Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Merete Badger [verfasserIn] Aito Fujita [verfasserIn] Krzysztof Orzel [verfasserIn] Daniel Hatfield [verfasserIn] Mark Kelly [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 16(2023), 9, p 3819
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:9, p 3819

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en16093819

Katalog-ID:	DOAJ090375440

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ090375440
003	DE-627
005	20240413035720.0
007	cr uuu---uuuuu
008	230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/en16093819 \|2 doi
035			\|a (DE-627)DOAJ090375440
035			\|a (DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	0		\|a Merete Badger \|e verfasserin \|4 aut
245	1	0	\|a Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.
650		4	\|a wind energy
650		4	\|a resource assessment
650		4	\|a offshore
650		4	\|a wind retrieval
650		4	\|a Synthetic Aperture Radar (SAR)
650		4	\|a satellite
653		0	\|a Technology
653		0	\|a T
700	0		\|a Aito Fujita \|e verfasserin \|4 aut
700	0		\|a Krzysztof Orzel \|e verfasserin \|4 aut
700	0		\|a Daniel Hatfield \|e verfasserin \|4 aut
700	0		\|a Mark Kelly \|e verfasserin \|4 aut
773	0	8	\|i In \|t Energies \|d MDPI AG, 2008 \|g 16(2023), 9, p 3819 \|w (DE-627)572083742 \|w (DE-600)2437446-5 \|x 19961073 \|7 nnns
773	1	8	\|g volume:16 \|g year:2023 \|g number:9, p 3819
856	4	0	\|u https://doi.org/10.3390/en16093819 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7 \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/1996-1073/16/9/3819 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1996-1073 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 16 \|j 2023 \|e 9, p 3819

