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On the Aliasing and Resolving Power of Sea Level Low-Pass Filtered APRIL 2008 TAI 617 On the Aliasing and Resolving Power of Sea Level Low-Pass Filtered onto a Regular Grid from Along-Track Altimeter Data of Uncoordinated Satellites: The Smoothing Strategy CHANG-KOU TAI NOAA/NESDIS, Camp Springs, Maryland (Manuscript received 14 July 2006, in final form 20 June 2007) ABSTRACT It is shown that smoothing (low-pass filtering) along-track altimeter data of uncoordinated satellites onto a regular space–time grid helps reduce the overall energy level of the aliasing from the aliasing levels of the individual satellites. The rough rule of thumb is that combining N satellites reduces the energy of the overall aliasing to 1/N of the average aliasing level of the N satellites. Assuming the aliasing levels of these satellites are roughly of the same order of magnitude (i.e., assuming that no special signal spectral content signifi- cantly favors one satellite over others at certain locations), combining data from uncoordinated satellites is clearly the right strategy. Moreover, contrary to the case of coordinated satellites, this reduction of aliasing is not achieved by the enhancement of the overall resolving power. In fact (by the strict definition of the resolving power as the largest bandwidths within which a band-limited signal remains free of aliasing), the resolving power is reduced to its smallest possible extent. If one characterizes the resolving power of each satellite as a spectral space within which all band-limited signals are resolved by the satellite, then the combined resolving power of the N satellite is characterized by the spectral space that is the intersection of all N spectral spaces (i.e., the spectral space that is common to all the resolved spectral spaces of the N satellites, hence the smallest). It is also shown that the least squares approach is superior to the smoothing approach in reducing the aliasing and upholding the resolving power of the raw data. To remedy one of the shortcomings of the smoothing approach, the author recommends a multismoother smoothing strategy that tailors the smoother to the sampling characteristics of each satellite. Last, a strategy based on the least squares approach is also described for combining data from uncoordinated satellites. 1. Introduction satellites are not coordinated, the combined resolving power and aliasing have been somewhat obscure. For a single altimetric satellite in exact-repeat sam- Greenslade et al. (1997) have shown that when the data pling mode or for a coordinated constellation of satel- from uncoordinated satellites are combined, it does not lites (wherein the coordination is such that the collec- appear to enhance the original resolving power of any tive samplings appear as if they were carried out by a of the individual satellites. Thus combining the data single exact-repeat satellite; see more elaborations in appears to offer few advantages. Yet, Le Traon and appendix A), Tai (2004, hereafter T04) has been able to Dibarboure (1999) as well as Ducet et al. (2000) have determine the resolving power (i.e., the largest band- found that the mapping errors are much reduced if data widths within which a band-limited signal can remain from uncoordinated missions are combined [as has the aliasing-free; see more elaborations in appendix B). overall error level computed in Greenslade et al. Moreover, Tai (2006, hereafter T06) has been able to (1997)]. The main purpose of the present investigation derive the aliasing formulas (i.e., the aliasing from spec- is to document a proof that they are both right and that tral components beyond these bandwidths into compo- there is no contradiction here, because even if the com- nents within these bandwidths). However, when the bination does not enhance the resolving power it may reduce the aliasing. In addition, this paper recommends the optimal strategies for combining data from uncoor- Corresponding author address: Chang-Kou Tai, E/RA3, dinated satellites. NOAA/NESDIS, 5200 Auth Road, Camp Springs, MD 20746. T06 has shown that the aliasing computed using the E-mail: [email protected] least squares (i.e., the best-fitting results to the raw DOI: 10.1175/2007JTECHO514.1 Unauthenticated | Downloaded 10/02/21 08:04 AM UTC JTECHO514 618 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 25 data) is different from the aliasing computed if the raw called ␣, may be indistinguishable from up to two spec- ␤ ␥ data are first low-pass filtered onto a regular space– tral components inside Rc, called and , along the time grid. The aliasing results favor the least squares ascending or descending tracks. For this to occur, ␣ has approach, which incurs the least amount of aliasing. to reside in special regions in the spectral space. Section (Hereafter, the low-pass-filtered data are referred to as 5 (section 8) of T06 gives the LS (smoothing) solutions the smoothed data, the