scholarly journals Observation impact, domain length and parameter estimation in data assimilation for flood forecasting

2018 ◽  
Vol 104 ◽  
pp. 199-214 ◽  
Author(s):  
E.S. Cooper ◽  
S.L. Dance ◽  
J. Garcia-Pintado ◽  
N.K. Nichols ◽  
P.J. Smith
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Flood Forecasting ◽  
Observation Impact ◽  
Domain Length

scholarly journals Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

2019 ◽  
Vol 577 ◽  
pp. 123924 ◽  
Author(s):  
Matteo G. Ziliani ◽  
Rabih Ghostine ◽  
Boujemaa Ait-El-Fquih ◽  
Matthew F. McCabe ◽  
Ibrahim Hoteit
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
State Parameter ◽  
Flood Forecasting ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Joint State

An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting

2014 ◽  
Vol 519 ◽  
pp. 2722-2736 ◽  
Author(s):  
Yuan Li ◽  
Dongryeol Ryu ◽  
Andrew W. Western ◽  
Q.J. Wang ◽  
David E. Robertson ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Real Time ◽  
Flood Forecasting ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme ◽  
Error Parameter ◽  
Integrated Error

scholarly journals Parameter Estimation Using Ensemble-Based Data Assimilation in the Presence of Model Error

2015 ◽  
Vol 143 (5) ◽  
pp. 1568-1582 ◽  
Author(s):  
Juan Ruiz ◽  
Manuel Pulido
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Model Error ◽  
Forecast Skill ◽  
Moist Convection ◽  
Treatment Techniques ◽  
Correction Technique ◽  
Error Treatment ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with model error treatment techniques without parameter estimation. The improvement produced by parameter estimation is mainly a consequence of the optimization of the parameter values. The estimated parameters do not converge to the value used to generate the observations in the imperfect model scenario; however, the analysis error is reduced and the forecast skill is improved.

A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

2018 ◽  
Vol 146 (2) ◽  
pp. 447-465 ◽  
Author(s):  
Mark Buehner ◽  
Ping Du ◽  
Joël Bédard
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
New Approach ◽  
Regional Prediction ◽  
Observation Impact ◽  
Adjoint Approach ◽  
The Impact

Abstract Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

scholarly journals Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model

Agriculture ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 606
Author(s):  
Alaa Jamal ◽  
Raphael Linker
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Assimilation ◽  
Particle Filter ◽  
Sequential Estimation ◽  
Measurement Noise ◽  
Genetic Operators ◽  
True Value

Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

Sign in / Sign up

Export Citation Format

Share Document