Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja
ilustraciones, diagramas, fotografías, mapas
- Autores:
-
Ramírez Tamayo, Jorge Iván
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/85752
- Palabra clave:
- Radar
Bloqueos
Estimación Cuantitativa de Precipitación
Índice de Calidad del Radar
Pluviógrafo
Cluttering
Quantitative Precipitation Estimation
Radar Quality Index
Rain Gauge
Control meteorológico
Radar
Weather modification
Radar
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
| id |
UNACIONAL2_623a21e0191adece00dc7c258f155e26 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/85752 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja |
| dc.title.translated.eng.fl_str_mv |
Evaluation of the incidence of clutter in the quantitative estimation of rainfall present in the Barrancabermeja meteorological radar |
| title |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja |
| spellingShingle |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja Radar Bloqueos Estimación Cuantitativa de Precipitación Índice de Calidad del Radar Pluviógrafo Cluttering Quantitative Precipitation Estimation Radar Quality Index Rain Gauge Control meteorológico Radar Weather modification Radar |
| title_short |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja |
| title_full |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja |
| title_fullStr |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja |
| title_full_unstemmed |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja |
| title_sort |
Evaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de Barrancabermeja |
| dc.creator.fl_str_mv |
Ramírez Tamayo, Jorge Iván |
| dc.contributor.advisor.none.fl_str_mv |
Piña Fulano, Adriana Patricia |
| dc.contributor.author.none.fl_str_mv |
Ramírez Tamayo, Jorge Iván |
| dc.contributor.researchgroup.spa.fl_str_mv |
Hydrodynamics of the Natural Media (HYDS) |
| dc.subject.proposal.spa.fl_str_mv |
Radar Bloqueos Estimación Cuantitativa de Precipitación Índice de Calidad del Radar Pluviógrafo |
| topic |
Radar Bloqueos Estimación Cuantitativa de Precipitación Índice de Calidad del Radar Pluviógrafo Cluttering Quantitative Precipitation Estimation Radar Quality Index Rain Gauge Control meteorológico Radar Weather modification Radar |
| dc.subject.proposal.eng.fl_str_mv |
Cluttering Quantitative Precipitation Estimation Radar Quality Index Rain Gauge |
| dc.subject.unesco.spa.fl_str_mv |
Control meteorológico Radar |
| dc.subject.unesco.eng.fl_str_mv |
Weather modification Radar |
| description |
ilustraciones, diagramas, fotografías, mapas |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-12-01 |
| dc.date.accessioned.none.fl_str_mv |
2024-03-01T15:52:15Z |
| dc.date.available.none.fl_str_mv |
2024-03-01T15:52:15Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/85752 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/85752 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
Alfieri, L., Claps, P., & Laio, F. (2010). Time-dependent Z-R relationships for estimating rainfall fields from radar measurements. Natural Hazards and Earth System Sciences, 10, 149–158. www.nat-hazards-earth-syst-sci.net/10/149/2010/ Alku, L., Moisseev, D., Aittomäki, T., & Chandrasekar, V. (2015). Identification and Suppression of Nonmeteorological Echoes Using Spectral Polarimetric Processing. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3628–3638. https://doi.org/10.1109/TGRS.2014.2380476 American Meteorological Socety. (2020). Glossary of Meteorology. https://glossary.ametsoc.org/wiki/Welcome Anagnostou, E. N., Krajewski, W. F., & Smith, J. (1998). Uncertainty Quantification of Mean-Areal Radar-Rainfall Estimates. Journal of Atmospheric and Oceanic Technology, 16, 206–215. https://doi.org/https://doi.org/10.1175/1520-0426(1999)016<0206:UQOMAR>2.0.CO;2 Atlas, D. (1990). Radar in Meteorology (1st ed.). American Meteorological Socety. Austin, P. M. (1986). Relation between Measured Radar Reflectivity and Surface Rainfall. Monthly Weather Review, 115, 1053–1070. https://doi.org/https://doi.org/10.1175/1520-0493(1987)115<1053:RBMRRA>2.0.CO;2 Battan. L. J. (1973). Radar Observation of the Atmosphere. Univ. of Chicago Press. . Bech, J., & Bebbington, D. (2003). The Sensitivity of Single Polarization Weather Radar Beam Blockage Correction to Variability in the Vertical Refractivity Gradient. Journal of Atmospheric and Oceanic Technology, 20, 845–855. https://doi.org/https://doi.org/10.1175/1520-0426(2003)020<0845:TSOSPW>2.0.CO;2 Bech, J., Gjertsen, U., & Haase, G. (2007). Modelling weather radar beam propagation and topographical blockage at northern high latitudes. Quarterly Journal of the Royal Meteorological Society, 133(626 A), 1191–1204. https://doi.org/10.1002/qj.98 Berenguer, M., Sempere-Torres, D., Corral, C., & Sánchez-Diezma, R. (2006). A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans. Journal of Atmospheric and Oceanic Technology , 23, 1157–1180. https://doi.org/https://doi.org/10.1175/JTECH1914.1 Bergström, S., & Graham, L. P. (1998). On the scale problem in hydrological modelling. Journal of Hydrology, 211(1–4), 253–265. https://doi.org/10.1016/S0022-1694(98)00248-0 Berne, A., & Krajewski, W. F. (2013). Radar for hydrology: Unfulfilled promise or unrecognized potential? Advances in Water Resources, 51, 357–366. https://doi.org/10.1016/j.advwatres.2012.05.005 Bohórquez, C. (2023). Caracterización de la convección atmosférica nocturna en el Magdalena Medio. Universidad Nacional de Colombia. Bøvith, T. (2008). Detection of Weather Radar Clutter [Technical University of Denmark]. www.imm.dtu.dk Bowman, K. P., Collier, J. C., North, G. R., Wu, Q., Ha, E., & Hardin, J. (2005). Diurnal cycle of tropical precipitation in Tropical Rainfall Measuring Mission (TRMM) satellite and ocean buoy rain gauge data. Journal of Geophysical Research Atmospheres, 110(21), 1–14. https://doi.org/10.1029/2005JD005763 Canfield, H. E., & Goodrich, D. C. (2003). Studies of Scale and Processes in Hydrologic Modeling on the Lucky Hills Watershed. Proc. 1st Interagency Conf. on Research in the Watersheds. 