Patent Application
20080312836
The present disclosure relates to remote measurement, and more particularly to remotely measuring atmospheric variables used for weather prediction.
Previous systems used for obtaining atmospheric information used to predict weather patterns have involved using Global Positioning System satellite (GPS) signals to perform GPS occultation. GPS occultation involves measurement of a GPS signal's phase shift due to travel through the atmosphere to calculate the refractivity of a column of the atmosphere. The refractivity can then be used to deduce water vapor content, and more particularly the precipitable water vapor (PWV) in a designated area. GPS and related constellations like Glonass and Galileo (often identified collectively as “Global Navigation Satellite Systems, or GNNS”) use frequencies that are insensitive to atmospheric effects. Therefore, very sensitive occultation receivers are needed to measure the slight changes in refractivity due to natural variation in the atmosphere. These measurements are especially challenging from mobile platforms such as aircraft because small motions of the aircraft can change the phase of arriving GPS signals. Also, attempts to use aircraft have proven less than satisfactory for this purpose because measurements from an aircraft have only been able to provide refractivity data within an approximate 150 m by 150 km corridor. This area is too long to be easily used in computational models that provide PWV data.
Another aspect of the problem is the sparse coverage afforded by GPS occultation. Occultation measurements require that a GPS satellite appear within a few angular degrees of the observer's horizon. If the satellite is too low, it is occluded by the Earth. If it is too high, the signal's path through the atmosphere does not traverse the troposphere on its way to an aircraft flying at a typical cruise altitude (typically 30,000-40,000 ft, or 9100 m-12,133 m). This makes the data nearly useless for weather prediction. The times when a GPS satellite is near the horizon for a given aircraft are infrequent-typically once an hour or so. Given that a jet aircraft typically covers about 1000 kilometers in an hour (when operating at a cruise speed), the distance between occultation measurements is so large that the measurements give relatively little value for weather models.
With GPS, the primary function is to let GPS receivers compute their positions based on relative phase shift among GPS signals transmitted from several GPS satellites. Therefore, current approaches to measuring atmosphere properties rely on measuring the GPS phase shift. For weather estimation, prior art methods measure the excess phase shift induced by GPS signals following a bent (refracted) path through the atmosphere to the receiver. As the GPS satellite rises or sets, the path length through the atmosphere changes. The phase shift changes with the path length and the refractivity. Phase measurements taken along various lines of sight are fed to tomography algorithms that estimate the best-fit refractivity as a function of altitude, which is termed a “refractivity profile.” This method can result in poor vertical or horizontal resolution. This is because each phase measurement is the sum of all phase shifts occurring anywhere along the signal's long path through the atmosphere. For example, using receivers aboard the COSMIC constellation of Low Earth orbit (LEO) satellites for this measurement can produce poor lateral resolution.
Networks of GPS receivers on land currently exist. There are currently two large scale GPS networks in the United States designed for real-time sensing of atmospheric water vapor: the NSF-UCAR SuomiNet, and the NOAA-FSL GPS-MET network. SuomiNet is designed primarily for university-based research and education while the FSL network is designed primarily for operational demonstration. SuomiNet is an international network, configured and managed to generate near real-time estimates of precipitable water vapor in the atmosphere, total electron content in the ionosphere, and other meteorological and geodetic information.
Many, if not most, conventional methods measure the phase shift directly and require precise knowledge of the receiving antenna's location. Meeting these requirements can be especially difficult on a moving platform like a high speed jet aircraft.
Other methods for gathering water vapor data over the oceans have been explored, however, they all have significant limitations. Radiosondes may be sent out over an ocean, but these can be expensive to gather the frequency of data required. Currently, the National Weather Service (NWS) obtains information on the water vapor distribution from satellite information and from twice daily radiosonde balloon launches at 93 sites around the continental United States. The radiosonde network is expensive to operate. In addition to the expense, the balloons carrying the sonde packages take about an hour to reach the tropopause. Therefore, the PWV data is not available for some time. Because there are not many radiosonde balloons available, the horizontal spatial density is too low and time between launches too high to observe rapid changes of the PWV with time and position. This is especially so over large bodies of the water such as oceans, where the PWV can vary significantly in short periods of time, giving rise to rapidly changing weather patterns.