low-pass-filtering approach as to the spatial aliasing. The temporal aliasing involves the smoothing approach, and the least squares ap- one extra step. First, pure temporal aliasing causes ␣ to proach as the LS approach.) It is no surprise that how appear as ␣Ј (because of the constant sampling time one processes the data can affect the aliasing. But the difference ␶ between adjacent parallel tracks) along the difference here extends to the resolving power. That is, ascending or descending tracks (see section 3 of T06) ␣Ј ␣Ј the resolving power of the smoothed data is smaller and may or may not be inside Rc already. Then than what the raw data are capable of resolving. With may be indistinguishable from up to two spectral com- few exceptions, most studies utilizing altimeter data ponents inside Rc along the ascending or descending have used the smoothing approach, including the seem- tracks as well. Again, for this to occur, ␣Ј has to reside ingly contradictory studies cited in the previous para- in special regions in the spectral space. Section 6 (8) of graph. And the proof that will be presented here is also T06 gives the LS (smoothing) solutions to the temporal for the smoothed data, whereas the proof for the LS aliasing. approach is still elusive because of the extreme diffi- b. The advantage of the LS approach over the culty in trying to get the analytic solutions. But clearly smoothing approach a numerical LS solution in combining the data from uncoordinated satellites would incur the least amount T06 has made the assertion that the LS approach of aliasing. However, even in the absence of such a produces the least aliasing, but the explanation leaves solution, it is still possible to combine the LS results something to be desired. We will use this opportunity to obtained from individual satellites in the most advan- offer a better explanation. In comparing the two ap- tageous way. proaches, one needs to distinguish two sources of alias- Because the proof cannot be developed coherently ing, namely, the inherent aliasing and the artificial alias- without a brief description of how the aliasing occurs ing. By the inherent aliasing, we mean the aliasing that for the smoothed data of a single satellite, this is what arises from the sampling’s inability to distinguish two will be outlined first but the details will be left to T06 spectral components from each other, such as men- (see section 8). Furthermore, this paper will provide tioned in section 2a, an outside spectral component ␣ is more detailed explanations where T06 has failed to ex- indistinguishable from up to two inside spectral com- plain fully. ponents ␤ and ␥ along the ascending or descending tracks. Thus the inherent aliasing is unavoidable no 2. Aliasing for the smoothed data from one matter what method is used to process the data. The exact-repeat satellite artificial aliasing, on the other hand, refers to the inef- ficiencies of the method deployed to treat the data. In a. The resolving power and aliasing of altimetric the LS approach, all the aliasing arises from the sam- exact-repeat sampling pling’s inability to distinguish two spectral components The peculiar sampling patterns of the exact-repeat from each other. There is no artificial aliasing in the LS sampling are characterized by four parameters. Spa- approach. However, the smoothing approach induces a tially, the sampling is carried out along two sets of par- lot of artificial aliasing in addition to the inherent alias- allel tracks (ascending and descending). The east–west ing (see elaborations below). Despite its superior qual- (north–south) distance between adjacent parallel tracks ity, it is much harder to apply the LS methodology. In is a constant called X (Y). Temporally, the entire sam- practice, almost all investigations have adopted the pling pattern is repeated after a repeat period called T. smoothing approach. Additionally, the sampling time difference between ad- c. The artificial aliasing induced in the smoothing jacent parallel tracks is also a constant called ␶. T04 has approach shown that the resolving power is characterized by a resolved spectral space Rc, where the wavenumbers One would get a better grasp of the situation if we Ͻ ϭ ␲ Ͻ ϭ ␲ |k| kc 2 /X and |l| lc 2 /Y and the frequencies contrast the altimetric sampling with the ideal textbook ␻ Ͻ ␻ ϭ ␲ | | c /T. case in which data are available continuously, thus fil- T06 has shown that the spatial aliasing occurs in the tering can be regarded as a convolution operation. following manner: a spectral component outside Rc, Then the Fourier transform of the filtered data is the Unauthenticated | Downloaded 10/02/21 08:04 AM UTC APRIL 2008 TAI 619 ˆ multiplication of the Fourier transforms of the data and where h(xi, yi, ti) are the smoothed data on a regular the filter [e.g., the convolution theorem in Bracewell space–time grid of (xi, yi, ti); h(xj, yj, tj) are the raw data (1986, 108–112)].
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