27-30 de Octubre. Casallas Garcia, A. (2020). Estudio de eventos de convección profunda asociados a vientos del Oeste en superficie en la Sabana de Bogotá. Universidad Nacional de Colombia. Casallas-García, A., Hernández-Deckers, D., & Mora-Páez, H. (2023). Understanding convective storms in a tropical, high-altitude location with in-situ meteorological observations and GPS-derived water vapor. Atmosfera, 36(2), 225–238. https://doi.org/10.20937/ATM.53051 Ciach, G. J., & Krajewski, W. F. (1999). Radar-Rain Gauge Comparisons under Observational Uncertainties. Journal of Applied Meteorology, 38, 1519–1525. https://doi.org/https://doi.org/10.1175/1520-0450(1999)038<1519:RRGCUO>2.0.CO;2 Copete Ríos, J. (2009). Herramientas matemáticas e informaticas aplicables en la calibración y funcionamiento de un radar meteorológico concebido para el estudio del drenaje urbano de la ciudad de bogotá. Universidad Nacional de Colombia. Copete Ríos, J. (2013). Determinación de los parámetros en la relación Z-R de reflectividad e intensidad de precipitación para la ciudad de Bogotá apoyado en obsrvables de Disdrómetros y Pluviografos. Pontificia Universidad Javeriana. Creutin, J. D., Delrieu, G., & Lebel. T. (1988). Rain Measurement by Raingage-Radar Combination: A Geostatistical Approach. Journal of Atmospheric and Oceanic Technology, 5(1), 102–115. https://doi.org/https://doi.org/10.1175/1520-0426(1988)005<0102:RMBRRC>2.0.CO;2 DANE. (2021). Pobreza monetaria y grupos de ingreso en Colombia: RESULTADOS 2021. do Amaral, L. M. C., Barbieri, S., Vila, D., Puca, S., Vulpiani, G., Panegrossi, G., Biscaro, T., Sanò, P., Petracca, M., Marra, A. C., Gosset, M., & Dietrich, S. (2018). Assessment of ground-reference data and validation of the H-SAF precipitation products in Brazil. Remote Sensing, 10(11). https://doi.org/10.3390/rs10111743 Doviak, R., & Zrnic, D. (1984). Doppler Radar and Weather Observations (1st ed., Vol. 1). Academic Press, Inc. Dufton, D. R. L., & Collier, C. G. (2015). Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments. Atmospheric Measurement Techniques, 8(10), 3985–4000. https://doi.org/10.5194/amt-8-3985-2015 Fattorelli, S., Casale, R., Borga, M., & Da Ros, D. (1996). Integrating radar and remote sensing techniques of rainfall estimation in hydrological applications for flood hazard mitigation (1st ed.). Associazione Italiana di Idronomia (A.I.D.I.). Figueroa, A., & Rodríguez, E. (2016, September 30). Análisis temporal de la precipitación en una microcuenca urbana densamente instrumentada. XXVII Congreso Latinoamericano de Hidráulica Figueroa Ortiz, C. A. (2016). Caracterización del campo de precipitación sobre la microcuenca urbana del campus Universidad Nacional de Colombia - sede Bogotá. Universidad Nacional de Colombia Friedrich, K., Hagen, M., & Einfalt, T. (2006). A Quality Control Concept for Radar Reflectivity, Polarimetric Parameters, and Doppler Velocity. Journal of Applied Meteorology and Climatology, 23(10), 865–887. Gabella, M., & Notarpietro, R. (2002). Ground clutter characterization and elimination in mountainous terrain. Proceedings of ERAD, 305–311. Giangrande, S. E., & Ryzhkov, A. V. (2008). Estimation of rainfall based on the results of polarimetric echo classification. Journal of Applied Meteorology and Climatology, 47(9), 2445–2462. https://doi.org/https://doi.org/10.1175/2008JAMC1753.1 Hagen, M., & Meischner, P. (2000). Estimation of Rainfall Parameters from Polarimetric Radar Measurements with POLDIRAD. Phys. Chem. Earrh (E), 25(12), 849–853. https://doi.org/https://doi.org/10.1016/S1464-1909(00)00114-3 Harrison, D. L., Driscoll, S. J., & Kitchen, M. (2000). Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorological Applications, 7(2), 135–144. https://doi.org/10.1017/S1350482700001468 He, X., Vejen, F., Stisen, S., Sonnenborg, T. O., & Jensen., K. H. (2011). An Operational Weather Radar-Based Quantitative Precipitation Estimation and its Application in Catchment Water Resources Modeling. Vadose Zone Journal, 10(1), 8–24. https://doi.org/10.2136/vzj2010.0034 Heistermann, M., Jacobi, S., & Pfaff, T. (2013). Technical Note: An open source library for processing weather radar data (wradlib). Hydrology and Earth System Sciences, 17(2), 863–871. https://doi.org/10.5194/hess-17-863-2013 Helmus, J. J., & Collis, S. M. (2016a). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software, 4(1), 25. https://doi.org/10.5334/jors.119 Hitschfield, W., & Bordan, J. (1954). Errors inherent in the radar measurement of rainfall at attenuating wavelengths. Journal of Meteorology, 11(1), 58–67. https://doi.org/https://doi.org/10.1175/1520-0469(1954)011<0058:EIITRM>2.0.CO;2 Huff, F. A. (1967). Time distribution of rainfall in heavy storms. Water Resour. Res. Water Resources Research 3, 3(4), 1007–1019. https://doi.org/https://doi.org/10.1029/WR003i004p01007 Huuskonen, A., & Holleman, I. (2007). Determining weather radar antenna pointing using signals detected from the sun at low antenna elevations. Journal of Atmospheric and Oceanic Technology, 24(3), 476–483. https://doi.org/10.1175/JTECH1978.1 IDEAM. (2018). Red de radares del IDEAM. http://www.pronosticosyalertas.gov.co/archivos-radar IDEAM. (2019). Glosario Meteorológico. Instituto de Hidrología Meteorologia y Estudios Ambientales(IDEAM). Illingworth, A. (2004). Improved Precipitation Rates and Data Quality by Using Polarimetric Measurements. In P. Meischner (Ed.), Weather Radar: Principles and Advanced Applications (pp. 130–166). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-05202-0_5 Ivić, I. R., Krause, J. C., Boydstun, O. E., Daniel, A. E., Free, A. D., & Zittel, W. D. (2014). Effects of radial-based noise power estimation on spectral moment estimates. Journal of Atmospheric and Oceanic Technology, 31(12), 2671–2691. https://doi.org/10.1175/JTECH-D-14-00048.1 Jacobi, S., & Heistermann, M. (2016). Benchmarking attenuation correction procedures for six years of single-polarized C-band weather radar observations in South-West Germany. Geomatics, Natural Hazards and Risk, 7(6), 1785–1799. https://doi.org/10.1080/19475705.2016.1155080 Joss, J., Waldvogel, A., & Collier, C. G. (1990). Precipitation Measurement and Hydrology. In D. Atlas (Ed.), Radar in Meteorology: Battan Memorial and 40th Anniversary Radar Meteorology Conference (pp. 577–606). American Meteorological Society. https://doi.org/10.1007/978-1-935704-15-7_39 Kite, G. W., & Haberlandt, U. (1999). Atmospheric model data for macroscale hydrology. Journal of Hydrology, 217(3–4), 303–313. https://doi.org/10.1016/S0022-1694(98)00230-3 Krajewski, W. F., Kruger, A., & Nespor, V. (1998). Experimental and numerical studies of small-scale rainfall measurements and variability. Water Science and Technology, 37(11), 131–138. https://doi.org/10.1016/S0273-1223(98)00325-4 Krämer, S., & Verworn, H. R. (2009). Improved radar data processing algorithms for quantitative rainfall estimation in real time. Water Science and Technology, 60(1), 175–184. https://doi.org/10.2166/wst.2009.282 Krause, J. M. (2016). A simple algorithm to discriminate between meteorological and nonmeteorological radar echoes. Journal of Atmospheric and Oceanic Technology, 33(9), 1875–1885. https://doi.org/10.1175/JTECH-D-15-0239.1 Krivoruchko, K. (2012). Empirical Bayesian Kriging Implemented in ArcGIS Geostatistical Analyst. Krivoruchko, K., & Gribov, A. (2014). Pragmatic bayesian kriging for non-stationary and moderately non-Gaussian data. Lecture Notes in Earth System Sciences, 0, 61–64. https://doi.org/10.1007/978-3-642-32408-6_15 Krivoruchko, K., & Gribov, A. (2019). Evaluation of empirical Bayesian kriging. Spatial Statistics, 32. https://doi.org/10.1016/j.spasta.2019.100368 Kumjian, M. (2013). Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables. Journal of Operational Meteorology, 1(19), 226–242. https://doi.org/10.15191/nwajom.2013.0119 Ladino, A. (2017). Caracterización de la microfísica de la precipitación mediante información de disdrómetros y radar polarimétrico para la estimación cuantitativa de lluvia en el área metropolitana del Valle de Aburrá. Universidad Nacional de Colombia. Lang, T. J., Nesbitt, S. W., & Carey, L. D. (2009). On the correction of partial beam blockage in polarimetric radar data. Journal of Atmospheric and Oceanic Technology, 26(5), 943–957. https://doi.org/10.1175/2008JTECHA1133.1 Li, J., Yu, R., & Sun, W. (2013). Duration and seasonality of hourly extreme rainfall in the central eastern China. Acta Meteorologica Sinica, 27(6), 799–807. https://doi.org/10.1007/s13351-013-0604-y Liu, C., & Zipser, E. J. (2008). Diurnal cycles of precipitation, clouds, and lightning in the tropics from 9 years of TRMM observations. Geophysical Research Letters, 35(4). https://doi.org/10.1029/2007GL032437 Maidment, D. R. (1993). Handbook of Hydrology (1st ed.). McGraw-Hill. Marshall, J. S., & Palmer, McK. W. (1948). The distribution of raindrop sizes. Journal of the Atmospheric Sciences, 5(4), 165–166. https://doi.org/https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2 Monsalve, G. (1999). Hidrología en la Ingeniería (2nd ed.). Escuela Colombiana de Ingeniería: Julio Garavito Armero. Moore, R. J., Jones, D. A., Cox, D. R., & Isham, V. S. (2000). HYREX: the HYdrological Radar Experiment. Hydrology and Earth System Sciences, 4, 523–530. https://hal.science/hal-00304682 National Oceanic and Atmospheric Administration. (2023). Radar Quality Index (RQI). https://vlab.noaa.gov/web/wdtd/-/radar-quality-index-rqi-#:~:text=The%20Radar%20Quality%20Index%20(RQI,blockage%20and%20higher%20beam%20heights. National Weather Service. (2023). NWS Weather Radar. https://radar.weather.gov/ Ochoa-Rodriguez, S., Wang, L. P., Willems, P., & Onof, C. (2019). A Review of Radar-Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications. Water Resources Research, 55(8), 6356–6391. https://doi.org/10.1029/2018WR023332 Parzen, E. (1962). On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics, 33(3), 1065–1076. https://doi.org/10.1214/aoms/1177704472 Paturel, E., Desbodes, M., & Masson, J. M. (1986). Evaluation de l`influence de la densité des réseaux pluviométriques sur la determination des lames précipitées. Informe LHM, 14–86. Poveda, G. (2004). La hidroclimatología en Colombia: Una síntesis desde la escala inter-decadal hasta la escala diurna. Revista Academia Colombiana de Ciencias, 28(107), 201–222. https://doi.org/10.18257/raccefyn.28(107).2004.1991 Ramírez, E. (2021). Hacia una cadena de procesamiento de datos de un radar meteorológico. Perspectivas para el radar PIUXX. Universidad De Piura. Ramirez, J., Cuavas, J., Niño, G., Noel Chartano, S. V., Sacristan, D., & Rodriguez, E. (2021, September 19). REDAP UN: Una herramienta para el análisis de datos de precipitación capturados en la microcuenca urbana del campus de la Universidad Nacional de Colombia -Sede Bogotá. XXIV Seminario Nacional de Hidráulica e Hidrología. Rauber, R. M., & Nesbitt, S. W. (2014). Radar Meteorology: A first Course (1st ed.). Wiley. Rinehart, R. (2007). El Radar para los Meteorólogos (Knight Printing, Ed.). Rinollo, A., Vulpiani, G., Puca, S., Pagliara, P., Kaňák, J., Lábó, E., Okon, L., Roulin, E., Baguis, P., Cattani, E., Laviola, S., & Levizzani, V. (2010). Definition and impact of a quality index for radar-based reference measurements in the H-SAF precipitation product validation. Natural Hazards and Earth System Sciences, 13(10), 2695–2705. https://doi.org/10.5194/nhess-13-2695-2013 Rodriguez, E., Camacho, L. A., Villarreal, J., Jimenez, A., Santos, A. C., & Duarte, M. del P. (2008). De La Precipitación En Una Microcuenca Urbana ,. Revista Colombiana de Geografía, 17(0121-215X), 139–153. Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. The Annals of Mathematical Statistics, 27(3), 832–837. https://doi.org/10.1214/aoms/1177728190 Sachidananda, M., & Zrinc, D. S. (1987). Rain Rate Estimates from Differential Polarization Measurements. Journal of Atmospheric and Oceanic Technology, 4(4), 588–598. https://doi.org/https://doi.org/10.1175/1520-0426(1987)004<0588:RREFDP>2.0.CO;2 Sahlaoui, Z., & Mordane, S. (2019). Radar rainfall estimation in Morocco: Quality control and gauge adjustment. Hydrology, 6(2). https://doi.org/10.3390/hydrology6020041 Saltikoff, E., Cho, J. Y. N., Tristant, P., Huuskonen, A., Allmon, L., Cook, R., Becker, E., & Joe, P. (2016). The threat to weather radars by wireless technology. Bulletin of the American Meteorological Society, 97(7), 1159–1167. https://doi.org/10.1175/BAMS-D-15-00048.1 Saltikoff, E., Friedrich, K., Soderholm, J., Lengfeld, K., Nelson, B., Becker, A., Hollmann, R., Urban, B., Heistermann, M., & Tassone, C. (2019). An overview of using weather radar for climatological studies successes, challenges, and potential. Bulletin of the American Meteorological Society, 100(9), 1739–1751. https://doi.org/10.1175/BAMS-D-18-0166.1 Salvo, C., Baldassini, P., & Di Bella, C. (2021). Identificación de campos de precipitación mediante el uso de radares meteorológicos: una alternativa para mejorar las estimaciones espaciales de las lluvias. RIA. Revista de Investigaciones Agropecuarias, 47(2), 240–248. Sánchez-Diezma, R., & Corral, C. (2000). Curso de Postgrado de Climatología Aplicada: El radar meteorológico y sus aplicaciones hidrológicas. Universidad