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GPS Water Vapor and Its Comparison with Radiosonde and ERA-Interim Data in Algeria

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GPS Water Vapor and Its Comparison with Radiosonde and ERA-Interim Data in Algeria ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 34, MAY 2017, 623–634 • Original Paper • GPS Water Vapor and Its Comparison with Radiosonde and ERA-Interim Data in Algeria Houaria NAMAOUI∗1;2, Salem KAHLOUCHE2, Ahmed Hafid BELBACHIR1, Roeland Van MALDEREN3, Hugues BRENOT4, and Eric POTTIAUX5 1Facult´ede Physique, Universit´edes Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouer, 31000 Oran, Alg´erie 2D´epartementde G´eod´esieSpatiale, Centre des Techniques Spatiales, 31200 Arzew, Alg´erie 3Royal Meteorological Institute of Belgium, 1180, Uccle, Belgium 4Royal Belgian Institute for Space Aeronomy, 1180, Uccle, Belgium 5Royal Observatory of Belgium, 1180, Uccle, Belgium (Received 26 April 2016; revised 17 November 2016; accepted 24 November 2016) ABSTRACT Remote sensing of atmospheric water vapor using global positioning system (GPS) data has become an effective tool in meteorology, weather forecasting and climate research. This paper presents the estimation of precipitable water (PW) from GPS observations and meteorological data in Algeria, over three stations located at Algiers, Bechar and Tamanrasset. The objective of this study is to analyze the sensitivity of the GPS PW estimates for the three sites to the weighted mean temperature (Tm), obtained separately from two types of Tm–Ts regression [one general, and one developed specifically for Algeria (Ts stands for surface temperature)], and calculated directly from ERA-Interim data. The results show that the differences in Tm are of the order of 18 K, producing differences of 2.01 mm in the final evaluation of PW. A good agreement is found between GPS-PW and PW calculated from radiosondes, with a small mean difference with Vaisala radiosondes. A comparison between GPS and ERA-Interim shows a large difference (4 mm) in the highlands region. This difference is possibly due to the topography. These first results are encouraging, in particular for meteorological applications in this region, with good hope to extend our dataset analysis to a more complete, nationwide coverage over Algeria. Key words: GPS, atmospheric water vapor, radiosonde, ERA-Interim Citation: Namaoui, H., S. Kahlouche, A. H. Belbachir, R. Van Malderen, H. Brenot, and E. Pottiaux, 2017: GPS wa- ter vapor and its comparison with radiosonde and ERA-Interim data in Algeria. Adv. Atmos. Sci., 34(5), 623–634, doi: 10.1007/s00376-016-6111-1. 1. Introduction Wang and Zhang, 2008). In recent years, the use of the global positioning sys- Water vapor plays an important role in atmospheric pro- tem (GPS) satellites to sense water vapor in the troposphere cesses, but its quantification is difficult because of its tem- has increased. In addition to its use for geodetic position- poral variation in space and time, depending on the com- ing, it also allows the estimation of atmospheric water va- plex interaction of several phenomena like convection, pre- por. Specifically, it is possible to convert the propagation cipitation and turbulence. Radiosonde profiles, as classical delay of electromagnetic waves in atmospheric precipitable methods of collecting data on atmospheric water vapor, do water (PW) when at least the surface temperature [T , from not generally offer the spatial and temporal resolution nec- s which the weighted mean temperature (T ) can be derived] essary for in-depth studies of weather and climate (Ware et m and pressure at the same site are known (Bevis et al., 1992; al., 2000). These sondes are launched twice a day at most Rocken et al., 1997). Many studies have shown that GPS sites, and are located hundreds of miles from each other. is an efficient tool that can complement other remote sens- Moreover, these methods are affected by issues of calibration, ing techniques for measuring the water vapor content, which poor quality of data, and long-term reliability (Elliott, 1995, is a useful quantity for climatological and weather forecast- ing applications (Guerova et al., 2003, 2016). Recent exper- ∗ Corresponding author: Houaria NAMAOUI iments have proved the ability of GPS to measure the wa- Email: [email protected], [email protected] ter vapor with the same accuracy as other instruments, such © Institute of Atmospheric Physics=Chinese Academy of Sciences; and Science Press and Springer-Verlag Berlin Heidelberg 2017 624 GPS WATER VAPOUR AND ITS COMPARISON WITH RADIOSONDES VOLUME 34 as radiosondes, radiometers and photometers (Torres et al., the gravity field f with respect to the latitude ' and the height 2010; Van Malderen et al., 2014). above the geoid h (in km), (Saastamoinen, 1972; Davis et al., The first objective of this study is to provide preliminary 1985): retrievals of PW from GPS receivers in Algeria using dif- Psur