Referencias
Aksoy, A., Dowell, D.C., and Snyder, C., 2010. A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part II: Short-Range Ensemble Forecasts. Monthly Weather Review, 138, 4, 1273–1292.
Allaire, J., Horner, J., Xie, Y., Marti, V., and Porte, N., 2019. Markdown: Render markdown with the c library ’sundown’.
Andersson, E., Hollingsworth, A., Kelly, G., Lönnberg, P., Pailleux, J., and Zhang, Z., 1991. Global Observing System Experiments on Operational Statistical Retrievals of Satellite Sounding Data. Monthly Weather Review, 119, 8, 1851–1865.
Arruti, A., Maldonado, P., Rugna, M., Sacco, M., Ruiz, J.J., and Vidal, L., 2021. Sistema de Control de Calidad de Datos de Radar en el Servicio Meteorológico Nacional. Parte I: Descripción del algoritmo.
Bae, J.-H., and Min, K.-H., 2022. Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems. Remote Sensing, 14, 3, 3, 605.
Banos, I.H., Sapucci, L.F., Cucurull, L., Bastarz, C.F., and Silveira, B.B., 2019. Assimilation of GPSRO Bending Angle Profiles into the Brazilian Global Atmospheric Model. Remote Sensing, 11, 3, 3, 256.
Bao, Y., Xu, J., Powell Jr., A.M., Shao, M., Min, J., and Pan, Y., 2015. Impacts of AMSU-A, MHS and IASI data assimilation on temperature and humidity forecasts with GSI–WRF over the western United States. Atmospheric Measurement Techniques, 8, 10, 4231–4242.
Barton, N., Metzger, E.J., Reynolds, C.A., Ruston, B., Rowley, C., Smedstad, O.M., Ridout, J.A., Wallcraft, A., Frolov, S., and Hogan, P. and others, 2021. The Navy’s Earth System Prediction Capability: A New Global Coupled Atmosphere-Ocean-Sea Ice Prediction System Designed for Daily to Subseasonal Forecasting. Earth and Space Science, 8, 4, e2020EA001199.
Bauce Machado, V., gustavo de goncalves, luis, Vendrasco, E., Sinhori, N., Herdies, D., Sapucci, L., Levien, C., Quadro, M., Rodrigues, T., and Cardoso, C. and others, 2017. Investigating the impacts of convective scale hazardous weather events in Santa Catarina State through the CPTEC/INPE local data assimilation system. In. Presented at the Seventh International WMO Symposium on Data Assimilation.
Bauer, P., Geer, A.J., Lopez, P., and Salmond, D., 2010. Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Quarterly Journal of the Royal Meteorological Society, 136, 652, 1868–1885.
Boukabara, S.-A., Garrett, K., Chen, W., Iturbide-Sanchez, F., Grassotti, C., Kongoli, C., Chen, R., Liu, Q., Yan, B., and Weng, F. and others, 2011. MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System. IEEE Transactions on Geoscience and Remote Sensing, 49, 9, 3249–3272. Presented at the IEEE Transactions on Geoscience and Remote Sensing.
Bousquet, O., Montmerle, T., and Tabary, P., 2008. Using operationally synthesized multiple-Doppler winds for high resolution horizontal wind forecast verification: OPERATIONAL DOPPLER RADAR NETWORKS. Geophysical Research Letters, 35, 10.
Brier, G.W., 1950. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY. Monthly Weather Review, 78, 1, 1–3.
Campitelli, E., 2020, April. metR: Tools for Easier Analysis of Meteorological Fields.
Candille, G., Côté, C., Houtekamer, P.L., and Pellerin, G., 2007. Verification of an Ensemble Prediction System against Observations. Monthly Weather Review, 135, 7, 2688–2699.
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G., 2018. Data assimilation in the geosciences: An overview of methods, issues, and perspectives. WIREs Climate Change, 9, 5, e535.
Casaretto, G., Dillon, M.E., Salio, P., Skabar, Y.G., Nesbitt, S.W., Schumacher, R.S., García, C.M., and Catalini, C., 2022. High-Resolution NWP Forecast Precipitation Comparison over Complex Terrain of the Sierras de Córdoba during RELAMPAGO-CACTI. Weather and Forecasting, 37, 2, 241–266.
