Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events

During extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison...

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Autores:
Acosta-Coll, Melisa
Morales, Abel
Zamora-Musa, Ronald
Butt, Shariq Aziz
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/10887
Acceso en línea:
https://hdl.handle.net/11323/10887
https://repositorio.cuc.edu.co
Palabra clave:
Cross-evaluation
Reflectivity
NEXRAD
GPM
Hurricane
Ground validation system
Ground radar
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
id RCUC2_c93d97c28413bdc8b0cdf26e197f6192
oai_identifier_str oai:repositorio.cuc.edu.co:11323/10887
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
title Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
spellingShingle Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
Cross-evaluation
Reflectivity
NEXRAD
GPM
Hurricane
Ground validation system
Ground radar
title_short Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
title_full Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
title_fullStr Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
title_full_unstemmed Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
title_sort Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events
dc.creator.fl_str_mv Acosta-Coll, Melisa
Morales, Abel
Zamora-Musa, Ronald
Butt, Shariq Aziz
dc.contributor.author.none.fl_str_mv Acosta-Coll, Melisa
Morales, Abel
Zamora-Musa, Ronald
Butt, Shariq Aziz
dc.subject.proposal.eng.fl_str_mv Cross-evaluation
Reflectivity
NEXRAD
GPM
Hurricane
Ground validation system
Ground radar
topic Cross-evaluation
Reflectivity
NEXRAD
GPM
Hurricane
Ground validation system
Ground radar
description During extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison between several sensors used to monitor these phenomena such as ground-based and satellite instruments, must maintain a high degree of correlation in order to issue alerts with an accuracy that allows for timely decision making. This study presents a cross-evaluation of the radar reflectivity from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement Mission (GPM) and the U.S. National Weather Service (NWS) Next-Generation Radar (NEXRAD) ground-based instrument located in the Caribbean island of Puerto Rico, USA, to determine the correlation degree between these two sensors' measurements during extreme weather events and normal precipitation events during 2015-2019. GPM at Ku-band and Ka-band and NEXRAD at S-band overlapping scanning regions data of normal precipitation events during 2015-2019, and the spiral rain bands of four extreme weather events, Irma (Category 5 Hurricane), Beryl (Tropical Storm), Dorian (Category 1 hurricane), and Karen (Tropical Storm), were processed using the GPM Ground Validation System (GVS). In both cases, data were classified and analyzed statistically, paying particular attention to variables such as elevation angle mode and precipitation type (stratiform and convective). Given that ground-based radar (GR) has better spatial and temporal resolution, the NEXRAD was used as ground-truth. The results revealed that the correlation coefficient between the data of both instruments during the analyzed extreme weather events was moderate to low; for normal precipitation events, the correlation is lower than that of studies that compared GPM and NEXRAD reflectivity located in other regions of the USA. Only Tropical Storm Karen obtained similar results to other comparative studies in terms of the correlation coefficient. Furthermore, the GR elevation angle and precipitation type have a substantial impact on how well the rain reflectivity correlates between the two sensors. It was found that the Ku-band channel possesses the least bias and variability when compared to the NEXRAD instrument's reflectivity and should therefore be considered more reliable for future tropical storm tracking and tropical region precipitation estimates in regions with no NEXRAD coverage.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-08-02
dc.date.accessioned.none.fl_str_mv 2024-03-19T15:44:45Z
dc.date.available.none.fl_str_mv 2024-03-19T15:44:45Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.citation.spa.fl_str_mv Acosta-Coll, M.; Morales, A.; Zamora-Musa, R.; Butt, S.A. Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events. Sensors 2022, 22, 5773. https://doi.org/10.3390/s22155773
dc.identifier.issn.spa.fl_str_mv 1424-8220
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11323/10887
dc.identifier.doi.none.fl_str_mv 10.3390/s22155773
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co
identifier_str_mv Acosta-Coll, M.; Morales, A.; Zamora-Musa, R.; Butt, S.A. Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events. Sensors 2022, 22, 5773. https://doi.org/10.3390/s22155773
1424-8220
10.3390/s22155773
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/10887
https://repositorio.cuc.edu.co
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Sensors
dc.relation.references.spa.fl_str_mv 1. de Beurs, K.M.; McThompson, N.S.; Owsley, B.C.; Henebry, G.M. Hurricane damage detection on four major Caribbean islands. Remote Sens. Environ. 2019, 229, 1–13. [CrossRef]
2. NSF and The University of Rhode Island. Rainfall and Inland Flooding. 2010. Available online: http://hurricanescience.org/ society/impacts/rainfallandinlandflooding/ (accessed on 20 October 2021).