ferent estimations of Tm. Specifically, Tm is determined by ZHD = (0:0022768 ± 0:0000024) ; (5) f (';h) linear regression from Ts using the known equation model of Bevis et al. (1992), and using the equation model estab- f (';h) = 1 − 0:00266cos' − 0:00026h : (6) lished for Algeria by Boutiouta and Lahcene (2013). Tm is also calculated directly by numerical integration of the ver- The zenith wet delay (ZWD) represents the contribution tical profiles of temperature and humidity, measured by ra- of water vapor, and is expressed by diosondes or taken from the numerical weather model output " Z Z # −6 Pw Pw of ERA-Interim. The second objective of this study is then ZWD = 10 k2 dz + k3 dz ; (7) T T 2 the validation of those different parameterizations by com- paring the resulting GPS-PW retrievals with the PW values where Pw and T are the partial pressure of water vapor (in obtained with radiosonde data and from ERA-Interim data. hPa) and the atmospheric temperature (in K), respectively. k2 and k3 are the coefficients of refractivity obtained experimen- tally; their errors are negligible in the error budget for pro- 2. Methodology cessing of the PW (Bevis et al., 1994; Brenot et al., 2006): 2.1. Physics of the atmospheric propagation delay k2 = (64:8 ± 0:08) ; The troposphere is the lower part of the neutral atmo- (8) k = (373900 ± 0:04) : sphere, extending from Earth’s surface up to an altitude of 3 approximately 16 km at the equator and 8 km at the poles, and composed of dry gases and water vapor (Kos et al., 2009). 3. Determination of PW from ZWD Global Navigation Satellite System (GNSS) signals are affected by refraction due to molecules in the troposphere, The total PW (generally expressed in mm) is the amount and this introduces delay in the arrival time of the signal due of liquid water that would be obtained if all the water vapor in to bending and delay along the propagation path. The path the atmosphere within a vertical column were compressed to is determined from knowledge of the refraction index (n), the point of condensation. Considering the density of water −3 which is conveniently expressed in terms of refractivity (N) being equal to 1 g cm , PW is equivalent to the integrated −2 (Bevis et al., 1994): water vapor (IWV) content (generally expressed in kg m ). The relationship between PW and the wet delay can be ex- 6 N = 10 (n − 1) : (1) pressed as follows (Askne and Nordius, 1987): An often used expression for N has the following form (Thayer, 1974): PW = kZWD ; (9) 6 0 −1 k = [10 (k3=Tm + k2)Rvρ] ; (10) 6 Pd(z) Pw(z) Pw(z) N(z) = 10 (n − 1) = k1 + k2 + k3 ; (2) 2 −1 T T T where Rv (= 461.5181 kg K ) is the gas constant for the 0 where Pd, T and Pw are the partial pressure of dry air, water vapor, ρ is air density, k2 = (17 ± 10) and Tm is the temperature, and the partial pressure of water vapor, re- weighted mean temperature of the atmosphere. The weighted spectively, and vary with height z. The coefficients k1 = mean temperature Tm is defined as (Davis et al., 1985) −1 −1 (77:60 ± 0:05)K hPa , k2 = (64:8 ± 0:08)K Pa and k3 = R 2 −1 PV (373900 ± 0:04)K Pa . T dz Tm = R ; (11) PV dz 2.2. Zenith total delay (ZTD) T 2 The phase delay along the zenith direction is related to where pv is the partial pressure of water vapor, T is the ab- the atmospheric refractivity, N(z), by solute temperature, and z is the vertical coordinate. Tm can Z 1 either be calculated from vertical profile data provided by ra- ZTD = 10−6 N(z)dz : (3) diosondes or global reanalysis data, or estimated from Ts (in receiver K) observations using a linear empirical relationship (e.g. Be- The refractivity can be divided into two parts—the hydro- vis et al., 1992)—the so-called Tm–Ts relationship: static (Nhydr) and the wet delay (Nwet): Tm = 70:2 + 0:72Ts : (12) N = Nhydr + Nwet: (4) The zenith hydrostatic delay (ZHD) is the delay due to This Bevis et al. (1992) regression was based on an anal- the whole density of the neutral atmosphere and can be accu- ysis of 8718 radiosonde profiles spanning approximately a rately estimated, under the assumption of hydrostatic equilib- two-year interval from 13 sites in the United States with a ◦ ◦ rium, from only the surface pressure Psur and the variation of latitudinal range of 27 (West Palm Beach, Florida) to 65 MAY 2017 NAMAOUI ET AL. 625 (Fairbanks, Alaska) and a height range of these stations from It can therefore be concluded that these three stations 0 to 1.6 km. are located in distinct regions from a climatological point of Based on a period of three years (2005–07) of radiosonde view. observations at five permanent sites in Algeria (Dar-El-Beida, To process the GPS data, we include the three stations Bechar, Tindouf, In-Salah and Tamanrasset), Boutiouta and of VILL (Villafranca, Spain), RABT (Rabat, Morocco) and Lahcene (2013) derived the following Tm–Ts relationship for CAGL (Cagliari, Italy) from the International GNSS Ser- Algeria: vice (IGS).
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