Chang, W., Jacques, D., Fillion, L., and Baek, S.-J., 2017. Assimilation of Hourly Surface Observations with the Canadian High-Resolution Ensemble Kalman Filter. Atmosphere-Ocean, 55, 4-5, 247–263.
Chen, F., and Dudhia, J., 2001. Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Monthly Weather Review, 129, 4, 569–585.
Chen, Q., Fan, J., Hagos, S., Gustafson, W.I., and Berg, L.K., 2015. Roles of wind shear at different vertical levels: Cloud system organization and properties. Journal of Geophysical Research: Atmospheres, 120, 13, 6551–6574.
Chen, X., Zhao, K., Sun, J., Zhou, B., and Lee, W.-C., 2016. Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case. Advances in Atmospheric Sciences, 33, 10, 1106–1119.
Cherubini, T., Businger, S., Velden, C., and Ogasawara, R., 2006. The Impact of Satellite-Derived Atmospheric Motion Vectors on Mesoscale Forecasts over Hawaii. Monthly Weather Review, 134, 7, 2009–2020.
Clark, A.J., 2017. Generation of Ensemble Mean Precipitation Forecasts from Convection-Allowing Ensembles. Weather and Forecasting, 32, 4, 1569–1583.
Clark, A.J., Gallus, W.A., Xue, M., and Kong, F., 2009. A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles. Weather and Forecasting, 24, 4, 1121–1140.
Collard, A.D., 2007. Selection of IASI channels for use in numerical weather prediction: SELECTION OF IASI CHANNELS FOR NWP. Quarterly Journal of the Royal Meteorological Society, 133, 629, 1977–1991.
Computational and Information Systems Laboratory, 2019. Cheyenne: HPE/SGI ICE XA System (University Community Computing). National Center for Atmospheric Research Boulder, CO.
Crews, A., Blackwell, W.J., Leslie, R.V., Grant, M., Osaretin, I.A., DiLiberto, M., Milstein, A., Leroy, S., Gagnon, A., and Cahoy, K., 2021. Initial Radiance Validation of the Microsized Microwave Atmospheric Satellite-2A. IEEE Transactions on Geoscience and Remote Sensing, 59, 4, 2703–2714. Presented at the IEEE Transactions on Geoscience and Remote Sensing.
Cutraro, F., Galligani, V.S., and Skabar, Y.G., 2021. Evaluation of synthetic satellite images computed from radiative transfer models over a region of South America using WRF and GOES-13/16 observations. Quarterly Journal of the Royal Meteorological Society, 147, 738, 2988–3003.
de Elía, R., Vidal, L., and Lohigorry, P., 2017. El SMN y la red argentina de radares meteorológicos (http://hdl.handle.net/20.500.12160/625).
de Moraes, R.J., Hajibeygi, H., and Jansen, J.D., 2020. A multiscale method for data assimilation. Computational Geosciences, 24, 2, 425–442.
Desroziers, G., Berre, L., Chapnik, B., and Poli, P., 2005. Diagnosis of observation, background and analysis-error statistics in observation space. Quarterly Journal of the Royal Meteorological Society, 131, 613, 3385–3396.
Dillon, L.M.E., 2017. Asimilación de datos reales a escala regional en Argentina.
Dillon, M.E., Maldonado, P., Corrales, P., Skabar, Y.G., Ruiz, J., Sacco, M., Cutraro, F., Mingari, L., Matsudo, C., and Vidal, L. and others, 2021. A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign. Atmospheric Research, 105858.
Dowle, M., and Srinivasan, A., 2020, July. Data.Table: Extension of ’data.frame’.
Earth Observing Laboratory, U. -, 2020. Multi-network composite highest resolution radiosonde data. Version 1.3. UCAR/NCAR - earth observing laboratory.
English, S.J., Renshaw, R.J., Dibben, P.C., Smith, A.J., Rayer, P.J., Poulsen, C., Saunders, F.W., and Eyre, J.R., 2000. A comparison of the impact of TOVS arid ATOVS satellite sounding data on the accuracy of numerical weather forecasts. Quarterly Journal of the Royal Meteorological Society, 126, 569, 2911–2931.
