In order to obtain accurate prediction of ocean wind energy, long term data are needed. However, one data sources might not able to provide long duration data. Therefore, the data need to be combined with other sources of data. However, before combining the data, it is important to compare and validate them to confirm their accuracy. In the present study, wind speed data collected by QuikScat and SSM/I (SSMIS) missions are compared and analyzed. QuikScat data were collected by a satellite with the same name, while Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data are processed and offered by Remote Sensing System (RSS). SSM/I (SSMIS) are passive microwave radiometers carried onboard Defense Meteorological Satellite Program (DMSP). For the comparison, 5 (five) arbitrary positions over Sulawesi and Maluku islands sea areas are chosen for the analyses. For the evaluation purposes, beside time series of daily data from several chosen positions in research location, several statistical parameters are also computed and compared such as mean, standard deviation, root mean square (RMS), correlation coefficient.

Mahmuddin, Faisal

English

KnE Publishing Platform

 ICoSE Conference ProceedingsICoSE Conference on Instrumentation, Environment andRenewable Energy (2015), Volume 2016Conference PaperComparison of Remotely Sensed Wind Dataover Sulawesi and Maluku Islands Sea AreasFaisal Mahmuddin, Misliah Idrus, Hamzah, Juswan Sade, and RosmaniNaval Architecture Department, Engineering Faculty, Hasanuddin University, Jalan PerintisKemerdekaan km. 10, Tamalanrea, Makassar, IndonesiaAbstractIn order to obtain accurate prediction of ocean wind energy, long term data areneeded. However, one data sources might not able to provide long duration data.Therefore, the data need to be combined with other sources of data. However,before combining the data, it is important to compare and validate them to confirmtheir accuracy. In the present study, wind speed data collected by QuikScat andSSM/I (SSMIS) missions are compared and analyzed. QuikScat data were collectedby a satellite with the same name, while Special Sensor Microwave Imager (SSM/I)and Special Sensor Microwave Imager Sounder (SSMIS) data are processed andoffered by Remote Sensing System (RSS). SSM/I (SSMIS) are passive microwaveradiometers carried onboard Defense Meteorological Satellite Program (DMSP). Forthe comparison, 5 (five) arbitrary positions over Sulawesi and Maluku islands seaareas are chosen for the analyses. For the evaluation purposes, beside time seriesof daily data from several chosen positions in research location, several statisticalparameters are also computed and compared such as mean, standard deviation, rootmean square (RMS), correlation coefficient.Keywords: Energy Density, Satellite Data Comparison, Remote Sense Data, Sulawesiand Maluku islands1. IntroductionIt is known that wind characteristics such as speed and direction, have seasonal varia-tion. They are varied depending on time and location. Therefore, in order to maximizethe harvested power, wind turbine system needs to be relocated from time to time.For this purpose, the National Institute for Environmental Studies (NIES) of Japan hasstudied and proposed a sailing-type floating turbine structure [1]. The structure ispontoon shaped and has no mooring system. It is supposed to be able to navigatewith its own sails and azimuth thrusters. More detail studies about the structure canalso be found in references 2 and 3.Similar concept has also been introduced by Mahmuddin et al. [4, 5] to harvestocean wind energy around Sulawesi and Maluku islands sea areas. The structure isknown to be mobile floating structure (MFS). The MFS is designed not to sail but itcan be relocated to other locations with higher energy density every certain time. Inorder to determine the location of the MFS, in the same studies, the energy densityof the sea areas around Sulawesi and Maluku islands has also been assessed andHow to cite this article: Faisal Mahmuddin, Misliah Idrus, Hamzah, Juswan Sade, and Rosmani, “Comparison ofRemotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas,” KnE Engineering, vol. 2016, 6 pages. DOI10.18502/keg.v1i1.513Page 1Corresponding Author: FaisalMahmuddin; email:f.mahmuddin@gmail.comReceived: 1 August 2016Accepted: 18 August 2016Published: 6 September 2016Publishing services providedby Knowledge EFaisal Mahmuddin etal. This article is distributedunder the terms of theCreative CommonsAttribution License, whichpermits unrestricted use andredistribution provided thatthe original author andsource are credited.Selection and Peer-reviewunder the responsibility ofthe ICoSE ConferenceCommittee. ICoSE Conference Proceedingssimulated in order to predict the power production of the MFS in the areas. In theprevious analysis, data collected by satellite QuikScat had been used. The satelliteQuikScat used a scatterometer to measure the wind speed 10m above sea surface.However, QuikScat satellite operated only for around 10 years which are from year1999 to 2009. Therefore, other sources of