GPS Radio Occultation A New Data Source for Improvement of Antarctic Pressure Field

Transcription

1 GPS Radio Occultation A New Data Source for Improvement of Antarctic Pressure Field Ge Shengjie, Shum C. K. Laboratory for Space Geodesy and Remote Sensing Research, The Ohio State University, 47 Hitchcock Hall, 7 Neil Avenue, Columbus, Ohio 43, USA. Tel: , ; Fax: ; ge.8@osu.edu, ckshum@osu.edu Wickert Jens, Reigber Chris Division I, Kinematics and Dynamics of the Earth, GeoForschungsZentrum Potsdam (GFZ), Telegrafenberg, 4473 Potsdam, Germany. jens.wickert@gfz-potsdam.de, reigber@gfz-potsdam.de Abstract: Radio occultation concept, first tested on planetary satellite missions, can also be applied to Low- Earth-Orbiting (LEO) satellites with GPS occultation receivers. Successfully demonstrated for the first time by the GPS/MET experiment in 995 [7], GPS occultation technique shows great prospective to provide accurate pressure, temperature and water vapor profiles in the Earth s neutral atmosphere. This atmospheric data source provides enhancement in temporal and spatial resolution, in addition to the traditional measurements (e.g., radiosonde, nadir-viewing satellite based radiometers) and ground-based GPS networks (antenna zenith delays) for precipitable water vapor (PWV) measurements. The new generation Blackjack GPS receivers onboard the current operating LEOs (SAC-C, CHAMP and GRACE), the future COSMIC mission and European proposed multisatellite occultation constellations--ace+ and METOP based on GRAS GPS receiver provide an unprecedented global coverage and continuous GPS atmospheric limbsounding data for research in climate, meteorology and space weather. For poor data regions, the near-real time use of these data could potentially improve regional weather forecasting. In this paper, occultation concept and retrieval procedure are overviewed at the beginning. Major technical problems including lower troposphere inhomogeneities and signal penetration, multipath, water vapor ambiguity etc. are discussed. This paper mainly focuses on the possibility of using occultation data to improve global surface pressure fields and quantify atmospheric mass redistribution on Earth, allowing other climate-sensitive mass variation signals such as hydrological, oceanic and cryospheric to be separated from atmospheric loading. The background and limitations of current weather analysis models and their pressure products applied to geodetic applications are given in detail. Preliminary results by processing CHAMP occultation data over Antarctica are presented along with their validations using radiosonde, European Center for Medium-range Weather Forecasts (ECMWF) and National Center for Enviroental Prediction (NCEP) data. The potential of improving global pressure field by assimilating GPS occultation measurement is discussed at the end Received date: -- Key words: GPS Occultation; Limb-Sounding; Time variable gravity; Atmosphere remote sensing; Pressure; Water Vapor Introduction Radio occultation, as a new remote sensing technology to explore the atmosphere, was first introduced in NASA s planetary satellite missions to sense the planetary atmosphere []. This concept can also be applied to Earth s atmosphere by using LEO satellite carrying GPS receiver tracking the higher altitude GPS signal. The fundamental principle is that transmitted GPS signal will be delayed when LEO setting or rising from the Earth s atmosphere. This delay could be converted to Doppler shift and further to bending angle. Finally atmospheric refractivity, temperature, pressure, air density as well as water vapor information can be retrieved (see Figure ). Figure : Remote sensing of the atmosphere using GPS radio occultation. The bending angle α of the ray path from GPS satellite to the LEO (here CHAMP) is a characteristic parameter of the occultation. Vertical profiles of pressure, temperature and specific humidity can be derived (modified from [3] ). Current global weather prediction models, like ECMWF and NCEP, and regional or mesoscale models, like MM5, are still heavily depending on traditional measurements, e.g., radiosonde, nadir-looking space based water vapor radiometer, research aircraft and ship measurements. The distribution of these data varies greatly over regions and time. By comparing GPS/MET and ECMWF, better agreements are found in northern hemisphere than southern hemisphere [][]. This indicates that the ECMWF analysis, which is heavily based on radiosonde data, is less accurate in data sparse regions like

2 the southern Pacific than over the Europe and USA continent [3]. By using the fully deployed GPS constellation, occultation can provide a very efficient way to obtain all weather and nearly uniform global coverage, especially for data sparse regions, e.g., southern ocean and Antarctica. These measurements represent an invaluable data source for climate and meteorological research and better understanding of the Earth s atmosphere. GPS occultation can also provide profiles with a high vertical resolution []. After the success of the GPS/MET proof-of-concept mission, current German CHAllenging Minisatellite Payload (CHAMP, launched in July [9] ) and Argentina SAC-C (November ) carrying a new generation GPS flight receiver ( BlackJack ) provide quasi-continuously GPS occultation measurements. In addition, the U.S.