Silvia Puca

A comprehensive analysis of the July 2021 event that occurred on Lake Como (Italy), during which heavy hailstorms and floods affected the surroundings of Lake, is presented. The study provides a detailed analysis of the event using different observation sources currently available. The employed techniques include both conventional (rain gauges, radar, atmospheric sounding) and non-conventional (satellite-based Earth observation products, GNSS, and lightning detection network) observations for hydro-meteorological analysis. The study is split in three main topics: event description by satellite-based observations; long-term analysis by the ERA5 model and ASCAT soil water index; and short-term analysis by lightning data, GNSS delays and radar-VIL. The added value of the work is the near-real-time analysis of some of the datasets used, which opens up the potential for use in alerting systems, showing considerable application possibilities in NWP modeling, where it can also be useful fo...

Because of the ongoing climate change, the frequency of extreme rainfall events at the global scale is expected to increase, resulting in higher social and economic impacts. Thus, improving the forecast accuracy and the risk communication is a fundamental goal to limit social and economic damages. Both Numerical Weather Prediction (NWP) and radar-based nowcasting systems still have open issues, mainly in terms of precipitation correct time/space localization predictability and rapid forecast accuracy decay, respectively. Trying to overcome these issues, this work aims to present a nowcasting system combining an NWP model (WRF), using a 3 h rapid update cycling 3DVAR assimilation of radar reflectivity data, with the radar-based nowcasting system PhaSt through a blending technique. Moreover, an innovative post-processing algorithm named SWING (Score-Weighted Improved NowcastinG) has been developed in order to take into account the timely and spatial uncertainty in the convective field...

<p><span xml:lang="EN-GB" data-contrast="none"><span>Monitoring the state of the cryosphere </span><span>in real time </span><span>is a key to improved risk and water resources management, especially in a warming climate. </span><span>All around the world, this goal is achieved </span><span>through forecasting chains combining models with in-situ and remote-sensing measurements. Here, we discuss lessons learned while developing S3M Italy, one such chain delivering </span><span>h</span><span>ourly estimates of snow water equivalent, density, </span><span>snow and glacier </span><span>melt, and bulk liquid water content across the Italian territory (300k+ km2, 200 m resolution</span><span>, </span><span>1.5 hour</span><span> turnaround</span><span>). S3M Italy includes downloaders to ingest input data from automatic weather stations, spatialization t</span><span>ools </span><span>to convert these data into weather-input maps, blending routines for deriving daily snow-covered-area maps from ESA Sentinel 2, NASA MODIS, and EUMETSAT H-SAF products, mapping algorithms based on multilinear regressions for assimilating on-the-ground snow-depth data, as well as algorithms to manage parallelized runs a</span><span>nd then mosaic model outputs</span><span>.</span><span> S3M Italy has been developed to support decisions by the Italian Civil Protection Agency and is fully open source, not only in terms of underlying models (</span></span><span xml:lang="EN-GB" data-contrast="none"><span>https://github.com/c-hydro/s3m-dev),</span></span><span xml:lang="EN-GB" data-contrast="none"><span> but </span><span>also</span><span> in terms of all pre-processing routines (</span></span><span xml:lang="EN-GB" data-contrast="none"><span>https://github.com/c-hydro/fp-hyde</span></span><span xml:lang="EN-GB" data-contrast="none"><span>, </span></span><span xml:lang="EN-GB" data-contrast="none"><span>https://github.com/c-hydro/fp-s3m</span></span><span xml:lang="EN-GB" data-contrast="none"><span>).</span></span><span> </span></p>

The MSG data in Meteorogical Service of Italian Air Force has been implemented in the NEFODINA algorithm and the first images are shown. The time availability and the resolution of the MSG data allowed us to define a refurbishment of the model and a new output format has been realized. The new output format shows the morphology of convective structures and so this design helps the forecasters to detect the structures modification. Also a multi-channel approach (6.2 µm, 7.3 µm and 10.8 µm) has been adopted for an early detection and characterization of the life phases of a convective cell. The combined use of window and absorbing channels shows the possibilities of increase the performance of the model. The first results and our experience about the Neural Network (NN) with METEOSAT 6 and 7 data strengthen the improvement of the skill of the model to track the convective cells and to forecast them phases of them is pointed out. 1.

Numerical models are operationally used for weather forecasting activities to reduce the risks of several hydro-meteorological disasters. The overarching goal of this work is to evaluate the Weather Research and Forecasting (WRF) model predictive capabilities over the Italian national territory in the year 2018, in two specific cloud resolving configurations. The validation is carried out with a fuzzy logic approach, by comparing the precipitation predicted by the WRF model, and the precipitation observed by the national network. The fuzzy logic technique, by considering different intensity thresholds, allows to identify the reliable spatial scales of the forecasts. The same approach is applied to evaluate the performances of COSMO-2I model, a state-of-the-art numerical model configuration used for operational activities. For the entire year, except for summer, the model predictive capabilities are high, with useful forecasts for structures of medium intensities down to O(10 km) len...

