A Simple Data Assimilation Method for Improving Estimation of MODIS LAI Time-series Data Products Based on the 2-Dimensional LMS Adaptive Filter

2006 ◽  
Author(s):  
B. He ◽  
L. Tong ◽  
W. Xu ◽  
X. Han ◽  
H. Yuan ◽  
...  
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Adaptive Filter ◽  
Time Series Data ◽  
Series Data ◽  
Data Products ◽  
Modis Lai

scholarly journals Observation error covariance specification in dynamical systems for data assimilation using recurrent neural networks

2021 ◽  
Author(s):  
Sibo Cheng ◽  
Mingming Qiu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Dynamical Systems ◽  
Data Assimilation ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Observation Data ◽  
Observation Error ◽  
Error Covariance

AbstractData assimilation techniques are widely used to predict complex dynamical systems with uncertainties, based on time-series observation data. Error covariance matrices modeling is an important element in data assimilation algorithms which can considerably impact the forecasting accuracy. The estimation of these covariances, which usually relies on empirical assumptions and physical constraints, is often imprecise and computationally expensive, especially for systems of large dimensions. In this work, we propose a data-driven approach based on long short term memory (LSTM) recurrent neural networks (RNN) to improve both the accuracy and the efficiency of observation covariance specification in data assimilation for dynamical systems. Learning the covariance matrix from observed/simulated time-series data, the proposed approach does not require any knowledge or assumption about prior error distribution, unlike classical posterior tuning methods. We have compared the novel approach with two state-of-the-art covariance tuning algorithms, namely DI01 and D05, first in a Lorenz dynamical system and then in a 2D shallow water twin experiments framework with different covariance parameterization using ensemble assimilation. This novel method shows significant advantages in observation covariance specification, assimilation accuracy, and computational efficiency.

Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data

2021 ◽  
Vol 173 ◽  
pp. 262-277
Author(s):  
Xuejian Li ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Modis Lai

scholarly journals Data-Space Inversion With a Recurrent Autoencoder for Naturally Fractured Systems

2021 ◽  
Vol 7 ◽  
Author(s):  
Su Jiang ◽  
Mun-Hong Hui ◽  
Louis J. Durlofsky
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Time Series Data ◽  
Flow Simulation ◽  
Series Data ◽  
Model Parameters ◽  
Fractured Reservoir ◽  
Data Space ◽  
Naturally Fractured ◽  
Space Inversion

Data-space inversion (DSI) is a data assimilation procedure that directly generates posterior flow predictions, for time series of interest, without calibrating model parameters. No forward flow simulation is performed in the data assimilation process. DSI instead uses the prior data generated by performing O(1000) simulations on prior geomodel realizations. Data parameterization is useful in the DSI framework as it enables representation of the correlated time-series data quantities in terms of low-dimensional latent-space variables. In this work, a recently developed parameterization based on a recurrent autoencoder (RAE) is applied with DSI for a real naturally fractured reservoir. The parameterization, involving the use of a recurrent neural network and an autoencoder, is able to capture important correlations in the time-series data. RAE training is accomplished using flow simulation results for 1,350 prior model realizations. An ensemble smoother with multiple data assimilation (ESMDA) is applied to provide posterior DSI data samples. The modeling in this work is much more complex than that considered in previous DSI studies as it includes multiple 3D discrete fracture realizations, three-phase flow, tracer injection and production, and complicated field-management logic leading to frequent well shut-in and reopening. Results for the reconstruction of new simulation data (not seen in training), using both the RAE-based parameterization and a simpler approach based on principal component analysis (PCA) with histogram transformation, are presented. The RAE-based procedure is shown to provide better accuracy for these data reconstructions. Detailed posterior DSI results are then presented for a particular “true” model (which is outside the prior ensemble), and summary results are provided for five additional “true” models that are consistent with the prior ensemble. These results again demonstrate the advantages of DSI with RAE-based parameterization for this challenging fractured reservoir case.

scholarly journals An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data

NeuroImage ◽  
2006 ◽  
Vol 33 (4) ◽  
pp. 1072-1081 ◽  
Author(s):  
Roel H.R. Deckers ◽  
Peter van Gelderen ◽  
Mario Ries ◽  
Olivier Barret ◽  
Jeff H. Duyn ◽  
...  
Keyword(s):  
Time Series ◽  
Adaptive Filter ◽  
Time Series Data ◽  
Series Data

scholarly journals Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm

2018 ◽  
Vol 11 (1) ◽  
pp. 56 ◽  
Author(s):  
Luqi Xing ◽  
Xuejian Li ◽  
Huaqiang Du ◽  
Guomo Zhou ◽  
Fangjie Mao ◽  
...  
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Bayesian Network ◽  
Leaf Area Index ◽  
Time Series Data ◽  
Series Data ◽  
Bamboo Forest ◽  
Area Index ◽  
Moso Bamboo Forest ◽  
Modis Lai

The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of > 0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest.

Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

2011 ◽  
Vol 115 (2) ◽  
pp. 427-437 ◽  
Author(s):  
Joseph P. Spruce ◽  
Steven Sader ◽  
Robert E. Ryan ◽  
James Smoot ◽  
Philip Kuper ◽  
...  
Keyword(s):  
Time Series ◽  
Gypsy Moth ◽  
Time Series Data ◽  
Series Data ◽  
Modis Ndvi ◽  
Data Products ◽  
Ndvi Time Series

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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