-- RobertIacovazzi - 13 Apr 2009

# Process Name: Selecting Orbits

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2008-12-18 Bob Iacovazzi, Jr. / NOAA Original Document 2009-04-15 Bob Iacovazzi, Jr. / NOAA Removed software references, updated methodology

## 1a.iii.1 Scope

This document outlines the process used to determine the time and location of simultaneous nadir overpass (SNO) events.

## 1a.iii.2 Relevance to ATBD

Acquisition of raw satellite data is obviously a critical first step in an SNO analysis. To facilitate the acquisition of data for the purpose of inter-comparison of low-earth-orbit (LEO) satellite instruments, prediction of the time and location of SNO events is also important.

## 1a.iii.3 Method Background

Johanes Kepler discovered that all orbital motion of planets around the Sun can be described by an ellipse; the area of the ellipse swept out by planetary orbital motion is constant over equal time intervals; and the average distance between the sun and a planet is related to the planetary period of orbital motion. Sir Isaac Newton was able to support Kepler's Laws for the two-body orbit problem in a much more rigorious manner.

When describing orbital motion of a satellite, one can define the orbit of the satellite, and the position of that satellite on that orbit, using classical orbital elements. These classical orbital elements are 1) semi-major axis, 2) eccentricity, 3) inclination, 4) right ascention of the ascending node, 5) argument of perigee, and 6) true anomaly (eg. Science@NASA Classical Orbital Elements). For weather satellites, these six parameters are given along with the time, spacecraft id, and orbital period in a two-line data format, which gives rise to the name "two-line elements" or "TLEs." Two-line elements can give the initial conditions to equation solutions that describe orbit motion of two bodies in space. Ideally, given a two line element and the equation solutions, one could predict the motion of a satellite forever. On the other hand, there are other forces - Earth oblateness, atmospheric drag, third-body gravity, solar wind and radiation pressure, and electromagnetic drag - that cause perturbations of satellite orbits. For this reason, models that take into account these other perturbing affects on the satellite orbit are called orbital perturbation models. The Simplified General Perturbations Satellite Orbit Model 4 (SGP4) is used to predict SNO events for low-earth-orbiting (LEO) satellites. The model is described in documentation compiled by Kelso (1988). Also, for a more in depth treatment of this topic, please see Bates et al. (1971) and the AU Space Primer.

## 1a. References

Air University Space Primer, 2003: Chapter 8 (Orbital Mechanics), United States Air Force.

Bates, R. R. , J. E. White, and D. D. Mueller, 1971: Fundamentals of Astrodynamics. New York: Dover Publications, Inc.

Kelso, T. S., 1988: Spacetrack Report Number 3 (Compiled from Hoots F. R. and R. L. Roehrich, 1980: Models for propagating NORAD elements sets), 87 pp.

# Process Name: Collocate Pixels

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-01-05 Bob Iacovazzi, Jr. / NOAA Original Document 2009-04-15 Bob Iacovazzi, Jr. / NOAA Removed software references, and updated methodology

## 1b.iii.1 Scope

This document outlines the process used to collocate pixels from two space-borne instruments on different low-earth-orbit (LEO) satellites at simultaneous nadir overpass (SNO) events.

## 1b.iii.2 Relevance to ATBD

The inter-comparison of LEO satellite instrument data at SNO events can be expedited by choosing and storing only those raw data necessary to carry out the inter-comparison. A major step in reducing the data burden of performing LEO-to-LEO inter-satellite calibration is to collocate those pixels that will be used in the analysis and discarding the remaining data. In addition, carefully determining appropriate data collocation techniques, and their associated thresholds, is critical to reducing LEO-to-LEO SNO inter-comparison uncertainties related to geolocation and resolution mismatch between measurements of different satellite instruments.

## 1b.iii.3 Method Background

In the SNO analysis, for a given SNO event, bilinear-interpolation or weighted-average data collocation techniques are harnessed.

The assumptions and technical considerations to perform data collocation between LEO satellite instruments at SNO events is summarized below:
• All data chosen for the analysis are near-nadir, which eliminates the need to consider azimuthal differences between measurements from two instruments; and
• Effectiveness of bilinear-interpolation versus weighted-average data collocation techniques can be indicated by the ratio of the areas of the pixel sizes of the two instruments.

When the instrument 1 to instrument 2 ratio of the pixel areas is greater than three, then the weighted-average collocation technique is considered to be better than the bilinear-interpolation collocation technique. Note that the instrument with the larger pixel is considered to be instrument 1.

