Assimilating visible satellite images for convective‐scale numerical weather prediction: A case‐study

Abstract

Satellite images in the visible spectral range contain high‐resolution cloud information, but have not been assimilated directly before. This paper presents a case‐study on the assimilation of visible Meteosat SEVIRI images in a convective‐scale data assimilation system based on a local ensemble transform Kalman filter (LETKF) in a near‐operational set‐up. For this purpose, a fast look‐up table‐based forward operator is used to generated synthetic satellite images from the model state. Single‐observation experiments show that the assimilation of visible reflectances improves cloud cover under most conditions and often reduces temperature and humidity errors. In cycled experiments for two summer days with convective precipitation, the assimilation strongly reduces the errors of cloud cover and improves the precipitation forecast. While these results are promising, several issues are identified that limit the efficacy of the assimilation process. First, the linearity assumption of the LETKF can lead to errors as reflectance is a nonlinear function of the model state. Second, errors can arise from the fact that visible reflectances alone are ambiguous and only weakly sensitive to the water phase and cloud‐top height. And lastly, it is not obvious how to localise vertical covariances as visible reflectances are sensitive to clouds at all heights. For the latter reason, no vertical localisation was used in this study. To investigate the robustness of the results, the horizontal localisation scale, the assigned observation error and the spatial density of observations were varied in sensitivity experiments. The best results were obtained for an observation error close to the Desroziers estimate. High observation density combined with small localisation radii resulted in the smallest 1 hr forecast error. These settings were also beneficial for 3 hr forecasts, but forecasts at that lead time were less sensitive to the observation density and the localisation scale.

Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection

“…The number of satellite observations is reduced by ''superobbing'' (i.e., by averaging the satellite image on a certain length scale) (see, e.g., Scheck et al 2020). For this purpose, the observation operator is called for each column of the model grid and then the results are averaged over blocks of 6 by 6 grid cells, corresponding to a superobbing scale of 12 km.…”

“…In contrast to the assimilation experiments by Scheck et al (2020) and Hutt et al (2020), no multiplicative or additive inflation (Zeng et al 2019) of the error covariance matrix is used. To conserve positivity of relative humidity, we employ saturation adjustment in the LETKF (Schraff et al 2016).…”

“…To improve the prediction of local severe weather, infrared radiances were assimilated above the pacific (Sawada et al 2019) with 10-min temporal resolution. Scheck et al (2020) conducted the first study assimilating a visible satellite channel in a regional model for two cases with summertime convective precipitation over Germany. The simultaneous assimilation of visible and infrared channels has not been investigated so far.…”

“…Challenges for the assimilation of cloud-affected radiances include the errors of forward operators (Scheck et al 2018), correlated observation errors (Janjić et al 2017), the non-Gaussian distribution of errors (Geer et al 2010), systematic errors in the representation of clouds (Otkin et al 2018) and the ambiguity of observed integrated radiation in one channel resulting from the sensitivity to various model variables (e.g., water clouds, ice clouds, humidity, and temperature). Various methods have been developed to address these challenges.…”

“…To calculate synthetic infrared satellite observations from the model state, we simulate the cloud-affected infrared radiances with the radiative transfer code RTTOV (Saunders et al 1999;Matricardi and Saunders 1999). For synthetic observations in the visible channel, we use the method Method for Fast Satellite Image Simulation (MFASIS) recently put forward by Scheck et al (2016Scheck et al ( , 2018, which is by now also included in RTTOV. Compared to the assimilation of conventional observations (Schraff et al 2016;Necker et al 2018), a larger number (.6000) of all-sky radiance measurements can be assimilated every hour in a model domain covering (e.g., central Europe).…”