Evensen, G., 2009. Data Assimilation, Springer,
Eyre, J.R., English, S.J., and Forsythe, M., 2020. Assimilation of satellite data in numerical weather prediction. Part I: The early years. Quarterly Journal of the Royal Meteorological Society, 146, 726, 49–68.
Eyre, J.R., Bell, W., Cotton, J., English, S.J., Forsythe, M., Healy, S.B., and Pavelin, E.G., 2022. Assimilation of satellite data in numerical weather prediction. Part II: Recent years. Quarterly Journal of the Royal Meteorological Society, 148, 743, 521–556.
Ferreira, R.C., Herdies, D.L., Vendrasco, É.P., Beneti, C.A.A., and Biscaro, T.S., 2017. Impacto da Assimilação de Dados de Radar em Sistemas Convectivos de Mesoescala: Um Estudo de Caso. Revista Brasileira de Meteorologia, 32, 3, 447–458.
Ferreira, R.C., Alves Júnior, M.P., Vendrasco, éder P., Aravéquia, J.A., Nolasco Junior, L.R., Biscaro, T.S., Ferreira, R.C., Alves Júnior, M.P., Vendrasco, éder P., and Aravéquia, J.A. and others, 2020. The Impact of Microphysics Parameterization on Precipitation Forecast Using Radar Data Assimilation. Revista Brasileira de Meteorologia, 35, 1, 123–134.
Gao, F., Huang, X.-Y., Jacobs, N.A., and Wang, H., 2015. Assimilation of wind speed and direction observations: Results from real observation experiments. Tellus A: Dynamic Meteorology and Oceanography, 67, 1, 27132.
Garcia, F., Ruiz, J., Salio, P., Bechis, H., and Nesbitt, S., 2019. Argentina mesonet data. Version 1.1. UCAR/NCAR - earth observing laboratory.
García Skabar, Y., 1997. Análisis objetivo regional para inicializar un modelo de diez niveles en forma operativa. Tesis de licenciatura en ciencias de la atmósfera.
Gasperoni, N.A., Wang, X., Brewster, K.A., and Carr, F.H., 2018. Assessing Impacts of the High-Frequency Assimilation of Surface Observations for the Forecast of Convection Initiation on 3 April 2014 within the Dallas–Fort Worth Test Bed. Monthly Weather Review, 146, 11, 3845–3872.
Goncalves de Goncalves, L.G., Sapucci, L., Vendrasco, E., de Mattos, J.G., Ferreira, C., Khamis, E., and Cruz, N., 2015. A rapid update data assimilation cycle over South America using 3DVar and EnKF. In The 20th International TOVS Study Conference (ITSC-20).
Grell, G.A., and Freitas, S.R., 2013. A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmospheric Chemistry and Physics Discussions, 13, 9, 23845–23893.
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., and Bučánek, A. and others, 2018. Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres. Quarterly Journal of the Royal Meteorological Society, 144, 713, 1218–1256.
Ha, S.-Y., and Snyder, C., 2014. Influence of Surface Observations in Mesoscale Data Assimilation Using an Ensemble Kalman Filter. Monthly Weather Review, 142, 4, 1489–1508.
Heidinger, A., and Straka III, W.C., 2013, June 11. ABI Cloud Mask.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Rozum, I. and others, 2018. ERA5 hourly data on pressure levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), (Accessed on \(<\)08-08-2022\(>\)).
Hobouchian, M.P., García Skabar, Y., Salio, P., Viale, M., and Matsudo, C.M., 2018. Evaluación de estimaciones de precipitación por satélite en el sur de Sudamérica.
Hohenegger, C., and Schar, C., 2007. Atmospheric Predictability at Synoptic Versus Cloud-Resolving Scales. Bulletin of the American Meteorological Society, 88, 11, 1783–1794.
Honda, T., Miyoshi, T., Lien, G.-Y., Nishizawa, S., Yoshida, R., Adachi, S.A., Terasaki, K., Okamoto, K., Tomita, H., and Bessho, K., 2018. Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015). Monthly Weather Review, 146, 1, 213–229.
Hong, S.-Y., Noh, Y., and Dudhia, J., 2006. A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Monthly Weather Review, 134, 9, 2318–2341.
