scholarly journals Characterizing Frequency Stability Measurements having Multiple Data Gaps

2021 ◽  
Author(s):  
David Howe ◽  
Noah Schlossberger
Keyword(s):  
Time Series ◽  
Frequency Stability ◽  
Multiple Data ◽  
Data Gaps ◽  
Stability Measurements

Describes a method of imputing data into gaps in a time series.

scholarly journals Characterizing Frequency Stability Measurements having Multiple Data Gaps

2021 ◽  
Author(s):  
David Howe ◽  
Noah Schlossberger
Keyword(s):  
Time Series ◽  
Frequency Stability ◽  
Multiple Data ◽  
Data Gaps ◽  
Stability Measurements

Describes a method of imputing data into gaps in a time series.

An optimum data editing technique for the reduction of noise in multi-channel geophysical data

1984 ◽  
Vol 74 (3) ◽  
pp. 1059-1078
Author(s):  
P. A. Tyraskis ◽  
O. G. Jensen ◽  
D. E. Smylie ◽  
J. A. Linton
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Seismic Data ◽  
Data Model ◽  
Optimum Interpolation ◽  
Long Period ◽  
Data Editing ◽  
Spectral Peaks ◽  
Data Gaps ◽  
The Individual

Abstract We develop a data editing method, for the optimum interpolation of multichannel time series containing time-coincident data gaps, in one, several, or all channels based upon the autoregressive data model. The method is applied to a set of very long-period seismic data recorded during the 19 August 1977 Indonesian earthquake, which shows several unassociated bursts of noise. Spectral analysis following editing and interpolation of the record indicates existence of systematic signals with periods higher than 1 hr and perhaps as long as 2 hr. The individual spectral peaks in this subseismic band have not been identified.

scholarly journals Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions

2020 ◽  
Vol 12 (15) ◽  
pp. 2471
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
High Latitude ◽  
High Latitudes ◽  
Series Analysis ◽  
Data Gaps ◽  
Northern High Latitude ◽  
Study Sites ◽  
Sentinel 2 ◽  
Permafrost Regions

Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances.

scholarly journals Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2

2019 ◽  
Vol 11 (9) ◽  
pp. 1010 ◽  
Author(s):  
Christian Schwatke ◽  
Daniel Scherer ◽  
Denise Dettmering
Keyword(s):  
Time Series ◽  
Surface Area ◽  
Time Variable ◽  
New Approach ◽  
Data Gaps ◽  
Lakes And Reservoirs ◽  
Variable Water ◽  
Land Water ◽  
Sentinel 2

In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).

A method of editing time series observations

Geophysics ◽  
1984 ◽  
Vol 49 (5) ◽  
pp. 521-524 ◽  
Author(s):  
John Halpenny
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Input Data ◽  
Random Noise ◽  
Data Value ◽  
Data Gaps ◽  
Before And After ◽  
Noise Data ◽  
Fixed Delay ◽  
Time Point

Data from automatic recording systems often require editing and filtering before they are suitable for computer analysis. The procedure described in this paper produces edited values at regular intervals from input data containing random noise, data gaps, and sudden steps or resets. It uses a Kalman filter with a fixed delay time to estimate the most probable data value at any time, based on information both before and after the time point. Isolated portions of a bad record can be recognized and removed, and steps or offsets are identified and measured. An example is shown of clean output produced from input which suffers from a variety of instrumental problems.

scholarly journals Radial Differential Rotation of Solar Corona using Radio Emissions

2021 ◽  
Author(s):  
Vivek Kumar Singh ◽  
Satish Chandra ◽  
Sanish Thomas ◽  
Som Kumar Sharma ◽  
Hari Om Vats
Keyword(s):  
Time Series ◽  
Solar Corona ◽  
Solar Rotation ◽  
Rotation Period ◽  
Solar Radio Emission ◽  
Correlation Technique ◽  
Emission Data ◽  
Height Range ◽  
Data Gaps ◽  
Frequency Radiation

Abstract The present work is an effort to investigate possible radial variations in the solar coronal rotation by analyzing the solar radio emission data at 15 different frequencies (275-1755 MHz) for the period starting from July 1994 to May 1999. We used a time series of disk-integrated radio flux recorded daily at these frequencies through radio telescopes situated at Astronomical Observatory of the Jagellonian University in Cracow. The different frequency radiation originates from different heights in the solar corona. Existing models, indicate its origin at the height range from nearly ∼12, 000 km (for emission at 275 MHz), below up to ∼2, 400 km (for emission at 1755 MHz). There are some data gaps in the time series used for the study, so we used statistical analysis using the Lomb-Scargle Periodogram (LSP). This method has successfully estimated the periodicity present in time series even with such data gaps. The rotation period estimated through LSP shows variation in rotation period, which is compared with the earlier reported estimate using auto correlation technique. The present study indicates some similarity as well as contradiction with studies reported earlier. The radial and temporal variation in solar rotation period are presented and discussed for the whole period analyzed.

scholarly journals Filling the gaps of in-situ hourly PM<sub>2.5</sub> concentration data with the aid of empirical orthogonal function constrained by diurnal cycles

2019 ◽  
Author(s):  
Kaixu Bai ◽  
Ke Li ◽  
Jianping Guo ◽  
Yuanjian Yang ◽  
Ni-Bin Chang
Keyword(s):  
Time Series ◽  
Empirical Orthogonal Function ◽  
Diurnal Cycle ◽  
Spline Interpolation ◽  
Gap Filling ◽  
Concentration Data ◽  
Diurnal Variability ◽  
Orthogonal Function ◽  
Data Gaps ◽  
Pm2.5 Concentration

Abstract. Data gaps are frequently observed in the hourly PM2.5 mass concentration records measured from the China national air quality monitoring network. In this study, we proposed a novel gap filling method called the diurnal cycle constrained empirical orthogonal function (DCCEOF) to fill in data gaps present in hourly PM2.5 concentration records. This method mainly calibrates the diurnal cycle of PM2.5 that is reconstructed from discrete PM2.5 neighborhood fields in space and time to the level of valid PM2.5 data values observed at adjacent times. Prior to gap filling, possible impacts of varied number of data gaps in the time series of hourly PM2.5 concentration on PM2.5 daily averages were examined via sensitivity experiments. The results showed that PM2.5 data suffered from the gaps on about 40% of days, indicating a high frequency of missing data in the hourly PM2.5 records. These gaps could introduce significant bias to daily-averaged PM2.5. Particularly, given the same number of gaps, larger biases would be introduced to daily-averaged PM2.5 during clean days than polluted days. The cross-validation results indicate that the predicted missing values from the DCCEOF method with the consideration of the local diurnal phases of PM2.5 are more accurate and reasonable than those from the conventional spline interpolation approach, especially for the reconstruction of daily peaks and/or minima that cannot be restored by the latter method. To fill the gaps in the hourly PM2.5 records across China during 2014 to 2019, as a practical application, the DCCEOF method can be able to reduce the averaged frequency of missingness from 42.6 % to 5.7 %. In general, the present work implies that the DCCEOF method is realistic and robust to be able to handle the missingness issues in time series of geophysical parameters with significant diurnal variability and can be expectably applied in other data sets with similar barriers because of its self-consistent capability.

Sign in / Sign up

Export Citation Format

Share Document