Identify and interpret fields and derived products
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.
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.
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).
Ján Kanák presents the operational satellite products for precipitation detection, the procedure for their validation and a case study showing the use of these products in evaluating the long-term accumulated precipitation.
Primary satellite data processed into higher-level products are still used less frequently, especially in the context of processing by NWC SAF software, or directly by SAF products received by the EUMETCast Satellite receiving system. Such products include the hydrology support products of the EUMETSAT H SAF (Hydrological Satellite Application Facility). SHMÚ, as a member of the consortium, has long been involved in the task of validation of products for precipitation detection and hydrological applications of these products. In this article we present the operational satellite products for precipitation detection, the procedure for their validation and a case study presenting the use of these products in evaluating the long-term accumulated precipitation. Accumulated precipitation can be used to monitor periods of droughts with precipitation deficits and surpluses. The ambition of this work is to show future users of satellite data that satellite products of a higher level of processing have the potential for climatological studies. A significant increase in this potential is expected in the near future with the launch of the new generation of MTG (third generation Meteosat) and EPS-SG (second generation European Polar System) satellites.
Hamidreza Mosaffa presents a study that aims 1) to develop the long-term climatological SM2RAIN datasets for the period of 1998–2020 by merging two rainfall SM2RAIN products including SM2RAIN-CCI and SM2RAIN-ASCAT, and 2) to the analysis of drought based on standardized precipitation index over the USA.
Investigation of drought variability requires long term rainfall dataset with high spatial and temporal resolution. The goal of this study are as follow: 1) to develop the long-term climatological SM2RAIN datasets for the period of 1998–2020 at 0.25° spatial and monthly temporal resolution by merging two rainfall SM2RAIN products including SM2RAIN-CCI and SM2RAIN-ASCAT, and 2) to the analysis of drought based on standardized precipitation index over the USA. Results indicated that the most significant decreases in the monthly rainfall trends appear in November. In addition, drought occurred during 2003, 2007, and 2012 over most parts of the USA.
Jean-Christophe Calvet presents a neural network that has been trained using the modelled surface soil moisture (SSM), soil temperature, rainwater interception by leaves, and satellite-derived LAI observations from Copernicus.
In the context of climate warming, the frequency and the intensity of extreme events such as droughts is increasing, and better modelling the response of vegetation to climate is needed. Monitoring the impact of extreme events on terrestrial surfaces involves a number of variables of the soil-plant system such as surface albedo, the soil water content and the vegetation leaf area index (LAI). These variables can be monitored by either using the unprecedented amount of data from the Earth observation satellite fleet, or using land surface models. Another solution consists in combining all available sources of information by assimilating satellite observations into models. In this work, level 1 ASCAT backscatter values (sigma0) are assimilated in the ISBA land surface model of Meteo-France using the LDAS-Monde tool. First, an observation operator is built using machine learning. A neural network (NN) is trained using the modelled surface soil moisture (SSM), soil temperature, rainwater interception by leaves, and satellite-derived LAI observations from Copernicus. The NN is then used for simulating sigma0, making LDAS-Monde capable of assimilating ASCAT sigma0 observations. It is shown that the assimilation of sigma0 alone is able to markedly improve the simulated LAI and soil moisture.