Politécnica de Cataluña. Sauvageot, H. (1994). Rainfall measurement by radar: a review. Atmospheric Research, 35(1), 27–54. https://doi.org/https://doi.org/10.1016/0169-8095(94)90071-X Schilling, W., Bauwens, W., Borchardt, D., Krebs, P., Rauch, W., & Vanrolleghem, P. A. (1997). On the Relation Between Urban Wastewater Management Needs and Receiving Water Objectives. Seliga, T. A., & Bringi, V. N. (1976). Potential Use of Radar Differential Reflectivity Measurements at Orthogonal Polarizations for Measuring Precipitation. Journal of Applied Meteorology and Climatology, 15(1), 69–76. https://doi.org/https://doi.org/10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2 Sempere-Torres, D., Delrieu, G., Zawadzki, I., & Sánchez-Diezma, R. (2001). An improved methodology for ground clutter substitution based on a pre-classification of precipitation types. In American Meteorological Socety (Ed.), 30th International Conference on Radar Meteorology (pp. 271–273). https://www.researchgate.net/publication/242368835 Sepúlveda, J. (2015). Estimación cuantitativa de precipitación a partir de la información de Radar Meteorológico deí Area Metropolitana del Valle de Aburrá. Universidad Nacional de Colombia. Shahriari, F. (2018). The Relationship between Iranian EFL Instructors’ Multiple Intelligence Ability and Reflective Teaching. Revista Romaneasca Pentru Educatie Multidimensionala, 10(1.SP), 97. https://doi.org/10.18662/rrem/39 Silver, M., Svoray, T., Karnieli, A., & Fredj, E. (2020). Improving weather radar precipitation maps: A fuzzy logic approach. Atmospheric Research, 234. https://doi.org/10.1016/j.atmosres.2019.104710 Skolnik, M. (1980). Introduction to radar systems. McGraw-Hill. Smith, P. L. (1998). On the Minimum Useful Elevation Angle for Weather Surveillance Radar Scans. Journal of Atmospheric and Oceanic Technology, 15(3), 841–843. https://doi.org/https://doi.org/10.1175/1520-0426(1998)015<0841:OTMUEA>2.0.CO;2 Szturc, J., Ośródka, K., & Jurczyk, A. (2011). Quality index scheme for quantitative uncertainty characterization of radar-based precipitation. Meteorological Applications, 18(4), 407–420. https://doi.org/10.1002/met.230 Vargas, E. G. (2015). Modelo para la estimación cuantitativa de precipitación a partir de datos de radares polarimétricos. Pontificia Universidad Javeriana. Ven Te Chow, David R. Maidment, & Larry W. Mays. (1988). Applied Hydrology (1st ed., Vol. 1). McGraw-Hill. Vulpiani, G., Baldini, L., & Roberto, N. (2015). Characterization of Mediterranean hail-bearing storms using an operational polarimetric X-band radar. Atmospheric Measurement Techniques, 8(11), 4681–4698. https://doi.org/10.5194/amt-8-4681-2015 Vulpiani, G., Tabary, P., Du Chatelet, J. P., & Marzano, F. S. (2008). Comparison of advanced radar polarimetric techniques for operational attenuation correction at C band. Journal of Atmospheric and Oceanic Technology, 25(7), 1118–1135. https://doi.org/10.1175/2007JTECHA936.1 Wang, X., Zheng, X., He, J., & Li, X. (2012). Using fuzzy logic to discriminate convective and statiform precipitation in Doppler weather radar. 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 623–627. https://doi.org/10.1109/FSKD.2012.6233819 Wood, E. F. (1998). Scale Analysis for Land Surface Hydrology. En Scale Dependence and Scale Invariance in Hydrology (G. Sposito, Ed.). Cambridge: University Cambridge Press. Yang, Y., Chen, X., & Qi, Y. (2013). Classification of convective/stratiform echoes in radar reflectivity observations using a fuzzy logic algorithm. Journal of Geophysical Research Atmospheres, 118(4), 1896–1905. https://doi.org/10.1002/jgrd.50214 Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X Zhang, G. (2019). Weather Radar Polarimetry (CRC Press, Ed.; 1st ed.). Zhang, J. (2011). Radar Quality Index (RQI)-A combined measure of beam blockage and VPR effects in a national network Probabilistic Precipitation Estimates View project. https://www.researchgate.net/publication/268061344 Zhang, Y., Liu, L., Wen, H., Yu, B., Wang, H., & Zhang, Y. (2022). Combined Radar Quality Index for Quantitative Precipitation Estimation of Heavy Rainfall Events. Remote Sensing, 14(13). https://doi.org/10.3390/rs14133154 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Reconocimiento 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Reconocimiento 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
138 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ingeniería - Maestría en Ingeniería - Recursos Hidráulicos |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ingeniería |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/85752/2/1018493841.2023.pdf https://repositorio.unal.edu.co/bitstream/unal/85752/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/85752/3/1018493841.2023.pdf.jpg |
| bitstream.checksum.fl_str_mv |
03f5fa02a255c535c71fb21077ddb8df eb34b1cf90b7e1103fc9dfd26be24b4a 58aa7cf7c1cb36c5316796f7674a8773 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814090197205254144 |
| spelling |
Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Piña Fulano, Adriana Patricia8fbaf0420a6d82efcc55e0e91ba6f710Ramírez Tamayo, Jorge Ivánf6b16c86ac15c1a520235a291f3ff2d8Hydrodynamics of the Natural Media (HYDS)2024-03-01T15:52:15Z2024-03-01T15:52:15Z2023-12-01https://repositorio.unal.edu.co/handle/unal/85752Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografías, mapasLa variabilidad espacial de la precipitación es difícil de medir debido a la falta de estaciones pluviométricas y automáticas en tierra. Como resultado, los radares meteorológicos se han convertido en fuentes de información cruciales para estimar campos de precipitación. Sin embargo, los bloqueos, que se refieren a factores externos de la naturaleza que afectan la calidad de los datos del radar, plantean un desafío importante y contribuyen a errores e incertidumbres en la estimación cuantitativa de precipitación. Este trabajo tiene como enfoque principal analizar el impacto de los bloqueos en el radar meteorológico de banda C de Barrancabermeja, ubicado entre las Cordilleras Oriental y Central de los Andes colombianos, específicamente para la estimación cuantitativa de precipitación (QPE). El análisis de bloqueos se realizó utilizando información de radar recopilada entre 2019 y 2020. Para identificar zonas de bloqueo, se identificaron los días sin lluvia que fueron insumo para un análisis de frecuencia de la reflectividad, que tuvo una alta correlación con las