A moisture‐incrementing operator for the assimilation of humidity‐ and cloud‐sensitive observations: formulation and preliminary results. (2nd February 2018) - Migliorini, Stefano; Lorenc, Andrew C.; Bell, William
Abstract : Being able to assimilate satellite radiances that are affected by cloud and precipitation is a key goal for the Met Office, as it is expected to provide improvements to forecast skill both at global scale and over the UK, at locations that are meteorologically important. The set of 'control' variables currently used for the minimization of the data assimilation cost function includes a single moist variable that accounts for total water (i.e. from all its phases) increments arising from moisture‐sensitive observations. The control variable defines a total water increment, which is partitioned by a suitably designed operator into specific humidity and cloud (liquid or frozen) water increments to be added to the forecast model's short‐range predictions. These incremented values are fitted to the observations during the minimization procedure and used to start the next forecast. This article discusses the design and initial testing of a new moisture‐incrementing operator with the following characteristics: (i) it is linearly related to the perturbation forecast (PF) model state; (ii) its physically based formulation is consistent with that of the cloud scheme operating within the PF model; and (iii) it is trained statistically to fit cloud‐water perturbations from the Met Office Global and Regional Ensemble Prediction System (MOGREPS), when the operator is fed with specific humidity and temperature perturbations also from MOGREPS. The moisture‐incrementing operator's formulation described in this article is relevant to meteorological centres that (plan to) make use of moist control variables that are linearly dependent on total (i.e. vapour plus cloud) water data‐assimilation increments. The initial validation results of the new operator show a neutral impact, arising mostly from changes in wind forecasts in the Tropics. Future work will test the performance of the new moisture‐incrementing operator with a set of observations that include cloud‐affected microwave radiances. Abstract : This article discusses the design and performance of a new moisture‐incrementing operator that is suitable for assimilation of moisture‐sensitive observations, including those from satellite radiances that may be affected by cloud. The new operator can be used to partition total water data‐assimilation increments into vapour, liquid and frozen cloud water (and possibly cloud fraction) increments. The incrementing operator implicitly builds in situation‐dependent covariances between these variables, with the covariances ultimately derived from the ensemble training data.