3. Ortega-Gonzalez, L.; Acosta-Coll, M.; Piñeres-Espitia, G.; Butt, S.A. Communication protocols evaluation for a wireless rainfall monitoring network in an urban area. Heliyon 2021, 18, 7. [CrossRef] [PubMed]
4. Ren, Y.; Zhang, J.; Guimond, S.; Wang, X. Hurricane Boundary Layer Height Relative to Storm Motion from GPS Dropsonde Composites. Atmposphere 2019, 10, 339. [CrossRef]
5. Trepanier, J. North Atlantic Hurricane Winds in Warmer than Normal Seas. Atmposphere 2020, 11, 293. [CrossRef]
6. Yang, K.; Davidson, R.A.; Blanton, B.; Colle, B.; Dresback, K.; Kolar, R.; Nozick, L.K.; Trivedi, J.; Wachtendorf, T. Hurricane evacuations in the face of uncertainty: Use of integrated models to support robust, adaptive, and repeated decision-making. Int. J. Disaster Risk Reduct. 2019, 36, 101093. [CrossRef]
7. Luitel, B.; Villarini, G.; Vecchi, G. Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones. J. Hydrol. 2018, 556, 1026–1037. [CrossRef]
8. Ramirez-Cerpa, E.; Acosta-Coll, M.; Velez-Zapata, J. Analysis of the climatic conditions for short-term precipitation in urban areas: A case study Barranquilla, Colombia. Idesia 2017, 35, 2. [CrossRef]
9. Neeck, S.P.; Kakar, R.K.; Azarbarzin, A.A.; Hou, A.Y. Global Precipitation Measurement (GPM) launch, commissioning, and early operations. Sens. Syst. Next-Gener. Satell. XVIII 2014, 9241, 31–44. [CrossRef]
10. Omranian, E.; Sharif, H.O.; Tavakoly, A.A. How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey. Remote Sens. 2018, 10, 1150. [CrossRef]
11. Furukawa, K.; Yamamoto, K.; Kubota, T.; Oki, R.; Iguchi, T. Current status of the Dual-frequency precipitation Radar on the Global Precipitation Measurement core spacecraft and scan pattern change test operations results. Remote Sens. Atmos. Clouds Precip. VII 2018, 10776, 1077602. [CrossRef]
12. Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The Global Precipitation Measurement Mission; American Metereological Society: Boston, MA, USA, 2014. Available online: https: //pdfs.semanticscholar.org/c2c9/e1aca77adaf560d24f08ca72e58b4484e66d.pdf (accessed on 9 August 2021).
13. Skofronick-Jackson, G.; Petersen, W.A.; Berg, W.; Kidd, C.; Stocker, E.F.; Kirschbaum, D.B.; Kakar, R.; Braun, S.A.; Huffman, G.J.; Iguchi, T.; et al. The Global Precipitation Measurement (GPM) Mission for Science and Society. Bull. Am. Meteorol. Soc. 2017, 98, 1679–1695. [CrossRef] [PubMed]
14. Baldini, L.; Roberto, N.; Montopoli, M.; Adirosi, E. Ground-Based Weather Radar to Investigate Thunderstorms. In Remote Sensing of Clouds and Precipitation; Springer: Cham, Switzerland, 2018; pp. 113–135. [CrossRef]
15. Acosta-Coll, M.; Ballester-Merelo, F.; de la Hoz-Franco, E.; Martinez-Peiró, M. Real-time early warning system design for pluvial flash floods—A review. Sensors 2018, 18, 2255. [CrossRef] [PubMed]
16. Keem, M.; Seo, B.C.; Krajewski, W.F.; Morris, K.R. Intercomparison of Reflectivity Measurements between GPM DPR and NEXRAD Radars. Atmos. Res. 2019, 226, 49–65. [CrossRef]
17. Biswas, S.; Chandrasekar, V. Cross-Validation of Observations between the GPM Dual-Frequency Precipitation Radar and Ground Based Dual-Polarization Radars. Remote Sens. 2018, 10, 1773. [CrossRef]
18. Kim, K.; Bui, L. Learning from Hurricane Maria: Island ports and supply chain resilience. Int. J. Disaster Risk Reduct. 2019, 39, 101244. [CrossRef]
19. López-Marrero, T.; Castro-Rivera, A. Let’s not forget about non-land-falling cyclones: Tendencies and impacts in Puerto Rico. Nat. Hazards 2019, 98, 809–815. [CrossRef]
20. Bacopoulos, P. Extreme low and high waters due to a large and powerful tropical cyclone: Hurricane Irma (2017). Nat. Hazards 2019, 98, 3. [CrossRef]
21. Benach, J.; Diaz, M.R.; Muñoz, N.J.; Martinez-Herera, E.; Pericas, J.M. What the Puerto Rican hurricanes make visible: Chronicle of a public health disaster foretold. Soc. Sci. Med. 2019, 238, 112367. [CrossRef] [PubMed]
22. Colom, J.G.; Cruz-Pol, S.; Pablos, G.; Córdoba, M.F.; Castellanos, W.; Acosta, M.; Ortiz, J.A.; de Jesús, B.; Trabal, J. Uprm Weather Radars at the Central American and Caribbean Games at Mayagüez 2010. IEEE Geosci. Remote Sens. Lett. 2010, 156, 34–39.