Instrumentation on marine vessels such as ships does not provide sufficient frequency of PWV data to be useful for weather predicting purposes. In addition, ships are expensive to operate.
Land-based GPS receivers, the land-based receivers are unable to gather data for most of the Earth's surface, e.g., over the oceans. Poor coverage over the oceans leads to unreliable weather forecasts for the western United States, western Europe, Australia, and occasionally Japan. Using airborne platforms would allow meteorologists to have more expansive coverage, but current methods suffer from problems of wide resolution and infrequent coverage that limit the usefulness of information gathered for refractivity determination, and for weather prediction purposes.
The present disclosure is directed to a method and system for using an airborne mobile platform to provide refractivity profile data that is useful for weather prediction purposes.
In one exemplary methodology an airborne mobile platform, for example a high speed aircraft, is used to receive GPS signals from an orbiting GPS space vehicle. The aircraft receives a first GPS signal directly from the GPS signal source, and a second GPS signal that is reflected from a surface of the Earth before being received by the aircraft. The two signals are analyzed to determine phase information, and the phase information is used to determine the refractivity of the atmosphere that the second GPS signal traverses. From the determined refractivity, valuable weather related information for the atmospheric column between the surface and the aircraft can be determined.
In one specific implementation the aircraft is used to fly over a body of water. A first antenna of the aircraft, located adjacent a crown of the fuselage of the aircraft, is used to receive the first GPS signal. A second antenna located adjacent to an undersurface of the fuselage is used to receive the reflected second GPS signal. The distances that the two GPS signals each travel are known from information concerning the altitude of the aircraft and the elevation of the GPS satellite, relative to the aircraft.
In one specific implementation the phase difference information comprises an absolute phase difference between the first and second GPS signals. In another implementation the Doppler shift between the two GPS signals is used to determine the needed phase information.
In another specific implementation the effect of the waves on the surface of the body of water is taken into account by the methodology in analyzing the phase of the reflected GPS signal. In still another implementation the effect of the changing level of the body of water is taken into account in analyzing the phase difference between the two GPS signals.
In another specific implementation the use of GPS signals from more than one transponded satellite, or the change in elevation angle of a single GPS signal, may be used to obtain refractivity information, which in turn can be used to determine weather related information.
The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.
The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses.
Referring to
Referring further to
The system 10 includes a GPS receiver system 28 in communication with a processor 30. The GPS receiver system 28 is also in communication with both antennas 18 and 22. In general operation, the GPS receiver system 28 receives the direct and reflected GPS signals 24,26 and outputs the signals to the processor 30. Using the direct and reflected GPS signals 24,26, the processor 30 determines phase difference information between the signals. The determined phase difference information is used by the processor 30 to determine an average refractivity of the air (i.e., atmosphere) between the aircraft 12 and the ocean surface 16a. From the determined refractivity, and using additional algorithms to be described in the following paragraphs, the processor 30 is able to determine the PWV for a column 32 of the atmosphere between the ocean surface 16a and the aircraft 12.
Specific GPS frequencies that may be used with the present system 10 are both the L1 and the L2 frequencies. The L1 carrier is 1575.42 MHz and carries both the status message and a pseudo-random code for timing. The L2 carrier is 1227.60 MHz and is used for the more precise military pseudo-random code.
In
Φr=Φ0+ΔΦ1+ΔΦ2 Equation 1
where φ0=phase of incoming GPS signal 24 being directly received by the aircraft 12; (Equation 1a)
where ΔΦ1=D1*n1=change in phase of the incoming GPS signal 26a along distance D1; (Equation 1b); and
where ΔΦ2=D2*n2=change in phase of the incoming GPS signal portion 26b along distance D2.
