SM2RAIN-ASCAT is a new global scale rainfall product obtained from ASCAT satellite soil moisture data through the SM2RAIN algorithm (Brocca et al., 2014). The SM2RAIN-ASCAT rainfall dataset (in mm/day) is provided over an irregular grid at 12.5 km on a global scale. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The SM2RAIN method was applied to the ASCAT soil moisture product (Wagner et al., 2013) for the period from January 2007 to 31 August 2019 (12 years and 8 months), for version 1.1. The rainfall dataset is provided in netCDF format. A total of 13 netCDF files, one per year, are provided. The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests. A GeoTIFF version of the dataset is available here: https://zenodo.org/record/3520620 A sample dataset that can be used for testing SM2RAIN algorithm is available here: https://zenodo.org/record/2580285 Details on the dataset development and its assessment with ground and reanalysis observations are provided as: Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. Earth System Science Data, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. https://doi.org/10.5194/essd-11-1583-2019. Simple Python and Matlab codes for the extraction of SM2RAIN-ASCAT rainfall at one and multiple station(s) \location(s) are available at: https://zenodo.org/record/3451685 The SM2RAIN code in Python is available here: https://zenodo.org/record/2203560 The SM2RAIN code in Matlab is available here: http://hydrology.irpi.cnr.it/download-area/sm2rain-code/ Acknowledgements EUMETSAT Global SM2RAIN project (contract n° EUM/CO/17/4600001981/BBo) EUMETSAT “Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)” CDOP 3 (EUM/C/85/16/DOC/15).
<p>In order to enhance our understanding of the hydrologic cycle, frequent, reliable and detailed information on precipitation are fundamental. In-situ measurements are the traditional source of this information, but they have limited spatial representativeness and the number of stations worldwide is declining and their access is often troublesome. Satellite products are able to overcome these issues and actually are the main, if not the only, source of information over many areas of the world. Notwithstanding this, the spatial resolution is still limited to tens or hundreds of kilometers, limiting their usefulness for hydrological applications. In the recent decade, a new approach for estimating rainfall from satellite-derived soil moisture observations has been proposed, named SM2RAIN (Brocca et al., 2014) and based on the inversion of the soil water balance equation. The application of SM2RAIN to Sentinel-1 satellites carrying a C-band Synthetic Aperture Radar (CSAR) sensor can provide rainfall data at unprecedented spatial and temporal resolution.</p><p>In this study, we combined the soil moisture data retrieved from backscatter observations of Sentinel-1 (1.5/4 days temporal frequency over Europe, 500 m sampling) with the soil moisture data obtained from ASCAT sensor, onboard of METOP satellites (8-24 h temporal frequency, 12.5 km sampling) through a data fusion algorithm. The result is an innovative soil moisture dataset with a temporal resolution of 1 day and a spatial resolution of 1 km (Bauer-Marschallinger et al., 2018). These data are used as input for SM2RAIN, obtaining as output a rainfall product with temporal and spatial sampling of 1 day and 1 km, respectively.</p><p>The approach was applied over test regions in Italy and Austria obtaining promising results. Specifically, the comparison with high density observations from raingauges and meteorological radars has allowed the assessment of the method at high spatial resolution and varying temporal resolution. Results show that good quality rainfall estimates at 1 km of spatial resolution can be obtained in reproducing 3- to 5-day rainfall accumulations. Further testing will be carried out in the next months and presented at the conference.</p><p><strong>Acknowlodgments</strong></p><p>The activity is funded by DWC radar project, Austrian Space Applications Programme, FFG Project 873658.</p><p><strong>Reference</strong></p><p>Bauer-Marschallinger, B., Paulik, C., Mistelbauer, T., Hochst&#246;ger, S., Modanesi, S., Ciabatta, L., Massari, C., Brocca, L. & Wagner, W. (2018). Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering. Remote Sensing, 10(7), 1030. doi:10.3390/rs10071030</p><p>Brocca L., Ciabatta L., Massari C., Moramarco T., Hahn S., Hasenauer S., Kidd R., Dorigo W., Wagner W., Levizzani V. &#8211; &#8220;Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data&#8221;. J. Geophys. Res. Atmos. vol. 119, pp. 5128&#8211;5141, 2014. doi: 10.1002/2014JD021489</p>
Climate change is increasing the challenges related to extreme weather events, shifting precipitation patterns, causing water scarcity and increasing the occurrence of natural disasters. Accurate and timely precipitation data are critical for understanding and mitigating these events, as well as for informing decision-makers. Specifically, Europe climatic and physiographic features make capturing fine-scale (1 km-daily) variations crucial to improve the precision of climate models and facilitate targeted adaptation strategies in this area. This can be achieved by using the recent remote sensing technologies, which allow to systematically monitor wide areas without the need of maintaining ground networks. In particular, for satellite precipitation estimation, both the top-down and bottom-up approaches have been exploited in recent years to obtain information related to rainfall. Both the methodologies carry advantages and limitations. Their merging, coupled with high spatial resolution ancillary information, is therefore recommended to reach the final aim of detailed and accurate precipitation data. In this study, the rainfall data obtained from IMERG Late Run and SM2RAIN ASCAT (H SAF) are downscaled and merged over the whole Europe. The downscaling is obtained by leveraging high spatial resolution statistical information from CHELSA product, while a triple collocation technique is applied to merge the two downscaled datasets. The resulting high resolution rainfall is subsequently compared against multiple products, including coarse resolution ones such as H SAF, IMERG-LR, ERA5, EOBS, PERSIANN, CHIRP, GSMAP, and high-resolution products like EMO, INCA, SAIH, COMEPHORE, MCM, 4DMED. These comparisons, spanning ground, model and satellite data, serve to assess its capabilities in estimating precipitation over Europe.