23. Zhong, L.; Yang, R.; Wen, Y.; Chen, L.; Gou, Y.; Li, R.; Zhou, Q.; Hong, Y. Cross-evaluation of reflectivity from the space-borne precipitation radar and multi-type ground-based weather radar network in China. Atmos. Res. 2017, 196, 200–210. [CrossRef]
24. Morris, K.R.; Greenbelt, S.; Schwaller, M.R. Sensitivity of Spaceborne and Ground Radar Comparison Results to Data Analysis Methods and Constraints. In Proceedings of the 35th Conference on Radar Meteorology, Pittsburgh, PA, USA, 26–30 September 2011. Available online: https://ams.confex.com/ams/35Radar/webprogram/Paper191729.html (accessed on 5 July 2021).
25. Biswas, S.K.; Chandrasekar, V. Cross validation of observations from GPM dual-frequnecy precipitation radar with S-band ground radar measurents over the Dallas—Fort worth region. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017; pp. 2085–2088. [CrossRef]
26. Arias, I.; Chandrasekar, V. Cross Validation of GPM and Ground-Based Radar in Latin America and the Caribbean. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 3891–3893. [CrossRef]
27. Goddard Space Flight Center. Global Precipitation Mission (GPM) Ground Validation System Validation Network Data Product User’s Guide. 2013. Available online: https://gpm.nasa.gov/sites/default/files/document_files/Val_Network_Users_Guide_v4 .1.pdf (accessed on 9 June 2021).
28. National Hurricane Center. Hurrican Beryl. Available online: https://www.nhc.noaa.gov/data/tcr/AL022018_Beryl.pdf (accessed on 6 June 2021).
29. National Weather Service. Hurricane Dorian. 2019. Available online: https://www.weather.gov/mhx/Dorian2019 (accessed on 6 June 2021).
30. National Weather Service. Tropical Storm Karen. 2019. Available online: https://www.weather.gov/sju/karen2019 (accessed on 6 June 2021).
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dc.rights.license.spa.fl_str_mv Atribución 4.0 Internacional (CC BY 4.0)
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spelling Atribución 4.0 Internacional (CC BY 4.0)2022 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Acosta-Coll, MelisaMorales, AbelZamora-Musa, RonaldButt, Shariq Aziz2024-03-19T15:44:45Z2024-03-19T15:44:45Z2022-08-02Acosta-Coll, M.; Morales, A.; Zamora-Musa, R.; Butt, S.A. Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events. Sensors 2022, 22, 5773. https://doi.org/10.3390/s221557731424-8220https://hdl.handle.net/11323/1088710.3390/s22155773Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.coDuring extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison between several sensors used to monitor these phenomena such as ground-based and satellite instruments, must maintain a high degree of correlation in order to issue alerts with an accuracy that allows for timely decision making. This study presents a cross-evaluation of the radar reflectivity from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement Mission (GPM) and the U.S. National Weather Service (NWS) Next-Generation Radar (NEXRAD) ground-based instrument located in the Caribbean island of Puerto Rico, USA, to determine the correlation degree between these two sensors' measurements during extreme weather events and normal precipitation events during 2015-2019. GPM at Ku-band and Ka-band and NEXRAD at S-band overlapping scanning regions data of normal precipitation events during 2015-2019, and the spiral rain bands of four extreme weather events, Irma (Category 5 Hurricane), Beryl (Tropical Storm), Dorian (Category 1 hurricane), and Karen (Tropical Storm), were processed using the GPM Ground Validation System (GVS). In both cases, data were classified and analyzed statistically, paying particular attention to variables such as elevation angle mode and precipitation type (stratiform and convective). Given that ground-based