-German Gravity Recovery and Climate Experiment (GRACE, launched in March ), the Taiwan-U.S. multi-satellites Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC, 5) [] and the European 4-satellite Atmosphere Climate Experiment (ACE+, 7) mission will provide unprecedented measurement sources to continuously observe the Earth s atmosphere by the GPS radio occultation technique [6]. Lower troposphere is one of the main focuses in atmosphere science. Understanding Planetary Boundary Layer (PBL), water vapor circulation, heat and energy transportations etc. is very critical for further improving the weather prediction and analysis. Penetrating the signal to lower troposphere is still a big challenge both in algorithm and hardware tracking because of the rapid change of the moist contents, lower tropospheric inhomogeneities, multipath problem, etc. Besides the application in atmosphere science and meteorology, GPS occultation data can also be used to improve the global surface pressure field to facilitate the dedicated gravity missions and time variable gravity filed recovery. The global climate change is directly linked to dynamic processes of the Earth system. Having one of the two largest ice sheets over the globe, Antarctica plays an important role in the process of global warming and sea level rise. Quantifying the contribution of ice sheet mass balance to the total budget of sea level rise is very critical to the understanding of the relation between mass movement within the Earth and the consequent sea level rise. Dedicated gravity satellite missions, such as CHAMP, GRACE and Gravity Field and Steady-State Ocean Circulation Mission (GOCE), are anticipated to significantly improve the current knowledge of the Earth s mean gravity field and its time variable part climate sensitive gravity signals, which could be measured with sub-centimeter accuracy in units of column of water movement near the Earth s surface with a spatial resolution of 5 km or longer, and a temporal resolution of one month [6] (primarily by GRACE). In order to detect the climate sensitive hydrological, oceanic and cryospheric signals, pressure loading has to be first removed. Current weather analysis data from ECMWF and NCEP are still not sufficient to support these missions, partly because only very sparse and unevenly distributed measurements can be assimilated into the analysis. In this paper, we will put our main focus on the improvement of the pressure field of Antarctica. Problems and limitations of the current weather analysis models and possible improvement of pressure field using GPS occultation are discussed in detail. First, we briefly overview the occultation retrieval procedures and describe how we can obtain the pressure profile. Challenges and technical difficulties of occultation retrieval, including lower troposphere penetration and multipath, are also discussed. We also found that user must pay more attention to 3 major aspects before using the weather analysis model to geodetic applications, i.e., model accuracy and consistency, topography and aliasing. Results are shown with inter-model comparison and model validation with Automatic Weather Station (AWS) data. Preliminary results using CHAMP data over Antarctica region along with its validation with radiosonde, NCEP and ECMWF are also presented in the paper. The possibility of using GPS occultation to improve the global pressure field is discussed at the end. Retrieval Procedures Different methods can be used to retrieve occultation profiles. The method introduced here is based on the geometric optics. Detailed descriptions of the retrieval are given e.g. by Hocke [], Kursinski [] or Melbourne et al. [6]. A LEO carrying GPS receiver can receive signals from the GPS constellation. When GPS signal penetrating the neutral atmosphere, it can be bended dominantly by the vertical gradient of the refractive index field. The length of the ray path increases accordingly (geometrical effect) and the optical ray path also is becoming longer due to traveling through a medium with refractive index > (optical effect), both effects form the atmospheric excessive phase of the occultation link. A double difference technique is used to eliminate satellite clock errors and to derive the atmospheric excess phase of the occultation link. The 5 Hz carrier phases (L and L) measured by the LEO (from the occulting, and a second, referencing GPS satellite), are synchronized with interpolated Hz data of a global distributed fiducial network of GPS ground receivers [8]. Alternatively, in result of the termination of the Selective Availability (SA) mode of the GPS (resulting in enhanced GPS clock stability) the application of Space-based single differencing techniques for precise occultation processing became feasible and was recently demonstrated by Wickert et al. [3]. Additional data needed for the excess phase derivation are the precise orbit information (position and velocity) of the GPS and LEO satellites. Atmospheric bending angles are derived from the time derivative of the calibrated atmospheric excess phases after appropriate filtering. The ionospheric influence is corrected by linear combination of the L and L bending angles [5]. The point on the ray which is the closest to the Earth s surface is called the ray perigee or tangent point. Another quantity impact parameter the distance from the tangent point to the local center of curvature (which is