Hong, S.-Y., Kim, J.-H., Lim, J.-o., and Dudhia, J., 2006. The WRF Single Moment 6-Class Microphysics Scheme (WSM6). Journal of the Korean Meteorological Society, 42, 129–151.
Hotta, D., Chen, T.-C., Kalnay, E., Ota, Y., and Miyoshi, T., 2017. Proactive QC: A Fully Flow-Dependent Quality Control Scheme Based on EFSO. Monthly Weather Review, 145, 8, 3331–3354.
Hu, H., and Han, Y., 2021. Comparing the Thermal Structures of Tropical Cyclones Derived From Suomi NPP ATMS and FY-3D Microwave Sounders. IEEE Transactions on Geoscience and Remote Sensing, 59, 10, 8073–8083. Presented at the IEEE Transactions on Geoscience and Remote Sensing.
Hu, H., Weng, F., Han, Y., and Duan, Y., 2019. Remote Sensing of Tropical Cyclone Thermal Structure from Satellite Microwave Sounding Instruments: Impacts of Background Profiles on Retrievals. Journal of Meteorological Research, 33, 1, 89–103.
Hu, M., Ge, G., Zhou, C., Stark, D., Shao, H., Newman, K., Beck, J., and Zhang, X., 2018. Grid-point Statistical Interpolation (GSI) User’s Guide Version 3.7, Developmental Testbed Center, p. 149.
Huffman, G., Bolvin, D., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E., Sorooshian, S., Tan, J., and Xie, P., 2018. NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), National Aeronautics and Space Administration (NASA), p. 35.
Hunt, B.R., Kostelich, E.J., and Szunyogh, I., 2007. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena, 230, 1-2, 112–126.
Iacono, M.J., Delamere, J.S., Mlawer, E.J., Shephard, M.W., Clough, S.A., and Collins, W.D., 2008. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13, D13103.
Iacovazzi, R., Lin, L., Sun, N., and Liu, Q., 2020. NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings. Remote Sensing, 12, 5, 5, 828.
Ismay, C., and Solomon, N., 2022. Thesisdown: An updated r markdown thesis template using the bookdown package.
Janjić, T., Bormann, N., Bocquet, M., Carton, J.A., Cohn, S.E., Dance, S.L., Losa, S.N., Nichols, N.K., Potthast, R., and Waller, J.A. and others, 2018. On the representation error in data assimilation. Quarterly Journal of the Royal Meteorological Society, 144, 713, 1257–1278.
Janjić, Z.I., 1994. The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes. Monthly Weather Review, 122, 5, 927–945.
Jones, T.A., Otkin, J.A., Stensrud, D.J., and Knopfmeier, K., 2013. Assimilation of Satellite Infrared Radiances and Doppler Radar Observations during a Cool Season Observing System Simulation Experiment. Monthly Weather Review, 141, 10, 3273–3299.
———, 2014. Forecast Evaluation of an Observing System Simulation Experiment Assimilating Both Radar and Satellite Data. Monthly Weather Review, 142, 1, 107–124.
Jones, T.A., Skinner, P., Yussouf, N., Knopfmeier, K., Reinhart, A., Wang, X., Bedka, K., Smith, W., and Palikonda, R., 2020. Assimilation of GOES-16 Radiances and Retrievals into the Warn-on-Forecast System. Monthly Weather Review, 148, 5, 1829–1859.
Kain, J.S., 2004. The Kain–Fritsch Convective Parameterization: An Update. JOURNAL OF APPLIED METEOROLOGY, 43, 12.
Kalnay, E., 2002, November 6. Atmospheric Modeling, Data Assimilation and Predictability (https://www.cambridge.org/highereducation/books/atmospheric-modeling-data-assimilation-and-predictability/C5FD207439132836E85027754CE9BC1A).
Kelly, G.a.M., Mills, G.A., and Smith, W.L., 1978. Impact of Nimbus-6 Temperature Soundings on Australian Region Forecasts. Bulletin of the American Meteorological Society, 59, 4, 393–406.
Kleist, D.T., Parrish, D.F., Derber, J.C., Treadon, R., Wu, W.-S., and Lord, S., 2009. Introduction of the GSI into the NCEP Global Data Assimilation System. Weather and Forecasting, 24, 6, 1691–1705.