In this case, the phase has units of radians. This can be interpreted as an optical path along a physical distance, for example the physical distance separating the GPS satellite 14 and the aircraft 12. The number of wavelengths within this distance varies when the index of refraction, “n”, varies. As n increases, the number of wavelengths that can fit within this distance increases without changing frequency. Thus, the measurement is an effective phase shift which has units of radians as shown in Equation 2 below:
φ=2π Ln/λ Equation 2
For a ground-based receiver, the excess path length that the GPS signal must travel when the GPS satellite 14 is at its zenith relative to the receiver (due to changes in the index of refraction) is given by Equation 3, where refractivity, N(r), is related to the index of refraction n by N=106(n−1). The limits of integration are expressed as rs and ra where, rs is the geodetic radius of the Earth's surface and ra is the geodetic radius of the top of the neutral atmosphere (i.e., the “neutral” atmosphere being the portion of the Earth's atmosphere below the ionosphere).
The term Δr can be measured as GPS phase shift relative to the theoretical phase assumed if the Earth had no atmosphere. Equations for predicting Δr when a GPS satellite is not at zenith are known in the art.
Some meteorologists use Δr to help predict the weather using computer models. However, when h (aircraft 12 altitude) is approximately equal to ra, not much is learned by meteorologists from Δr looking upwards (above the aircraft 12). Therefore, it is necessary to consider the signal reflected from the ocean surface 16a.
An empirical formula can be used to calculate the refractivity of a parcel of air as shown in Equation 4. In this formula “T” is the temperature in Kelvin, “pd” is the partial pressure of dry air, “pv” is the partial pressure of water vapor, “Zd” is the inverse compressibility factor for dry air and “Zw” is the inverse compressibility factor for wet air. The constants “k1”, “k2” and “k3” are empirically determined.
This formula can also be expressed in Equation 5 below with the constants determined as:
(n−1)×106=N=77.6(pd/T)Zd−1+64.8(e/T)Zw−1+3.776×105(e/T2)Zw−1 Equation 5
An average PWV measurement can be calculated for the column of air below the altitude of the aircraft by determining n2 (the average refractivity over the distance D2, discussed further below) from the phase shift difference between the two signals. With the quantity n2, Equation 5 can be used, with tomographic algorithms, to determine the partial pressure of the water vapor, pv.
The system 10 may also be able to determine the needed phase change between the two GPS signals 24,26 by measuring the Doppler shift between the two signals rather than the absolute phase difference. In this instance the angular velocity of the signals relative to the Earth would need to be different. The general results will be approximately the same, however, although the Doppler shift may be computationally easier to measure.
The total distance traveled by the incoming GPS signal 26 is represented by length 26a, between the GPS satellite 14 and the surface of the ocean 16, and by length 26b, which is the reflected portion between the ocean surface 16a and the aircraft 12. This total distance can be represented by the following equations, where D2 (Equation 6a) represents length 26b and D1 (Equation 6b) represents length 26a:
The reflected signal 26b will travel the additional distance D1 and D2 before being received by the antenna 22 on the bottom of the aircraft 12. The quantities D1 and D2 are known from the aircraft's 12 altitude and the GPS satellite 14 elevation angle relative to the aircraft. An average refractivity measurement can be calculated for the column of air 32 below the altitude of the aircraft 12 by determining n2 from the phase shift difference between the two signals 24 and 26. The quantity n2 is the average over the distance D2 and n1 is the average over the distance D1.
Referring to
The measurement of n2 assumes the ocean surface 16a is a flat surface. This is often not the case, so in one embodiment the system 10 and methodology of the present disclosure may use a model for the reflection of electromagnetic radiation from waves on the ocean's surface 16a to obtain even more accurate phase information from the reflected GPS signal 26. This model corrects the apparent radius rs of the ocean surface 16a to account for waves. For example, researchers have developed a general bistatic scattering model that yields the cross section for the specular and resonant reflections of GPS signals from an ocean's surface. See, for example, Thompson, D. R. et. al., “Surface Roughness Estimation from GPS Sea Reflections,” NASA Earth Science Enterprise, IEEE Geoscience and Remote Sensing Symposium, 2002, which is hereby incorporated by reference into the present application. The scattering model predicts the behavior of a GPS signal reflected from ocean waves, and particularly changes in φ1 and φ2 due to different reflective strengths of wave troughs and peaks.