3 a little bit different from the Earth s center due to the oblateness of the Earth) can be determined from the time derivative of the atmospheric excessive phase measurement as well (e.g. Syndergaard [3] ). Figure : Overview of processing steps for the derivation of vertical atmospheric profiles from GPS radio occultation measurements. Vertical profiles of atmospheric refractivity are derived from the corrected bending angle profiles by Abel inversion: a a n( r ) exp π a α( a) a a da. () The integration is performed from impact parameter a to infinity using the bending angle profiles. The refractivity N of the neutral atmosphere can be related to pressure and temperature through the following formulas: 6 P 5 Pw N ( n ) , T T () P ρrt, m (3) P gρ h. (4) Here P is pressure (hpa), T temperature (K), P w water vapor partial pressure (hpa), ρ air density, R gas constant, m the gas effective molecular weight, h height and g the gravitational acceleration. If there s no water vapor and, the above function reduced to: N P T (5) From (3) and (5), eliminate P/T, Nm 77.6R ρ (6) By integrating (4), the pressure at different altitude is given by: h P ( h) g( h) ρ ( h) dh (7) Equation (6) computes the density from the refractivity, integrating by equation (7) obtains the pressure profile. The formulas above are based on the no water vapor assumption. A schematic view to the retrieval steps, starting from the phase occultation measurements, is given in Figure. Water Vapor In GPS occultation, the retrieval of pressure and temperature from the refractivity also requires the knowledge of water vapor pressure P w. In the upper troposphere and stratosphere, the atmosphere can be assumed to be dry with negligible error. However, in the lower troposphere, where the contribution of wet component to the refractivity is significant, water vapor can not be easily separated from the dry components. Another important reason is that hydrostatic equation (3) only works with the total pressure. From (), P w can be derived as: N T 77.6 P T P w (8) In order to compute water vapor pressure, accurate and independent estimate of temperature must be known. This may come from the NCEP or ECMWF meteorological analyses or forecasts respectively. With known refractivity from the occultation, water vapor can be computed from an iterative process [5]. This algorithm suffers from a high sensitivity to even small errors in the analyses temperatures, resulting in large uncertainties of the derived water vapor profiles [5]. Combining spaceborne and ground based measurements provides another possible way to solve water vapor [3]. More elaborate water vapor retrieval methods based on optimal estimation of both temperature and humidity show more potential for obtaining water profiles with high accuracy. Following the discussion of Healy and Eyre [8], a penalty function is set up in the form: J ( x) ( y ( x x H ( x)) T b ) T B ( E + F) ( x x ( y b ) + H ( x)) The mathematical task here is to minimize the penalty function. x in this case is a vector including a profile of pressure, temperature and water vapor (i.e. the most probable atmospheric state). x b is the state of the atmospheric background (a priori, derived from the background information). B is the co-variance matrix of the a priori information. H(x) is the forward model, which maps the background information to a background measurement, in this case, could be bending angle or refractivity N. E and F are the expected covariances of the measurements and forward modeling respectively. By solving this penalty equation (minimizing) using the least square principle, profile of pressure, temperature and water vapor can be obtained simultaneously. Details and results (9)

4 of applying variational retrieval methods are given e.g. by Healy and Eyre [8] or Palmer et al. [7]. The mean spatial distribution of the water vapor is similar to the spatial distribution of the temperature, decreasing from the equator to the pole. This would make the pressure retrieval in polar region more easy and accurate and less affected by the water vapor. Some researchers suggest that water vapor does not need to be considered until the temperature beyond 5 K (e.g. Kursinski et al. [] ). Therefore, in most of the cases, it is fair to neglect the water vapor when retrieving the profiles at Antarctica region. 3 Lower troposphere One of the limitations of atmospheric occultation is its difficulty to extract accurate soundings in the lowest several kilometers of troposphere, which is primarily due to the relatively low gain GPS antenna and the phase lock tracking algorithm. Multipath occurs when strong refractivity gradient is present. During multipath, you have more than one signal arriving at the receiver simultaneously. These superposed signals as seen by the receiver coming from different directions and with different phases and travel paths. The signal input to the receiver is the superposition of the different signals which contains different phases and amplitudes. This signal contaminated with strong oscillations of the phase and amplitude due to the diffraction of the small scale tropospheric irregularities, impedes tracking of the signal and can result in the receiver losing lock of the signal. The spectrum of the signal would be broader than the higher altitude spectrum. Phase Lock Loop (PLL), a optimal tracking mechanism with feedback loop used by the receiver to perform carrier acquisition, will attempt to track the strongest signal, i.e. the signal with the largest amplitude but, if the signals are similar in amplitude and frequency, the correlator in PLL will have trouble to detect signal peak and generally, losing lock will happen (e.g. Sokolovskiy [] ). One suggestion to improve this problem is to have the receiver tracking in open loop mode. In this mode, the tracking loop does not use feedback and the receiver delivers the output raw correlator. From these time series which include the superimposed multipath signal, one can reconstruct the refractivity profiles, in the ideal scenario. However this scenario requires the elaboration of special method in order to have correct interpretation of the measurements of the signal in the multipath area. The most complicated problems of interpretation of radio occultation data arise in the lower troposphere. Gorbunov [6] classifies these difficulties as follows:. Algorithmic problems: Processing the phase and amplitude measurements in multipath area requires a special technique for the separation of interfering rays.. Technical problem: The receiver used in occultation satellites can not track the signal with the strong amplitude and phase scintillations resulting from lower tropospheric inhomogeneities and multipath propagation, on the background of significant refractive attenuation of the signal. Two different methods coping with the occultation data in multipath area:. Radio optic method analysis of spatial spectra. Back propagation technique or diffraction correction method. Both methods are based on the analysis of records of complex radio signals radio hologram. However, first results of the recent GPS occultation experiments (CHAMP and SAC-C), have shown, that the state-of-the-art GPS receiver Blackjack (provided by Jet Propulsion Laboratory, JPL) in combination with optimized high-gain occultation antenna allows for sounding deeper into troposphere (e.g. for CHAMP see [9] ). At Arctic or Antarctic regions one can expect a high percentage of profiles reaching the Earth s surface because of the lack of large refractivity gradients (caused by strong gradients in water vapor distribution) due to the very dry atmosphere. An example is shown in Figure 3. Nearly 9 % of,58 temperature profiles, located in the Arctic Region between 8 and 9 N reached the first Kilometer above the Earth s surface. Figure 3: Minimum altitude of,58 CHAMP dry temperature profiles measured at Polar region, located between 8 and 9 N (recorded between February and August, see also [3] ). 4 Pressure Atmospheric gravitational effect could be modeled as invert barometer (IB) over the ocean and loading over the solid earth. Invert barometer effect is usually used to correct the radar altimeter measured sea level [8]. Pressure loading over the land can cause crustal deformation of the Earth and has influence on the accurate extraction of mass balance signals from ice sheet and due to ground water circulation. In geodesy, gravity signal is usually represented by spherical harmonics. Pressure data can be easily transformed into mass signal expressed by the spherical harmonic coefficients. Gravitational potential outside the Earth due to the anomalous mass distribution can be represented by normalized spherical harmonic coefficients of degree n and order m as follows: (Heiskanen and Moritz [9], 967, pp58-59, see reference for notations):

5 n GM V P (cos θ )( C cos m λ + S sin m λ ) r n m () C n r P m dm n n + Ma (cos θ ) cos λ ( ) Earth () S n r P m dm n n + Ma (cos θ ) sin λ ( ) Earth () dm ρ dv ρ r dr sin θ dθd λ (3) C n + r dr P m d d (4) n n + Ma ρ (cos θ ) cos λ sin θ θ λ ( ) Earth S ( n + ) Ma ρ dr dp g q ρ dr P surface g n Earth n + r ρ dr P (cos θ ) sin m λ sin θ dθ dλ (5) (6) (7) (a) Global view, hr Theoretically, pressure can enter into the two equations (4) and (5) by using the hydrostatic formula (6). This is the fully correct form and requires pressure information along different vertical layers. Another alternative is neglecting vertical structure of the atmospheric mass and assuming that all the mass locate in a single surface layer, and then using formula (7), a simplified version of formula containing surface pressure can be obtained. Gruber et al. [7] and Swenson and Wahr [] use the first rigorous formulation to vertically integrate geopotential level of atmospheric pressure and account for aspherical shape of the Earth, as opposed to the computation of mass load using surface pressure. These studies found that their approach is preferable. Despite of these improvements, atmospheric loading correction remains a significant problem, especially over Antarctica continent and southern ocean. The current data product to correct for the atmospheric effect on GRACE is the 6-hourly ECMWF data. The limitations of using this data product and NCEP include: () inadequate spatial ( km) and temporal (6- hourly) sampling of data which cause errors and highfrequency aliasing, () substantial errors in the surface pressure data (or geopotential levels of pressure) especially in Antarctica, and (3) topography is poorly known in Antarctica (up to several hundred meters) while this error is diminished in temporal gravity field solutions, the mean gravity field would be significantly biased if the actual atmospheric pressure is not well known. Meanwhile, topography used in the weather analysis is an artificial topography, which has been adjusted to best fit the physical model and its resolution [Pekker, personal communication]. This artificial topography is different from the real topography, comparison results will be shown in later section. Velicogna and Wahr [4] did a research over continent U.S. and North Africa and conclude that analyzed pressure field is adequate to remove the atmospheric contribution form GRACE hydrological estimates, this is too optimistic for southern hemisphere [4]. Advanced gravity missions (CHAMP, GRACE, GOCE) carrying sensors capable of measuring < cm equivalent of surface water movement, require pressure accuracy of < hpa [6]. (b) Antarctica view Figure 4: 6-hourly NCEP-ECMWF surface pressure difference using NCEP topography. 4. Model Pressure Difference Figure 4 (a) and (b) show a 4 times daily example of pressure difference between NECP and ECMWF based on the same topography. Only hr difference is plotted as a global view in (a). Significant difference (±8 hpa) can be found over southern ocean and Antarctic continent. This big disagreement between the two models shows the uncertainty and inconsistency of these two data products over the southern hemisphere. Figure 5: 6-hourly ECMWF surface pressure difference based on different topography.