Weighted-average interpolation is carried out simply by averaging all SNO pixels from satellite 2 that fall within each satellite 1 SNO pixel. Currently, a "top-hat" weight of 1.0 is used for all satellite 2 pixels whose centers lies within the boundaries of a satellite 1 pixel, while this weight is 0.0 for all other satellite 2 pixels.

In the bilinear-interpolation collocation method. The four nearest satellite 2 pixels surrounding a given satellite 1 pixel are bilinearly interpolated to the location of the satellite 1 SNO pixel. In addition, screening collocated measurements with an indicator of the scene inhomogeneity helps to lower uncertainties in the analysis. Note that this second step is discussed in more detail in the section on Uniformity Test (3a). The reference available for the biliner-interpolation techique is Iacovazzi and Cao (2008).

## 1b. References

Iacovazzi, Jr., R. A. and C. Cao, 2008: Reducing uncertainties of SNO-estimated inter-satellite AMSU-A brightness temperature biases for surface-sensitive channels. J. Atmos. Ocean. Tech., 25, 1,048–1,054.

# Process Name: Calculate Intercalibration Units (Reflectance, Radiance, or Brightness Temperature)

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-04-15 Bob Iacovazzi, Jr. / NOAA Original Document

## 2a.iii.1 Scope

This document briefly outlines the process used to compute reflectances, radiances or brightness temperatures (Tbs) from two space-borne instruments on different low-earth-orbit (LEO) satellites at simultaneous nadir overpass (SNO) events.

## 2a.iii.2 Relevance to ATBD

Since discrepancies between the digital number, or count, data of two instruments at an SNO do not signify an error in measurement from either instrument, the instruments calibration protocol must be applied to the digital number, or count, data before they can be intercompared. Thus, computing reflectance, radiance or Tb from the native instrument digital number, or count, data becomes a standard step in performing an SNO analysis. Also, if the reflectance, radiance or Tb units from the two instruments are different, they must be converted such that the units match.

## 2a.iii.3 Method Background

In this SNO analysis, as a general rule the calibration protocol applied at the time of analysis by a given instruments satellite operations center is used. In some instances, data from a data provider is already in units of reflectance factor, radiance or Tb, eliminating this step completely. Unit matching is performed depending on the instrument pair.

# Process Name: Spectral Matching

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-04-22 Bob Iacovazzi, Jr. / NOAA Original Document

## 2b.iii.1 Scope

This document briefly outlines the process, if any, used to to account for systematic errors of measurements from two space-borne instruments on different low-earth-orbit (LEO) satellites at simultaneous nadir overpass (SNO) events due to spectral response function (SFR) differences.

## 2b.iii.2 Relevance to ATBD

A major source of systematic measurement error between similar instrument channels on different satellites can be due to discrepancies between the channel SRFs. Therefore, a firm statement regarding how these systematic errors are assessed and removed is essential to the SNO data processing.

## 2b.iii.3 Method Background

Band pass filters in meteorological satellite instruments are typically interference filters. The exception to this is microwave instruments, which have band pass determine by an electronic circuit. An interference filter consists of multiple thin layers of dielectric or metallic material having different refractive indices, that are coated on a glass substrate. The basis of such a filter is that these coatings can reflect (transmit) incoming radiation at unwanted (desired) wavelengths, based on the coating materials and thicknesses. The reflected wavelengths often destructively interfere to inhibit the total amount of reflected radiation, thus the name interference filter. Since the process of making an interference filter can be challenging, the same channel from two identical instruments do not have identical SRFs, since they are not made from the same filter lot, or run.

In the Earth, Atmosphere, Ocean EAO system, reflectance, absorptance, and scattering of radiation can be wavelength dependent. Thus, the response of the "same" channel or band from two different instruments can also be different, since the SRFs are not necessarily the same. This means any measurement error between instruments with even slightly different SRFs needs to be account for in some manner before calibration errors between these instruments can be inferred.