interferencias topográficas causadas por los rangos circundantes del radar. Posteriormente, se calculó el índice de calidad del radar (RQI) para condiciones de lluvia, teniendo en cuenta factores como el mapa de frecuencia de bloqueos, el bloqueo parcial del haz, los efectos de la pérdida de resolución por la distancia, el ruido del radar y la atenuación. La evaluación reveló un área de bloqueos aproximada del 50% para el barrido más bajo de 0.5°, principalmente asociada con las interferencias topográficas, lo que indica un impacto directo de las cordilleras en la calidad de los datos del radar. Posteriormente, se analizaron eventos de precipitación específicos (mayores a 10 mm por evento) para determinar los parámetros de tres relaciones reflectividad – precipitación (metodologías de Marshall & Palmer, Seliga & Bringi, y Sachidananda & Zrinc) para el radar de Barrancabermeja, utilizando datos de 91 estaciones automáticas disponibles en el área de influencia del radar. Al emplear estas relaciones, se calcularon mapas de incertidumbre de la estimación de lluvia, obteniendo valores de incertidumbre cercanos al 65%. En general, se destaca la influencia de los bloqueos en la estimación de los campos de precipitación del radar de Barrancabermeja y la importancia de tener en cuenta las interferencias topográficas en la interpretación de datos de radar, como elemento fundamental para la estimación cuantitativa de la precipitación en la región Andina. (Texto tomado de la fuente)The spatial variability of rainfall is difficult to measure due to the lack of ground weather rain gauges. As a result, meteorological radars have become crucial sources of information for estimating precipitation fields. However, radar cluttering, which refers to external factors of nature that affect radar data quality, poses a significant challenge, and contributes to errors and uncertainties in the estimation process. In this study, we focused on analyzing the impact of cluttering on the Barrancabermeja C-band weather radar, situated between the Eastern and Central Ranges in the Colombian Andes, specifically for the Quantitative Precipitation Estimation (QPE). The analysis was conducted using radar information collected between 2019 and 2020. To identify cluttering areas, rainless days were identified that were input for a frequency analysis of reflectivity, which had a high interference with topographic interferences caused by the surrounding radar ranges. Subsequently, we calculated the radar quality index (RQI) for both rainy conditions, considering factors such as clutter frequency map, partial beam blockage, effects of range distance quality, radar noise, and attenuation. The evaluation revealed an approximate clutter area of 50% in a beam elevation of 0.5°, primarily associated with the topographical interferences, indicating a direct impact of the Andean region on radar data quality. Subsequently, we focused on intense rainfall events (greater than 10mm per event) to determine the parameters of three reflectivity-rainfall intensity relationships (Marshall & Palmer, Seliga & Bringi, and Sachidananda & Zrinc methodologies) for the Barrancabermeja radar, utilizing data from 91 available rain gauges. By employing these relationships, we calculated uncertainty maps of the quantitative precipitation estimation, obtaining an uncertainty of 65% from cluttering in the Quantitative Precipitation Estimation of the meteorological radar. Overall, our findings emphasize the significant role of cluttering in the estimation of precipitation fields from the Barrancabermeja radar. The study underscores the importance of addressing cluttering effects and accounting for the topographical interferences in radar data interpretation to enhance the accuracy of quantitative precipitation estimates in the Andean region.MaestríaMagíster en Ingeniería - Recursos HidráulicosHidrología y Meteorología138 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Recursos HidráulicosFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede BogotáEvaluación de la incidencia de bloqueos en la estimación cuantitativa de precipitación presente en el radar meteorológico de BarrancabermejaEvaluation of the incidence of clutter in the quantitative estimation of rainfall present in the Barrancabermeja meteorological radarTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAlfieri, L., Claps, P., & Laio, F. (2010). Time-dependent Z-R relationships for estimating rainfall fields from radar measurements. Natural Hazards and Earth System Sciences, 10, 149–158. www.nat-hazards-earth-syst-sci.net/10/149/2010/Alku, L., Moisseev, D., Aittomäki, T., & Chandrasekar, V. (2015). Identification and Suppression of Nonmeteorological Echoes Using Spectral Polarimetric Processing. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3628–3638. https://doi.org/10.1109/TGRS.2014.2380476American Meteorological Socety. (2020). Glossary of Meteorology. https://glossary.ametsoc.org/wiki/WelcomeAnagnostou, E. N., Krajewski, W. F., & Smith, J. (1998). Uncertainty Quantification of Mean-Areal Radar-Rainfall Estimates. Journal of Atmospheric and Oceanic Technology, 16, 206–215. https://doi.org/https://doi.org/10.1175/1520-0426(1999)016<0206:UQOMAR>2.0.CO;2Atlas, D. (1990). Radar in Meteorology (1st ed.). American Meteorological Socety.Austin, P. M. (1986). Relation between Measured Radar Reflectivity and Surface Rainfall. Monthly Weather Review, 115, 1053–1070. https://doi.org/https://doi.org/10.1175/1520-0493(1987)115<1053:RBMRRA>2.0.CO;2Battan. L. J. (1973). Radar Observation of the Atmosphere. Univ. of Chicago Press. .Bech, J., & Bebbington, D. (2003). The Sensitivity of Single Polarization Weather Radar Beam Blockage Correction to Variability in the Vertical Refractivity Gradient. Journal of Atmospheric and Oceanic Technology, 20, 845–855. https://doi.org/https://doi.org/10.1175/1520-0426(2003)020<0845:TSOSPW>2.0.CO;2Bech, J., Gjertsen, U., & Haase, G. (2007). Modelling weather radar beam propagation and topographical blockage at northern high latitudes. Quarterly Journal of the Royal Meteorological Society, 133(626 A), 1191–1204. https://doi.org/10.1002/qj.98Berenguer, M., Sempere-Torres, D., Corral, C., & Sánchez-Diezma, R. (2006). A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans. Journal of Atmospheric and Oceanic Technology , 23, 1157–1180. https://doi.org/https://doi.org/10.1175/JTECH1914.1Bergström, S., & Graham, L. P. (1998). On the