6 4. Topography Surface pressure used in Figure 5 is derived from two different topography model topography and ETOPO5 topography using only the ECMWF data. Very big difference (±6 hpa) can be found over Antarctica, coastal lines and mountain area. This indicates that big error will occur if without interpolating the data to the real Earth s surface. Care must be taken before using any meteorological product, since geodetic applications will require the real topography instead of model topography. For time variable gravity recovery of GRACE, the topography error may not be significant because of the differencing effect of the two satellites. However, this error will corrupt the mean gravity field without the accurate pressure on Earth s surface. Some of the errors displayed in Figure 5 may be caused by interpolation, since the real topography is not the best fit for the physical models, but this part would be assumed small and not on the same order of magnitude as topography error. (a) (b) Figure 7: Pressure difference between ECMWF and AWS at station locations. Figure 6: AWS locations. 4.3 Model compared with AWS In order to quantify the model error, meteorological analysis results must be compared or validated with some other independent information. Automatic Weather Station (AWS) barometric measurements were chosen and assumed as ground truth for this comparison. AWS data from May, to June 3, ( month) were obtained from the ftp site of Antarctic Meteorological Research Center (AMRC), University of Wisconsin. Total of 45 stations (Figure 6) are available for this study period. Table : Statistics of the ECMWF and AWS results. Mean Press. Difference (hpa) Standard Deviation (hpa) Avg. 4.8 Avg..65 rms 5.8 rms.3 Max.6 Max 6.38 Min.3 Min.75 Surface pressure from ECMWF is interpolated to the station elevation. Due to the extreme low number of valid observations at Clean Air, this station is excluded from the statistics of Table. From Table, we can see that rms of standard deviation is still above hpa for this month analysis. month statistics could be worse than this result. The rms of the mean value (5.8 hpa) is rather big, this difference will corrupt the static or mean gravity field solution if we assume the AWS measurements representing the ground truth. Obviously, this assumption is not always true. Figure 7 (a) (b) shows this comparison for two selected stations, one (Relay Station) agrees well with the model, the other (Bonaparte Point) deviates around hpa, represents a bad agreement. 4.4 Aliasing Aliasing effect is caused by inadequate sampling of the real signal by instruments (signal is not band limited), meanwhile representing the signal using a limited set of coefficients, e.g., spherical harmonic coefficients up to a certain degree (EGM96, 36x36). The higher frequency signals, although it is very small, will corrupt the lower frequency part if not properly filtered out. High frequency mass variation in the atmosphere has impacts to all 3 gravity missions (CHAMP, GRACE and GOCE) and must be properly handled.

