Evaluation of the regional reanalysis COSMO-REA6 vs ERA-Interim for improving the dealiasing analysis of GRACE/GRACE-FO mission data

2021 ◽  
Author(s):  
Shashi Dixit ◽  
Petra Friederichs ◽  
Andreas Hense
Keyword(s):  
Water Mass ◽  
Hydrological Cycle ◽  
Scale Effects ◽  
Principal Component ◽  
Grid Size ◽  
Sensor Data ◽  
German Research Foundation ◽  
Direct Evaluation ◽  
Hourly Data ◽  
Atmospheric Mass

<p>This work is part of the Research Group New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV), which is funded by the German Research Foundation (DFG). The goal of NEROGRAV is to develop new analysis methods and modeling approaches to improve the resolution, accuracy, and long-term consistency of mass transport series from the GRACE and GRACE-FO missions. This can only be obtained by improving the sensor data, background models, and processing strategies for satellite gravimetry. Within NEROGRAV, the joint Geodesy and Meteorology group at the University of Bonn is responsible for the investigation of the atmospheric and hydrological effects on the dealiasing of GRACE/GRACE-FO observations of the Earth’s gravity field.</p><p>In the present study we compare 3-hourly data from the ERA-Interim realanysis with a grid size of 50 km based on a hydrostatic model of the atmosphere and the houly data of the non-hydrostatic COSMO reanalysis with a grid size of 6 km (COSMO-REA6, Bollmeyer et.al (2015), QJRMS, <em>141</em>(686), 1-15.). To date, atmospheric mass variability has been studied largely through data from hydrostatic models of the atmosphere. Therefore a direct evaluation of the total atmospheric mass variability including non-hydrostatic effects compared to a hydrostatic background model is necessary. Further, GRACE/GRACE-FO is expected to be sensitive to the atmospheric water mass variability. Since a high resolution atmospheric model provides an intensified water cycle, a more localised and enhanced mass variability within all water components is expected in COSMO-REA6.</p><p>The objectives of this talk are to (1) present the evaluation results of non-hydrostatic effects and water mass transports on the atmospheric mass variability and (2) assess the scale effects of a coarse vs a fine resolution representation of the atmospheric mass. Both objectives place an emphasis on the contributions of the atmospheric hydrological cycle in two views: the systematic effects are investigated by the mean values, while spatial variability effects are investigated using a principal component analysis. The study concentrates on the summer season 2007 over the CORDEX (North Atlantic, European region) domain.</p>

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

2014 ◽  
Vol 41 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Suyong Yeon ◽  
ChangHyun Jun ◽  
Hyunga Choi ◽  
Jaehyeon Kang ◽  
Youngmok Yun ◽  
...  
Keyword(s):  
Principal Component ◽  
Point Clouds ◽  
Previous Method ◽  
Divide And Conquer ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Content Type ◽  
Extraction Algorithm ◽  
Plane Extraction ◽  
Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

scholarly journals Regional GRACE-based estimates of water mass variations over Australia: validation and interpretation

2013 ◽  
Vol 17 (12) ◽  
pp. 4925-4939 ◽  
Author(s):  
L. Seoane ◽  
G. Ramillien ◽  
F. Frappart ◽  
M. Leblanc
Keyword(s):  
Water Mass ◽  
Drainage Basin ◽  
Principal Component ◽  
Component Analysis ◽  
Climate Index ◽  
Accurate Estimation ◽  
Total Water ◽  
Spatial And Temporal Patterns ◽  
Regional Solutions

Abstract. Time series of regional 2° × 2° Gravity Recovery and Climate Experiment (GRACE) solutions have been computed from 2003 to 2011 with a 10-day resolution by using an energy integral method over Australia (112° E–156° E; 44° S–10° S). This approach uses the dynamical orbit analysis of GRACE Level 1 measurements, and specially accurate along-track K-band range rate (KBRR) residuals with a 1 μm s−1 level of errors, to estimate the total water mass over continental regions. The advantages of regional solutions are a significant reduction of GRACE aliasing errors (i.e. north–south stripes) providing a more accurate estimation of water mass balance for hydrological applications. In this paper, the validation of these regional solutions over Australia is presented, as well as their ability to describe water mass change as a response of climate forcings such as El Niño. Principal component analysis of GRACE-derived total water storage (TWS) maps shows spatial and temporal patterns that are consistent with independent data sets (e.g. rainfall, climate index and in situ observations). Regional TWS maps show higher spatial correlations with in situ water table measurements over Murray–Darling drainage basin (80–90%), and they offer a better localization of hydrological structures than classical GRACE global solutions (i.e. Level 2 Groupe de Recherche en Géodésie Spatiale (GRGS)) products and 400 km independent component analysis solutions as a linear combination of GRACE solutions provided by different centers.

scholarly journals Volunteered Geographic Information: Interpretation, Visualisation and Social Computing (VGIscience)

