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A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of number of modeled data. The algorithm uses a weight error function during the learning phase, to improve the modeling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangle storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods. [ABSTRACT FROM AUTHOR]

Many severe precipitation events occurred in Europe during the last decade and caused casualties and damages to the historical heritage and natural environment. Protection of civilians and safeguard of the territory are the kay mandates of a civil Protection (CP) agency. This is achieved through activities that mitigate hydro-meteorological risks, such as flooding and droughts. European CP agencies, supported by meteorological and hydrological institutes, aim to assess risk scenarios, to monitor and supervise events and risk levels, providing early warning to National and local authorities.Hydro meteorological risk management consists of three phases: pre-event (forecast), event (early warning, and monitoring) and post-event (recovery and survey).Near real time accurate estimations of hydrological variables such as precipitation and soil moisture are invaluable to the CP agencies, enable them to issue early warnings and plan for disaster relief at the local level.Besides measurements of key hydrological variables by ground-based instruments, often affected by a limited spatial coverage, advanced satellite-based precipitation and soil moisture products developed within different international programs, are becoming available and accessible to users in near-real time. The assessment of the accuracy and reliability of such products is necessary in order to be able to optimally exploit them for hydro-meteorological applications.The comparison of satellite-derived rainfall and soil moisture estimates with respect to ground-based measurements is a challenging task, both because of the temporal and spatial variability of the fields and the inherently different measurement types. This activity is particularly complex in Europe due to the heterogeneity in the ground-based networks of the different countries, due to sampling strategy and processing methodologies. For the validation and assessment of satellite-derived soil moisture products, these issues are exacerbated by the limited availability of ground observation networks that can be used as benchmark. Moreover, the measuring stations cover only small areas over Europe. As an example, in Central Italy only 14 probes are distributed over an area of about 8000 km2.In this study, a comparison between satellite derived products and ground data is presented, according to the EUMETSAT H SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) Validation protocols. Since 2005, eleven European countries joined in the framework of the H SAF to perform the satellite products monitoring and ground verification in their own regions of interest. Moreover, recent severe rainfall events are selected in order to understand how the main satellite product characteristics, i.e. accuracy, spatial pattern and resolution, update frequency and latency, impact the efficiency of a hydro-meteorological early warning system at a local level in an operational framework. State-of-the-art satellite rainfall and soil moisture products, obtained through international space agencies and programmes (EUMETSAT H SAF, ESA, NASA, and JAXA), are used over different European countries as forcing input for an early warning system for meteorological/hydrological events. [ABSTRACT FROM AUTHOR]

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 10 3 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at 10.5281/zenodo.7034956 and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km²) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the 5 period 01/09/2010 - 31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22% on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52% of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23%), and central Apennines (5%). IT-SNOW is freely available with the following DOI: https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources - a crucial endevor in a warming and drier climate. [ABSTRACT FROM AUTHOR]

We show, for the first time over Italy and over part of the central Mediterranean Basin, the impact of lightning data assimilation (LDA) on the strokes forecast for a long period. We use the Weather Research and Forecasting (WRF) model coupled with the Dynamic Lightning Scheme (DLS) at convection allowing horizontal resolution (3 km). We carried out a two-seasons experiment (summer 2020 and fall 2021) providing the forecast of lightning and precipitation for the next 6 h (nowcasting), considering two sub-periods (0-3 h and 3-6 h) for verification. The LDA is done through a nudging scheme that increases the water vapor mass in the mixed-phase region based on observed flash density rates and simulated graupel mixing ratio. No changes are made to the model run if spurious convection is predicted or no flashes are observed. LDA can trigger convection missed by the control forecast, without LDA, and/or can redistribute the strokes predicted to be more consistent with observations. LDA has a positive impact on strokes forecast, improving correct forecasts and reducing false alarms. This improvement is however confined to the first three-hours of forecast with negligible to negative impact for longer time ranges, in line with other studies. The improvement pattern is different in summer and fall, depending on the convection development. The analysis of the Fraction Skill Score shows the usefulness of the forecast for practical purposes, considering the current areas used by the Civil Protection Department to issue meteorological alerts for intense convective events over Italy. Finally, it is shown that the forecast at the short-range (0−3 h) using LDA can improve the strokes forecast issued on the previous day, not using LDA, and the methodology of this paper can be applied to issue warnings and alerts as the storm is approaching. A brief examination of rainfall forecast shows positive impact of LDA at the short-range (0-3 h), with neutral impact for longer time ranges. The different impact of LDA on the strokes and precipitation forecasts is also highlighted. • Lightning data assimilation improves the lightning and precipitation forecast for the first three-hours in summer and fall. • The pattern of improvement is different in summer and fall. • The very short-term forecast refines the previous day forecast as the storm is approaching. [ABSTRACT FROM AUTHOR]

