A SYNERGISTIC GLOBAL OCEAN AND ATMOSPHERIC ALGORITHM FOR THE COPERNICUS IMAGING MICROWAVE RADIOMETER

C. Jimenez^1,2, J. Tenerelli³, C. Prigent^2,1, T. Meissner⁴, R. Ekelund⁵

¹Estellus, ²Observatoire De Paris, ³Ocean Data Lab, ⁴Remote Sensing System , ⁵EUMETSAT

The Copernicus Imaging Microwave Radiometer (CIMR) is a satellite mission developed under the European Union Copernicus program expansion. While designed primarily for polar observations, CIMR will also provide unprecedented, coincident multi-frequency measurements over the global ocean at 1.414, 6.925, 10.65, 18.7, and 36.5 GHz, for all four Stokes parameters (Donlon et al., 2023). These observations will enable the estimation of key ocean and atmospheric parameters at global scale. The instrument features a large rotating deployable mesh reflector, providing real-aperture resolutions from <60 km at 1.4 GHz to <5 km at 36.5 GHz.

A physically based algorithm is proposed to consistently and simultaneously retrieve key global ocean parameters, including sea surface temperature, ocean wind speed and direction, sea surface salinity, together with atmospheric parameters above the ocean (the total column water vapor, the liquid water path, and the precipitation rate).

CIMR provides multi-frequency, multi-polarization observations at multiple spatial resolutions but with a complex observing geometry. Compared with WindSat and AMSR2, the CIMR sampling is particularly complicated, with 8 feeds per band at Ku and Ka bands, and 4 feeds per band at C and X bands. The feeds are arranged to minimize gaps between successive scans, causing their boresights to trace distinct arcs on the surface, each with a slightly different incidence angle (within 1.5°). Since the retrieval algorithm requires antenna temperatures from the different bands to be collocated and footprint-matched, an appropriate resampling strategy is necessary and a possible methodology based on the Backus-Gilbert method (Stogryn, 1978) will be described.

To exploit CIMR multi-band capability while addressing its varying spatial resolutions, a cascade multi-parameter, multi-frequency, multi-scale retrieval algorithm has been designed, based a physically grounded optimal estimation method (Rodger et al., 2000). The retrieval incorporates SURFEM-Ocean, a recent fast radiative transfer ocean model implemented in RTTOV (Kilic et al., 2023). The algorithm applies three successive optimal estimation steps at spatial resolutions of 60 km, 15 km, and 5 km, following methodologies suggested by Wilheit (1987) or Wentz and Meissner et al. (2000). At each resolution, geophysical parameters are retrieved, along with their associated uncertainties. Depending on the geophysical parameter, the environmental context, the application, and the estimated accuracy, the retrieval result from a given resolution is selected as the preferred result. This method maximizes the use of CIMR capabilities by leveraging a detailed understanding of its complex observing geometry and measurement characteristics.

Retrieval results for the multiple geophysical parameters will be presented, under a large range of environments from tropical conditions to the poles, for simulated scenes. The algorithms will also be applied to existing close-to-CIMR observations (SMAP and AMSR2), for evaluation under real conditions.

References:

• Donlon, C. J. et al. (2023). The Copernicus Imaging Microwave Radiometer (CIMR) Mission Requirements Document, v5.0.

• Kilic, L., Prigent, C., Jimenez, C., Turner, E., Hocking, J., English, S., … & Dinnat, E. (2023). Development of the surface fast emissivity model for ocean (SURFEM-Ocean) based on the PARMIO radiative transfer model. Earth and Space Science, 10(11), e2022EA002785.

• Rodgers, C. D. (2000). Inverse methods for atmospheric sounding: theory and practice (Vol. 2). World scientific.

• Stogryn, A. (1978). Estimates of brightness temperatures from scanning radiometer data, IEEE Transactions on Antennas and Propagation, 26(5) 720-726.

• Wentz, F. J., and Meissner, T. (2000). Algorithm theoretical basis document (ATBD): AMSR ocean algorithm. California: Remote Sensing Systems.

• Wilheit, T.T., and A.T.C. Chang (1980). An algorithm for retrieval of ocean surface and atmospheric parameters from the observations of the Scanning Multichannel Microwave Radiometer (SMMR), Radio Science, 15, 525-544.