radar (GR) has better spatial and temporal resolution, the NEXRAD was used as ground-truth. The results revealed that the correlation coefficient between the data of both instruments during the analyzed extreme weather events was moderate to low; for normal precipitation events, the correlation is lower than that of studies that compared GPM and NEXRAD reflectivity located in other regions of the USA. Only Tropical Storm Karen obtained similar results to other comparative studies in terms of the correlation coefficient. Furthermore, the GR elevation angle and precipitation type have a substantial impact on how well the rain reflectivity correlates between the two sensors. It was found that the Ku-band channel possesses the least bias and variability when compared to the NEXRAD instrument's reflectivity and should therefore be considered more reliable for future tropical storm tracking and tropical region precipitation estimates in regions with no NEXRAD coverage.21 páginasapplication/pdfengMultidisciplinary Digital Publishing Institute (MDPI)Switzerlandhttps://www-webofscience-com.ezproxy.cuc.edu.co/wos/woscc/full-record/WOS:000839716900001Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather EventsArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Sensors1. de Beurs, K.M.; McThompson, N.S.; Owsley, B.C.; Henebry, G.M. Hurricane damage detection on four major Caribbean islands. Remote Sens. Environ. 2019, 229, 1–13. [CrossRef]2. NSF and The University of Rhode Island. Rainfall and Inland Flooding. 2010. Available online: http://hurricanescience.org/ society/impacts/rainfallandinlandflooding/ (accessed on 20 October 2021).3. Ortega-Gonzalez, L.; Acosta-Coll, M.; Piñeres-Espitia, G.; Butt, S.A. Communication protocols evaluation for a wireless rainfall monitoring network in an urban area. Heliyon 2021, 18, 7. [CrossRef] [PubMed]4. Ren, Y.; Zhang, J.; Guimond, S.; Wang, X. Hurricane Boundary Layer Height Relative to Storm Motion from GPS Dropsonde Composites. Atmposphere 2019, 10, 339. [CrossRef]5. Trepanier, J. North Atlantic Hurricane Winds in Warmer than Normal Seas. Atmposphere 2020, 11, 293. [CrossRef]6. Yang, K.; Davidson, R.A.; Blanton, B.; Colle, B.; Dresback, K.; Kolar, R.; Nozick, L.K.; Trivedi, J.; Wachtendorf, T. Hurricane evacuations in the face of uncertainty: Use of integrated models to support robust, adaptive, and repeated decision-making. Int. J. Disaster Risk Reduct. 2019, 36, 101093. [CrossRef]7. Luitel, B.; Villarini, G.; Vecchi, G. Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones. J. Hydrol. 2018, 556, 1026–1037. [CrossRef]8. Ramirez-Cerpa, E.; Acosta-Coll, M.; Velez-Zapata, J. Analysis of the climatic conditions for short-term precipitation in urban areas: A case study Barranquilla, Colombia. Idesia 2017, 35, 2. [CrossRef]9. Neeck, S.P.; Kakar, R.K.; Azarbarzin, A.A.; Hou, A.Y. Global Precipitation Measurement (GPM) launch, commissioning, and early operations. Sens. Syst. Next-Gener. Satell. XVIII 2014, 9241, 31–44. [CrossRef]10. Omranian, E.; Sharif, H.O.; Tavakoly, A.A. How Well Can Global Precipitation Measurement (GPM) Capture Hurricanes? Case Study: Hurricane Harvey. Remote Sens. 2018, 10, 1150. [CrossRef]11. Furukawa, K.; Yamamoto, K.; Kubota, T.; Oki, R.; Iguchi, T. Current status of the Dual-frequency precipitation Radar on the Global Precipitation Measurement core spacecraft and scan pattern change test operations results. Remote Sens. Atmos. Clouds Precip. VII 2018, 10776, 1077602. [CrossRef]12. Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The Global Precipitation Measurement Mission; American Metereological Society: Boston, MA, USA, 2014. Available online: https: //pdfs.semanticscholar.org/c2c9/e1aca77adaf560d24f08ca72e58b4484e66d.pdf (accessed on 9 August 2021).13. Skofronick-Jackson, G.; Petersen, W.A.; Berg, W.; Kidd, C.; Stocker, E.F.; Kirschbaum, D.B.; Kakar, R.; Braun, S.A.; Huffman, G.J.; Iguchi, T.; et al. The Global Precipitation Measurement (GPM) Mission for Science and Society. Bull. Am. Meteorol. Soc. 2017, 98, 1679–1695. [CrossRef] [PubMed]14. Baldini, L.; Roberto, N.; Montopoli, M.; Adirosi, E. Ground-Based Weather Radar to Investigate Thunderstorms. In Remote Sensing of Clouds and Precipitation; Springer: Cham, Switzerland, 2018; pp. 113–135. [CrossRef]15. Acosta-Coll, M.; Ballester-Merelo, F.; de la Hoz-Franco, E.; Martinez-Peiró, M. Real-time early warning system design for pluvial flash floods—A review. Sensors 2018, 18, 2255. [CrossRef] [PubMed]16. Keem, M.; Seo, B.C.; Krajewski, W.F.; Morris, K.R. Intercomparison of Reflectivity Measurements between GPM DPR and NEXRAD Radars. Atmos. Res. 2019, 226, 49–65. [CrossRef]17. Biswas, S.; Chandrasekar, V. Cross-Validation of Observations between the GPM Dual-Frequency Precipitation Radar and Ground Based Dual-Polarization Radars. Remote Sens. 2018, 10, 1773. [CrossRef]18. Kim, K.; Bui, L. Learning from Hurricane Maria: Island ports and supply chain resilience. Int. J. Disaster Risk Reduct. 2019, 39, 101244. [CrossRef]19. López-Marrero, T.; Castro-Rivera, A. Let’s not forget about non-land-falling cyclones: Tendencies and impacts in Puerto Rico. Nat. Hazards 2019, 98, 809–815. [CrossRef]20. Bacopoulos, P. Extreme low and high waters due to a large and powerful tropical cyclone: Hurricane Irma (2017). Nat. Hazards 2019, 98, 3. [CrossRef]21. Benach, J.; Diaz, M.R.; Muñoz, N.J.; Martinez-Herera, E.; Pericas, J.M. What the Puerto Rican hurricanes make visible: Chronicle of a public health disaster foretold. Soc. Sci. Med. 2019, 238, 112367. [CrossRef] [PubMed]22. Colom, J.G.; Cruz-Pol, S.; Pablos, G.; Córdoba, M.F.; Castellanos, W.; Acosta, M.; Ortiz, J.A.; de Jesús, B.; Trabal, J. Uprm Weather Radars at the Central American and Caribbean Games at Mayagüez 2010. IEEE Geosci. Remote Sens. Lett. 2010, 156, 34–39.23. Zhong, L.; Yang, R.; Wen, Y.; Chen, L.; Gou, Y.; Li, R.; Zhou, Q.; Hong, Y. Cross-evaluation of reflectivity from the space-borne precipitation radar and multi-type ground-based weather radar network in China. Atmos. Res. 2017, 196, 200–210. [CrossRef]24. Morris, K.R.; Greenbelt, S.; Schwaller, M.R. Sensitivity of Spaceborne and Ground Radar Comparison Results to Data Analysis Methods and Constraints. In Proceedings of the 35th Conference on Radar Meteorology, Pittsburgh, PA, USA, 26–30 September 2011. Available online: https://ams.confex.com/ams/35Radar/webprogram/Paper191729.html (accessed on 5 July 2021).25. Biswas, S.K.; Chandrasekar, V. Cross validation of observations from GPM dual-frequnecy precipitation radar with S-band ground radar measurents over the Dallas—Fort worth region. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23–28 July 2017; pp. 2085–2088. [CrossRef]26. Arias, I.; Chandrasekar, V. Cross Validation of GPM and Ground-Based Radar in Latin America and the Caribbean. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018; pp. 3891–3893. [CrossRef]27. Goddard Space Flight Center. Global Precipitation Mission (GPM) Ground Validation System Validation Network Data Product User’s Guide. 2013. Available online: https://gpm.nasa.gov/sites/default/files/document_files/Val_Network_Users_Guide_v4 .1.pdf (accessed on 9 June 2021).28. National Hurricane Center. Hurrican Beryl. Available online: https://www.nhc.noaa.gov/data/tcr/AL022018_Beryl.pdf (accessed on 6 June 2021).29. National Weather Service. Hurricane Dorian. 2019. Available online: https://www.weather.gov/mhx/Dorian2019 (accessed on 6 June 2021).30. National Weather Service. Tropical Storm Karen. 2019. 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