This paper presents the first detailed analysis of cloud-to-ground (CG) and intra-cloud (IC) strokes characteristics from the Lightning Detection Network (LINET) over Italy and the Central Mediterranean area, a lightning active area in south Europe. We study the strokes over a 13-year period from 2010 to 2022, aiming to understand how it varies with different temporal scales (hourly, monthly, seasonally, and yearly), surface types (sea and land), and ground levels (0–100 m ; 100–200 m ; 200–400 m ; 400–800 m ; 800–1200 m ; 1200–2000 m and above 2000 m). We found that the stroke's maximum activity was observed in August; specifically, July has the maximum activity over the land with a maximum diurnal peak in the afternoon, while in September, the convection shifts over the sea with a secondary daily maximum in the morning. The largest current intensities are observed in January, over sea and during nighttime. Moreover we found that stroke current intensities, polarity and IC height emissions are influenced by ground altitude level. Our paper provides new insights into the spatio-temporal patterns and characteristics of lightning over Italy and the Central Mediterranean area, which can be useful for improving weather forecasting, climate modeling, risk assessment, and damage mitigation strategies in this area. • Orography has strong impact on lightning density, polarity, intensity and discharge height. • Vigorous convection develops over the land in summer and over the sea in autumn. • Overall stroke density reaches its peak in August. • Atmospheric instability decreases the breakdown threshold increasing weaker strokes. [ABSTRACT FROM AUTHOR]

Lightning is an important threat to life and properties and its forecast is important for several applications. In this paper, we show the performance of the "dynamic lightning scheme" for next-day total strokes forecast. The predictions were compared against strokes recorded by a ground observational network for a forecast period spanning one year. Specifically, a total of 162 case studies were selected between 1 March 2020 and 28 February 2021, characterized by at least 3000 observed strokes over Italy. The events span a broad range of lightning intensity from about 3000 to 600,000 strokes in one day: 69 cases occurred in summer, 46 in fall, 18 in winter, and 29 in spring. The meteorological driver was the Weather Research and Forecasting (WRF) model (version 4.1) and we focused on the next-day forecast. Strokes were simulated by adding three extra variables to WRF, namely, the potential energies for positive and negative cloud to ground flashes and intracloud strokes. Each potential energy is advected by WRF, it is built by the electrification processes occurring into the cloud, and it is dissipated by lightning. Observed strokes were remapped onto the WRF model grid with a 3 km horizontal resolution for comparison with the strokes forecast. Results are discussed for the whole year and for different seasons. Moreover, statistics are presented for the land and the sea. In general, the results of this study show that lightning forecast with the dynamic lightning scheme and WRF model was successful for Italy; nevertheless, a careful inspection of forecast performance is necessary for tuning the scheme. This tuning is dependent on the season. A numerical experiment changing the microphysics scheme used in WRF shows the sensitivity of the results according to the choice of the microphysics scheme. [ABSTRACT FROM AUTHOR]

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events. [ABSTRACT FROM AUTHOR]

On 10 October 2018 an intense storm, characterized by heavy rainfall, hit the Sardinia island, reaching a peak of 452 mm of rain measured in 24 h. Among others, two particularly intense phases were registered between 3 and 6 UTC (Universal Coordinated Time), and between 18 and 24 UTC. The forecast of this case study is challenging because the precipitation was heavy and localized. In particular, the meteorological model used in this paper, provides a good prediction only for the second period over the eastern part of the Sardinia island. In this work, we study the impact of lightning data assimilation and horizontal grid resolution on the Very Short-term Forecast (VSF, 3 and 1 h) for this challenging case, using the RAMS@ISAC meteorological model. The comparison between the 3 h VSF control run and the simulations with lightning data assimilation shows the considerable improvement given by lightning data assimilation, especially for the precipitation that occurred in the eastern part of the island. Reducing the VSF range to 1 h, resulted in higher model performance with a good precipitation prediction over eastern and south-central Sardinia. In addition, the comparison between simulated and observed reflectivity shows an important improvement of simulations with lightning data assimilation compared to the control forecast. However, simulations assimilating lightning overestimated the precipitation in the last part of the day. The increasing of the horizontal resolution to 2 km grid spacing reduces the false alarms and improves the model performance. [ABSTRACT FROM AUTHOR]

Satellite rainfall products (SRPs) are becoming more accurate with ever increasing spatial and temporal resolution. This evolution can be beneficial for hydrological applications, providing new sources of information and allowing to drive models in ungauged areas. Despite the large availability of rainfall satellite data, their use in rainfall-runoff modelling is still very scarce, most likely due to measurement issues (bias, accuracy) and the hydrological community acceptability of satellite products. In this study, the real-time version (3B42-RT) of Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis, TMPA, and a new SRP based on the application of SM2RAIN algorithm ( Brocca et al., 2014 ) to the ASCAT (Advanced SCATterometer) soil moisture product, SM2R ASC , are used to drive a lumped hydrologic model over four basins in Italy during the 4-year period 2010–2013. The need of the recalibration of model parameter values for each SRP is highlighted, being an important precondition for their suitable use in flood modelling. Results shows that SRPs provided, in most of the cases, performance scores only slightly lower than those obtained by using observed data with a reduction of Nash–Sutcliffe efficiency ( NS ) less than 30% when using SM2R ASC product while TMPA is characterized by a significant deterioration during the validation period 2012–2013. Moreover, the integration between observed and satellite rainfall data is investigated as well. Interestingly, the simple integration procedure here applied allows obtaining more accurate rainfall input datasets with respect to the use of ground observations only, for 3 out 4 basins. Indeed, discharge simulations improve when ground rainfall observations and SM2R ASC product are integrated, with an increase of NS between 2 and 42% for the 3 basins in Central and Northern Italy. Overall, the study highlights the feasibility of using SRPs in hydrological applications over the Mediterranean region with benefits in discharge simulations also in well gauged areas. [ABSTRACT FROM AUTHOR]

