We have developed instrumentation to measure and characterize the moisture field transpired by irrigated trees, to demonstrate that IPR is working as expected. This inexpensive technology is known as the Atmospheric Moisture Observation System (AMOS), with a patent pending. A demonstration test at the waste water treatment plantation in Missoula Montana is pending. An aerial view of the plantation, along with a notional network of sensors to be used for collecting data, is shown in Figure 1.

Figure 1: Overhead view of wastewater treatment plantation, and notional placement of measurement sensors

Figure 1: Overhead view of wastewater treatment plantation, and notional placement of measurement sensors

The primary purpose of these tests is to understand how the moisture field generated by the plantation evolves with time. In other words, does it usually form a plume that extends downwind, a cloud that settles over the area, or something else? Or does the moisture just dissipate? This is important to know when designing IPR projects.

How AMOS works

AMOS uses raw data from a network of GPS receivers (serving as sensors) to determine the amount of water in the atmosphere. Water vapor can’t be measured directly using GPS data, however it affects the GPS signal transmitted from the satellite to the receiver. The “measurement” must be extracted indirectly from the signal. What provides this information is actually an error that is introduced to the data during transmission.

Determining water vapor levels from the Tropospheric delay

Figure 2: Refraction of a GPS signal as it passes through the atmosphere

Figure 2: Refraction of a GPS signal as it passes through the atmosphere

Carrier phase differential GPS processing is a technique used to measure positional accuracy within centimeters (this is the basis for survey-grade GPS systems).  In addition, very accurate estimates of errors (those that can’t be eliminated) are also determined by this technique. Once these errors are known, corrections can be applied to mitigate their effect.

There are several different errors that must be corrected to determine position and time with high accuracy using GPS (Just about any GPS tutorial on the internet will provide a basic description of the primary error sources). One of these errors is known as tropospheric delay. Figure 2 depicts the cause in a simplified manner.

When determining GPS positions, the time for the signal to travel the “Straight Line Distance” between the satellite and receiver is required. However, the actual path the signal travels between the satellite and the receiver is bent by refraction, which increases travel time. The signal experiences other delays as well as it passes through the troposphere, which further increases the travel time. This increase in travel time is the Tropospheric delay, which is an error that must be subtracted from the measured time.

Part of this delay is caused by water vapor in the atmosphere. Once the total Tropospheric delay has been determined, the amount of delay due to water vapor can be determined. From this value, there are mathematical formulas that have been developed to determine the amount of water vapor along the signal transmission path. This is the “measurement” that is extracted from the GPS signal.

GPS Tomography

GPS tomography is used to determine the distribution of water vapor in a region above the sensor network, and is similar to taking a “CAT scan” of the atmosphere. A large number of individual measurements are used to observe the three-dimensional distribution of water vapor. Individual receivers are combined to serve as a sensor network, each collecting data from multiple satellites, as depicted in Figure 3.

Figure 3: GPS receiver sensor network configured for observing atmospheric water vapor fields

Figure 3: GPS receiver sensor network configured for observing atmospheric water vapor fields

This diagram represents a portion of a network of GPS receivers, and the potential precision they can provide when estimating atmospheric moisture fields and profiles. The profile grid represents atmospheric volume elements, technically known as voxels (analogous to pixels on a 2-D image) above the sensor network.  The grid encompasses the volume of coverage, and the size of each voxel determines the resolution of the profile.  While the details of a tomographic inversion routine are beyond the scope of this description, the algorithms and underlying theory are similar to those employed with a medical CAT scan.

For GPS tomography to measure moisture within a voxel, a GPS signal transmission path, or line of sight (LOS) vector, must cross through the voxel. With more LOS vectors crossing a voxel, the accuracy of the results improves. Additional accuracy improvements result when LOS vectors associated with different receivers intersect within a voxel. Therefore, the spacing between receivers and the total number of receivers in the network are important factors affecting the accuracy of the moisture field profiles.

Beyond the demonstration test

AMOS will be used with all IPR validation tests, as well as environmental transformation projects as they are developed. The data provided by AMOS will be very useful for understanding local climate effects, and for optimizing placement and operations of an IPR plantation. There are other uses for AMOS that are being pursued at the same time, since this technology has promising applications for weather forecasting and research as well.