Identify and interpret fields and derived products
Simone Gabellani addresses the potential of using Sentinel-2 high-resolution imagery to validate moderate-resolution snow products.
In this part we will address the potential of using Sentinel-2 high-resolution imagery to validate moderate-resolution snow products supplied by the Hydrological Satellite Facility (HSAF) Project of EUMETSAT.
The consistency of Sentinel-2 observations has been assessed against both in-situ snow measurements and webcam digital imagery and they can be used as reference data (Piazzi et al 2019). We will show the comparison of HSAF products and Sentinel 2 dataset in different region of the world with different snow seasonality.
Alexander Toniazzo discusses the validation of H SAF snow detection products.
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 a large scale.
In this session, the validation of the products will be discussed in detail. The presentation will begin with a brief recap on the various snow product types: snow detection, Snow status (D/W), Fractional SC and SWE. Some of these products are over European areas, whereas the new products developed during CDOP3 are global products of full disk satellite products.
Following there will be a short discussion about performances and limitations of each product, based on the Operational Reviews of the last years. Validation of these products requires a great effort due to sparse availability of snow observation, especially, over extra-European areas. Therefore, new validation strategies were developed.
Matias Takala presents practical examples of how to use the H13 product (Snow Water Equivalent) will be presented using Jupyter notebook.
The Snow Water Equivalent (SWE) is a parameter that describes the water content of snow mass. If snow would melt in its place the SWE tells the depth of the resulting water layer. Spaceborne microwave radiometers are well suited for the detection of SWE. Even though the spatialresolution of radiometer data is rather coarse (tens of kilometres) a polar orbiting satellite can cover most of the globe in 24h period. Unlike optical instruments radiometer depends only on natural thermal radiation of objects and doesn’t require illumination from sun. In addition, radiometers are quite insensitive to weather phenomena. The EUMETSAT H SAF SWE products H13 and H65 are described in detail in this presentation. The products are merged products containing Finnish Meteorological Institute (FMI) contribution for flat lands and Turkish State Meteorological Service (TSMS) contribution for mountainous areas. Both products use Helsinki University of Technology (HUT) model as basis for the estimates. The FMI algorithm is a data assimilation algorithm combining ground-based snow depth measurements with spaceborne derived SWE estimates and the TSMS algorithm uses modified HUT model for mountains. The nominal resolution for H13 is 0.25° and for H65 25 km. Product H13 is provided for Europe in so called H-SAF area [25-75°N lat, 25°W-45°E long]. The upcoming product H65 will provided for Northern Hemisphere in EASE2 format. The products are validated against independent SWE snow course measurements.
In thissession, practical examples of how to use the H13 product will be presented using Jupyter notebook.
Overview on the H SAF satellite derived snow products.
Reliable snow cover extent is of vital importance to have a comprehensive understanding for present and future climate, hydrological, and ecological dynamics. Development of methodologies to obtain reliable snow cover information by means of optical and microwave remote sensing (RS) has long been one of the most active research topics of the RS community. Operational snow products namely H10 (Snow detection (snow mask) by VIS/IR radiometry), H11 (dry/wet by MW radiometry), H12 (Effective snow cover by VIS/IR radiometry AVHRR), H13 (Snow Water Equivalent(SWE)by MW radiometry), H31 (Snow detection by VIS/IR radiometry), H32 (Effective snow cover by VIS/IR radiometry AVHRR) have been developed since 2008 within HSAF. Considering different characteristics of snow for mountainous and flat areas, various algorithms are used in producing the snow products for flat and mountainous areas, and then the products are merged to have a single snow product. The development of new snow products is in progress. The presentation will provide an overview of existing and future operational satellite-derived snow products of H SAF portfolio. In the last part of the presentation, there will be a short introduction of quality assessment. After a brief recap of all available operational and pre-operational products, the performances of the products and the new validation strategy using high-resolution satellite data will be discussed, with some interesting case studies of the latter.
Lecture slides (Arslan and Akyurek)...
Overview on the H SAF satellite derived precipitation products.
The EUMETSAT Satellite Application Facility for Operational Hydrology and Water Management (H SAF) provides satellite products and user services in support to Operational Hydrology, Meteorology, Risk Management and Water Management. Since 2005, H SAF science and research bridge into operations through the development and dissemination of soil moisture, precipitation and snow products based on the exploitation of primary EUMETSAT missions. During the fourth Continuous Development and Operations Phase (CDOP-4, from 2022 to 2027), H SAF products will be primarily based on the Meteosat Third Generation (MTG) and the EUMETSAT Polar System -Second Generation (EPS-SG) missions. Current products are based on the use of the full constellation of microwave (MW) radiometers for Level 2 passive microwave (MW) precipitation products and for MW/IR combined products for near-real time applications over the Meteosat Second Generation (MSG) full disk area. The presentation will provide a full overview of the current status and future development of the operational precipitation product portfolio as well as the product quality assessment strategy and results. Examples of applications for specific case studies will be also presented.
Lecture slides (Panegrossi)...
Overview on the H SAF satellite derived soil moisture products.
The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) develops and provides operational satellite products for precipitation, snow and soil moisture. These satellite products have a wide range of applications, but especially play a key role in numerical weather prediction.