At present, visible instruments usually do not have SRF measurement differences accounted for, since the bidirectional reflectance distribution function is usually not known as a function of wavelength for most EAO features. On the other hand, currently operating hyperspectral infrared sounding instruments - e.g., the Earth Observing System Aqua Atmospheric Infrared Sounder (AIRS) and the Metop Infrared Atmospheric Sounding Interferometer (IASI) - are used to estimate SRF-related differences. Meanwhile, in the microwave the difference between identical bands of two instruments are very small, because electronic circuits can be tuned for band-pass in a much more reproducible way than interference filters. On the other hand, instrument response differences arising from similar but different microwave band passes are often handled using forward radiative transfer models (RTMs), coupled with numerical weather prediction model output to establish RTM boundary conditions. More details and references will be furnished in instrument specific sections of this ATBD.

# Process Name: Spatial Matching

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-04-30 Bob Iacovazzi, Jr. / NOAA Original Document

## 2c.iii.1 Scope

Spatial matching in the Simultaneous Nadir Overpass (SNO) method has been defined in Section 1b on Collocation.

## 2c.iii.2 Relevance to ATBD

Although spatial matching is very important to any satellite instrument inter-comparison method, it has been handled in Section 1b on collocation.

## 2c.iii.3 Method Background

Spatial matching is the art of collocating two data sets in space using an interpolation method. In the SNO method, only those cross-track pixels that are within 5 degrees scan angle from nadir are used in the analysis. Since all pixels are considered near-nadir pixels, other than spatial interpolation, no filters have been placed on the data related to differences in satellite instrument view elevation and azimuth angle in the SNO event scene. Thus, spatial matching has been absorbed into Section 1b on collocation.

# Process Name: Temporal Matching

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-04-30 Bob Iacovazzi, Jr. / NOAA Original Document

## 2d.iii.1 Scope

Temporal matching in the Simultaneous Nadir Overpass (SNO) method has been defined in Section 1b on Collocation.

## 2d.iii.2 Relevance to ATBD

Although temporal matching is very important to any satellite instrument inter-comparison method, it has been handled in Section 1b on collocation.

## 2d.iii.3 Method Background

Temporal matching is the art of collocating two data sets in time using an interpolation method. In the SNO method, the time interval of observations of the same scene is defined as a criteria. At this point, this criteria is set to 30 seconds.

# Process Name: Uniformity Test

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-04-30 Bob Iacovazzi, Jr. / NOAA Original Document

## 3a.iii.1 Scope

Currently, no SNO scene uniformity tests are implemented.

## 3a.iii.2 Relevance to ATBD

The idea of scene uniformity tests in regards to SNO satellite instrument inter-comparison analysis is that SNO scenes with the relatively high variability, which cause the greatest uncertainties, can be removed or given a smaller weight in the analysis. For the SNO method, this can be a double edged sword. In the case of infrared and microwave sounders, large pixel size and separation can lead to less than 10 collocated pixels for a given SNO event. If a SNO scene is highly variable, entire SNO events can be removed from the analysis, which brings down the active sample number. Since in many cases SNO events occur only every few days, loss of a number of events can increase the standard error. For this reason, no method is used to evaluate scene uniformity in the SNO method of LEO-LEO inter-calibration.

# Process Name: Outlier Rejection

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-04-30 Bob Iacovazzi, Jr. / NOAA Original Document

## 3b.iii.1 Scope

Data rejection is used minimally to help maintain the stability of the satellite instrument data comparison analysis.

## 3b.iii.2 Relevance to ATBD

Data rejection can be used in any measurement process to increase the fidelity of that measurement process. If not careful though, it can also be used subjectively as a way to make the results of a measurement analysis conform to expectations of that measurement. Therefore, in this these low-earth-orbit satellite instrument inter-comparison analyses, data rejection is used minimally to help maintain the stability of the satellite instrument data comparison analysis.

## 3b.iii.3 Method Background

Data rejection is limited only to undefined data values that have been flagged in the raw satellite data product.

# Process Name: Auxillary Data Sets

Document History:
 Date (yr-mon-day) Responsible Official and Organization Nature of Change or Revision 2009-05-04 Bob Iacovazzi, Jr. / NOAA Original Document

## 3c.iii.1 Scope

Auxillary data are those data, other than radiances, needed to perform the SNO data analysis.

## 3c.iii.2 Relevance to ATBD

Auxillary data sets - cloud or land/sea masks, surface emissivity, surface type, etc. - are not utilized in the current implementation SNO method.

I Attachment Action Size Date Who Comment
pdf spacetrackReportNum3.pdf manage 473 K 14 Apr 2009 - 13:16 RobertIacovazzi Spacetrack Report Number 3
Topic revision: r11 - 04 May 2009, RobertIacovazzi

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