scholarly journals Data assimilation of incoherent scatter radar observation into a one-dimensional midlatitude ionospheric model by applying ensemble Kalman filter

Radio Science ◽  
2007 ◽  
Vol 42 (6) ◽  
Author(s):  
Xinan Yue ◽  
Weixing Wan ◽  
Libo Liu ◽  
Fei Zheng ◽  
Jiuhou Lei ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Radar Observation ◽  
Ionospheric Model ◽  
Incoherent Scatter Radar ◽  
One Dimensional ◽  
Incoherent Scatter ◽  
Scatter Radar

scholarly journals Ionospheric longitudinal variations at midlatitudes: Incoherent scatter radar observation at Millstone Hill

2012 ◽  
Vol 55 (5) ◽  
pp. 1153-1160 ◽  
Author(s):  
ShunRong Zhang ◽  
Anthea J. Coster ◽  
John M. Holt ◽  
John C. Foster ◽  
Phil Erickson
Keyword(s):  
Radar Observation ◽  
Incoherent Scatter Radar ◽  
Incoherent Scatter ◽  
Scatter Radar ◽  
Longitudinal Variations

One-Dimensional Soil Moisture Simulation Using Ensemble Kalman Filter

2012 ◽  
Vol 212-213 ◽  
pp. 177-180
Author(s):  
Xiao Lei Fu ◽  
Zhong Bo Yu ◽  
Yu Li ◽  
Hai Shen Lv ◽  
Di Liu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Soil Layer ◽  
Model Parameters ◽  
Deep Soil ◽  
One Dimensional ◽  
Initial Soil Moisture ◽  
Initial Soil

Data assimilation is a method which integrates model and observation. In hydrology, ensemble Kalman filter (EnKF) as a sequential data assimilation method is often used to correct model parameters, thus improve the simulated accuracy. In this study, we conduct one numerical experiment to predict soil moisture using the one-dimensional soil moisture system based on ensemble Kalman filter and Simple Biosphere (SiB2) Model at Meilin study area, China. The simulated period is divided into two parts: 0-60h and 60-240h. The results show that EnKF is an efficient method in assimilating the soil moisture, especially in soil surface layer and deep soil layer; in addition, the efficiency of EnKF depends on the selection of initial soil moisture profile. With different initial soil moisture profiles, the performance of EnKF is different at the first few assimilated time, but with time grows, it can improve the simulated accuracy significantly.

scholarly journals Incoherent scatter radar observation of E-region vertical electric field at Arecibo

2011 ◽  
Vol 38 (1) ◽  
pp. n/a-n/a ◽  
Author(s):  
Q. H. Zhou ◽  
Y. T. Morton ◽  
C. M. Huang ◽  
N. Aponte ◽  
M. Sulzer ◽  
...  
Keyword(s):  
Electric Field ◽  
Radar Observation ◽  
Incoherent Scatter Radar ◽  
Incoherent Scatter ◽  
Scatter Radar ◽  
Vertical Electric Field ◽  
E Region

scholarly journals Comparison of data assimilation methods based on the classical, ensemble and local Kalman filter by the example of the advection equation and Lorenz system

2018 ◽  
pp. 507-515
Author(s):  
Д.А. Ростилов ◽  
М.Н. Кауркин ◽  
Р.А. Ибраев
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Lorenz System ◽  
Parallel Implementation ◽  
Advection Equation ◽  
Ensemble Kalman Filters ◽  
One Dimensional ◽  
The Mean ◽  
The Lorenz System

Статья посвящена сравнению трех методов усвоения данных наблюденй: фильтр Калмана (Kalman Filter, KF), ансамблевый фильтр Калмана (Ensemble Kalman Filter, EnKF) и локальный фильтр Калмана (Local Kalman Filter, LKF). Выполнены численные эксперименты по усвоению синтетических данных этими методами в двух разных моделях, описываемых системами дифференциальных уравнений. Первая описывается одномерным линейным уравнением адвекции, а вторая - системой Лоренца. Проведено сравнение средних ошибок и времени исполнения этих методов при различных размерах модели, которые согласуются с теоретическим оценками. Показано, что вычислительная сложность ансамблевого и локального фильтров Калмана растет линейно с увеличением размера модели, в то время как у первого метода эта сложность растет со скоростью куба. Рассмотрена эффективность одной из возможных параллельных реализаций локального фильтра Калмана. The paper is devoted to the comparison of three data assimilation methods: the Kalman Filter (Kalman Filter, KF), the ensemble Kalman Filter (EnKF), and the local Kalman Filter (LKF). A number of numerical experiments on data assimilation by these methods are performed on two different models described by systems of differential equations. The first one is a simple one-dimensional linear equation of advection and the second one is the Lorenz system. The mean errors and the execution time of these assimilation methods are compared for different model sizes. The numerical results are consistent with the theoretical estimates. It is shown that the computational complexity of local and ensemble Kalman filters grows linearly with the size of the model, whereas in the classical Kalman Filter this complexity increases according to the cubic law. The efficiency of parallel implementation of the local Kalman filter is considered.

Application of the Ensemble Kalman Filter to a One-Dimensional Linear Advection Model

2015 ◽  
Vol 804 ◽  
pp. 287-290
Author(s):  
Somsiri Payakkarak ◽  
Dusadee Sukawat
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Ensemble Member ◽  
Observation Data ◽  
State Estimate ◽  
One Dimensional ◽  
Weather Forecasts

Data assimilation is used in numerical weather prediction to improve weather forecasts by incorporating observation data into the model forecast. The Ensemble Kalman Filter (EnKF) is a method of data assimilation which updates an ensemble of states to provide a state estimate and associated error at each step. The atmospheric model that is used in this research is a one-dimensional linear advection model. This model describes the motion of a scalar field as it is advected by a known speed field. The result shows that by selecting appropriate initial ensemble, model noise and measurement perturbations, it is possible to achieve a significant improvement in the EnKF results. The accuracy of the EnKF increases when the number of ensemble member grows. That is, the larger ensemble sizes perform better than those of smaller sizes.

Data assimilation with the weighted ensemble Kalman filter

2010 ◽  
Author(s):  
Nicolas Papadakis ◽  
Etienne Mémin ◽  
Anne Cuzol ◽  
Nicolas Gengembre
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter
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