Finally, the signal reflection measurement for the reflected GPS signal 26 may also benefit from corrections for sea level changes due to tidal variations and the local air pressure. It will be appreciated that high air pressure depresses the local ocean surface 16a. These corrections may be determined by comparing the phases of two separate incoming, reflected signals arriving at different angles from the vertical, e.g. signals from two GPS satellites. The signal at the lower angle gets refracted more for a given mean value of n, so it effectively travels a longer overall path to the aircraft 12. From two phase measurements at different elevation angles, values for the quantity h+Ah and the average refractivity, n2, can be determined. This is explained with reference to
The present system 10 provides a number of significant advantages of previous approaches to obtain atmospheric information for weather prediction purposes. For one, the ionosphere has a strong effect on radio signals, which adds to the phase shift of GPS signals. In previous systems, this complicates the approach to measuring phase shift and refraction in the neutral atmosphere because the phase shift due to the ionosphere must be subtracted from the total observed phase shift to obtain the phase shift due to neutral atmosphere. This can introduce uncertainty in estimates of temperature and water content in the neutral atmosphere. The system 10 avoids this problem. The signal arriving directly from the satellite (signal component 24) and the signal reflected from the ocean surface 16a (signal component 26) have both traversed the ionosphere and have therefore incurred the same phase shift (to within the limits of local isotropy in the ionosphere). This greatly reduces the effect of ionospheric delay, thereby simplifying the calculation of temperature and water content in the neutral atmosphere.
Previously developed systems have also used absolute phase measurements to determine phase shift in the atmosphere. Thus, with previously developed systems it was necessary to use GPS-like satellites where absolute phase information is encoded in the signal. In the present system 10, only the relative phase of the direct signal and the reflected signal needs to be measured. Therefore, the system 10 is not constrained to use GPS-like satellites. Rather, temporal correlation can be used to measure the phase shift between the direct signal and the reflected signal for any satellite with a non-repeating signal and known orbital parameters. As an example, the present system 10 can be used with Iridium satellites. Iridium satellites are more numerous than GPS satellites, which provides better observing opportunities and thereby improves the spatial and temporal resolution of atmosphere models used for weather prediction. Iridium satellites also provide a signal that is roughly 1000 times stronger signal than a GPS signal, which greatly improves the signal-to-noise ratio of the received signal at the present system 10 and thereby improves the accuracy of temperature and humidity measurements obtained with the present system 10. Other candidates that may be suitable for use with the system 10 include direct-broadcast TV satellites.
The present system 10 can also minimize horizontal drift of the measurement point during each set of measurements. Conventional airborne GNSS occultation requires observing a single GNSS satellite as it moves through several degrees of elevation near the horizon. It is known that such a measurement point can drift by as much as 450 km during one occultation. This drift (through potentially different weather conditions) is the major source of error for standard GNSS occultation. Because the system 10 can observe at two or more elevation angles concurrently (using multiple satellites), this source of error can be avoided.
The present system 10 can also use another aircraft, rather than a satellite, to produce the direct and reflected signal. While such an arrangement would probably not be highly preferred for routine meteorology operations, it nevertheless could be used for targeted measurements. Such targeted measurements could be used, for example, in applications involving high-resolution study of moisture profiles in air feeding a hurricane near the U.S. coast.
The system and methodology described herein thus enables temperature and precipitable water vapor to be determined through the use of an airborne mobile platform. This enables highly useful precipitable water vapor information to be obtained over oceans and other large bodies of water where weather fronts often develop.
While various embodiments have been described, those skilled in the art will recognize modifications or variations which might be made without departing from the present disclosure. The examples illustrate the various embodiments and are not intended to limit the present disclosure. Therefore, the description and claims should be interpreted liberally with only such limitation as is necessary in view of the pertinent prior art.
