica (SBA), which geographically is located at 77°51’S latitude, 166°46’E longitude and 15.85 m altitude ellipsoid.

When determining the PWV from GPS observation, the lower atmosphere (so-called troposphere) was approximated flat and atmospheric layer is considered to have azimuthal asymmetry. Beside use the dual-frequency GPS receiver, the ionospheric delay effect in the GPS processing thus yields the

Proceeding of the 2009 International Conference on Space Science and Communication 26-27 October 2009, Port Dickson, Negeri Sembilan, Malaysia

978-1-4244-4956-9/09/$25.00 ©2009 IEEE 212

total zenith tropospheric delay (ZTD) can be minimized by adopted the precise point positioning (PPP) strategy, as implemented in the JPL GIPSY-OASIS software [6]. The observation operator for ZTD is sum of the zenith hydrostatic delay (ZHD) and the zenith wet delay (ZWD), which ZTD is calculated based on the improved Modified Hopfield model. While the ZHD is calculated based on the Saastamoinen approach. Due to instabilities of local weather phenomena and a poorly modeled the water vapour distribution, the ZWD is computed by subtracting the ZHD from ZTD. Before calculation of ZWD, the ZTD is mapped to all satellites view to obtain the total water vapour content in the zenith direction. The ZWD then transformed into an estimate of precipitable water vapour (PWV) by employing the surface temperature measured at the site. Readers are referred to Suparta et al. [5] for more details the PWV determination. In this term, PWV or total precipitable water is the amount of water that can be obtained from the surface to the "top" of the atmosphere if all of the water and water vapour were condensed to a liquid phase. The PWV data are sampled at 10-minute interval. All PWV data were presented in kg/m2 or millimeter (1 kg/m2 equal to 1 millimeter). A Matlab program suite namely the tropospheric water vapour program (TroWav) was developed by author to process and analysis the all above parameters from the collected data [7]. The analysis is supported by a Trimble Reference Station (TRS) software that further enhances GPS surveying.

For solar activity parameters, association of solar activity and the global geomagnetic variability over the six-year period of 2003-2008 are presented. The sunspot number was obtained from the Solar Influences Data analysis Center (SIDC) at Royal observatory of Belgium, the solar wind speed and density acquired by the ACE spacecraft SWEPAM instrument are used to determine the dynamic solar wind pressure, and 3-hourly a global geomagnetic activity (aa index) was obtained from World Data Center (WDC) for Solar Terrestrial Physics, Moscow, Russia. In this work, surface temperature measurement at SBA supported by National Institute of Water and Atmospheric Research, Ltd (NIWA) is also presented together with PWV data as an comparable indicator of climate response to solar forcing.

III. RESULTS AND DISCUSSIONS

Variations in the number of sunspots (SSN), solar wind dynamic pressure (SWP) and global geomagnetic activity (aa index) during declining phase of solar cycle 23 are plotted in Fig. 1. The plotted of solar-geomagnetic parameters are related to the transmission of the solar energy to the terrestrial atmosphere and are as an indicator of solar forcing on the Earth’s climate. From the top panel, SSN is common parameter have been used as an indicator of solar activity. It is plotted for daily, monthly and smoothed to show their trend during the declining phase. On the second panel, SWP is like momentum flux, brings the energy, drive the Earth’s magnetosphere, controlling the polar aurora and is an important driver of substorms [8]. It is a function of speed and density of solar wind, which can be determined using the following formula:

2VnmSWP p= (1) where SWP is in nPa (nano Pascals), mp is mass of proton in kg, n is the density of particles in cm-3 and V is the bulk flow speed in km s-1. The high two SWP peaks in Fig. 1b, is clear correlated with geomagnetic storm of October 2003 and November 2004, respectively. On the bottom panel of Fig. 1, we display the aa index, representing the global geomagnetic activity. The aa index is three hourly index of geomagnetic activity determined from Kp indices scaled at two antipodal subauroral stations. Its activity is driven by the solar coronal magnetic field strength. The Kp and Ap indices have been used extensively in studies linking geomagnetic activity to the lower atmosphere. Cliver at al. [9], use the aa index as a proxy for solar irradiance to linking the Earth’s climate through terrestrial surface temperature records, and found that the two are strongly correlated (r = 0.90). In Fig. 1c, the highest peak of aa index was well-correlated with the October 2003 geomagnetic storm and their monthly maximum variation was shown decreasing, following the SSN activity. A similar trend was also shown by SW pressure profile in Fig. 1b.

Fig. 1. Sunspot, solar wind dynamic pressure and aa index variations during

the declining phase of 23rd solar cycle Fig. 2 shows the lower atmosphere parameters represented

by PWV and surface temperature. As shown in Fig. 2a, the blank GPS data of about 3 months for year of 2008 at SBA was due to frozen the system. During the observation period, the daily PWV mean values are ranging from -0.80 to 11.33 mm (~3.11 mm, on average) and their monthly mean values are from 0.79 to 8.27 mm (~3.14 mm, on average), and the yearly average is 3.25 mm. During summer and winter

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periods, the PWV mean are maximum at 6.83 mm and minimum at 1.15 mm, respectively. As clear shown in the figure, the PWV show a large seasonal variation, as local differences in the seasonality of precipitation; largest in summer and lowest in winter, respectively. In the bottom panel of Fig. 2, surface temperature measurement at SBA was shown similar variation to the PWV. During summer and winter periods, the monthly temperature mean are maximum at -4.94 °C and minimum at -32.83 °C, respectively (-19.14 °C, on average). By comparing both profiles of PWV and temperature on monthly mean values, their correlation is strongest with a correlation coefficient (r) of 0.87 at the 99% confidence level. One interesting to note from monthly and yearly means profiles of PWV, their trend during the observation period (declining phase of solar cycle 23) are shown decreased. The monthly PWV mean variation look like a envelope of the wave packet propagates at the group PWV. Wave ripples propagate at the phase PWV. It is believed that the wave energy from the solar activity is trapped in this wave packet and is thereby constrained to move with the wave packet. The PWV of this packet is called the group PWV. In addition to the PWV decreasing, increasing water vapour due to global warming is potential in increasing of greenhouse gases content in the atmosphere. This is contrast to the trend of surface temperature, when the PWV decreased, the temperature tend to be increase.

