Cloud-affected satellite microwave radiances

Improvements in the partition of water vapour and cloud increments from satellite observations and a better specification of observation errors for cloud-affected satellite radiances have led to significant increase in the amount of satellite data used to produce operational global weather forecasts. This should lead to measurable improvements in forecast skill, particularly up to 2 days ahead.

One of the main avenues to improve the skill of a numerical weather prediction system is to increase the accuracy of the initial conditions for prediction through the assimilation of a larger number of observations. Despite their importance, as measured by impact indicators such as the Forecast Sensitivity to Observations Impact (FSOI), most cloud-affected microwave data are currently discarded so as to avoid detrimental effects as they are much more difficult to predict than clear-sky radiances.

In order to benefit from cloud-affected radiances from satellite microwave sounders we had to address two separate issues: one related to the larger spread and bias of the non-Gaussian distribution of observed minus predicted radiances when affected by cloud and another due to shortcomings in the assimilation of liquid-and-frozen-water-sensitive observations.

The first issue was addressed by devising specifically-tailored observation error models that are parametrised as a function of observed and predicted liquid water path in the observed scene (Migliorini and Candy, 2018). To ease the second issue we redesigned the so-called moisture-incrementing operator (Migliorini et al. 2018) so as to produce liquid and/or frozen water increments from moisture-sensitive observations at observation locations where the liquid and/or frozen cloud fraction forecast field is positive.

As a result of this work, from the next global UM operational release we will be assimilating cloud-affected radiances from microwave temperature sounding channels of the Advanced Microwave Sounding Unit A (AMSU-A) satellite sensor, which are currently under-used by the Met Office operational data assimilation system.


Migliorini, S., and Candy, B. (2018), All-sky satellite data assimilation of microwave temperature sounding channels at the Met Office. Q.J.R. Meteorol. Soc., under revision.

Migliorini, S., Lorenc, A. C. and Bell, W. (2018), A moisture-incrementing operator for the assimilation of humidity- and cloud-sensitive observations: formulation and preliminary results. Q.J.R. Meteorol. Soc.. doi:10.1002/qj.3216