Revisiting error models for the assimilation of infrared satellite radiances

Cloud-affected infrared satellites constitute a promising data source for numerical weather prediction models as they contain crucial information on atmospheric clouds and convective activity. Their sensitivity to both hydrometeor content and cloud top height, however, leads to a very non-Gaussian distribution of first-guess (FG) departures, which violates a fundamental assumption of current data assimilation schemes. To mitigate this issue, various cloud-dependent error models for normalizing the departures have been proposed (Geer and Bauer, 2011; Harnisch et al., 2016; Okamoto et al., 2014). In the current presentation, we revisit these error models and propose a refined approach that leads to a more Gaussian distribution.

We quantify the performance of these error methods in detail when applied to one-month infrared observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation and simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over Austria. While all methods can successfully yield approximately Gaussian FG departure distributions when normalized by the observation error, an additional quality control and a minimum error threshold is necessary for some of them.

While previously published methods estimate the observation error using the average cloud effects from the model and observation spaces, we also introduce a new method that uses the maximum values from these two spaces for observation error calculation. Results show that the new method systematically outperforms the previous methods at no additional cost. Lastly, we analyze the performance of different practical implementation choices, such as using a linear or polynomial fit.

Reference:

Geer, A. J. and Bauer, P.: Observation errors in all-sky data assimilation, Quarterly Journal of the Royal Meteorological Society, 137, 2024–2037, https://doi.org/10.1002/qj.830, 2011.

Harnisch, F., Weissmann, M., and Periáñez, Á.: Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system, Quarterly Journal of the Royal Meteorological Society, 142, 1797–1808, https://doi.org/10.1002/qj.2776, 2016.

Okamoto, K., McNally, A., and Bell, W.: Progress towards the assimilation of all-sky infrared radiances: An evaluation of cloud effects, Quarterly Journal of the Royal Meteorological Society, 140, 1603–1614, https://doi.org/10.1002/qj.2242, 2014.