FX-Net to Provide Internet Access to WFO-Advanced Workstation ProductsBy Sean Madine and Ning Wang
FSL is developing FX-Net, an Internet-based meteorological PC workstation, that can create sophisticated products using the WFO-Advanced system and transmit them over the Internet to a client workstation. The client application provides an AWIPS-like user interface with the processing tools needed to interact with products such as satellite, radar, and model imagery.
FX-Net is intended for users who have only modest communications and computing capability. It will serve as an inexpensive, simplified forecast workstation system for use in a variety of forecast, training/educational, and research environments. The less complex system can simulate many WFO-Advanced workstation features, but with some drawbacks, such as reduced resolution.
Two critical challenges that faced the FX-Net developers were to be able to effectively fulfill client requests as quickly as possible, and to accurately represent each meteorological product. Of course the size of the product is an important consideration when preparing it for transmittal over the Internet.
The products associated with this project, particularly weather data, can be categorized into four groups: model imagery, satellite imagery, model graphics, and radar imagery. Of these, satellite imagery is the most difficult to handle because of its large size; for example, an average raw image requires about 700 Kilobytes of space. Model imagery is problematic because of its size as well, and the fact that there are so many products available from so many different models. After investigating all available options, we decided to use a relatively new mathematical tool, the wavelet transform, to compress both model and satellite products. For the purposes of FX-Net, we determined that a small loss of fidelity would be tolerable in exchange for a high compression ratio. Model graphics are represented in a standard vector graphics format, whereas radar imagery is encoded in a standard lossless image compression format. The actual processing time involved in transmitting a given product representation naturally adds to the total time that it takes to fulfill a client request.
Here we discuss the selection and development of a compression technique that best meets the needs of the products transmitted by FX-Net, and the processing strategies implemented for the server and the client in order to optimize delivery time.
Compression Techniques for Product RepresentationModel and Satellite Imagery - These images are grouped together because their size demands many common requirements related to compression. The model image contains much less information than a satellite image. In comparison, the compression ratio for the model images is very high (about 80:1). After decompression, both model and satellite images continue to show meteorological detail even with a small loss of fidelity (see figures below). The processing time associated with decompression must not offset the time that is saved during transmission. Further, for processing reasons (discussed more later), the decompression of a particular image frame must be independent of adjacent image frames. The need for high compression ratios along with the tolerance for some loss of fidelity eliminated the use of any of the lossless schemes.
In the context of the above requirements, we investigated the following four compression schemes:
Because the wavelet transform contains some spatial information in addition to the frequency information, it can achieve excellent compression of meteorological images. Besides this, the wavelet transform also fulfills the other representation requirements. The loss of fidelity is acceptable (see the figures), the decompression processing is reasonable, and the image frames are individually compressed. By exploiting the time continuity of the series of frames, the three-dimensional wavelet compression might yield a higher compression ratio, but it also introduces some interframe dependencies. (This is being investigated.) Batch generation of the compressed products and real-time decompression of images by the client are discussed later.
The three types of satellite imagery that are available through FX-Net are infrared, visible, and water vapor. Each of these has different characteristics in terms of the wavelet transform. In dealing with these variables, we took advantage of the wavelet transform feature that enables us to "tune" the compression with a judicious choice of basis functions. For example, an infrared satellite image transformed with one choice of wavelet basis has higher fidelity than that of an image transformed with another choice of wavelet basis. The compression scheme that is used follows the standard transform-quantization-entropy coding procedure, which can be used in other types of schemes.
The images below illustrate the lossy nature of the wavelet transform compression scheme. An original satellite image of water vapor is shown in Figure 1a, with Figure 1b showing a magnified view of a cloudy moist area. Figure 2a shows a 20:1 compressed representation of the full image (1a), with the same magnified view in Figure 2b. Very little information is lost at 20:1, and it is still very useful to the meteorological forecaster or researcher. Figure 3a shows a 50:1 compressed representation of the original full image, with the same magnified view in 3b. The degradation is more objectionable at 50:1, and the image may no longer be useful.
Figure 1a. An original, raw satellite image showing water vapor.