Lazarus, S.M., Splitt, M.E., Lueken, M.D., Ramachandran, R., Li, X., Movva, S., Graves, S.J., and Zavodsky, B.T., 2010. Evaluation of Data Reduction Algorithms for Real-Time Analysis. Weather and Forecasting, 25, 3, 837–851.
Lee, J.-R., Li, J., Li, Z., Wang, P., and Li, J., 2019. ABI Water Vapor Radiance Assimilation in a Regional NWP Model by Accounting for the Surface Impact. Earth and Space Science, 6, 9, 1652–1666.
Lim, A.H., Jung, J.A., Huang, H.-L.A., Ackerman, S.A., and Otkin, J.A., 2014. Assimilation of clear sky Atmospheric Infrared Sounder radiances in short-term regional forecasts using community models. Journal of Applied Remote Sensing, 8, 1, 083655.
Lin, H., Weygandt, S.S., Benjamin, S.G., and Hu, M., 2017. Satellite Radiance Data Assimilation within the Hourly Updated Rapid Refresh. Weather and Forecasting, 32, 4, 1273–1287.
Liu, H., Collard, A., Derber, J., Nebuda, S., and Jung, J.A., 2019. Evaluation of GOES-16 Clear-sky Radiance Data and Preliminary Assimilation Results at NCEP, 2 pags.
Liu, Q., Weng, F., Han, Y., and van Delst, P., 2008. Community Radiative Transfer Model for Scattering Transfer and Applications. In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium Vol. 4, pp. IV–1193–IV–1196. Presented at the IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.
Lorenz, E.N., 1965. A study of the predictability of a 28-variable atmospheric model. Tellus, 17, 3, 321–333.
Maejima, Y., Miyoshi, T., Kunii, M., Seko, H., and Sato, K., 2019. Impact of Dense and Frequent Surface Observations on 1-Minute-Update Severe Rainstorm Prediction: A Simulation Study. Journal of the Meteorological Society of Japan. Ser. II, 97, 1, 253–273.
Maldonado, P., Ruiz, J., and Saulo, C., 2020. Parameter Sensitivity of the WRF–LETKF System for Assimilation of Radar Observations: Imperfect-Model Observing System Simulation Experiments. Weather and Forecasting, 35, 4, 1345–1362.
———, 2021. Sensitivity to Initial and Boundary Perturbations in Convective-Scale Ensemble-Based Data Assimilation: Imperfect-Model OSSEs. SOLA, 17, 0, 96–102.
Markowski, P., and Richardson, Y., 2010. Organization of Isolated Convection. In Mesoscale Meteorology in Midlatitudes pp. 201–244.
Matsudo, C., Salles, M.A., and García Skabar, Y., 2021. Verificación de los pronósticos del esquema determinístico del modelo WRF para el año 2020.
Nakanishi, M., and Niino, H., 2009. Development of an Improved Turbulence Closure Model for the Atmospheric Boundary Layer. Journal of the Meteorological Society of Japan, 87, 5, 895–912.
National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce, 2015. NCEP GFS 0.25 degree global forecast grids historical archive.
Necker, T., Geiss, S., Weissmann, M., Ruiz, J., Miyoshi, T., and Lien, G., 2020. A convective‐scale 1,000‐member ensemble simulation and potential applications. Quarterly Journal of the Royal Meteorological Society, 146, 728, 1423–1442.
Nesbitt, S.W., Salio, P.V., Ávila, E., Bitzer, P., Carey, L., Chandrasekar, V., Deierling, W., Dominguez, F., Dillon, M.E., and Garcia, C.M. and others, 2021. A storm safari in Subtropical South America: Proyecto RELAMPAGO. Bulletin of the American Meteorological Society, -1, aop, 1–64.
Ohring, G., 1979. Impact of Satellite Temperature Sounding Data on Weather Forecasts. Bulletin of the American Meteorological Society, 60, 10, 1142–1147.
Orlanski, I., 1975. A Rational Subdivision of Scales for Atmospheric Processes. Bulletin of the American Meteorological Society, 56, 5, 527–530.