Fig. 2. PWV and surface temperature profiles at Scott Base station, Antarctica

for the observation period from 2003 to 2008

To analyze possible coupling between the solar activity and the lower atmosphere through PWV observation, we display monthly profiles of solar-climate parameters, which all data are smoothed using 11-point moving average. All parameters are plotted in Fig. 3. During declining phase, solar and geomagnetic disturbances are reduced their activities due to recurrent high speed streams from coronal holes. If not most coronal mass ejections (CMEs), the corotating stream interaction regions (CIRs) are cause the strongest recurrent

geomagnetic activity during the descending phase [10]. The SSN and aa index are the two parameters which both strongly correlated than other parameters. The slope of descending phase represented by regression coefficient and their correlation is given in Table I. The monthly correlation between solar activity and PWV was shown moderate with a correlation coefficient of about 0.59 on average. When we looking on yearly average (annual), their average correlation is strongest (r = 0.80). This significant signal indicating that when the geomagnetic activity of the Sun is stronger, warmer the Earth surface temperature will trigger the precipitation clouds, this produces fewer water vapour through cloud droplet distribution and thereby heats the Earth.

Fig. 3. Monthly profiles of solar-climate parameters (all data have been

smoothed using 11-point moving average) during the declining phase of 23rd solar cycle

TABLE I. CORRELATION COEFFICIENT AND REGRESSION COEFICIENT

No.

Analysis during declining phase of 23rd solar cycle

Parameters

Monthly correlation coefficient

(r) a

Yearly correlation coefficient

(r) a

Regression coefficient

(slope)

1 SSN – SWP 0.65 0.90 SSN = -20.67

2 SSN – aa 0.97 0.97

3 SSN – PWV 0.52 0.79 SWP = -2.70

4 SSN – Temp 0.40 0.70

5 SWP – aa 0.70 0.92 aa = - 4.38

6 SWP – PWV 0.66 0.87

7 SWP – Temp 0.61 0.72 PWV = -0.30

8 aa – PWV 0.60 0.74

9 aa – Temp 0.64 0.73 Temp. = 0.02

10 PWV - Temp 0.87 0.97 a. All correlations are at the 99% confidence level

To show solar-geomagnetic and terrestrial interaction, the frequency distributions of the aa, PWV and temperature are plotted in Fig. 4. Their nature characters are shown in lognormal distributions. The association between geomagnetic activity (aa index) and climate (PWV) distribution is controvertible and shown here is for first time. The frequency peak of PWV was occurred between 1 and 2 mm, while aa index was frequently occurred between 9 and 12 nT which is categorized as a low geomagnetic activity. The confirmation of relation between PWV and temperature is also seen in Figs. 4b and 4c, which the temperature rises the proportion water

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vapour in the air is increases. In other word, warm air can hold more water vapour (increases PWV content in the atmosphere) than cold air. Perhaps there is some solar or interplanetary geomagnetic activity behavior difference that also somehow affects the PWV and surface temperature variations. When the surface temperature below -40 °C (which is well-known as anomaly or extreme values), the PWV content will be decreased? It is the cooling of Antarctic atmosphere more GPS signals would be delayed? Careful examination to answer all above questions within a GPS perspective is further needed to find out a reasonable coupling and to explain their physical mechanisms.

Fig. 4. Frequency distribution of the PWV and surface temperature at daily

means during the observation period from 2003 to 2008

IV. CONCLUSION AND FURTHER WORK We have examined the connection between solar activity

and terrestrial processes during the declining phase of solar cycle 23. Based on our understanding of their interaction, a number of climate-related physical processes such as month of solar cycle 23 minimum, it is predicted close minimum at the occurrence of ‘Total Solar Eclipse’ of 22 July 2009, the PWV trend is descending and the temperature tend to be growing up. With high correlation coefficient and slope values shown in Table I, the SWP can be used as a proxy for surface temperature. The high correlation between PWV and solar activity, indicating a possible solar-climate link. The influence

of geomagnetic activity on the weather within the Antarctic polar cap is initial promising results. However, proper statistical analysis by concerning the classification of geomagnetic activity and the driving factors during the descending phase events are left a later work to point out the understandable of PWV signals in declining phase of solar cycle 23. The use of PWV data from GPS measurements to relate its response to solar and geomagnetic activities is novel and the comprehensive work was reported in Suparta et al. [5].

ACKNOWLEDGMENT Thanks the Institute of Space Science (ANGKASA) UKM

to support this work. I would like to express my gratitude to Dr. Dean Peterson and the managerial staff and science technicians of Antarctica New Zealand (ANZ) for maintaining the GPS data and Andrew R. Harper at the National Institute of Water and Atmospheric Research Ltd., New Zealand (NIWA) for supporting the surface meteorological data. We would also like to thank to the Solar Influences Data analysis Center (SIDC), Royal observatory of Belgium for sunspots data, D. J. McComas at SWRI and CDAWeb for providing the ACE/SWEPAM Solar Wind Experiment Data, and the World Data Center (WDC) for Solar Terrestrial Physics, Moscow for maintaining the global geomagnetic data (aa-index).

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