Figure 1b. A magnified view of a moist cloudy area (1a, lower center) showing a good deal of fine-scale structure.
Figure 2a. A 20:1 compressed representation of the original image (Figure 1a).
Figure 2b. A magnified view of Figure 2a showing a slight but acceptable degradation.
Figure 3a. A 50:1 compressed representation of the original image (Figure 1a).
Figure 3b. A magnified view of Figure 3a showing that the image has now become more objectionable.
Model Graphics - Model graphics are represented in a vector graphics format called Dare Graphics Metafile (DGM). An important aspect of this format is that it offers a compact representation of the model graphics; e.g., a typical CONUS 500-mb Height Contour DGM file is on the order of 15 kilobytes in size. The FX-Net system also uses the DGM to encode progressive disclosure information about the graphics, which enhances the client's ability to perform efficient zoom display operations.
Radar Imagery - Radar imagery must be represented differently than the satellite and model imagery because there is no tolerance for loss in the radar signal. For instance, the use of wavelet compression could introduce an artificial feature or even mask a dangerous storm feature that does in fact exist. Since radar imagery also contains much less information (in terms of signal) than satellite imagery, and there is no tolerance for loss, FX-Net compresses radar imagery in a lossless manner. This was accomplished using a standard format, Graphical Interchange Format (GIF); tests verified that it works well for radar images. The compression ratio varies significantly depending on the amount of information in the radar image. The size of even the largest resultant radar files is manageable using the FX-Net scheme.
Processing StrategiesServer - It is important to address the processing associated with the various formats that have been chosen for the FX-Net products. The vector graphics format is inexpensive for both encoding on the server and decoding (displaying) on the client computer/workstation. Similarly, GIF encoding and decoding do not contribute significantly to the total time associated with product delivery. The wavelet transform, however, is compute-intensive, particularly on the server side.
Besides the actual wavelet transform, the compression routine on the server involves a dynamic search for the optimal set of basis function coefficients that are used to represent the image. This dynamic search greatly enhances the ability to compress the image at the cost of increased processing time, especially for satellite imagery where the compression ratio is critical. The cost in time can be on the order of 20 seconds per image frame. For the set of available satellite imagery, frames are compressed immediately upon arrival from the data ingest system. Thus, any requests for satellite imagery by a client can be fulfilled without performing the compression processing.
The production of model imagery is quite different. Because there is a huge matrix of possible products, model image products cannot be generated before an actual request. However, the high compressibility of the model images allows the compression routine to bypass the dynamic search for coefficients. Then a best guess set of coefficients is used, and the processing time is reasonable for the necessary on-demand compression.
Client - The decompression of the wavelet-compressed files on the client is also computationally expensive. The processing takes about two to three seconds per image frame when run on a 400-MHz PC. The client takes advantage of the multithreading feature offered by the Java programming language.
By executing decompression and communication threads concurrently, the capabilities of the client hardware are optimally utilized. Each individual image frame is displayed for the user as the decompression completes, thus minimizing the perceived wait for the product arrival.
Concluding Remarks and Future WorkNow that Internet communications have become faster, and available client hardware provides better processing capability, the concept of a network meteorological workstation is more viable. Many factors go into the design of such a workstation. Effective delivery of the products from the server to the client is an integral consideration in the FX-Net project. This includes not only accurate representation of the various products, but efficient processing associated with those representations.
The next major goal for the FX-Net project is the installation of an operational real-time system that will support an undergraduate meteorology curriculum at Plymouth State College in New Hampshire. As development continues, future enhancements could include access to case study data and support of multiple local radar datasets.
With the advent of wireless Internet connectivity and powerful laptop computers, FX-Net can be accessed even in remote areas. One example is that forecasters could be called upon to provide an on-site weather analysis in an area where a blazing fire is out of control. FX-Net would enable the forecasters near the site to access AWIPS products available hundreds of miles away at a National Weather Service forecast office.
Note: Special thanks to Steve Albers, a researcher in the Forecast Research Division, for offering his expertise regarding the satellite images.
(Sean Madine and Ning Wang are computer programmers in the International Division, headed by Dr. Wayne Fischer. They can be reached here.)