Indexfelder

author_variant	m b mb a f af k o ko d h dh m k mk
matchkey_str	article:19961073:2023----::idereafocntlainosalastlieptniloofh
hierarchy_sort_str	2023
publishDate	2023
allfields	10.3390/en16093819 doi (DE-627)DOAJ090375440 (DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7 DE-627 ger DE-627 rakwb eng Merete Badger verfasserin aut Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms. wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite Technology T Aito Fujita verfasserin aut Krzysztof Orzel verfasserin aut Daniel Hatfield verfasserin aut Mark Kelly verfasserin aut In Energies MDPI AG, 2008 16(2023), 9, p 3819 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:16 year:2023 number:9, p 3819 https://doi.org/10.3390/en16093819 kostenfrei https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7 kostenfrei https://www.mdpi.com/1996-1073/16/9/3819 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 9, p 3819
spelling	10.3390/en16093819 doi (DE-627)DOAJ090375440 (DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7 DE-627 ger DE-627 rakwb eng Merete Badger verfasserin aut Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms. wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite Technology T Aito Fujita verfasserin aut Krzysztof Orzel verfasserin aut Daniel Hatfield verfasserin aut Mark Kelly verfasserin aut In Energies MDPI AG, 2008 16(2023), 9, p 3819 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:16 year:2023 number:9, p 3819 https://doi.org/10.3390/en16093819 kostenfrei https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7 kostenfrei https://www.mdpi.com/1996-1073/16/9/3819 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 9, p 3819
allfields_unstemmed	10.3390/en16093819 doi (DE-627)DOAJ090375440 (DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7 DE-627 ger DE-627 rakwb eng Merete Badger verfasserin aut Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms. wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite Technology T Aito Fujita verfasserin aut Krzysztof Orzel verfasserin aut Daniel Hatfield verfasserin aut Mark Kelly verfasserin aut In Energies MDPI AG, 2008 16(2023), 9, p 3819 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:16 year:2023 number:9, p 3819 https://doi.org/10.3390/en16093819 kostenfrei https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7 kostenfrei https://www.mdpi.com/1996-1073/16/9/3819 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 9, p 3819
allfieldsGer	10.3390/en16093819 doi (DE-627)DOAJ090375440 (DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7 DE-627 ger DE-627 rakwb eng Merete Badger verfasserin aut Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms. wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite Technology T Aito Fujita verfasserin aut Krzysztof Orzel verfasserin aut Daniel Hatfield verfasserin aut Mark Kelly verfasserin aut In Energies MDPI AG, 2008 16(2023), 9, p 3819 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:16 year:2023 number:9, p 3819 https://doi.org/10.3390/en16093819 kostenfrei https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7 kostenfrei https://www.mdpi.com/1996-1073/16/9/3819 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 9, p 3819
allfieldsSound	10.3390/en16093819 doi (DE-627)DOAJ090375440 (DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7 DE-627 ger DE-627 rakwb eng Merete Badger verfasserin aut Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms. wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite Technology T Aito Fujita verfasserin aut Krzysztof Orzel verfasserin aut Daniel Hatfield verfasserin aut Mark Kelly verfasserin aut In Energies MDPI AG, 2008 16(2023), 9, p 3819 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:16 year:2023 number:9, p 3819 https://doi.org/10.3390/en16093819 kostenfrei https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7 kostenfrei https://www.mdpi.com/1996-1073/16/9/3819 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 9, p 3819
language	English
source	In Energies 16(2023), 9, p 3819 volume:16 year:2023 number:9, p 3819
sourceStr	In Energies 16(2023), 9, p 3819 volume:16 year:2023 number:9, p 3819
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite Technology T
isfreeaccess_bool	true
container_title	Energies
authorswithroles_txt_mv	Merete Badger @@aut@@ Aito Fujita @@aut@@ Krzysztof Orzel @@aut@@ Daniel Hatfield @@aut@@ Mark Kelly @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	572083742
id	DOAJ090375440
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ090375440</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413035720.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/en16093819</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ090375440</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Merete Badger</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wind energy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">resource assessment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">offshore</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wind retrieval</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Synthetic Aperture Radar (SAR)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">satellite</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Aito Fujita</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Krzysztof Orzel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daniel Hatfield</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mark Kelly</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Energies</subfield><subfield code="d">MDPI AG, 2008</subfield><subfield code="g">16(2023), 9, p 3819</subfield><subfield code="w">(DE-627)572083742</subfield><subfield code="w">(DE-600)2437446-5</subfield><subfield code="x">19961073</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:9, p 3819</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/en16093819</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/1996-1073/16/9/3819</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1996-1073</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2023</subfield><subfield code="e">9, p 3819</subfield></datafield></record></collection>
author	Merete Badger
spellingShingle	Merete Badger misc wind energy misc resource assessment misc offshore misc wind retrieval misc Synthetic Aperture Radar (SAR) misc satellite misc Technology misc T Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment
authorStr	Merete Badger
ppnlink_with_tag_str_mv	@@773@@(DE-627)572083742
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	DOAJ
remote_str	true
illustrated	Not Illustrated
issn	19961073
topic_title	Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment wind energy resource assessment offshore wind retrieval Synthetic Aperture Radar (SAR) satellite