satellite data are needed to obtain longerdata period to be used in analysis. Longer data period would improve the accuracy oflong term assessment results.There are numerous other satellite data sources that can be used for analysis. Thesummary of satellite data and their duration can be found in reference 6. Becauseof the convenience way to obtain the data, the present study uses data processedand offered by Remote Sensing System (RSS). RSS processed and analyzed data fromseveral satellites. The data can be downloaded freely from their website. The datasource processed by RSS which has the longest period is the data obtained from theSpecial Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave ImagerSounder (SSMIS) missions. SSM/I (SSMIS) are passive microwave radiometers carriedonboard Defense Meteorological Satellite Program (DMSP). The measurement deviceis different with the one used by QuikScat because QuikScat utilized scatterometer forits observations. However, before combining the QuikScat and the SSM/I (SSMIS) datain the analysis, it is important to compare and validate the data to confirm their accu-racy. In the present study, wind speed data collected from QuikScat and SSM/I (SSMIS)missions are compared and analyzed. For this purpose, 5 (five) arbitrary positions overSulawesi and Maluku islands sea areas are chosen for the analyses.For the evaluation purposes, beside time series of daily data from several chosenpositions in research location, several statistical parameters are also computed andcompared such as mean, standard deviation, root mean square (RMS) and correlationcoefficient.2. Wind Data ModelsThere are numerous wind data models that could be used in a long term prediction ofwind speed. The present study uses 2 (two) products of RSS which are SSM/I (SSMIS)and QuikScat.3. SSM/I and SSMISThe Special Sensor Microwave Imager (SSM/I) and the Special Sensor MicrowaveImager Sounder (SSMIS) are satellite passive microwave radiometers. This seriesof instruments has been carried onboard Defense Meteorological Satellite Program(DMSP) satellites since 1987. The instruments are referred to by satellite numberstarting from F08 to F18. Ocean measurements derived from the radiometer observa-tions include Surface Wind Speed, Atmospheric Water Vapor, Cloud Liquid Water, andRain Rate [7]. The spatial resolution of the data is 25 x 25 km.DOI 10.18502/keg.v1i1.513 Page 2 ICoSE Conference ProceedingsFigure 1: Location of the research and chosen positions.3.1. QuikScatThe US launched SeaWinds scatterometer in June 1999. It continued to operate untilNovember 2009. The SeaWinds scatterometer is active microwave radar with dual-beam, conical scan 1 m diameter reflector antenna, operating in Ku-band at 13.402 GHz[8]. The primarymission of these SeaWinds scatterometerswas tomeasurewinds nearthe ocean surface. They are also useful for some land and sea ice applications. TheSeaWinds instruments are the third in a series of NASA scatterometers that operateat Ku-band (i.e., a frequency near 14 GHz). The first instrument was flown in 1978 onSeaSat mission, which was followed by the second Ku-Band scatterometer flown 18(eighteen) years later on Japan’s Midori-I (ADEOS-I) spacecraft in August 1996 namedNSCAT.4. Research AreaThe location of the present study is the sea areas around Sulawesi and Maluku islandsof Indonesia. Over the sea areas, 5 positions are chosen arbitrarily for data analysis.The map of research area and chosen positions are shown in the following Fig. 1. Thebasic image of the figure was produced using Google Earth version 7.1.2.2041.The chosen positions are shown as yellow dotswith their numbers in themap above.The exact coordinates of the chosen locations are shown in the following table.In the present study, 1 (one) year data which is in year 2000 are extracted from eachdata sources to be compared. From all type of data available from RSS website, 3-dayaveraged data were chosen.DOI 10.18502/keg.v1i1.513 Page 3 ICoSE Conference ProceedingsNo Longitude Latitude1 119 3.502 120 -7.003 126 -0.504 127 3.005 129 -6.00T 1: Coordinates of computed positions.Figure 2: Daily wind speed comparison.5. Results and AnalysisAfter extracting the wind speed information of the chosen positions from the datasources, the average of daily wind speed from chosen locations are computed andshown in the following figure. It can be seen from the Figure 2 that the general ten-dency are similar. However, magnitudes are found to be slightly different. It can also benoted that a line from each data sources reaches zero wind speed which indicates thatno data available for the day. In order to observe more detail about the difference, thedata above are averaged to obtain monthly averaged data. The computation resultsare shown in the following figure.From Fig. 3, it can be seen that general tendencies of the wind speed are in agood agreement. However, significantly different magnitude of the wind speed canbe observed. Moreover, in order to evaluate more closely the data, the statisticalparameters of the data are computed and compared. The