Chiara works on improving the estimation of the climatological background error covariance using different types of training datasets such as Met Office Global and Regional Ensemble System, ensembles of analyses and analysis increments.
Chiara's areas of expertise include:
Chiara works on improving the estimation of the forecast error covariances in the Met Office data assimilation system.
Estimates of forecast error covariances are at the heart of any data assimilation system and yet the way they are modeled in any operational assimilation scheme is limited by the compromises made for practical implementation and the available knowledge of the statistical properties of the forecast error.
In current NWP systems the dimension of the forecast error covariance matrices is far too large to be represented explicitly. They must be approximated. One additional problem is the lack of sufficiently large population of errors to calculate all elements of the forecast error covariance matrices. The aim of Chiara's work is to test different strategies to measure the main properties of forecast error covariances. This involves using different methods to estimate the forecast errors, such as ensemble of forecasts or analysis increments, and compare the results with the operational method which uses differences of varying length forecasts.
Chiara is also interested in using adaptive mesh methods to better represent the background error. A particular application is the representation of the background error in the presence of stratocumulus clouds.
Chiara started her career at the Met Office in Summer 2007. Before joining the Met Office, Chiara worked for six years as a postdoc at the AOPP at the University of Oxford on infrared remote sensing from satellite instruments and retrieval theory to get the information content when using multi-channels instruments. This work was a continuation of her PhD in Physics at University of Florence. The main focus of Chiara's PhD project was to develop and test the Optimised Retrieval Model used to retrieve in near-real time trace gases from the MIPAS instrument on board of Envisat platform. During her degree in Physics, Chiara was involved in developing an infrared diode-laser sensor to detect small tracers in atmosphere.