topic	misc wind energy misc resource assessment misc offshore misc wind retrieval misc Synthetic Aperture Radar (SAR) misc satellite misc Technology misc T
topic_unstemmed	misc wind energy misc resource assessment misc offshore misc wind retrieval misc Synthetic Aperture Radar (SAR) misc satellite misc Technology misc T
topic_browse	misc wind energy misc resource assessment misc offshore misc wind retrieval misc Synthetic Aperture Radar (SAR) misc satellite misc Technology misc T
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Energies
hierarchy_parent_id	572083742
hierarchy_top_title	Energies
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)572083742 (DE-600)2437446-5
title	Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment
ctrlnum	(DE-627)DOAJ090375440 (DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7
title_full	Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment
author_sort	Merete Badger
journal	Energies
journalStr	Energies
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Merete Badger Aito Fujita Krzysztof Orzel Daniel Hatfield Mark Kelly
container_volume	16
format_se	Elektronische Aufsätze
author-letter	Merete Badger
doi_str_mv	10.3390/en16093819
author2-role	verfasserin
title_sort	wind retrieval from constellations of small sar satellites: potential for offshore wind resource assessment
title_auth	Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment
abstract	The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.
abstractGer	The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.
abstract_unstemmed	The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	9, p 3819
title_short	Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment
url	https://doi.org/10.3390/en16093819 https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7 https://www.mdpi.com/1996-1073/16/9/3819 https://doaj.org/toc/1996-1073
remote_bool	true
author2	Aito Fujita Krzysztof Orzel Daniel Hatfield Mark Kelly
author2Str	Aito Fujita Krzysztof Orzel Daniel Hatfield Mark Kelly
ppnlink	572083742
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/en16093819
up_date	2024-07-03T14:24:14.523Z
_version_	1803568183535206400
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ090375440</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413035720.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/en16093819</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ090375440</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ0f59387f40944764a33de74fe9eb5ae7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Merete Badger</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The planning of offshore wind energy projects requires wind observations over long periods for the establishment of wind speed distributions. In the marine environment, high-quality in situ observations are sparse and restricted to point locations. Numerical modeling is typically used to determine the spatial variability of the wind resource. Synthetic Aperture Radar (SAR) observations from satellites can be used for retrieval of wind fields over the ocean at a high spatial resolution. The recent launch of constellations of small SAR satellites by private companies will improve the sampling of SAR scenes significantly over the coming years compared with the current sampling rates offered by multi-purpose SAR missions operated by public space agencies. For the first time, wind fields are retrieved from a series of StriX SAR scenes delivered by Synspective (Japan) and also from Sentinel-1 scenes delivered by the European Space Agency. The satellite winds are compared with wind speed observations from the FINO3 mast in the North Sea. This leads to root-mean-square errors of 1.4–1.8 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and negative biases of −0.4 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula< and −1.0 m s<inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mrow<</mrow<<mrow<<mo<−</mo<<mn<1</mn<</mrow<</msup<</semantics<</math<</inline-formula<, respectively. Although the Geophysical Model Functions (GMF) applied for wind retrievals have not yet been tuned for StriX SAR observations, the wind speed accuracy is satisfactory. Through conditional sampling, we estimate the wind resource from current and future SAR sampling scenarios where the number of SAR satellites in orbit is increasing over time. We find that hourly samples are needed to fully capture the diurnal wind speed variability at the site investigated. A combination of SAR samples from current missions with samples from clusters of small SAR satellites can yield the necessary number of wind speed samples for accurate wind resource estimation. This is particularly important for sites with pronounced diurnal wind speed variability. An additional benefit of small SAR satellites is that wind speed variability can be mapped at the sub-km scale. The very high spatial resolution is valuable for characterizing the wind conditions in the vicinity of existing offshore wind farms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wind energy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">resource assessment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">offshore</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wind retrieval</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Synthetic Aperture Radar (SAR)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">satellite</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Technology</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">T</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Aito Fujita</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Krzysztof Orzel</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Daniel Hatfield</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mark Kelly</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Energies</subfield><subfield code="d">MDPI AG, 2008</subfield><subfield code="g">16(2023), 9, p 3819</subfield><subfield code="w">(DE-627)572083742</subfield><subfield code="w">(DE-600)2437446-5</subfield><subfield code="x">19961073</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:16</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:9, p 3819</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/en16093819</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/0f59387f40944764a33de74fe9eb5ae7</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/1996-1073/16/9/3819</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1996-1073</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">16</subfield><subfield code="j">2023</subfield><subfield code="e">9, p 3819</subfield></datafield></record></collection>
score	7.4014626

Nicht das Richtige dabei?

Schreiben Sie uns!

Wind Retrieval from Constellations of Small SAR Satellites: Potential for Offshore Wind Resource Assessment

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?