Ouaraini, R.E., Berre, L., Fischer, C., and Sayouty, E.H., 2015. Sensitivity of regional ensemble data assimilation spread to perturbations of lateral boundary conditions. Tellus A: Dynamic Meteorology and Oceanography, 67, 1, 28502.
Patil, D.J., Hunt, B.R., Kalnay, E., Yorke, J.A., and Ott, E., 2001. Local Low Dimensionality of Atmospheric Dynamics. Physical Review Letters, 86, 26, 5878–5881.
Pondeca, M.S.F.V.D., Manikin, G.S., DiMego, G., Benjamin, S.G., Parrish, D.F., Purser, R.J., Wu, W.-S., Horel, J.D., Myrick, D.T., and Lin, Y. and others, 2011. The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development. Weather and Forecasting, 26, 5, 593–612.
Purser, R.J., Wu, W.-S., Parrish, D.F., and Roberts, N.M., 2003a. Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances. Monthly Weather Review, 131, 8, 1536–1548.
———, 2003b. Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part I: Spatially Homogeneous and Isotropic Gaussian Covariances. Monthly Weather Review, 131, 8, 1524–1535.
Rabier, F., Fourrié, N., Chafäi, D., and Prunet, P., 2002. Channel selection methods for Infrared Atmospheric Sounding Interferometer radiances. Quarterly Journal of the Royal Meteorological Society, 128, 581, 1011–1027.
R Core Team, 2020. R: A language and environment for statistical computing, R Foundation for Statistical Computing,
Robel, J., and Graumann, A., 2014, April. NOAA KLM Users Guide.
Roberts, B., Gallo, B.T., Jirak, I.L., Clark, A.J., Dowell, D.C., Wang, X., and Wang, Y., 2020. What Does a Convection-Allowing Ensemble of Opportunity Buy Us in Forecasting Thunderstorms? Weather and Forecasting, 35, 6, 2293–2316.
Roberts, N., 2008. Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model. Meteorological Applications, 15, 1, 163–169.
Ruiz, J.J., Saulo, C., and Nogués-Paegle, J., 2010. WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface Variables. Monthly Weather Review, 138, 8, 3342–3355.
Saucedo, M.A., 2015. Estudio de los efectos de diferentes fuentes de error sobre la calidad de los análisis generados por un sistema de asimilación por filtros de Kalman.
Sawada, M., Ma, Z., Mehra, A., Tallapragada, V., Oyama, R., and Shimoji, K., 2019. Impacts of Assimilating High-Resolution Atmospheric Motion Vectors Derived from Himawari-8 on Tropical Cyclone Forecast in HWRF. Monthly Weather Review, 147, 10, 3721–3740.
Shao, H., Derber, J., Huang, X.-Y., Hu, M., Newman, K., Stark, D., Lueken, M., Zhou, C., Nance, L., and Kuo, Y.-H. and others, 2016. Bridging Research to Operations Transitions: Status and Plans of Community GSI. Bulletin of the American Meteorological Society, 97, 8, 1427–1440.
Singh, R., Ojha, S.P., Kishtawal, C.M., Pal, P.K., and Kiran Kumar, A.S., 2016. Impact of the assimilation of INSAT-3D radiances on short-range weather forecasts: Assimilation of INSAT-3D Radiances. Quarterly Journal of the Royal Meteorological Society, 142, 694, 120–131.
Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W., and Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3 p. 125.
Sobash, R.A., and Stensrud, D.J., 2015. Assimilating Surface Mesonet Observations with the EnKF to Improve Ensemble Forecasts of Convection Initiation on 29 May 2012. Monthly Weather Review, 143, 9, 3700–3725.
Stensrud, D.J., Wicker, L.J., Xue, M., Dawson, D.T., Yussouf, N., Wheatley, D.M., Thompson, T.E., Snook, N.A., Smith, T.M., and Schenkman, A.D. and others, 2013. Progress and challenges with Warn-on-Forecast. Atmospheric Research, 123, 2–16.
Sun, J., Xue, M., Wilson, J.W., Zawadzki, I., Ballard, S.P., Onvlee-Hooimeyer, J., Joe, P., Barker, D.M., Li, P.-W., and Golding, B. and others, 2014. Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges. Bulletin of the American Meteorological Society, 95, 3, 409–426.