scale problem in hydrological modelling. Journal of Hydrology, 211(1–4), 253–265. https://doi.org/10.1016/S0022-1694(98)00248-0Berne, A., & Krajewski, W. F. (2013). Radar for hydrology: Unfulfilled promise or unrecognized potential? Advances in Water Resources, 51, 357–366. https://doi.org/10.1016/j.advwatres.2012.05.005Bohórquez, C. (2023). Caracterización de la convección atmosférica nocturna en el Magdalena Medio. Universidad Nacional de Colombia.Bøvith, T. (2008). Detection of Weather Radar Clutter [Technical University of Denmark]. www.imm.dtu.dkBowman, K. P., Collier, J. C., North, G. R., Wu, Q., Ha, E., & Hardin, J. (2005). Diurnal cycle of tropical precipitation in Tropical Rainfall Measuring Mission (TRMM) satellite and ocean buoy rain gauge data. Journal of Geophysical Research Atmospheres, 110(21), 1–14. https://doi.org/10.1029/2005JD005763Canfield, H. E., & Goodrich, D. C. (2003). Studies of Scale and Processes in Hydrologic Modeling on the Lucky Hills Watershed. Proc. 1st Interagency Conf. on Research in the Watersheds. 27-30 de Octubre.Casallas Garcia, A. (2020). Estudio de eventos de convección profunda asociados a vientos del Oeste en superficie en la Sabana de Bogotá. Universidad Nacional de Colombia.Casallas-García, A., Hernández-Deckers, D., & Mora-Páez, H. (2023). Understanding convective storms in a tropical, high-altitude location with in-situ meteorological observations and GPS-derived water vapor. Atmosfera, 36(2), 225–238. https://doi.org/10.20937/ATM.53051Ciach, G. J., & Krajewski, W. F. (1999). Radar-Rain Gauge Comparisons under Observational Uncertainties. Journal of Applied Meteorology, 38, 1519–1525. https://doi.org/https://doi.org/10.1175/1520-0450(1999)038<1519:RRGCUO>2.0.CO;2Copete Ríos, J. (2009). Herramientas matemáticas e informaticas aplicables en la calibración y funcionamiento de un radar meteorológico concebido para el estudio del drenaje urbano de la ciudad de bogotá. Universidad Nacional de Colombia.Copete Ríos, J. (2013). Determinación de los parámetros en la relación Z-R de reflectividad e intensidad de precipitación para la ciudad de Bogotá apoyado en obsrvables de Disdrómetros y Pluviografos. Pontificia Universidad Javeriana.Creutin, J. D., Delrieu, G., & Lebel. T. (1988). Rain Measurement by Raingage-Radar Combination: A Geostatistical Approach. Journal of Atmospheric and Oceanic Technology, 5(1), 102–115. https://doi.org/https://doi.org/10.1175/1520-0426(1988)005<0102:RMBRRC>2.0.CO;2DANE. (2021). Pobreza monetaria y grupos de ingreso en Colombia: RESULTADOS 2021.do Amaral, L. M. C., Barbieri, S., Vila, D., Puca, S., Vulpiani, G., Panegrossi, G., Biscaro, T., Sanò, P., Petracca, M., Marra, A. C., Gosset, M., & Dietrich, S. (2018). Assessment of ground-reference data and validation of the H-SAF precipitation products in Brazil. Remote Sensing, 10(11). https://doi.org/10.3390/rs10111743Doviak, R., & Zrnic, D. (1984). Doppler Radar and Weather Observations (1st ed., Vol. 1). Academic Press, Inc.Dufton, D. R. L., & Collier, C. G. (2015). Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments. Atmospheric Measurement Techniques, 8(10), 3985–4000. https://doi.org/10.5194/amt-8-3985-2015Fattorelli, S., Casale, R., Borga, M., & Da Ros, D. (1996). Integrating radar and remote sensing techniques of rainfall estimation in hydrological applications for flood hazard mitigation (1st ed.). Associazione Italiana di Idronomia (A.I.D.I.).Figueroa, A., & Rodríguez, E. (2016, September 30). Análisis temporal de la precipitación en una microcuenca urbana densamente instrumentada. XXVII Congreso Latinoamericano de HidráulicaFigueroa Ortiz, C. A. (2016). Caracterización del campo de precipitación sobre la microcuenca urbana del campus Universidad Nacional de Colombia - sede Bogotá. Universidad Nacional de ColombiaFriedrich, K., Hagen, M., & Einfalt, T. (2006). A Quality Control Concept for Radar Reflectivity, Polarimetric Parameters, and Doppler Velocity. Journal of Applied Meteorology and Climatology, 23(10), 865–887.Gabella, M., & Notarpietro, R. (2002). Ground clutter characterization and elimination in mountainous terrain. Proceedings of ERAD, 305–311.Giangrande, S. E., & Ryzhkov, A. V. (2008). Estimation of rainfall based on the results of polarimetric echo classification. Journal of Applied Meteorology and Climatology, 47(9), 2445–2462. https://doi.org/https://doi.org/10.1175/2008JAMC1753.1Hagen, M., & Meischner, P. (2000). Estimation of Rainfall Parameters from Polarimetric Radar Measurements with POLDIRAD. Phys. Chem. Earrh (E), 25(12), 849–853. https://doi.org/https://doi.org/10.1016/S1464-1909(00)00114-3Harrison, D. L., Driscoll, S. J., & Kitchen, M. (2000). Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorological Applications, 7(2), 135–144. https://doi.org/10.1017/S1350482700001468He, X., Vejen, F., Stisen, S., Sonnenborg, T. O., & Jensen., K. H. (2011). An Operational Weather Radar-Based Quantitative Precipitation Estimation and its Application in Catchment Water Resources Modeling. Vadose Zone Journal, 10(1), 8–24. https://doi.org/10.2136/vzj2010.0034Heistermann, M., Jacobi, S., & Pfaff, T. (2013). Technical Note: An open source library for processing weather radar data (wradlib). Hydrology and Earth System Sciences, 17(2), 863–871. https://doi.org/10.5194/hess-17-863-2013Helmus, J. J., & Collis, S. M. (2016a). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. Journal of Open Research Software, 4(1), 25. https://doi.org/10.5334/jors.119Hitschfield, W., & Bordan, J. (1954). Errors inherent in the radar measurement of rainfall at attenuating wavelengths. Journal of Meteorology, 11(1), 58–67. https://doi.org/https://doi.org/10.1175/1520-0469(1954)011<0058:EIITRM>2.0.CO;2Huff, F. A. (1967). Time distribution of rainfall in heavy storms. Water Resour. Res. Water Resources Research 3, 3(4), 1007–1019. https://doi.org/https://doi.org/10.1029/WR003i004p01007Huuskonen, A., & Holleman, I. (2007). Determining weather radar antenna pointing using signals detected from the sun at low antenna elevations. Journal of Atmospheric and Oceanic Technology, 24(3), 476–483. https://doi.org/10.1175/JTECH1978.1IDEAM. (2018). Red de radares del IDEAM. http://www.pronosticosyalertas.gov.co/archivos-radarIDEAM. (2019). Glosario Meteorológico. Instituto de Hidrología Meteorologia y Estudios Ambientales(IDEAM).Illingworth, A. (2004). Improved Precipitation Rates and Data Quality by Using Polarimetric Measurements. In P. Meischner (Ed.), Weather Radar: Principles and Advanced Applications (pp. 130–166). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-05202-0_5Ivić, I. R., Krause, J. C., Boydstun, O. E., Daniel, A. E., Free, A. D., & Zittel, W. D. (2014). Effects of radial-based noise power estimation on spectral moment estimates. Journal of Atmospheric and Oceanic Technology, 31(12), 2671–2691. https://doi.org/10.1175/JTECH-D-14-00048.1Jacobi, S., & Heistermann, M. (2016). Benchmarking attenuation correction procedures for six years of single-polarized C-band weather radar observations in South-West Germany. Geomatics, Natural Hazards and Risk, 7(6), 1785–1799. https://doi.org/10.1080/19475705.2016.1155080Joss, J., Waldvogel, A., & Collier, C. G. (1990). Precipitation Measurement and Hydrology. In D. Atlas (Ed.), Radar in Meteorology: Battan Memorial and 40th Anniversary Radar Meteorology Conference (pp. 577–606). American Meteorological Society. https://doi.org/10.1007/978-1-935704-15-7_39Kite, G. W., & Haberlandt, U. (1999). Atmospheric model data for macroscale hydrology. Journal of Hydrology, 217(3–4), 303–313. https://doi.org/10.1016/S0022-1694(98)00230-3Krajewski, W. F., Kruger, A., & Nespor, V. (1998). Experimental and numerical studies of small-scale rainfall measurements and variability. Water Science and Technology, 37(11), 131–138. https://doi.org/10.1016/S0273-1223(98)00325-4Krämer, S., & Verworn, H. R. (2009). Improved radar data processing algorithms for quantitative rainfall estimation in real time. Water Science and Technology, 60(1), 175–184. https://doi.org/10.2166/wst.2009.282Krause, J. M. (2016). A simple algorithm to discriminate between meteorological and nonmeteorological radar echoes. Journal of Atmospheric and Oceanic Technology, 33(9), 1875–1885. https://doi.org/10.1175/JTECH-D-15-0239.1Krivoruchko, K. (2012). Empirical Bayesian Kriging Implemented in ArcGIS Geostatistical Analyst.Krivoruchko, K., & Gribov, A. (2014). Pragmatic bayesian kriging for non-stationary and moderately non-Gaussian data. Lecture Notes in Earth System Sciences, 0, 61–64. https://doi.org/10.1007/978-3-642-32408-6_15Krivoruchko, K., & Gribov, A. (2019). Evaluation of empirical Bayesian kriging. Spatial Statistics, 32. https://doi.org/10.1016/j.spasta.2019.100368Kumjian, M. (2013). Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables. Journal of Operational Meteorology, 1(19), 226–242. https://doi.org/10.15191/nwajom.2013.0119Ladino, A. (2017). Caracterización de la microfísica de la precipitación mediante información de disdrómetros y radar polarimétrico para la estimación cuantitativa de lluvia en el área metropolitana del Valle de Aburrá. Universidad Nacional de Colombia.Lang, T. J., Nesbitt, S. W., & Carey, L. D. (2009). On the correction of partial beam blockage in polarimetric radar data. Journal of Atmospheric and Oceanic Technology, 26(5), 943–957. https://doi.org/10.1175/2008JTECHA1133.1Li, J., Yu, R., & Sun, W. (2013). Duration and seasonality of hourly extreme rainfall in the central eastern China. Acta Meteorologica Sinica, 27(6), 799–807. https://doi.org/10.1007/s13351-013-0604-yLiu, C., & Zipser, E. J. (2008). Diurnal cycles of precipitation, clouds, and lightning in the tropics from 9 years of TRMM observations. Geophysical Research Letters, 35(4). https://doi.org/10.1029/2007GL032437Maidment, D. R. (1993). Handbook of Hydrology (1st ed.). McGraw-Hill.Marshall, J. S., & Palmer, McK. W. (1948). The distribution of raindrop sizes. Journal of the Atmospheric Sciences, 5(4), 165–166. https://doi.org/https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2Monsalve, G. (1999). Hidrología en la Ingeniería (2nd ed.). Escuela Colombiana de Ingeniería: Julio Garavito Armero.Moore, R. J., Jones, D. A., Cox, D. R., & Isham, V. S. (2000). HYREX: the HYdrological Radar Experiment. Hydrology and Earth System Sciences, 4, 523–530. https://hal.science/hal-00304682National Oceanic and Atmospheric Administration. (2023). Radar Quality Index (RQI). https://vlab.noaa.gov/web/wdtd/-/radar-quality-index-rqi-#:~:text=The%20Radar%20Quality%20Index%20(RQI,blockage%20and%20higher%20beam%20heights.National Weather Service. (2023). NWS Weather Radar. https://radar.weather.gov/Ochoa-Rodriguez, S., Wang, L. P., Willems, P., & Onof, C. (2019). A Review of Radar-Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications. Water Resources Research, 55(8), 6356–6391. https://doi.org/10.1029/2018WR023332Parzen, E. (1962). On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics, 33(3), 1065–1076. https://doi.org/10.1214/aoms/1177704472Paturel, E., Desbodes, M., & Masson, J. M. (1986). Evaluation de l`influence de la densité des réseaux pluviométriques sur la determination des lames précipitées. Informe LHM, 14–86.Poveda, G. (2004). La hidroclimatología en Colombia: Una síntesis desde la escala inter-decadal hasta la escala diurna. Revista Academia Colombiana de Ciencias, 28(107), 201–222. https://doi.org/10.18257/raccefyn.28(107).2004.1991Ramírez, E. (2021). Hacia una cadena de procesamiento de datos de un radar meteorológico. Perspectivas para el radar PIUXX. Universidad De Piura.Ramirez, J., Cuavas, J., Niño, G., Noel Chartano, S. V., Sacristan, D., & Rodriguez, E. (2021, September 19). REDAP UN: Una herramienta para el análisis de datos de precipitación capturados en la microcuenca urbana del campus de la Universidad Nacional de Colombia -Sede Bogotá. XXIV Seminario Nacional de Hidráulica e Hidrología.Rauber, R. M., & Nesbitt, S. W. (2014). Radar Meteorology: A first Course (1st ed.). Wiley.Rinehart, R. (2007). El Radar para los Meteorólogos (Knight Printing, Ed.).Rinollo, A., Vulpiani, G., Puca, S., Pagliara, P., Kaňák, J., Lábó, E., Okon, L., Roulin, E., Baguis, P., Cattani, E., Laviola, S., & Levizzani, V. (2010). Definition and impact of a quality index for radar-based reference measurements in the H-SAF precipitation product validation. Natural Hazards and Earth System Sciences, 13(10), 2695–2705. https://doi.org/10.5194/nhess-13-2695-2013Rodriguez, E., Camacho, L. A., Villarreal, J., Jimenez, A., Santos, A. C., & Duarte, M. del P. (2008). De La Precipitación En Una Microcuenca Urbana ,. Revista Colombiana de Geografía, 17(0121-215X), 139–153.Rosenblatt, M. (1956). Remarks on Some Nonparametric Estimates of a Density Function. The Annals of Mathematical Statistics, 27(3), 832–837. https://doi.org/10.1214/aoms/1177728190Sachidananda, M., & Zrinc, D. S. (1987). Rain Rate Estimates