scholarly journals Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial System Modeling Platform (v1.1.0)

2018 ◽  
Author(s):  
Stefan Kollet ◽  
Fabian Gasper ◽  
Slavko Brdar ◽  
Klaus Goergen ◽  
Harrie-Jan Hendricks-Franssen ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
River Discharge ◽  
System Modeling ◽  
Groundwater Table ◽  
Lead Times ◽  
Terrestrial System ◽  
Consistent Picture ◽  
Forecasting System ◽  
And Storage

Operational weather and also flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g. groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over European in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.

scholarly journals Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial System Modeling Platform (v1.1.0)

2018 ◽  
Author(s):  
Stefan Kollet ◽  
Fabian Gasper ◽  
Slavko Brdar ◽  
Klaus Goergen ◽  
Harrie-Jan Hendricks-Franssen ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
River Discharge ◽  
System Modeling ◽  
Groundwater Table ◽  
Lead Times ◽  
Terrestrial System ◽  
Consistent Picture ◽  
Forecasting System ◽  
And Storage

Operational weather and also flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g. groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over European in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.

scholarly journals Introduction of an Experimental Terrestrial Forecasting/Monitoring System at Regional to Continental Scales Based on the Terrestrial Systems Modeling Platform (v1.1.0)

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1697 ◽  
Author(s):  
Stefan Kollet ◽  
Fabian Gasper ◽  
Slavko Brdar ◽  
Klaus Goergen ◽  
Harrie-Jan Hendricks-Franssen ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
River Discharge ◽  
Groundwater Table ◽  
Lead Times ◽  
Systems Modeling ◽  
Terrestrial System ◽  
Consistent Picture ◽  
Forecasting System ◽  
And Storage

Operational weather and flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g., groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over Europe in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.

scholarly journals GloFAS-ERA5 operational global river discharge reanalysis 1979-present

2020 ◽  
Author(s):  
Shaun Harrigan ◽  
Ervin Zsoter ◽  
Lorenzo Alfieri ◽  
Christel Prudhomme ◽  
Peter Salamon ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
River Discharge ◽  
Pearson Correlation ◽  
Weather Prediction ◽  
Global Network ◽  
Surface Model ◽  
Mean Flow ◽  
Time Step ◽  
Reanalysis Dataset

<p>Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of Earth system observations with advanced Numerical Weather Prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, known as a reanalysis. While the current generation of NWP output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly, and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/) by coupling surface and sub-surface runoff from the HTESSEL land surface model used within ECMWF’s latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model.</p><p>This work presents the new GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1° at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 days behind real time). The reanalysis was evaluated against a global network of 1801 river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling-Gupta Efficiency Skill Score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for large scale hydrology applications ranging from monitoring global flood and drought conditions, understanding hydroclimatic variability and change, initialising hydrological forecasts, and as raw input to post-processing and machine learning methods that can add further value.</p><p><strong>Data availibility:</strong> The dataset is openly available from the Copernicus Climate Change Service (C3S) Climate Data Store (C3S): https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview and further details and the evaluation of the dataset can be found in the accompanying data description paper: </p><p><strong>Data paper:</strong> Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-232, 2020.</p>

scholarly journals GloFAS-ERA5 operational global river discharge reanalysis 1979–present

2020 ◽  
Vol 12 (3) ◽  
pp. 2043-2060 ◽  
Author(s):  
Shaun Harrigan ◽  
Ervin Zsoter ◽  
Lorenzo Alfieri ◽  
Christel Prudhomme ◽  
Peter Salamon ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
River Discharge ◽  
Pearson Correlation ◽  
Weather Prediction ◽  
Global Network ◽  
Surface Model ◽  
Mean Flow ◽  
Time Step ◽  
Reanalysis Dataset

Abstract. Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1∘ at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling–Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions to the identification of hydroclimatic variability and change and as raw input for post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview (last access: 28 June 2020) with the following DOI: https://doi.org/10.24381/cds.a4fdd6b9 (C3S, 2019).

scholarly journals GloFAS-ERA5 operational global river discharge reanalysis 1979–present

2020 ◽  
Author(s):  
Shaun Harrigan ◽  
Ervin Zsoter ◽  
Lorenzo Alfieri ◽  
Christel Prudhomme ◽  
Peter Salamon ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
River Discharge ◽  
Pearson Correlation ◽  
Weather Prediction ◽  
Global Network ◽  
Surface Model ◽  
Mean Flow ◽  
Time Step ◽  
Reanalysis Dataset

Abstract. Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, known as a reanalysis. While the current generation of NWP output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly, and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/) by coupling surface and sub-surface runoff from the HTESSEL land surface model used within ECMWF’s latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1° at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (within 7 days behind real time). The reanalysis was evaluated against a global network of 1801 river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling-Gupta Efficiency Skill Score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions, identification of hydroclimatic variability and change, and as raw input to post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview with the following DOI: 10.24381/cds.a4fdd6b9 (C3S, 2019).

Real-Time Forecasting System of Winds, Waves and Surge in Tropical Cyclones

2003 ◽  
Author(s):  
Hans C. Graber ◽  
Mark A. Donelan ◽  
Michael G. Brown ◽  
Donald N. Slinn ◽  
Scott C. Hagen ◽  
...  
Keyword(s):  
Real Time ◽  
Tropical Cyclones ◽  
Forecasting System

scholarly journals Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

2019 ◽  
Vol 11 (3) ◽  
pp. 327 ◽  
Author(s):  
Xia Wang ◽  
Feng Ling ◽  
Huaiying Yao ◽  
Yaolin Liu ◽  
Shuna Xu
Keyword(s):  
Surface Water ◽  
Real Time ◽  
Spatial Resolution ◽  
Water Body ◽  
Land Surface ◽  
Clustering Algorithm ◽  
Body Mapping ◽  
Water Index ◽  
Land Surface Water ◽  
Body Map

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

scholarly journals NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

2020 ◽  
Vol 12 (17) ◽  
pp. 2861
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Jicheng Liu
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Vital Role ◽  
Product System ◽  
Global Soil ◽  
One Stop ◽  
Satellite Sensors ◽  
Blended Data

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

Sign in / Sign up

Export Citation Format

Share Document