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Ordinary kriging for on-demand average wind interpolation of in-situ wind sensor data

Ordinary kriging for on-demand average wind interpolation of in-situ wind sensor data
Ordinary kriging for on-demand average wind interpolation of in-situ wind sensor data
We have developed a domain agnostic ordinary kriging algorithm accessible via a standards-based service-oriented architecture for sensor networks. We exploit the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. We need on-demand interpolation maps so runtime performance is a major priority.

Our sensor data comes from wind in-situ observation stations in an area approximately 200km by 125km. We provide on-demand average wind interpolation maps. These spatial estimates can then be compared with the results of other estimation models in order to identify spurious results that sometimes occur in wind estimation.

Our processing is based on ordinary kriging with automated variogram model selection (AVMS). This procedure can smooth time point wind measurements to obtain average wind by using a variogram model that reflects the wind phenomenon characteristics. Kriging is enabled for wind direction estimation by a simple but effective solution to the problem of estimating periodic variables, based on vector rotation and stochastic simulation.

In cases where for the region of interest all wind directions span 180 degrees, we rotate them so they lie between 90 and 270 degrees and apply ordinary kriging with AVMS directly to the meteorological angle. Else, we transform the meteorological angle to Cartesian space, apply ordinary kriging with AVMS and use simulation to transform the kriging estimates back to meteorological angle.

Tests run on a 50 by 50 grid using standard hardware takes about 5 minutes to execute backward transformation with a sample size of 100,000. This is acceptable for our on-demand processing service requirements.
ordinary kriging, variogram, spatial estimation, spatial interpolation, spatial smoothing, wind velocity, wind direction, periodic variable, in-situ, sensor data
Zlatev, Z.
8f2e3635-d76c-46e2-85b9-53cc223fee01
Middleton, S.E.
404b62ba-d77e-476b-9775-32645b04473f
Veres, G.
3c2a37d2-3904-43ce-b0cf-006f62b87337
Zlatev, Z.
8f2e3635-d76c-46e2-85b9-53cc223fee01
Middleton, S.E.
404b62ba-d77e-476b-9775-32645b04473f
Veres, G.
3c2a37d2-3904-43ce-b0cf-006f62b87337

Zlatev, Z., Middleton, S.E. and Veres, G. (2009) Ordinary kriging for on-demand average wind interpolation of in-situ wind sensor data. EWEC 2009, France. 16 - 19 Mar 2009. 7 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

We have developed a domain agnostic ordinary kriging algorithm accessible via a standards-based service-oriented architecture for sensor networks. We exploit the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. We need on-demand interpolation maps so runtime performance is a major priority.

Our sensor data comes from wind in-situ observation stations in an area approximately 200km by 125km. We provide on-demand average wind interpolation maps. These spatial estimates can then be compared with the results of other estimation models in order to identify spurious results that sometimes occur in wind estimation.

Our processing is based on ordinary kriging with automated variogram model selection (AVMS). This procedure can smooth time point wind measurements to obtain average wind by using a variogram model that reflects the wind phenomenon characteristics. Kriging is enabled for wind direction estimation by a simple but effective solution to the problem of estimating periodic variables, based on vector rotation and stochastic simulation.

In cases where for the region of interest all wind directions span 180 degrees, we rotate them so they lie between 90 and 270 degrees and apply ordinary kriging with AVMS directly to the meteorological angle. Else, we transform the meteorological angle to Cartesian space, apply ordinary kriging with AVMS and use simulation to transform the kriging estimates back to meteorological angle.

Tests run on a 50 by 50 grid using standard hardware takes about 5 minutes to execute backward transformation with a sample size of 100,000. This is acceptable for our on-demand processing service requirements.

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More information

Published date: March 2009
Venue - Dates: EWEC 2009, France, 2009-03-16 - 2009-03-19
Keywords: ordinary kriging, variogram, spatial estimation, spatial interpolation, spatial smoothing, wind velocity, wind direction, periodic variable, in-situ, sensor data
Organisations: Electronics & Computer Science, IT Innovation

Identifiers

Local EPrints ID: 268436
URI: https://eprints.soton.ac.uk/id/eprint/268436
PURE UUID: 56744c6a-5750-4179-a83d-1ea045ee6f3d
ORCID for S.E. Middleton: ORCID iD orcid.org/0000-0001-8305-8176

Catalogue record

Date deposited: 29 Jan 2010 10:54
Last modified: 06 Jun 2018 12:51

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Contributors

Author: Z. Zlatev
Author: S.E. Middleton ORCID iD
Author: G. Veres

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