Speaker
Description
Despite the technological and scientific advances in recent decades, it is still a challenge to accurately forecast the onset and/or the spatio-temporal evolution of high impact weather events (HIWE), especially in complex topography coastal areas (present, for instance, in the Mediterranean Region). Nowadays, the skill of numerical weather prediction (NWP) models has improved thanks to the increasing model resolution from cloud-permitting (5 km) to cloud-resolving (1 km) grid spacing. However, challenges in predictive abilities with respect to HIWE derive from the poor knowledge of the initial state of both atmosphere and surface at small scales. Satellite data can be of great importance in this context, because forecast uncertainties can be significantly reduced by ingesting into models, operated at cloud-resolving grid spacing, high-resolution EO observational data. Hence, a challenging application of SAR could be the continuous and automatic generation of soil moisture (SM) maps used to routinely initialize high resolution NWP models. Sentinel-1 products in the Interferometric Wide swath mode are very effective for this purpose, allowing for a frequent and regular update (3-6 days considering both ascending and descending orbits) of soil moisture maps.
The STEAM (SaTellite Earth observation for Atmospheric Modelling) project, funded by ESA, aims at investigating new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels satellites. An example of synergy is just the Use of Sentinel-1-derived surface soil moisture to initialize a NWP model. In the framework of the STEAM project a number of experiments were carried out considering two HIWEs occurred in Italy, namely the floods that hit Livorno (Tuscany, Central Italy) on September 9, 2017 (casing nine casualties) and Silvi Marina (Abruzzo, Central Italy) on November 15, 2017. As numerical model, the Weather Research and Forecasting (WRF) one was chosen because it is well-established in the high resolution limit, has different physics parameterizations and enables the use of various data assimilation techniques. The soil moisture retrieval from Sentinel-1 was accomplished by applying a multi-temporal retrieval algorithm to time series of Ground Range detected Interferometric Wide Swath Sentinel-1 products. To perform the initialization experiments, the consistency between the original soil moisture data included in the land surface model (LSM) implemented into WRF (the rapid update cycle model) and those retrieved from Sentinel-1 must be taken into account. On the one hand, the soil moisture data are related to different soil depths. In fact, while the LSM included in WRF has six layers, the SAR derived SM is relative to approximately the first 5 cm of soil. On the other hand, the SAR derived maps have gaps in urban, forested, or densely vegetated areas, so that a direct insertion of SAR derived data to replace the LSM ones in the upped layer would generate strong inhomogeneities. Hence, the calculation of the difference between the retrieved SM values and the original (model) ones in the upper layer was firstly carried out. Then, this difference was spatially interpolated and, finally, the map of interpolated difference was added to the original (model) one. Obviously, in areas where gaps are not present, this procedure has no effect and the retrieved data are maintained. Successively, LSM derived vertical profile of SM was corrected through a linear interpolation of the difference between the S1-derived surface SM and the model one. The difference was assumed as equal to 0 m^3/m^3 at the deepest level.
The major outcomes of the initialization experiments will be shown at the conference.
