Hristo Chipilski, University of Reading, UK
The Search for Gaussian Moisture Variables at the Convective Scale
Abstract: Moisture data assimilation has gained much attention recently, not only due to the continuously growing number of moisture-related observations, but also because of the in-creasing role of moist processes in higher-resolution numerical weather prediction (NWP) models. Unfortunately, moisture control variables (MCVs) are hard to use in a conventional assimilation scheme due to i) their high spatial and temporal variability, ii) their nonlinear interactions with other model variables and iii) the extremely non-Gaussian distribution of their background errors. The last issue is directly connected to the strict physical bounds con-straining the majority of moisture variables. To this end, previous studies have approached the aforementioned issues in two distinct ways – through physically defining a new MCV or applying an appropriate statistical transformation to traditional MCVs.
Despite being largely successful, research efforts in the field of moisture data assimi-lation have mainly focused on global scale NWP models. As a result, this presentation exam-ines MCVs at the convective scale. To normalise their error distribution, a Rank-Based In-verse-Normal Transformation (RBINT) method, commonly used in the field of genetics, is introduced. Preliminary results reveal that the proposed method provides a superior Gaussian fit compared to earlier approaches and can be successfully implemented in current data as-similation algorithms.