from Differential Polarization Measurements. Journal of Atmospheric and Oceanic Technology, 4(4), 588–598. https://doi.org/https://doi.org/10.1175/1520-0426(1987)004<0588:RREFDP>2.0.CO;2Sahlaoui, Z., & Mordane, S. (2019). Radar rainfall estimation in Morocco: Quality control and gauge adjustment. Hydrology, 6(2). https://doi.org/10.3390/hydrology6020041Saltikoff, E., Cho, J. Y. N., Tristant, P., Huuskonen, A., Allmon, L., Cook, R., Becker, E., & Joe, P. (2016). The threat to weather radars by wireless technology. Bulletin of the American Meteorological Society, 97(7), 1159–1167. https://doi.org/10.1175/BAMS-D-15-00048.1Saltikoff, E., Friedrich, K., Soderholm, J., Lengfeld, K., Nelson, B., Becker, A., Hollmann, R., Urban, B., Heistermann, M., & Tassone, C. (2019). An overview of using weather radar for climatological studies successes, challenges, and potential. Bulletin of the American Meteorological Society, 100(9), 1739–1751. https://doi.org/10.1175/BAMS-D-18-0166.1Salvo, C., Baldassini, P., & Di Bella, C. (2021). Identificación de campos de precipitación mediante el uso de radares meteorológicos: una alternativa para mejorar las estimaciones espaciales de las lluvias. RIA. Revista de Investigaciones Agropecuarias, 47(2), 240–248.Sánchez-Diezma, R., & Corral, C. (2000). Curso de Postgrado de Climatología Aplicada: El radar meteorológico y sus aplicaciones hidrológicas. Universidad Politécnica de Cataluña.Sauvageot, H. (1994). Rainfall measurement by radar: a review. Atmospheric Research, 35(1), 27–54. https://doi.org/https://doi.org/10.1016/0169-8095(94)90071-XSchilling, W., Bauwens, W., Borchardt, D., Krebs, P., Rauch, W., & Vanrolleghem, P. A. (1997). On the Relation Between Urban Wastewater Management Needs and Receiving Water Objectives.Seliga, T. A., & Bringi, V. N. (1976). Potential Use of Radar Differential Reflectivity Measurements at Orthogonal Polarizations for Measuring Precipitation. Journal of Applied Meteorology and Climatology, 15(1), 69–76. https://doi.org/https://doi.org/10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2Sempere-Torres, D., Delrieu, G., Zawadzki, I., & Sánchez-Diezma, R. (2001). An improved methodology for ground clutter substitution based on a pre-classification of precipitation types. In American Meteorological Socety (Ed.), 30th International Conference on Radar Meteorology (pp. 271–273). https://www.researchgate.net/publication/242368835Sepúlveda, J. (2015). Estimación cuantitativa de precipitación a partir de la información de Radar Meteorológico deí Area Metropolitana del Valle de Aburrá. Universidad Nacional de Colombia.Shahriari, F. (2018). The Relationship between Iranian EFL Instructors’ Multiple Intelligence Ability and Reflective Teaching. Revista Romaneasca Pentru Educatie Multidimensionala, 10(1.SP), 97. https://doi.org/10.18662/rrem/39Silver, M., Svoray, T., Karnieli, A., & Fredj, E. (2020). Improving weather radar precipitation maps: A fuzzy logic approach. Atmospheric Research, 234. https://doi.org/10.1016/j.atmosres.2019.104710Skolnik, M. (1980). Introduction to radar systems. McGraw-Hill.Smith, P. L. (1998). On the Minimum Useful Elevation Angle for Weather Surveillance Radar Scans. Journal of Atmospheric and Oceanic Technology, 15(3), 841–843. https://doi.org/https://doi.org/10.1175/1520-0426(1998)015<0841:OTMUEA>2.0.CO;2Szturc, J., Ośródka, K., & Jurczyk, A. (2011). Quality index scheme for quantitative uncertainty characterization of radar-based precipitation. Meteorological Applications, 18(4), 407–420. https://doi.org/10.1002/met.230Vargas, E. G. (2015). Modelo para la estimación cuantitativa de precipitación a partir de datos de radares polarimétricos. Pontificia Universidad Javeriana.Ven Te Chow, David R. Maidment, & Larry W. Mays. (1988). Applied Hydrology (1st ed., Vol. 1). McGraw-Hill.Vulpiani, G., Baldini, L., & Roberto, N. (2015). Characterization of Mediterranean hail-bearing storms using an operational polarimetric X-band radar. Atmospheric Measurement Techniques, 8(11), 4681–4698. https://doi.org/10.5194/amt-8-4681-2015Vulpiani, G., Tabary, P., Du Chatelet, J. P., & Marzano, F. S. (2008). Comparison of advanced radar polarimetric techniques for operational attenuation correction at C band. Journal of Atmospheric and Oceanic Technology, 25(7), 1118–1135. https://doi.org/10.1175/2007JTECHA936.1Wang, X., Zheng, X., He, J., & Li, X. (2012). Using fuzzy logic to discriminate convective and statiform precipitation in Doppler weather radar. 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 623–627. https://doi.org/10.1109/FSKD.2012.6233819Wood, E. F. (1998). Scale Analysis for Land Surface Hydrology. En Scale Dependence and Scale Invariance in Hydrology (G. Sposito, Ed.). Cambridge: University Cambridge Press.Yang, Y., Chen, X., & Qi, Y. (2013). Classification of convective/stratiform echoes in radar reflectivity observations using a fuzzy logic algorithm. Journal of Geophysical Research Atmospheres, 118(4), 1896–1905. https://doi.org/10.1002/jgrd.50214Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-XZhang, G. (2019). Weather Radar Polarimetry (CRC Press, Ed.; 1st ed.).Zhang, J. (2011). Radar Quality Index (RQI)-A combined measure of beam blockage and VPR effects in a national network Probabilistic Precipitation Estimates View project. https://www.researchgate.net/publication/268061344Zhang, Y., Liu, L., Wen, H., Yu, B., Wang, H., & Zhang, Y. (2022). Combined Radar Quality Index for Quantitative Precipitation Estimation of Heavy Rainfall Events. Remote Sensing, 14(13). https://doi.org/10.3390/rs14133154RadarBloqueosEstimación Cuantitativa de PrecipitaciónÍndice de Calidad del RadarPluviógrafoClutteringQuantitative Precipitation EstimationRadar Quality IndexRain GaugeControl meteorológicoRadarWeather modificationRadarORIGINAL1018493841.2023.pdf1018493841.2023.pdfTesis de Maestría - Ingeniería de Recursos Hidráulicosapplication/pdf9352865https://repositorio.unal.edu.co/bitstream/unal/85752/2/1018493841.2023.pdf03f5fa02a255c535c71fb21077ddb8dfMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85752/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51THUMBNAIL1018493841.2023.pdf.jpg1018493841.2023.pdf.jpgGenerated Thumbnailimage/jpeg4865https://repositorio.unal.edu.co/bitstream/unal/85752/3/1018493841.2023.pdf.jpg58aa7cf7c1cb36c5316796f7674a8773MD53unal/85752oai:repositorio.unal.edu.co:unal/857522024-03-01 23:04:32.898Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |