Data Assimilation in high resolution Numerical Weather Prediction models to improve forecast skill of extreme hydrometeorological events.
Creators: name: LAGASIO, MARTINA
Publisher: name: Universit� degli studi di Genova
Year: 2019

description: The complex orography typical of the Mediterranean area supports the
formation, mainly during the fall season, of the so-called back-building
Mesoscale Convective Systems (MCS) producing torrential rainfall often
resulting into flash floods. These events are hardly predictable from a hydrometeorological
standpoint and may cause significant amount of fatalities and
socio-economic damages. Liguria region is characterized by small catchments
with very short hydrological response time, and it has been proven to be very
exposed to back-building MCSs occurrence. Indeed this region between 2011
and 2014 has been hit by three intense back-building MCSs causing a total
death toll of 20 people and several hundred million of euros of damages.
Building on the existing relationship between significant lightning activity and
deep convection and precipitation, the first part of this work assesses the
performance of the Lightning Potential Index, as a measure of the potential for
charge generation and separation that leads to lightning occurrence in clouds,
for the back-building Mesoscale Convective System which hit Genoa city (Italy)
in 2014. An ensemble of Weather Research and Forecasting simulations at
cloud-permitting grid spacing (1 km) with different microphysical
parameterizations is performed and compared to the available observational
radar and lightning data. The results allow gaining a deeper understanding of
the role of lightning phenomena in the predictability of back-building Mesoscale
Convective Systems often producing flash flood over western Mediterranean
complex topography areas. Despite these positive and promising outcomes for
the understanding highly-impacting MCS, the main forecasting issue, namely
the uncertainty in the correct reproduction of the convective field (location, 
timing, and intensity) for this kind of events still remains open. Thus, the second
part of the work assesses the predictive capability, for a set of back-building
Liguria MCS episodes (including Genoa 2014), of a hydro-meteorological
forecasting chain composed by a km-scale cloud resolving WRF model,
including a 6 hour cycling 3DVAR assimilation of radar reflectivity and
conventional ground sensors data, by the Rainfall Filtered Autoregressive
Model (RainFARM) and the fully distributed hydrological model Continuum. A
rich portfolio of WRF 3DVAR direct and indirect reflectivity operators, has been
explored to drive the meteorological component of the proposed forecasting
chain. The results confirm the importance of rapidly refreshing and data
intensive 3DVAR for improving first quantitative precipitation forecast, and,
subsequently flash-floods occurrence prediction in case of back-building MCSs
events. The third part of this work devoted the improvement of severe hydrometeorological
events prediction has been undertaken in the framework of the
European Space Agency (ESA) STEAM (SaTellite Earth observation for
Atmospheric Modelling) project aiming at investigating, new areas of synergy
between high-resolution numerical atmosphere models and data from
spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2
and 3 satellites and Global Positioning System stations. In this context, the
Copernicus Sentinel satellites represent an important source of data, because
they provide a set of high-resolution observations of physical variables (e.g. soil
moisture, land/sea surface temperature, wind speed, columnar water vapor) to
be used in NWP models runs operated at cloud resolving grid spacing . For this
project two different use cases are analyzed: the Livorno flash flood of 9 Sept
2017, with a death tool of 9 people, and the Silvi Marina flood of 15 November
2017. Overall the results show an improvement of the forecast accuracy by
assimilating the Sentinel-1 derived wind and soil moisture products as well as
the Zenith Total Delay assimilation both from GPS stations and SAR
Interferometry technique applied to Sentinel-1 data.