Double-Difference Method

Background

The Double-Difference (D-D) method facilitates inter-comparison between sensors of interest (SOIs) that do not have optimal temporal and/or spatial coincidence to allow for direct inter-comparison. The method depends on a stable sensor reference standard (SRS) of characterized uncertainty that can be inter-compared directly with two or more SOIs with similar radiometric characteristics as the reference standard. Directly inferred radiometric biases between each SOI and the SRS (SOI-SRS) comprise the elements used to compute the D-D radiometric bias values. Since the SRS is considered a stable source of measurement relative to all SOIs, it can be used as a common radiometric transfer standard between SOI pairs. This is accomplished by computing differences between pairs of (SOI-SRS) radiometric biases – e.g., (SOI2-SRS)-(SOI1-SRS) – which yields the radiometric bias between the two SOIs (SOI2-SOI1). This is illustrated in Figure 1 for three microwave sensors that share a common radiative transfer model reference standard [1]:

DD_Method_Example_Illustration.jpg

Figure 1. Illustration of D-D relationships between antenna temperatures from three operational microwave radiometer SOIs - NOAA-20 ATMS (SOI1), S-NPP ATMS (SOI2), and METOP-C AMSU-A (SOI3) - using RTM simulated Ta as a SRS.

Commonly Adopted Microwave Radiometric Standards

There are many commonly adopted microwave instrument reference standards that are used. These include the following:

1) RTM simulations of microwave antenna temperature (Ta) or brightness temperature (Tb) using various atmospheric sounding profiles and surface temperature and wind speed/direction data sources [1,2]
  • Atmospheric Profiles – Radiosondes, Global Navigation Satellite System Radio-Occultation (GNSS-RO), Numerical Weather Prediction (NWP)
  • Surface Data – NWP, Climatology, Buoys

2) Aircraft [3-5] or on-orbit instrument microwave instrument of well characterized and understood uncertainty, which can be used as a benchmark of inter-comparison using the SNO or SCO method.
  • Airborne Sensors – NPOESS Airborne Sounder Testbed - Microwave (NAST-M), High Altitude MMIC Sounding Radiometer (HAMSR), Hyperspectral Microwave Atmospheric Sounder (HYMAS – MIT/LL)
  • Satellite Sensors - S-NPP ATMS and METOP-A AMSU-A

3) Characterized relatively stable surface target, such as the Amazon forest [6].

Caveats

If SOIs have identical hardware characteristics, then a given D-D represents a radiometric bias between a pair of SOIs that is attributable to SOI performance differences. If the hardware characteristics of SOIs differ- e.g., scan angle and/or spectral response function - the radiometric contributions from these characteristics will be folded into the D-D value if they are not accounted for in the SRS.
  • D-D methods that use radiative transfer simulations as a SRS can usually accommodate for instrument hardware discrepancies.
  • If the SRS characteristics are not flexible, as is the case for most on-orbit satellite instruments, then radiative transfer simulations can be used to estimate hardware related radiometric biases between SOIs [7].
Author: Robbie Iacovazzi, NOAA/NESDIS/STAR, 5830 University Research Court, College Park, MD, USA, 20740-3823; Robert.Iacovazzi@noaa.gov.

References

  1. Iacovazzi, R.; Lin, L.; Sun, N.; Liu, Q., 2020: NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings. Remote Sens. 12, 828.
  2. Moradi, I., R. R. Ferraro, P. Eriksson and F. Weng, 2015: Intercalibration and Validation of Observations From ATMS and SAPHIR Microwave Sounders, in IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 11, pp. 5915-5925, doi: 10.1109/TGRS.2015.2427165.

  3. Blackwell, W. J., et al. (2001), NPOESS aircraft sounder testbed-microwave (NAST-M): Instrument description and initial flight results, IEEE Trans. Geosci. Remote Sens., 39, 2444-2453.

  4. Bjorn H. Lambrigtsen, A. L. Riley, “HAMSR - The High Altitude MMIC Sounding Radiometer,” Earth Science Technology Conference, University of Maryland, College Park, MD, August 28-30, 2001.

  5. L.M. Hilliard, P. E. Racette, W. Blackwell, C. Galbraith, E. Thompson, "Hyperspectral Microwave Atmospheric Sounder (HYMAS) Architecture and Design Accommodations," IEEE Aerospace Conference, Big Sky, MT, March 2-9, 2013, URL of presentation:http://tinyurl.com/lodaeg4

  6. N. Patel, L. Hong, W. Jones and S. Vasudevan, "Evaluation of the Amazon Rain Forrest as a Distributed Target for Satellite Microwave Radiometer Calibration," 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, 2006, pp. 85-88, doi: 10.1109/IGARSS.2006.27. Note that there is a Master's Thesis of the same title that can be found at https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=4294&context=etd.

  7. Iacovazzi, R., CY Cao, SA Boukabara, 2009: Analysis of Polar-orbiting Operational Environmental Satellite NOAA-14 MSU and NOAA-15 AMSU-A relative measurement biases for climate change detection. JGR Atmospheres, 114, DOI: 10.1029/2008JD011588.

-- RobbiIacovazzi - 21 Jul 2020
Topic attachments
I Attachment Action Size Date Who Comment
DD_Method_Example_Illustration.jpgjpg DD_Method_Example_Illustration.jpg manage 174 K 21 Jul 2020 - 22:15 RobbiIacovazzi Double-Difference Method Example Illustration
Topic revision: r4 - 05 Aug 2020, RobbiIacovazzi
This site is powered by FoswikiCopyright © by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding GSICS Wiki? Send feedback