2018 ◽  
Vol 1 ◽  
pp. 1-5 ◽  
Author(s):  
Dirk Burghardt ◽  
Wolfgang Nejdl ◽  
Jochen Schiewe ◽  
Monika Sester
Keyword(s):  
Visual Analysis ◽  
Spatial Information ◽  
Volunteered Geographic Information ◽  
Scientific Basis ◽  
Geographic Information ◽  
Sensor Data ◽  
Short Paper ◽  
German Research Foundation ◽  
Knowledge Presentation ◽  
Large Heterogeneity

In the past years Volunteered Geographic Information (VGI) has emerged as a novel form of user-generated content, which involves active generation of geo-data for example in citizen science projects or during crisis mapping as well as the passive collection of data via the user’s location-enabled mobile devices. In addition there are more and more sensors available that detect our environment with ever greater detail and dynamics. These data can be used for a variety of applications, not only for the solution of societal tasks such as in environment, health or transport fields, but also for the development of commercial products and services. The interpretation, visualisation and usage of such multi-source data is challenging because of the large heterogeneity, the differences in quality, the high update frequencies, the varying spatial-temporal resolution, subjective characteristics and low semantic structuring.<br> Therefore the German Research Foundation has launched a priority programme for the next 3&amp;ndash;6 years which will support interdisciplinary research projects. This priority programme aims to provide a scientific basis for raising the potential of VGI- and sensor data. Research questions described more in detail in this short paper span from the extraction of spatial information, to the visual analysis and knowledge presentation, taking into account the social context while collecting and using VGI.

Assessing the options for the operational mapping of the soil moisture content in the European Alps

2021 ◽  
Author(s):  
Felix Greifeneder ◽  
Klaus Haslinger ◽  
Georg Seyerl ◽  
Claudia Notarnicola ◽  
Massimiliano Zappa ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Temporal Dynamics ◽  
Hydrological Cycle ◽  
Balance Model ◽  
High Mountain ◽  
European Alps ◽  
Entire Area ◽  
Advantages And Disadvantages ◽  
Hourly Data

&lt;p&gt;Soil Moisture (SM) is one of the key observable variables of the hydrological cycle and therefore of high importance for many disciplines, from meteorology to agriculture. This contribution presents a comparison of different products for the mapping of SM. The aim was to identify the best available solution for the operational monitoring of SM as a drought indicator for the entire area of the European Alps, to be applied in the context of the Interreg Alpine Space project, the Alpine Drought Observatory.&lt;/p&gt;&lt;p&gt;The following datasets were considered: Soil Water Index (SWI) of the Copernicus Global Land Service [1]; ERA5 [2]; ERA5-Land [3]; UERRA MESCAN-SURFEX land-surface component [4]. All four datasets offer a different set of advantages and disadvantages related to their spatial resolution, update frequency and latency. As a reference, modelled SM time-series for 307 catchments in Switzerland were used [5]. Switzerland is well suited as a test case for the Alps, due to its different landscapes, from lowlands to high mountain.&lt;/p&gt;&lt;p&gt;The intercomparison was based on a correlation analysis of daily absolute SM values and the daily anomalies. Furthermore, the probability to detect certain events, such as persistent dry conditions, was evaluated for each of the SM datasets. First results showed that the temporal dynamics (both in terms of absolute values as well as anomalies) of the re-analysis datasets show a high correlation to the reference. A clear gradient, from the lowlands in the north to the high mountains in the south, with decreasing correlation is evident. The SWI data showed weak correlations to the temporal dynamics of the reference in general. Especially, during spring and the first part of the summer SM is significantly underestimated. This might be related to the influence of snowmelt, which is not taken into account in the two-layer water balance model used to model SM for deeper soil layers. Low coverage in the high mountain areas hampered a thorough comparison with the reference in these areas.&lt;/p&gt;&lt;p&gt;The results presented here are the foundation for selecting a suitable source for the operational mapping of SM for the Alpine Drought Observatory. The next steps will be to test the potential of MESCAN-SURFEX and ERA5-Land for the downscaling of ERA5 to take advantage of the low latency of ERA5 and the improved spatial detail of the other two datasets. &amp;#160;&lt;/p&gt;&lt;p&gt;Literature:&lt;/p&gt;&lt;p&gt;[1]&amp;#160; B. Bauer-marschallinger et al., &amp;#8220;Sentinel-1&amp;#8239;: Harnessing Assets and Overcoming Obstacles,&amp;#8221; IEEE Trans. Geosci. Remote Sens., vol. 57, no. 1, pp. 520&amp;#8211;539, 2019, doi: 10.1109/TGRS.2018.2858004.&lt;/p&gt;&lt;p&gt;[2]&amp;#160; H. Hersbach et al., &amp;#8220;ERA5 hourly data on single levels from 1979 to present.&amp;#8221; Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2018.&lt;/p&gt;&lt;p&gt;[3]&amp;#160; Copernicus Climate Change Service, &amp;#8220;ERA5-Land hourly data from 2001 to present.&amp;#8221; ECMWF, 2019, doi: 10.24381/CDS.E2161BAC.&lt;/p&gt;&lt;p&gt;[4]&amp;#160; E. Bazile, et al., &amp;#8220;MESCAN-SURFEX Surface Analysis. Deliverable D2.8 of the UERRA Project,&amp;#8221; 2017. Accessed: Jan. 11, 2020. [Online]. Available: http://www.uerra.eu/publications/deliverable-reports.html.&lt;/p&gt;&lt;p&gt;[5]&amp;#160; Brunner, et al.: Extremeness of recent drought events in &amp;#160;&amp;#160;&amp;#160;Switzerland: dependence on variable and return period choice, Nat. Hazards Earth Syst. Sci., 19, 2311&amp;#8211;2323, https://doi.org/10.5194/nhess-19-2311-2019, 2019.&lt;/p&gt;

scholarly journals Quantitative information extraction from gas sensor data using principal component regression