Tong, M., Zhu, Y., Zhou, L., Liu, E., Chen, M., Liu, Q., and Lin, S.-J., 2020. Multiple Hydrometeors All-Sky Microwave Radiance Assimilation in FV3GFS. Monthly Weather Review, 148, 7, 2971–2995.
Toshio Inouye, R., Calvetti, L., Gonçalves, J., Maske, B., Neundorf, R., Beneti, C., Diniz, F., Vendrasco, E., Herdies, D., and gustavo de goncalves, luis, 2017. Impact of radar data assimilation on a severe storm study in brazil. In. Presented at the 97th American Meteorological Meeting Annual Meeting.
Vera, C.S., 1992. Un sistema de asimilación de datos para la región extratropical de Sudamérica.
Wang, Z.Q., and Randriamampianina, R., 2021. The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA). Remote Sensing, 13, 3, 3, 426.
Weng, F., Zou, X., Sun, N., Yang, H., Tian, M., Blackwell, W.J., Wang, X., Lin, L., and Anderson, K., 2013. Calibration of Suomi national polar-orbiting partnership advanced technology microwave sounder. Journal of Geophysical Research: Atmospheres, 118, 19, 11, 187–11, 200.
Weston, P., Geer, A., Bormann, N., and Bormann, N., 2019. Investigations into the assimilation of AMSU-A in the presence of cloud and precipitation.
Wheatley, D.M., and Stensrud, D.J., 2010. The Impact of Assimilating Surface Pressure Observations on Severe Weather Events in a WRF Mesoscale Ensemble System. Monthly Weather Review, 138, 5, 1673–1694.
Whitaker, J.S., and Hamill, T.M., 2002. Ensemble Data Assimilation without Perturbed Observations. Monthly Weather Review, 130, 7, 1913–1924.
———, 2012. Evaluating Methods to Account for System Errors in Ensemble Data Assimilation. Monthly Weather Review, 140, 9, 3078–3089.
Whitaker, J.S., Hamill, T.M., Wei, X., Song, Y., and Toth, Z., 2008. Ensemble Data Assimilation with the NCEP Global Forecast System. Monthly Weather Review, 136, 2, 463–482.
Wickham, H., 2009. Ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag,
Wilks, D.S., 2011. Statistical Methods in the Atmospheric Sciences, 3rd ed., 676 pags Vol. 100.
Wu, T.-C., Liu, H., Majumdar, S.J., Velden, C.S., and Anderson, J.L., 2014. Influence of Assimilating Satellite-Derived Atmospheric Motion Vector Observations on Numerical Analyses and Forecasts of Tropical Cyclone Track and Intensity. Monthly Weather Review, 142, 1, 49–71.
Wu, W.-S., Purser, R.J., and Parrish, D.F., 2002. Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances. Monthly Weather Review, 130, 12, 2905–2916.
Xie, Y., 2015. Dynamic documents with R and knitr, Second., Chapman and Hall/CRC,
Zhu, K., Xue, M., Pan, Y., Hu, M., Benjamin, S.G., Weygandt, S.S., and Lin, H., 2019. The Impact of Satellite Radiance Data Assimilation within a Frequently Updated Regional Forecast System Using a GSI-based Ensemble Kalman Filter. Advances in Atmospheric Sciences, 36, 12, 1308–1326.
Zhu, Y., and Gelaro, R., 2008. Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System. Monthly Weather Review, 136, 1, 335–351.
Zhu, Y., Derber, J., Collard, A., Dee, D., Treadon, R., Gayno, G., and Jung, J.A., 2014. Enhanced radiance bias correction in the National Centers for Environmental Prediction’s Gridpoint Statistical Interpolation data assimilation system. Quarterly Journal of the Royal Meteorological Society, 140, 682, 1479–1492.
Zhu, Y., Liu, E., Mahajan, R., Thomas, C., Groff, D., Van Delst, P., Collard, A., Kleist, D., Treadon, R., and Derber, J.C., 2016. All-Sky Microwave Radiance Assimilation in NCEP’s GSI Analysis System. Monthly Weather Review, 144, 12, 4709–4735.