7 (d) Monthly average height anomaly at altitude Figure 9: Simulated aliasing error for GRACE. Figure 8: GRACE monthly Geoid Sensitivity. Figure 8 shows a simulated GRACE monthly geoid sensitivity. Dashed blue line represents an 8 hours atmosphere variation (NCEP data) expressed by geoid height. This figure points out that GRACE monthly solution can sense atmospheric signal up to approximately degree 3. It is also obvious from the figure that ocean signal is on the same order of magnitude or smaller than the atmospheric signal. Aliasing error in Figure 9 is defined as 6-hourly pressure minus 3 day mean NCEP pressure (scaled, 3x3 model). (a), (b) and (c) in Figure 6 are aliasing error at different epoch. (d) is the monthly average of all the aliasing errors at GRACE altitude. We can see from the figure that high frequency components are dominative at southern ocean and Antarctica region. 5 Occultation results 5. Data Coverage (a) Geoid anomaly Day hr Figure : Location of CHAMP occultations on September 7,. In total 46 occultations were recorded. (b) Geoid anomaly Day5 hr (c) Geoid anomaly Day3 hr CHAMP carries an aft-looking antenna and SAC-C carries both aft-looking and fore-looking antenna. For a typical day, one antenna could have about 5 occultation events as shown for CHAMP in Figure. Therefore, both missions could provide around 75 occultation events daily. If we consider the twin satellite GRACE mission, the total number of the occultation would increase to 5. Looking forward to the next few years, COSMIC (6 satellites), ACE+ (4 satellites) will bring the total number of occultation over several thousands. These data provide a valuable source for atmospheric research. Figure shows occultation events coverage over Antarctic region from February, to October 3,. There are totally 7389 quality checked profiles, 94 over Antarctica (.6%). It is obvious to see from the figure that these occultations can cover the whole

8 continent comparing with the sparse located radiosonde stations (5 stations for the entire Antarctic region). (a) Figure : Locations of all CHAMP Antarctic occultations (red dots) between February, and October 3, and radiosonde stations (blue stars). 5. CHAMP profile and its validation We randomly chose a CHAMP event near a radiosonde station over Antarctic region. The computation results based on the algorithm described in previous section are shown in Figure. The profile was recorded May, (8.4 W, 7. S), :54 UTC and the data is obtained from CHAMP data center at GFZ, Level products (atmospheric excess phase) were used to compute the profile and compared with vertical atmospheric profiles (GFZ level 3 product, JPL level product, UCAR COSMIC Data Analysis and Archive Center (CDAAC) level 3 product) and with corresponding ECMWF and NCEP data. From the comparison of the temperatures (Figure c), we can see that the occultation results agree well among the Ohio Sate University (OSU) solution, UCAR and GFZ solution below 3 km. The big disagreement of the temperature profile above 35 km between OSU and GFZ solution is primary due to different methods adopted in the upper atmosphere. OSU solution uses a statistical optimization method [] above a certain altitude, usually 35-4 km to optimize, at that heights, the noisy dominated bending angle. The GFZ retrieval software uses another approach for the optimization [9], this is the possible reason for the deviations observed above 3 km. JPL result agrees with OSU, GFZ, UCAR between around 5 5 km, but it agrees well with the NCEP and ECMWF data in the lower troposphere. All the results from other processing center also show a good agreement with the NCEP and ECMWF analysis results except in the lower 5 km. We also notice from the figure that GPS occultation has a much higher vertical resolution than the weather analysis products. (b) (c) Figure : CHAMP profile over Antarctic region.

9 Figure 3: Comparison of CHAMP vertical dry temperature profile with radiosonde data from the German Neumayer wintering station (occultation No. 7; 8. W, 7.4 S) on May,. GPS occultation can also be validated with other measurement techniques, like radiosonde. To demonstrate the potential of the GPS radio occultation technique for a precise and vertical high resolved temperature profiling above Antarctica, this solved CHAMP occultation profile (see Figure ) was compared with radiosonde data. The radiosonde was launched at the German Neumayer station (8. W, 7.4 S) at 9 UTC on May,. The data were recorded between 9 and UTC. The agreement between both profiles is very good even from Earth s surface (temperatures here about 35 C) up to about hpa pressure level (~3 km height). The very good resolution of the tropopause height and temperature (Figure 3) is remarkable. This indicates that occultation technique has comparable accuracy with radiosonde data. Statistical validations in next future with larger data sets from CHAMP and SAC-C mission as well as radiosonde and meteorological analysis data will be performed to give more detailed view to the potential of GPS occultation data for sounding the Antarctic atmosphere. 