2016 ◽  
Vol 24 ◽  
pp. 946-960
Author(s):  
Ahmet ÖZMEN ◽  
Bekir MUMYAKMAZ ◽  
Mehmet Ali EBEOĞLU ◽  
Cihat TAŞALTIN ◽  
İlke GÜROL ◽  
...  
Keyword(s):  
Information Extraction ◽  
Gas Sensor ◽  
Principal Component Regression ◽  
Principal Component ◽  
Quantitative Information ◽  
Sensor Data

Distribution des spectres de taille des particules en suspension dans le fjord du Saguenay

1979 ◽  
Vol 16 (2) ◽  
pp. 240-249 ◽  
Author(s):  
J. P. Chanut ◽  
S. A. Poulet
Keyword(s):  
Particle Size ◽  
Water Mass ◽  
Layer Structure ◽  
Principal Component ◽  
Water Masses ◽  
Size Spectra ◽  
Lawrence Estuary ◽  
Saguenay Fjord ◽  
Bottom Waters ◽  
St Lawrence Estuary

The spatial distribution of particle size spectra shows a two-layer stratification in May but reveals three-layer structure in September, both in the Saguenay fjord and in the adjacent waters of the St. Lawrence estuary, near the sill. In May, the particle size spectra in the surface layer show considerable variability whereas, in the bottom waters, they appear to be relatively homogeneous. In September, the deeper, more homogeneous water mass is less extensive. It is apparently eroded by diffusion and advection during summer months and becomes restricted to intermediate depths towards the head of the fjord. During the same period, a water mass with physical and particulate properties different from the upper layers occupies the bottom of the fjord. Principal component analysis shows that variations in particle size spectra are independent from one layer to another. Water masses with identical physical and particulate properties located in both sides of the sill illustrate the influence of the St. Lawrence estuary on the Saguenay fjord. These water masses, generally located below the sill depth, indicate the existence of powerful advective mechanisms in this region.

Principal component analysis for the early detection of mastitis and lameness in dairy cows

2013 ◽  
Vol 80 (3) ◽  
pp. 335-343 ◽  
Author(s):  
Bettina Miekley ◽  
Imke Traulsen ◽  
Joachim Krieter
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Error Rates ◽  
Sensor Data ◽  
Sequence Length ◽  
Feeding Patterns ◽  
Original Dataset ◽  
Block Sensitivity

This investigation analysed the applicability of principal component analysis (PCA), a latent variable method, for the early detection of mastitis and lameness. Data used were recorded on the Karkendamm dairy research farm between August 2008 and December 2010. For mastitis and lameness detection, data of 338 and 315 cows in their first 200 d in milk were analysed, respectively. Mastitis as well as lameness were specified according to veterinary treatments. Diseases were defined as disease blocks. The different definitions used (two for mastitis, three for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. Milk electrical conductivity, milk yield and feeding patterns (feed intake, number of feeding visits and time at the trough) were used for recognition of mastitis. Pedometer activity and feeding patterns were utilised for lameness detection. To develop and verify the PCA model, the mastitis and the lameness datasets were divided into training and test datasets. PCA extracted uncorrelated principle components (PC) by linear transformations of the raw data so that the first few PCs captured most of the variations in the original dataset. For process monitoring and disease detection, these resulting PCs were applied to the Hotelling's T2 chart and to the residual control chart. The results show that block sensitivity of mastitis detection ranged from 77·4 to 83·3%, whilst specificity was around 76·7%. The error rates were around 98·9%. For lameness detection, the block sensitivity ranged from 73·8 to 87·8% while the obtained specificities were between 54·8 and 61·9%. The error rates varied from 87·8 to 89·2%. In conclusion, PCA seems to be not yet transferable into practical usage. Results could probably be improved if different traits and more informative sensor data are included in the analysis.

scholarly journals NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA

2020 ◽  
Vol 20 (09) ◽  
pp. 2040017
Author(s):  
SEOK-WOO JANG ◽  
SANG-HONG LEE
Keyword(s):  
Pressure Sensor ◽  
Euclidean Distance ◽  
Performance Enhancement ◽  
Principal Component ◽  
Sensor Data ◽  
Second Step ◽  
Neuro Fuzzy ◽  
Euclidean Distances ◽  
Left And Right ◽  
The Individual

This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.

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