Marine Biogeochemical Modelling and Data Assimilation for Operational Forecasting, Reanalysis, and Climate Research

2018 ◽  
Author(s):  
David Ford ◽  
Susan Kay ◽  
Robert McEwan ◽  
Ian Totterdell ◽  
Marion Gehlen
Keyword(s):  
Data Assimilation ◽  
Climate Research ◽  
Operational Forecasting

CHAOS AND WEATHER FORECASTING: THE ROLE OF THE UNSTABLE SUBSPACE IN PREDICTABILITY AND STATE ESTIMATION PROBLEMS

2011 ◽  
Vol 21 (12) ◽  
pp. 3389-3415 ◽  
Author(s):  
ANNA TREVISAN ◽  
LUIGI PALATELLA
Keyword(s):  
Data Assimilation ◽  
Chaotic Systems ◽  
Weather Forecasting ◽  
Variational Assimilation ◽  
Forecasting Models ◽  
Operational Forecasting ◽  
Estimation Problems ◽  
Atmospheric Predictability ◽  
Unstable Subspace

In the first part of this paper, we review some important results on atmospheric predictability, from the pioneering work of Lorenz to recent results with operational forecasting models. Particular relevance is given to the connection between atmospheric predictability and the theory of Lyapunov exponents and vectors. In the second part, we briefly review the foundations of data assimilation methods and then we discuss recent results regarding the application of the tools typical of chaotic systems theory described in the first part to well established data assimilation algorithms, the Extended Kalman Filter (EKF) and Four Dimensional Variational Assimilation (4DVar). In particular, the Assimilation in the Unstable Space (AUS), specifically developed for application to chaotic systems, is described in detail.

scholarly journals eWaterCycle: a hyper-resolution global hydrological model for river discharge forecasts made from open source pre-existing components

2016 ◽  
Author(s):  
Rolf Hut ◽  
Niels Drost ◽  
Maarten van Meersbergen ◽  
Edwin Sutanudjaja ◽  
Marc Bierkens ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Open Source ◽  
River Discharge ◽  
Model Comparison ◽  
Weather Forecast ◽  
Model Ensemble ◽  
Basic Model ◽  
Operational Forecasting ◽  
The Impact

Abstract. eWaterCycle is an open source hyperresolution (10 km × 10 km) global hydrological forecasting framework that runs an ensemble of hydrological models. Forced with a weather forecast ensemble, it predicts river discharge and river discharge uncertainty nine days ahead. Daily satellite soil moisture observations are assimilated into the state of the model ensemble using an Ensemble Kalman Filter. We demonstrate that it is feasible to build such a system using pre-exisiting, open source, components that communicate through standard interfaces. The PCRGLOBWB2.0 (van Beek et al., 2011; Sutanudjaja et al., 2014) model is used to model hydrology globally, forced with GFS (Kanamitsu, 1989; Kanamitsu et al., 1991; Moorthi et al., 2001) weather forecast. The operational soil moisture product from the HSAF (Drusch et al., 2009; De Rosnay et al., 2011) service is assimilated into the model ensemble using OpenDA (Velzen et al., 2016), a data assimilation framework. Output of the model ensemble is presented in a Cesium (Analytical Graphics, 2011) based visualization. All communication between framework components is through standard file types (NetCDF)(Rew and Davis, 1990) and services (Web Map Service) (de La Beaujardiere, 2006). Communication between model and data assimilation framework is through the Basic Model Interface (BMI) (Peckham et al., 2013). The forecasts is available at forecast.ewatercycle.org. By using standard open interfaces, the different components of the model can be replaced with relative ease, facilitating future model comparison studies without the need of extensive Computer Science support. This makes eWaterCycle, in addition to an operational forecasting model, a testbed environment where the impact of different model structures, input sources and/or data assimilation schemes can easily be studied. Setup instructions to run the eWaterCycle project on local hardware are provided, allowing the hydrological community to build on this open source framework.

scholarly journals Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

2011 ◽  
Vol 15 (11) ◽  
pp. 3399-3410 ◽  
Author(s):  
C. M. DeChant ◽  
H. Moradkhani
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Water Storage ◽  
Initial Condition ◽  
Streamflow Prediction ◽  
Western Us ◽  
Operational Forecasting ◽  
Snow Water ◽  
Ensemble Streamflow Prediction ◽  
Using Data

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

scholarly journals Application of data assimilation in portable operational forecasting systems—the DATools assimilation environment

2007 ◽  
Vol 57 (4-5) ◽  
pp. 485-499 ◽  
Author(s):  
Ghada Y. El Serafy ◽  
Herman Gerritsen ◽  
Stef Hummel ◽  
Albrecht H. Weerts ◽  
Arthur E. Mynett ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Operational Forecasting

scholarly journals Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

2011 ◽  
Vol 8 (4) ◽  
pp. 7207-7235 ◽  
Author(s):  
C. M. DeChant ◽  
H. Moradkhani
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Water Storage ◽  
Initial Condition ◽  
Streamflow Prediction ◽  
Western Us ◽  
Operational Forecasting ◽  
Snow Water ◽  
Ensemble Streamflow Prediction ◽  
Using Data

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

Doppler Radar Data Assimilation in KMA's Operational Forecasting

2008 ◽  
Vol 89 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Qingnong Xiao ◽  
Eunha Lim ◽  
Duk-Jin Won ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Radar Data ◽  
Doppler Radar Data ◽  
Operational Forecasting ◽  
Radar Data Assimilation
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