6 Possible improvement of global pressure field Sections above point out the problems of the model analysis results and also demonstrate the characteristics and potentials of occultation application. Our next task is how to improve the global pressure field using the promising occultation data and technology. The direct use of the occultation measurements for gravity recovery is still a big challenge due to its dependency on time and location. Simultaneous observations over the globe are needed for geodetic applications. One way to use occultation measurements is the assimilation of GPS-observed bending angles and refractivities into regional 3- or 4-DVAR systems to provide the pressure field with finer spatial (<5 km) and temporal resolution (<3 hours). e. g., NSF funded Antarctic Mesoscale Prediction System (AMPS) [3][4]. AMPS, implemented by NCAR and Byrd Polar Research Center (BPRC) Ohio State University and based on MM5, can provide 9km, 3 km and km resolution output. With the improvement of the model, model input, boundary layer, initial condition and accurate topography, AMPS can be chosen as a best candidate for pressure field data source. Adopting more accurate topography model is also very critical for geodetic applications. Kuo et al. [4] further discussed the benefits of GPS occultation assimilation over Antarctica. The GPS occultation derived mass fields (temperature, moisture and pressure) are more efficiently assimilated into the model at higher latitude. Meanwhile, lack of moisture over Antarctica and its vicinity further improves the efficiency. Other alternatives could be using combined occultation events (SAC-C, CHAMP, GRACE, COSMIC), additional information, e.g., over-ocean IWV from passive (EOS Terra sensors) and from active (radar altimeters) radiometers, assuming small water vapor variations over Antarctic, empirically compute the improved pressure fields. 7 Summary and future works In this paper, the general procedure of GPS occultation retrieval is reviewed, with discussion of solving the water vapor ambiguity. Lower troposphere irregularity and the resulted tracking and multipath problem have to be treated from two main categories open loop hardware tracking and retrieval algorithm improvements. In this paper, we point out the existing problems with the global pressure field for the time variable gravity recovery, mainly from 3 aspects: inadequate spatial and temporal coverage and substantial errors are still exist in analysis model, especially in the southern hemisphere; accurate topography are hardly known and the inconsistent different topography could cause substantial error if not carefully treated; Aliasing errors could further corrupt the solution. Inter-model comparison and model-ground AWS data comparison shown in the paper prove these points. GPS occultation, as a very promising data collecting technology, shows very great potential to improve the current knowledge of our atmosphere with unbeatable global data coverage and very good vertical resolution over the traditional measurement techniques. CHAMP profile is derived in Antarctica region based on the described algorithm. Through the validation with radiosonde, NCEP and ECMWF, it is obvious that GPS occultation can provide comparable or even better sensing of the atmosphere. The current knowledge of Antarctica s pressure field is not adequate to support current and future gravity field mapping. Atmospheric (and other signal) aliasing is a severe problem for temporal gravity field recovery. GPS occultation is expected to improve surface pressure field over Antarctica continent and over southern ocean. For Antarctic applications, 4DVAR assimilation techniques, adopting more accurate topography, and potential

10 empirical computation of pressure profiles using multiplesatellite occultation data, are near future plans. Acknowledgements: We would like to express our appreciation to AMRC, University of Wisconsin-Madison, GFZ ISDC, UCAR CDAAC and JPL GENESIS for providing the data. We want to thank S. Han and T. Pekker for the help and useful discussions. References [] Anthes, R. A., C. Rocken, and Y.-H. Kuo, Applications of COSMIC to Meteorology and Climate, Terrestrial, Atmospheric and Oceanic Sciences,, 5 56,. [] Fjeldbo, G. F., V. R. Eshleman, and A. J. Kliore, The neutral atmosphere of Venus as studied with the Mariner V radio occultation experiments, Astron. J., 76, 3-4, 97 [3] Ge, S., C. Shum, C. Zhao, S. Gutman, and K. Hocke, GPS limb-sounding retrieval of water vapor using ground based network and spaceborne data, IAG Scientific Assembly, Vistas for Geodesy in the New Millennium, Budapest, Hungary, September -7,. [4] Ge, S., C. Shum, S. Han, B. Chao, and C. Cox, Contribution of COSMIC to climate change studies: Improvement of Antarctic Pressure field, Radio Occultation Science Workshop, NCAR Foothills Laboratory, Boulder, Colorado, August -3,. [5] Gorbunov, M. E., and S. V. Sokolovskiy, Space refractive tomography of the atmosphere: modeling of direct and inverse problems, Report, Max Planck institute for Meteorology, Hamburg, 996. [6] Gorbunov, M. E., Radioholographic methods for processing radio occultation data in multipath regions. Scientific report -, Danish meteorological institute, [7] Gruber, T., J. Wunsch, S. Bettadpur, and T. Pekker, Atmospheric and oceanic gravity field dealasing for GRACE science data processing, GRACE Satellite Mission Science Data System (SDS) Architectural Design Review,. [8] Healy, S., und J. Eyre, Retrieving temperature, water vapor and surface pressure information from refractive-index profiles derived by radio occultation: A simulation study, Quart. J. Roy. Meteorol. Soc., 6, ,. [9] Heiskanen, W., and H. Moritz, Physical Geodesy, W. H. Freeman Publication Co, San Francisco, 967. [] Hocke, K., Inversion of GPS meteorology data, Annales Geophysicae, 5, , 997. [] Kursinski, E. R., G. A. Hajj, W. I. Bertiger, S. S. Leroy, T. K. Meehan, L. J. Romans, J. T. Schofield, D. J. McCleese, W. G. Melbourne, C. L. Thornton, T. P. Yunck, J. R. Eyre, and R. N. Nagatani, Initial results of radio occultation observations of Earth's atmosphere using the Global Positioning System, Science, 7, 7-, 996. [] Kursinski, E. R., The GPS radio occultation concept: theoretical performance and initial results, PhD dissertation, California Institute of Technology, Pasadena, California, 997 [3] Kuo, Y.-H., S. Sokolovskiy, R. Anthes, and F. Vandenberghe, Assimilation of GPS Radio Occultation Data for Numerical Weather Prediction, Special issue of Terrestrial, Atmospheric and Oceanic Science,, 57-86, March. [4] Kuo, Y.-H., T.-K. Wee, and D. Bromwich, Potential Impact of COSMIC GPS Radio Occultation Data on Regional Weather Analysis and Prediction over the Antarctic, Radio Occultation Science Workshop, NCAR Foothills Laboratory, Boulder, Colorado, August -3,. [5] Marquardt, C., K. Labitzke, Ch. Reigber, T. Schmidt and J. Wickert, An assessment of the quality of GPS/MET radio limb soundings during February 997, Phys. Chem. Earth (A), Vol. 6, No. 3, pp. 5 3,. [6] Melbourne, W. G., E. S. Davis, C. B. Duncan, G. A. Hajj, K. R. Hardy, E. R. Kursinski, T. K. Meehan, L. E. Young, and T. P. Yunck, The Application of Spaceborne GPS to Atmospheric Limb Sounding and Global Change Monitoring, JPL Publication 94-8, April, 994. [7] Palmer, P. I., J. J. Barnett, J. R. Eyre, und S. B. Healy, A nonlinear optimal estimation inverse method for radio occultation measurements of temperature, humidity, and surface pressure, J. Geophys. Res., 5, ,. [8] Shum, C. K., J. C. Ries, and B. D. Tapley, The Accuracy and Applications of Satellite Altimetry, Geophys. J. Int.,, 3-336, 995. [9] Sokolovskiy, S. V., and D. Hunt, Statistical optimization approach for GPS/MET data inversions, URSI GPS/MET Workshop, Tucson, Arizona, 996. [] Sokolovskiy, S. V., Tracking tropospheric radio occultation signals from low Earth orbit, Radio Sci., 36, , [] Steiner, A. K., G. Kirchengast, and H. P. Ladreiter, Inversion, error analysis, and validation of GPS/MET occultation data, Ann. Geophys., 7, -38, 999 [] Swenson, S., and J. Wahr, Estimated effects of the vertical structure of atmospheric mass on the time-variable geoid, in review, J. Geophys. Res.,. [3] Syndergaard, S., Retrieval analysis and methodologies in atmospheric limb sounding using the GNSS radio occultation technique, Danish Meteorological Institute, 999 [4] Velicogna, I., J. Wahr, and H. van den Dool, Can Surface Pressure be used to remove the atmospheric contribution from GRACE data with sufficient accuracy to recover hydrological signals?, J. Geophys. Res., 6(B8), ,. [5] Vorob'ev, V. V., and T. G. Krasil'nikova, Estimation of the accuracy of the atmospheric refractive

11 index recovery from doppler shift measurements at frequencies used in the NAVSTAR system, Phys. of the Atmosphere and Ocean, 9, 6-69, 994. [6] Wahr, J., and M. Molenaar, Time variability of the Earth's gravity field: hydrological and oceanic effects and their possible detection using GRACE, J. Geophys., Res., 3 (B), 3,5-3,9, December, 998. [7] Ware, R., M. Exner, D. Feng, M. Gorbunov, K. Hardy, B. Herman, Y. Kuo, T. Meehan, W. Melbourne, C. Rocken, W. Schreiner, S. Sokolovskiy, F. Solheim, X. Zou, R. Anthes, S. Businger, and K. Trenberth, GPS sounding of the atmosphere from low earth orbit: Preliminary results, Bull. Am. Meteorol. Soc., 77, 9-4, 996. [8] Wickert, J., R. Galas, G. Beyerle, R. König, und Ch. Reigber, GPS Ground station data for CHAMP Radio Occultation Measurements, Phys. Chem. Earth (C), 6, 53-5, a. [9] Wickert, J., Ch. Reigber, G. Beyerle, R. König, C. Marquardt, T. Schmidt, L. Grunwaldt, R. Galas, T. K. Meehan, W. G. Melbourne, and K. Hocke, Atmosphere sounding by GPS radio occultation: First results from CHAMP, Geophys. Res. Lett., 8, , b. [3] Wickert, J., The CHAMP radio occultation experiment: Algorithms, Processing system, and First results, PhD thesis, (in German), Scientific Technical Report 7/, GFZ Potsdam, Germany,. [3] Wickert, J., Beyerle G., Hajj G. A., Schwieger V., Reigber Ch., GPS radio occultation with CHAMP: Atmospheric profiling utilizing the space-based single difference technique, Geophys. Res. Lett., 9(8),.9/GL398, b.