computation results areshown in the following table.From the table above, it can be seen that difference can be found in all parameters.The two first compared parameters are mean speed and root mean square (RMS)DOI 10.18502/keg.v1i1.513 Page 4 ICoSE Conference ProceedingsFigure 3: Monthly averaged wind speed comparison.Parameters SSMI QuikScatMean 4.480 5.188RMS 4.553 5.240Standard deviation 0.810 1.020Correlation coefficient -0.358 -0.404T 2: Statistical parameters comparison.which represent average of compared data. Both variables show difference of approx-imately 14%. Moreover, the table also shows that the standard deviation of QuikScatdata is higher 0.21 point as compared to the one computed from SSMI data whichmeans that Quikscat data has larger data range as compared to SSMI data.The last row of the table shows that both correlation coefficients are in negativewhich means that both data have same tendencies. Higher negative value of Quikscatcan be expected from higher standard variation as shown in third row. The differencesand tendencies of the parameters could confirm the tendency found in Fig. 3. Thefinding found above should be considered when using satellite data to determine thewind energy density in certain areas.6. SummaryThe present study compared the QuikScat and SSM/I (SSMIS) data. It is found from thecomputation results that the tendencies of the computed Quikscat and SSM/I (SSMIS)data are in a good agreement but the magnitudes are significantly different. The dif-ference between the data is approximately 14%. The finding should be considered inusing the data for long term assessment analysis including for wind energy prediction.DOI 10.18502/keg.v1i1.513 Page 5 ICoSE Conference ProceedingsAcknowledgmentThis work was supported by Unggulan Perguruan Tinggi (UPT) research grant fromIndonesian Directorate General of Higher Education for fiscal years 2014 and 2015.Moreover, the satellite data are produced by Remote Sensing Systems (RSS) andsponsored by the NASA Ocean Vector Winds Science Team. Data are available atwww.remss.com.References[1] National Institute for Environmental Studies (NIES), Report on research results forsailing type offshore wind turbine, Makuhari Messe, Japan, October 2007.[2] K. Takagi, K. Yamamoto, M. Kondo, T. Funaki, and Z. Kawazaki, Conceptual designof a very large mobile structure for the renewable energy plant., Proceedings of theAsia Pacific Workshop on Marine Hydrodynamics, 239–244, (2002).[3] K. Takagi, Hydroelastic behavior of VLMOS in beam seas, Proceeding of 14thInternational Offshore and Polar Engineering Conference, Vol. 4, 616–621, (2004).[4] F. Mahmuddin, M. Idrus, and Hamzah, Analysis of Ocean Wind Energy Densityaround Sulawesi and Maluku Islands with Scatterometer Data, Energy Procedia, 65,107–115, (2015).[5] F. Mahmuddin, M. Idrus, and Hamzah, Analysis of Wind Energy Potential witha Mobile Floating Structure around Sulawesi and Maluku Islands of Indonesia,Proceeding of the 34th Conference on Ocean, Offshore, and Arctic Engineering, 9, 64–73, (2015).[6] I. R. Young, S. Zieger, and A. V. Babanin, Development and Application of a GlobalSatellite Database of Wind And Wave Conditions, Proceeding of the 34th Conferenceon Ocean, Offshore, and Arctic Engineering, 7, 62–71, (2015).[7] Remote Sensing System, SSMI/SSMIS, URL at http://www.remss.com/missions/ssmi.[8] J. Winterfelt, in Comparison of Measured and Simulated Wind Speed Data in the NorthAtlantic, GKSS Report 2008/2, GKSS-Forschungszentrum, Geesthacht, 2008.DOI 10.18502/keg.v1i1.513 Page 6

Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

In order to obtain accurate prediction of ocean wind energy, long term data are needed. However, one data sources might not able to provide long duration data. Therefore, the data need to be combined with other sources of data. However, before combining the data, it is important to compare and validate them to confirm their accuracy. In the present study, wind speed data collected by QuikScat and SSM/I (SSMIS) missions are compared and analyzed. QuikScat data were collected by a satellite with the same name, while Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data are processed and offered by Remote Sensing System (RSS). SSM/I (SSMIS) are passive microwave radiometers carried onboard Defense Meteorological Satellite Program (DMSP). For the comparison, 5 (five) arbitrary positions over Sulawesi and Maluku islands sea areas are chosen for the analyses. For the evaluation purposes, beside time series of daily data from several chosen positions in research location, several statistical parameters are also computed and compared such as mean, standard deviation, root mean square (RMS), correlation coefficient

Mahmuddin, F. (Faisal)

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Comparison of Remotely Sensed Wind Data Over Sulawesi and Maluku Islands Sea Areas

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Comparison of Remotely Sensed Wind Data over Sulawesi and Maluku Islands Sea Areas

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