State-of-the-art rainfall products obtained by satellites are often the only way for measuring 13 rainfall in remote area of the world. However, it is well known that they may fail in properly 14 reproducing the amount of precipitation reaching the ground, which is of paramount importance for 15 hydrological applications. To address this issue, an integration between satellite rainfall and soil 16

This paper describes the first outcomes of an activity aiming at validating the H-SAF soil moisture products derived from METOP-ASCAT data. For this purpose, an extensive comparison between SMOS and ASCAT derived soil moisture retrievals has been accomplished by considering the 25 km resolution ASCAT products and the SMOS level 2 products. Both Europe and Northern Africa have been considered and data acquired during 2010 have been used. The procedure that has been followed to accomplish the comparison is described together with the first results. The way the ASCAT soil moisture relative index has been converted into a volumetric moisture content, which represents a critical aspect of the comparison, is also described. Results have demonstrated that, after the conversion of the H-SAF estimates into absolute volumetric soil moisture, the two products show a fairly good degree of correlation. Additional factors, such as spatial property features are also preliminary investigated.

Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10—Snow detection (SN-OBS-1) and H12—Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. ...

State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered: H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2RASC, considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010–13. In the ...

In the framework of the Hydrological-SAF Precipitation Product Validation Service, a common validation methodology has been defined to perform the validation of the satellite rainfall estimations using radar data as ground reference. The radar networks of Belgium, Germany, Hungary, Italy, Turkey, and Slovakia are used in the H-SAF Precipitation Product Validation Group (PPVG). A network of C-band and Ka-band radars throughout Europe ensures a wide area coverage with different orographies and climatological regimes, but the definition of a Quality Control Protocol for obtaining consistent ground precipitation fields across several countries is required. It is well known that radar-based rainfall estimation is affected by several sources of uncertainty, such as ground clutter, beam blocking, range distance, vertical variability, attenuation. Thus, among the hydro-meteorological community, the evaluation of the data quality is a quite consolidated practice, even though a unique definition of a common evaluation methodology between different countries and institutions has not been stated yet. The quality information is helpful in stating the reliability of data used for satellite precipitation products validation. Moreover, the algorithms for the calculation of rainfall estimation from radar raw data can keep into account the quality in the choice between radar maps at different elevations. Inside H-SAF, the first definition of the quality index on the radar rainfall observations has been introduced at the Italian Civil Protection Department (DPC). In the evaluation of the DPC quality index several parameters are considered, some measured by the RADAR itself (static clutter map, range distance, radial velocity, texture of differential reflectivity, texture of co-polar correlation coefficient and texture of differential phase shift) and some obtained by external sources (digital elevation model, freezing layer height). In some cases, corrections have been applied for clutter and beam blocking. The DPC quality index has been calculated and applied for a relevant meteorological event reported by a radar test site in Italy, already employed in the validation of the HSAF satellite rainfall measurements. The DPC quality index has been applied to compare precipitation field derived by radar data with satellite precipitation product derived by geostationary satellite data, comparing the validations of the same scene using data with varying conditions over quality index: radar data with quality lower than a certain threshold were rejected, but the threshold was varied between 0.0 (no threshold) and 0.8, evaluating the impact of the introduction of the quality index defined on the statistical results of the satellite product validation. Introduction Quantitative precipitation estimation from ground-based weather radars is a cumbersome task considering it is conditioned by several error sources. In spite some of them can be faced to a reasonably extent, any quantitative use of radar rainfall products should take into account the quality of input radar data and related precipitation estimates. This is especially recommendable either for radar data assimilation or for the validation of satellite-based precipitation products. A theoretical treatment over the radar quality index is here presented, and the procedure derived from it has been applied on data from a C-band radar belonging to DPC, located at mount Il Monte (Abruzzo region, Central Italy) at 700 m above the sea level, with significant orographical obstruction in W-SW direction. Then, the impact of the introduction of this quality information on the validation of satellite-based rainfall estimation from H-SAF has been evaluated, for different quality thresholds. 1. Quality concept Starting from the paradigm that the quality is a subjective quantity, there is not a unique way to determine it as well as there is not a unique way to deal with the radar error sources. However, it can be possible to reasonably provide a theoretical definition for data quality that might require specific set up for every radar system. The quality is a random variable ranging between 0 and 1 that depends on the considered quality indicators fi (i.e., random variables related to the error sources). For each quality indicator a relative quality index can be defined (q), the overall quality (Q) can then be computed as combination of the relative quality indices. Assuming, the radar systems are well maintained we will focus on the following quality indicators: clutter, beam blocking, distance from the radar, height of measurement and attenuation 1.1: Ground Clutter The ground clutter can be evaluated using several methods, those employing only the Doppler information (ground clutter is expected to be basically stationary) might produce a suppression of precipitation echoes having the radial component of velocity close to zero. Consequently, any efficient clutter identification algorithm…

Over the last few years, satellite data have progressively become a major (if not the predominant) source of information assimilated in Numerical Weather Prediction (NWP) models. This has been made possible thanks to a substantial enhancement of the remote sensing instruments measuring various atmospheric quantities but also largely to the improvements in data assimilation techniques to better exploit the information contained in such data. The advantage of satellite data is that they provide a uniform spatial and temporal coverage of the atmosphere. This advantage is however balanced by a general poor vertical resolution of the instruments currently used, and the difficulty to handle clouds, precipitations and surface contributions to the information content of the data. The future improvements of NWP models and a better handling of new observing techniques (radio-occultation, passive limb soundings, active sensors) in data assimilation schemes may overcome some of these limitations.

