scholarly journals Modified method of empirical orthogonal functions to retrieve the total amount of carbon dioxide from satellite data

2016 ◽  
Vol 19 (4) ◽  
pp. 87-90
Author(s):  
M.Yu. Kataev ◽  
◽  
A.K. Lukyanov ◽  
S. Maksyutov ◽  
◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Satellite Data ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Modified Method

scholarly journals TIEOF: Algorithm for Recovery of Missing Multidimensional Satellite Data on Water Bodies Based on Higher-Order Tensor Decompositions

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2578
Author(s):  
Leonid Kulikov ◽  
Natalia Inkova ◽  
Daria Cherniuk ◽  
Anton Teslyuk ◽  
Zorigto Namsaraev
Keyword(s):  
Missing Data ◽  
Lake Baikal ◽  
Satellite Data ◽  
Three Dimensional ◽  
Tensor Decomposition ◽  
Empirical Orthogonal Functions ◽  
Water Bodies ◽  
Atmospheric Conditions ◽  
Order Tensor ◽  
Orthogonal Functions

Satellite research methods are [DCh]actively involvedfrequently used in observations of water bodies. One of the most important problems in satellite observations is the presence of missing data due to internal malfunction of satellite sensors and poor atmospheric conditions. We proceeded on the assumption that the use of data recovery methods based on spatial relationships in data can increase the recovery accuracy. In this paper, we present a method for missing data reconstruction from remote sensors. We refer our method to as Tensor Interpolating Empirical Orthogonal Functions (TIEOF). The method relies on the two-dimensional nature of sensor images and organizes the data into three-dimensional tensors. We use high-order tensor decomposition to interpolate missing data [ZN] on chlorophyll a concentration in lake Baikal (Russia, Siberia). Using MODIS and SeaWiFS satellite data of lake Baikal we show that the observed improvement of TIEOF was 69% on average compared to the current state-of-the-art DINEOF algorithm measured in various preprocessing data scenarios including thresholding and different interpolating schemes.

scholarly journals Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter

2011 ◽  
Vol 11 (1) ◽  
pp. 1367-1384
Author(s):  
R. Zhuravlev ◽  
B. Khattatov ◽  
B. Kiryushov ◽  
S. Maksyutov
Keyword(s):  
Carbon Dioxide ◽  
Kalman Filter ◽  
Carbon Dioxide Emissions ◽  
Transport Model ◽  
Technical Note ◽  
Empirical Orthogonal Functions ◽  
Computationally Efficient ◽  
Orthogonal Functions ◽  
Novel Approach ◽  
Variable Surface

Abstract. In this work we propose an approach to solving a source estimation problem based on representation of carbon dioxide surface emissions as a linear combination of a finite number of pre-computed empirical orthogonal functions (EOFs). We used NIES transport model for computing response functions and Kalman filter for estimating carbon dioxide emissions. Our approach produces results similar to these of other models participating in the TransCom3 experiment, while being more advantageous in that it is more computationally efficient, produces smooth emission fields, and yields smaller errors than the traditional region-based approach. Additionally, the proposed approach does not require additional effort of defining independent self-contained emission regions.

scholarly journals Technical Note: A novel approach to estimation of time-variable surface sources and sinks of carbon dioxide using empirical orthogonal functions and the Kalman filter

2011 ◽  
Vol 11 (20) ◽  
pp. 10305-10315 ◽  
Author(s):  
R. Zhuravlev ◽  
B. Khattatov ◽  
B. Kiryushov ◽  
S. Maksyutov
Keyword(s):  
Carbon Dioxide ◽  
Kalman Filter ◽  
Carbon Dioxide Emissions ◽  
Transport Model ◽  
Technical Note ◽  
Empirical Orthogonal Functions ◽  
Surface Fluxes ◽  
Orthogonal Functions ◽  
Novel Approach ◽  
Variable Surface

Abstract. In this work we propose an approach to solving a source estimation problem based on representation of carbon dioxide surface emissions as a linear combination of a finite number of pre-computed empirical orthogonal functions (EOFs). We used National Institute for Environmental Studies (NIES) transport model for computing response functions and Kalman filter for estimating carbon dioxide emissions. Our approach produces results similar to these of other models participating in the TransCom3 experiment. Using the EOFs we can estimate surface fluxes at higher spatial resolution, while keeping the dimensionality of the problem comparable with that in the regions approach. This also allows us to avoid potentially artificial sharp gradients in the fluxes in between pre-defined regions. EOF results generally match observations more closely given the same error structure as the traditional method. Additionally, the proposed approach does not require additional effort of defining independent self-contained emission regions.

