Abstract
This study aims at introducing two conservative thermodynamic variables (moist-air entropy potential temperature and total water content) into a one-dimensional variational data assimilation system (1D-Var) to demonstrate the benefit for future operational assimilation schemes. This system is assessed using microwave brightness temperatures from a ground-based radiometer installed during the field campaign SOFGO3D dedicated to fog forecast improvement. An underlying objective is to ease the specification of background error covariance matrices that are currently highly dependent on weather conditions making difficult the optimal retrievals of cloud and thermodynamic properties during fog conditions. Background error covariance matrices for these new conservative variables have thus been computed by an ensemble approach based on the French convective scale model AROME, for both all-weather and fog conditions. A first result shows that the use of these matrices for the new variables reduces some dependencies to the meteorological conditions (diurnal cycle, presence or not of clouds) compared to usual variables (temperature, specific humidity). Then, two 1D-Var experiments (classical vs. conservative variables) are evaluated over a full diurnal cycle characterized by a stratus-evolving radiative fog situation, using hourly brightness temperatures. Results show, as expected, that analysed brightness temperatures by the 1D-Var are much closer to the observed ones than background values for both variable choices. This is especially the case for channels sensitive to water vapour and liquid water. On the other hand, analysis increments in model space (water vapour, liquid water) show significant differences between the two sets of variables.
Highlights
Numerical Weather Prediction (NWP) models at convective scale need accurate initial conditions for skillful forecasts of high impact meteorological events taking place at small-scale such as convective storms, wind gusts or fog
The aim of this study was to examine the interest of using moist-air entropy potential temperaturea and total water content qt to study fog initiation and dissipation at small scale
A 1D-Var system has been used for assimilating brightness temperature (T B) observations from the ground-based HATPRO microwave radiometer installed at Saint-Symphorien (Les Landes region over South-Western France) during the SOFOG3D measurement campaign
Summary
Numerical Weather Prediction (NWP) models at convective scale need accurate initial conditions for skillful forecasts of high impact meteorological events taking place at small-scale such as convective storms, wind gusts or fog. New ensemble approaches allow a better approximation of background error covariance matrices but rely on the capability of the 35 ensemble data assimilation to correctly represent model errors, which might not always be the case during fog conditions This is why it would be of interest to examine, in a data assimilation context, the use of variables that are more suitable when water phase changes take place. It is well-known that data assimilation systems used to be based on the assumptions of homogeneity and isotropy of background error correlations To release these hypotheses, Desroziers and Lafore (1993) and Desroziers (1997) implemented a 40 coordinate change inspired by the semi-geostrophic theory to test flow-dependent analyses with case studies from the Front-87 field campaign (Clough and Testud, 1988), where the local horizontal coordinates were transformed into the semi-geostrophic space during the assimilation process. The definition of the moist-air entropy potential temperature θs is introduced, as well as the formalism of the 1D-Var assimilation system, before describing the “conservative variable” conversion operator
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