Radiance measurements from spaceborne microwave instruments are the most impactful observations used in Numerical Weather Prediction (e.g. Eyre, English and Forsythe 2020). Sophisticated data assimilation methods such as 4D-Var have been critical to this success, enabling direct assimilation of raw radiances. However, until recently, different Earth System components such as ocean, land and atmosphere were always handled separately, meaning those radiances which are sensitive to more than one component are still assimilated sub-optimally. The development of coupled data assimilation methodologies enables us to take another big step in the use of radiances, simultaneously and consistently fitting the state in multiple sub-systems to the same observations. This requires improved surface radiative transfer models.
For the ocean, although in general physically based models are used in data assimilation, at least for modelling passive microwave observations, the uncertainty is not well known and often different models are used for different spectral bands, and for active and passive sensing instruments. Furthermore, for active-instrument, empirical Geophysical Model Functions are used, which are very accurate (~0.1 dB), but physically-based methods have lower accuracy (Fois, 2015). In attempting error budget closure, lack of knowledge of uncertainty in surface emission was a limiting factor (GAIA-CLIM: www.gaia-clim.eu/). An International Space Science Institute team was created (English et al. 2020) to address this gap, taking the best available model components, integrating, testing across all spectral bands and characterizing as far as possible the uncertainty. The resulting reference model will then be provided as community software on GitHub.
In this short presentation, the choices made assembling this model will be explained, building on the starting point of the LOCEAN model of Dinnat et al. (2003). Samples of characterization undertaken will also be summarized. This includes comparison to SMAP, AMSR2 and GMI (e.g. Kilic et al. 2019) and early work to evaluate in the infrared and for active sensors. Finally, the plans for making code available will be briefly presented. This model will also be used to generate training data for fast models, e.g. Fastem (English and Hewison 1998), as used in operational data assimilation and climate re-analysis.
Dinnat, E. P., Boutin, J., Caudal, G., and Etcheto, J., 2003 : Issues concerning the sea emissivity modeling at L band for retrieving surface salinity, Radio Sci., 38, 8060, https://doi.org/10.1029/2002RS002637
English, S., Prigent, C., et al., 2020: Reference-quality emission and backscatter modeling for the Ocean, B. American Meteorol. Soc., 101(10), 1593-1601. https://doi.org/10.1175/BAMS-D-20-0085.1
English S.J. and Hewison T.J., 1998: Fast generic millimeter-wave emissivity model, Proc. SPIE 3503, Microwave Rem. Sens. Atmos. Env., https://doi.org/10.1117/12.319490
Eyre, J.R., English, S.J., Forsythe, M., 2020: Assimilation of satellite data in numerical weather prediction. Part I: The early years. Q J R Meteorol Soc. 2020; 146: 49– 68. https://doi.org/10.1002/qj.3654
Fois, F., 2015, Enhanced ocean scatterometry, PhD Delft University of Technology, Delft, the Netherlands, doi = 10.4233/uuid:06d7f7ad-36a9-49fa-b7ae-ab9dfc072f9c .
Kilic, L., Prigent, C., Boutin, J., Meissner, T., English, S., & Yueh, S., 2019: Comparisons of ocean radiative transfer models with SMAP and AMSR2 observations., J. Geophys. Res.: Oceans, 124, 7683– 7699. https://doi.org/10.1029/2019JC015493