scholarly journals Analysis of the Monthly and Spring-Neap Tidal Variability of Satellite Chlorophyll-a and Total Suspended Matter in a Turbid Coastal Ocean Using the DINEOF Method

2021 ◽  
Vol 13 (4) ◽  
pp. 632
Author(s):  
Mengmeng Yang ◽  
Faisal Ahmed Khan ◽  
Hongzhen Tian ◽  
Qinping Liu
Keyword(s):  
Sea Level ◽  
Satellite Data ◽  
Temporal Variability ◽  
Total Suspended Matter ◽  
Empirical Orthogonal Functions ◽  
Spatial And Temporal Variability ◽  
Ariake Sea ◽  
Orthogonal Functions ◽  
Chl A ◽  
Tidal Variability

Missing spatial data is one of the major concerns associated with the application of satellite data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has been proven to be an effective tool for filling spatial gaps in various satellite data products. The Ariake Sea, which is a turbid coastal sea, shows the large spatial and temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM). However, ocean color satellite data for this region usually have large gaps, which affects the accurate analysis of Chl-a and TSM variability. In this study, we applied the DINEOF method to fill the missing pixels from the regionally tuned Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua (hereafter, MODIS) Chl-a and MODIS-derived TSM datasets for the period 2002–2017. The validation results showed that the DINEOF reconstructed data were accurate and reliable. Furthermore, the Empirical Orthogonal Functions (EOF) analysis based on the reconstructed data was used to quantitatively analyze the spatial and temporal variability of Chl-a and TSM at both monthly and individual events of spring-neap tidal scales. The first three EOF modes of Chl-a showed seasonal variability mainly caused by precipitation, the sea surface temperature (SST), and river discharge for the first EOF mode and the sea level amplitude for the second. The first three EOF modes of TSM exhibited both seasonal and spring-neap tidal variability. The first and second EOF modes of TSM displayed spring-neap tidal variability caused by the sea level amplitude. The second EOF mode of TSM also showed seasonal variability caused by the sea level amplitude. In this study, we first applied the DINEOF method to reconstruct the satellite data and to capture the major spatial and temporal variability of Chl-a and TSM for the Ariake Sea. Our results demonstrate that the DINEOF method can reconstruct patchy oceanic color datasets and improve spatio-temporal variability analysis.

Empirical Methods in Short-Term Climate Prediction

2006 ◽  
Author(s):  
Huug van den Dool
Keyword(s):  
Climate Prediction ◽  
Empirical Orthogonal Functions ◽  
Data Sets ◽  
Seasonal Forecasts ◽  
Short Term ◽  
Orthogonal Functions ◽  
Ocean Atmosphere Interaction ◽  
Global Coverage ◽  
Atmosphere Interaction ◽  
Global Data

This clear and accessible text describes the methods underlying short-term climate prediction at time scales of 2 weeks to a year. Although a difficult range to forecast accurately, there have been several important advances in the last ten years, most notably in understanding ocean-atmosphere interaction (El Nino for example), the release of global coverage data sets, and in prediction methods themselves. With an emphasis on the empirical approach, the text covers in detail empirical wave propagation, teleconnections, empirical orthogonal functions, and constructed analogue. It also provides a detailed description of nearly all methods used operationally in long-lead seasonal forecasts, with new examples and illustrations. The challenges of making a real time forecast are discussed, including protocol, format, and perceptions about users. Based where possible on global data sets, illustrations are not limited to the Northern Hemisphere, but include several examples from the Southern Hemisphere.

scholarly journals Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

2021 ◽  
Vol 13 (2) ◽  
pp. 265
Author(s):  
Harika Munagapati ◽  
Virendra M. Tiwari
Keyword(s):  
Temperature Anomaly ◽  
Mass Gain ◽  
Snow Accumulation ◽  
Empirical Orthogonal Functions ◽  
Total Water ◽  
Orthogonal Functions ◽  
Grace Data ◽  
Grace Satellite ◽  
Spatio Temporal ◽  
Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

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