The high resolution satellite precipitation product is based on the integration of multiple precipitation and rainfall datasets to generate a high spatial (1 km) and temporal (daily) resolution precipitation product over the Mediterranean area. The following precipitation and rainfall datasets are downscaled and merged together: GPM-Late run, CPC, SM2RAIN-ASCAT. All these products are originally at coarse spatial resolution (>10 km) and have been downscaled to 1 km spatial resolution using CHELSA (1 km) climatology. The three products are then merged with a triple collocation technique. In the areas covered by snow only GPM-Late run and CPC are merged together, again using the triple collocation to obtain the relative weights (third product: ERA5 Land precipitation). The product has been developed in the framework of the 4D-MED Hydrology project. The product is available in the period 2015-2022. Acknowledgements The work is supported by the European Space Agency (ESA) through the 4D-MED Hydrology project (grant no. ESA 4000136272/21/I-EF)
In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the "Green" Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments' development and satellite missions' evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion.
The alternation of extreme events is a source of great stress on the territory and forces us to adopt solutions to help mitigate their consequences. In this study, an attempt is made to exploit Earth Observation from space as a means to point out the interaction of inland waters and the coastal areas during hydrological extreme events, i.e. floods and droughts. During a flood event, large volume of water from the river reaches the coast, adding a considerable volume of freshwater. Conversely, during a drought event salt water from the sea enters inland causing severe damage to agriculture and the local population. With this study we attempt to investigate how the systems of sea and river interact during particularly intense events using satellite optical (Sentinel-2 and Sentinel-3) and altimeter (Sentinel-3, Cryosat-2, Icesat-2) sensor data. The area selected is the Po River delta (up to 200 km from the mouth), which in recent years has been exposed to severe events: in November 2019, the Po River was subject to a copious flood that had not occurred since 2000, while in the summer of 2022, it experienced the worst drought in the last 70 years. The analysis aims at evaluating three fundamental aspects: 1) the ability of satellite altimetry to identify extreme events in the river; 2) the potential of satellite altimetry to detect salt wedge intrusion in the Po River delta; and 3) the potential correlation between the altimetry observations and optical imagery of the river’s plume along the Adriatic coast. The analysis was conducted by analysing long time series (of about 10 years) for the first objective and by focusing on the drought event of 2022 and the flood events that occurred in the last 5 years for the other two objectives. The results of the analysis confirm that the satellite observed the significant increase and decrease in water levels in correspondence of the extreme events. In addition, the analysis of the data at the virtual stations in the downstream part of the Po River, together with the data along the tracks crossing the plume closer to the mouth of the river, showed the interaction between the sea and the river. In particular, the temporal series of the river clearly highlight the influence of the sea water several km upstream the river (more than 40 km as reported in the news), probably related to the salt wedge intrusion, which has caused significant damage to agriculture and drinking water aquifers for a long time after the event. The study qualitatively shows that extreme hydrological events can also be captured in the open sea in this region. The analysis illustrates the great potential of satellite sensors to monitor extreme events and the interaction of inland and coastal waters.
Abstract Satellite precipitation products have been largely improved in the recent years particularly with the launch of the global precipitation measurement (GPM) core satellite. Moreover, the development of techniques for exploiting the information provided by satellite soil moisture to complement/enhance precipitation products have improved the accuracy of accumulated rainfall estimates over land. Such satellite enhanced precipitation products, available with a short latency (< 1 day), represent an important and new source of information for river flow prediction and water resources management, particularly in developing countries in which ground observations are scarcely available and the access to such data is not always ensured. In this study, three recently developed rainfall products obtained from the integration of GPM rainfall and satellite soil moisture products have been used; namely GPM+SM2RAIN, PRISM-SMOS, and PRISM-SMAP. The prediction of observed daily river discharge at 10 basins located in Europe (4), West Africa (3) and South Africa (3) is carried out. For comparison, we have also considered three rainfall products based on: (1) GPM only, i.e., the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (GPM-ER), (2) rain gauges, i.e., the Global Precipitation Climatology Centre, and (3) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. Three different conceptual and lumped rainfall-runoff models are employed to obtain robust and reliable results over the 3-year data period 2015–2017. Results indicate that, particularly over scarcely gauged areas (West Africa), the integrated products outperform both ground- and reanalysis-based rainfall estimates. For all basins, the GPM+SM2RAIN product is performing the best among the short latency products with mean Kling–Gupta Efficiency (KGE) equal to 0.87, and significantly better than GPM-ER (mean KGE = 0.77). The integrated products are found to reproduce particularly well the high flows. These results highlight the strong need to disseminate such integrated satellite rainfall products for hydrological (and agricultural) applications in poorly gauged areas such as Africa and South America.
