GRAPES 3DVAR and its preliminary application on remote sensing data assimilation

2005 ◽  
Author(s):  
Shiyu Zhuang ◽  
Qilin Wan ◽  
Zhu Guofu ◽  
Xue Jishan
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Preliminary Application

scholarly journals Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

scholarly journals Remote sensing data assimilation for a prognostic phenology model

2008 ◽  
Vol 113 (G4) ◽  
Author(s):  
R. Stöckli ◽  
T. Rutishauser ◽  
D. Dragoni ◽  
J. O'Keefe ◽  
P. E. Thornton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Phenology Model

scholarly journals Remote sensing data assimilation in modeling urban dynamics: Objectives and methodology

2011 ◽  
Vol 7 ◽  
pp. 140-145 ◽  
Author(s):  
Johannes van der Kwast ◽  
Frank Canters ◽  
Derek Karssenberg ◽  
Guy Engelen ◽  
Tim Van de Voorde ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Urban Dynamics ◽  
Sensing Data

scholarly journals Data assimilation in sea-ice monitoring

2000 ◽  
Vol 31 ◽  
pp. 327-332 ◽  
Author(s):  
Ronald L. S. Weaver ◽  
Konrad Steffen ◽  
John Heinrichs ◽  
James A. Maslanik ◽  
Gregory M. Flato
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Sea Ice ◽  
Global Climate ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Physical Processes ◽  
Error Statistics ◽  
Sensing Data ◽  
Ice Model

AbstractThe detection of small changes in concentration or thickness in the Arctic or Antarctic ice cover is an important topic in the current global-climate-change debate. Change detection using satellite data alone requires rigorous error analysis for their derived ice products, including inter-satellite validation for long time series. All models of physical processes are only approximations, and the best models of complicated physical processes have errors and uncertainties. A promising approach is data assimilation, combining model, in situ data and satellite remote-sensing data. Sea-ice monitoring from satellite, ice-model estimates, and the potential benefit of combining the two are discussed in some detail. In a case-study we demonstrate how the sea-ice backscatter for the Beaufort Sea region was derived using a backscattering model in combination with an ice model. We conclude that, for data assimilation, the first steps include the use of simple models, moving, with success at this level, to progressively more complex models. We also recommend reconfiguring the current remote-sensing data to include precise time tags with each pixel. For example, the current Special Sensor Microwave Imager data might be reissued in a time-tagged orbital (or gridded) format as opposed to the currently available daily averaged gridded data. Finally, error statistics and quality-control information also need to be readily available in a form useful for assimilation. The effectiveness of data-assimilation techniques is directly linked to the availability of data error statistics.

Remote sensing data assimilation

2020 ◽  
pp. 1-33
Author(s):  
Akhilesh S. Nair ◽  
Rohit Mangla ◽  
Thiruvengadam P ◽  
J. Indu
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

2021 ◽  
Vol 13 (6) ◽  
pp. 1094
Author(s):  
Xingshuo Peng ◽  
Wenting Han ◽  
Jianyi Ao ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Maize Yield ◽  
Estimation Accuracy ◽  
Corn Yield ◽  
Sensing Data ◽  
Water Treatments

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.

scholarly journals Irrigation Scheduling Using Remote Sensing Data Assimilation Approach

2013 ◽  
Vol 02 (03) ◽  
pp. 258-268 ◽  
Author(s):  
Baburao Kamble ◽  
Ayse Irmak ◽  
Kenneth Hubbard ◽  
Prasanna Gowda
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Sensing Data

Using high-resolution groundwater data for the validation of a global hydrological model: evaluating WaterGAP and calibration/data assimilation (C/DA) performance over France

2021 ◽  
Author(s):  
Kuei-Hua Hsu ◽  
Laurent Longuevergne ◽  
Annette Eicker ◽  
Mehedi Hasan ◽  
Andreas Güntner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Freshwater Resources ◽  
Sensing Data

<p>The dynamic global water cycle is of ecological and societal importance as it affects the availability of freshwater resources and influences extreme events such as floods and droughts. This work is set in the frame of the GlobalCDA Research Unit, which has the goal of developing a calibration/data assimilation approach (C/DA) to improve the quantification of freshwater resources by combining the global hydrological model WaterGAP with geodetic (GRACE, altimetry) and remote sensing data. This presentation focuses on the validation of the C/DA results using an independent in-situ groundwater data set based on ~1500 monitoring boreholes in France.</p><p>The resulting validation data set is applied to independently assess the output of several C/DA experiments: data assimilation using different combinations of the available geodetic and remote sensing data sets and different methods of model calibration, based on either an ensemble Kalman filter approach or a Pareto-optimal calibration algorithm.</p><p>To further understand in-situ groundwater and WaterGAP data set, we subtract the coherent signals using Empirical orthogonal function (EOF).  Over 85% variances can be explained by the first 3 EOFs for both data sets.</p>

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