The H SAF soil moisture product suite is composed of surface and root zone soil moisture products available at various spatial resolution, ranging from 1 km to 50 km. Surface Soil Moisture (SSM) products are based on backscatter observations provided by the Advanced Scatterometer (ASCAT) onboard the series of Metop satellites using the EUMETSAT H SAF TU Wien soil moisture retrieval algorithm, whereas Root Zone Soil Moisture (RZSM) products assimilate H SAF SSM products within the ECMWF/H SAF land data assimilation system. At the moment, two ASCAT instruments are currently operational on-board Metop-B and Metop-C.
Introduction of the H SAF project, the history of the SAFs, the introduction of the HSAF Workshop and its agenda from the main organizers.
An introduction of the HSAF project, the history of the SAFs, the introduction of the HSAF Workshop and its agenda from the main organizers.
Humberto Barbosa presents a study which provides a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020.
The São Francisco River Basin (SFRB) plays a key role for the agricultural and hydropower sectors in Northeast Brazil (NEB). The purpose of this study is to provide a comprehensive evaluation of extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and soil moisture over different time windows between 1980 and 2020. The Standardized Precipitation-Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI) at 3- and 12- month time scales derived from ground data were used as benchmark drought indices. The selfcalibrating Palmer Drought Severity Index (scPDSI) and the Soil Moisture and Ocean Salinity-based Soil Water Deficit Index (SWDIS) were used to assess the agricultural drought. The Water Storage Deficit Index (WSDI) and the Groundwater Drought Index (GGDI) both derived from the Gravity Recovery and Climate Experiment (GRACE) were used to assess the hydrological drought. The SWDISa and WSDI showed the best performance in assessing agricultural and hydrological droughts across the whole SFRB.
Tommaso Abrate presents the efforts of WMO to coordinate with its Expert Network on updating the satellite data and product requirements for Flood Forecasting and seasonal and long term hydrological forecasts.
In order to better capture the complexity of interlinked natural phenomena related to the atmosphere, ocean, hydrosphere and cryosphere, WMO has adopted a holistic Earth System monitoring approach. The operational implementation of this approach is supported by WMO Congress decisions related to the establishment of a global basic observing network GBON, and the adoption of a unified data policy, aimed at improving the sharing and interoperability of data among users, contributing to better numerical weather prediction and more accurate flood and drought forecasts. To achieve these results, it is important to benefit from emerging approaches in order to combine different data sources such as satellites, citizen observations, low-cost devices, Internet of Things, Big Data. This approach also allows ensuring at least partial information overt hose vast areas of the world where conventional state-funded monitoring approaches are insufficient. WMO is developing technical solution (standards, best practices) to overcome the discrepancies in data quality and the multiplication of different data format. In this context satellite. WMO, in coordination with its Expert Network is working on updating the satellite data and product requirements for Flood Forecasting and seasonal and long term hydrological forecasts and outlook.
Christine Träger-Chatterjee presents the prototype Data Cube for Drought and Vegetation Monitoring, and tools to manipulate the data in the cube.
EUMETSAT provides a prototype Data Cube for Drought and Vegetation Monitoring, and tools to manipulate the data in the cube. This prototype consists of long-term data records on a regular latitude / longitude grid and in CF-compliant NetCDF via THREDDS.
The prototype seeks to explore how well EUMETSAT and partners can bring together data from multiple sources and from multiple grids to ease barriers to use of the data for thematic applications.
This presentation reports on the lessons learnt as regards the creation, provision and use of the data cube.
Mariette Vreugdenhil demonstrates the use of the EUMETSAT H SAF soil moisture (H116, SM) and SM2RAIN (H64) products to predict yields for Morocco and Senegal.
We demonstrate the use of the EUMETSAT HSAF soil moisture (H116, SM) and SM2RAIN (H64) products to predict yields for Morocco and Senegal. Root-zone SM was calculated from SM, and NDVI was used as a vegetation indicator. Data on yields was obtained from the Food and Agriculture Organization of the United Nations.
Yield prediction was done for main crops using multiple linear regression and a time for space approach. SM improved yield prediction, especially early in the growing season, improving early warning capabilities. NDVI showed better predictions later in the growing season. SM2RAIN outperformed other benchmark rainfall datasets.
Antonio Parodi presents a critical review of the forecasting performances of each model involved in the CIMA hydrometeorological chain on the example of Medicane Apollo.
During the last week of October 2021 an intense Mediterranean hurricane (medicane), named Apollo, affected many countries on the Mediterranean coasts. The deaths toll peaked up to 7 people, due to flooding from the cyclone in the countries of Tunisia, Algeria, Malta, and Italy.
The Apollo medicane persisted over such areas for about one week (24 October – 1 November 2021) and produced very intense rainfall phenomena and widespread flash flood and flood episodes especially over eastern Sicily on 25-26 October 2021.
CIMA Foundation hydro-meteorological forecasting chain, including the cloud-resolving WRF model assimilating radar data and in situ weather stations (WRF-3DVAR), the fully distributed hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, has been operated in real-time to predict the weather evolution and the corresponding hydrological and hydraulic impacts of the medicane Apollo, in support of the Italian Civil Protection Department early warning activities and in the framework of the H2020 LEXIS and E-SHAPE projects.
This work critically reviews the forecasting performances of each model involved in the CIMA hydrometeorological chain, with special focus on temporal scales ranging from very short-range (up to 6 hours ahead) to short-range forecasts (up to 48 hours ahead).