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 validation period 2012-13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2RASC is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42-RT, respectively. The integration of the products is found to improve the threat score for medium-high rainfall accumulations. Since SM2RASC, H05, and 3B42-RT datasets are provided in near-real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems. [ABSTRACT FROM AUTHOR]

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed. [ABSTRACT FROM AUTHOR]

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%. [ABSTRACT FROM AUTHOR]

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. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances. [ABSTRACT FROM AUTHOR]

H-SAF is the Satellite Application Facility on support to operational Hydrology and water management supervised and coordinated by EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) since 2005. Currently, the program entered in its Third Continuous Development and Operation Phase (CDOP-3), which will end on February 2022. During this phase, H-SAF contributes to the EUMETSAT MTG (Meteosat Third Generation) program by providing operational precipitation products based on combined microwave/infrared (MW/IR) techniques for the high spatial/temporal/spectral resolution Flexible Combined Imager (FCI) and for the MTG Lightning Imager (LI). Moreover, in view of the EUMETSAT Polar System Second Generation (EPS-SG) satellite mission, day-1 precipitation products for the Microwave Sounder (MWS), and Microwave Imager (MWI) will be delivered to provide precipitation estimates on a global scale. The collaboration with the NASA Precipitation Measurement Mission (PMM) Research Program promotes fruitful interactions on several critical aspects in precipitation retrieval algorithm development between H SAF and GPM (Global Precipitation Measurement). The nearly-global observational datasets built from coincident active and passive spaceborne MW observations are used as reference for the development of the EPS-SG precipitation products, and for the optimization of the existing PMW (Passive Microwave) products. The current work fits in this context. The H-SAF validation activities are performed by the Product Validation Group (PVG) composed by experts from the National Meteorological and Hydrological European Institutes under the coordination of the Italian Civil Protection Department (DPC). The product validation methodology is based on comparisons with radar and rain gauge data to produce yearly statistics (multi-categorical and continuous scores) through the use of a Unique Common Code (UCC) developed by the PVG. Recently, the validation over the MSG full disk area was performed with respect to the 2A DPR products. Comparison results obtained by the two H-SAF neural network algorithms retrieved by MW cross-track scanners (AMSU/MHS for H02B and ATMS for H18) over the European and full disk area are shown. [ABSTRACT FROM AUTHOR]

In the framework of the EUMETSAT H SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) project several soil moisture products, with different timeliness (e.g. NRT, offline, data records), spatial resolution, format (e.g. time series, swath orbit geometry) or the representation of the water content in various soil layers (e.g. surface, root-zone), are generated on a regular basis and distributed to users. The products are: H16 Metop ASCAT-B SSM NRT 12.5 km sampling, H101 Metop ASCAT-A SSM NRT 12.5 km sampling, H102 Metop ASCAT-A SSM NRT 25 km sampling, H103 Metop ASCAT-B SSM NRT 25 km sampling, H113 Metop ASCAT DR2018 SSM time series 12.5 km sampling, and the products H14 SM DAS 2, H27 SM DAS 3 of Soil wetness index in the roots region by scatterometer assimilation in a Land Data Assimilation System.In the framework of the H SAF project these products are evaluated with a validation protocol defined by the project partners. The validation methodology is based on Triple Collocation (TC) performed between the test data set, Noah GLDAS v2.1 and the passive ESA-CCI v04.3 soil moisture product, whereas the Pearson Correlation Coefficient (R) was only computed between the test data set and Noah GLDAS v2.1. In our study we will show, for each H SAF soil moisture product, the standard quality benchmark Signal-to-Noise Ratio (SNR) and the Pearson correlation coefficient (R) achieving a consistent and global validation of the different soil moisture products. [ABSTRACT FROM AUTHOR]

Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, 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 large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to validate moderate-resolution snow products, namely H10 – Snow detection (SN-OBS-1) and H12 – Effective snow cover (SN-OBS-3) supplied by the Hydrological Satellite Facility (HSAF) Project of EUMETSAT. With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is assessed against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, Italian Alps and Turkey to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over winter seasons 2016/17 and 2017/18 show a satisfying agreement of Sentinel-2 data with ground-based observations, both in terms of snow extent and fractional snow cover. HSAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances [ABSTRACT FROM AUTHOR]

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values. [ABSTRACT FROM AUTHOR]