ABSTRACT This paper describes the first outcomes of an activity aiming at validating the H-SAF soil moisture products derived from Metop-ASCAT data. For this purpose, an extensive comparison between SMOS and ASCAT derived soil moisture retrievals has been accomplished by considering the 25 km resolution ASCAT products and the SMOS L2 products. Both Europe and Northern Africa have been considered and data acquired during 2010 have been used. The procedure that has been followed to accomplish the comparison is described together with the first results. The way the ASCAT soil moisture relative index has been converted into a volumetric moisture content, which represents a critical aspect of the comparison, is also described. Results have demonstrated that, after the conversion of the H-SAF estimates into absolute volumetric soil moisture, the two products show a relatively good degree of correlation. Additional factors, such as spatial property features are also preliminary investigated.

A Neural Network (NN) model is proposed for the reconstruction of significant wave height time series, without any increase of the error of the NN output with the number of reconstructed data. The input of the NN model are correlated data, obtained from nearby stations: no data of the same series we are modelling are used. A weighted error function during the learning phase is also considered to improve the modelling of the higher significant wave height. Furthermore the equivalent triangular storm model is applied to test the ability of the NN model to reconstruct the sea storms. The comparison between actual data of a NOAA buoy moored off San Francisco (California) and the data reconstructed by NN model shows a good agreement, both during calm time periods and during storms.

The satellite rainfall product validation program established within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H‐SAF) makes use of the radar and rain gauge observations available among the partner countries to perform a continuous validation of the H‐ SAF precipitation products. H‐SAF precipitation products cover all the European area, and include microwave‐only instantaneous rainfall estimation (H01 and H02),combined infrared‐microwave instantaneous rain estimation (H03 and H04), and cumulated rainfall estimation (H05). Currently, the partner countries are doing big efforts to homogenize their operational procedure in order to reduce the intrinsic uncertainties related to the radar rainfall estimation and the network heterogeneity. This activity encourages the design and implementation of a common approach for the radar data quality evaluation to be used as constraint within the validation process. A radar‐based data quali...

The MSG data in Meteorogical Service of Italian Air Force has been implemented in the NEFODINA algorithm and the first images are shown. The time availability and the resolution of the MSG data allowed us to define a refurbishment of the model and a new output format has been realized. The new output format shows the morphology of convective structures

Results from Eumetsat research fellowship hosted at the Italian Meteorological Service (IMS) for improving the automatic detection and nowcasting of strong convective phenomena are presented. The study makes use of the operational application (NEFODINA) running at IMS Operations Centre. At present the application relays on the Meteosat 7 infrared (window) channel, it processes in real time the last available imagery

The Mesoscale Convective Systems (MCSs) are often correlated with heavy rainfall, thunderstorms and hail showers, frequently causing significant damages. The most intensive weather activities occur during the maturing stage of the development, which can be found in the case of a multi-cell storm in the centre of the convective complex systems. These convective systems may occur in several different unstable

At the Italian Air Force Meteorological Service a neural network model (NN) was defined in order to forecast the convective systems evolution in the Mediterranean area. This model, composed by a system of NNs, uses combination of water vapour absorption (WV) and infrared window (IR) data of Meteosat Second Generation (MSG). We realized that cloud top temperature, from IR window

The MSG data in Meteorogical Service of Italian Air Force has been implemented in the NEFODINA algorithm and the first images are shown. The time availability and the resolution of the MSG data allowed us to define a refurbishment of the model and a new output format has been realized. The new output format shows the morphology of convective structures and so this design helps the forecasters to detect the structures modification. Also a multi-channel approach (6.2 µm, 7.3 µm and 10.8 µm) has been adopted for an early detection and characterization of the life phases of a convective cell. The combined use of window and absorbing channels shows the possibilities of increase the performance of the model. The first results and our experience about the Neural Network (NN) with METEOSAT 6 and 7 data strengthen the improvement of the skill of the model to track the convective cells and to forecast them phases of them is pointed out.

Mesoscale Convective Systems (MCSs) are often correlated with heavy rainfall, thunderstorms and hail showers, frequently causing significant damages. The most intensive weather activities occur during the maturing stage of MCSs. Improving knowledge of the convective system phase and the forecast of its evolution is essential in giving better support to assistance in several fields as transports and civil protection where alert giving is an ordinary task. Different neural network techniques have been used at the Italian Meteorological Service of the Air Force to solve this problem. Nowcasting was focused on binary evolution status, only two phases. Best performance has been achieved with a system of back propagation network with a learning error designed respect the convective cell characteristic evolution phase. This system of neural networks has been learned to classify the status of the convective system and to forecast the evolution of each convective cells inside this. The input...