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P. Houtekamer, L. Lefaivre, J. Derome, H. Ritchie, H. Mitchell (1996)
A System Simulation Approach to Ensemble PredictionMonthly Weather Review, 124
Kazuo Saito, H. Seko, Masaru Kunii, T. Miyoshi (2012)
Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble predictionTellus A: Dynamic Meteorology and Oceanography, 64
J. Michalakes, J. Dudhia, Dave Gill, Tom Henderson, J. Klemp, W. Skamarock, Wei Wang (2005)
The Weather Research and Forecast Model: software architecture and performance [presentation]
W. McCarty, G. Jedlovec, T. Miller (2009)
Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecastsJournal of Geophysical Research, 114
Celeste Saulo, Soledad Cardazzo, J. Ruiz, C. Campetella, Alfredo Rolla (2008)
El sistema de pronóstico experimental del Centro de Investigaciones del Mar y la Atmósfera, 33
T. Warner, R. Peterson, R. Treadon (1997)
A Tutorial on Lateral Boundary Conditions as a Basic and Potentially Serious Limitation to Regional Numerical Weather PredictionBulletin of the American Meteorological Society, 78
M. Dillon, Y. Skabar, M. Nicolini (2013)
Desempeño del pronóstico de modelos de alta resolución, en un área limitada: análisis de la estación de verano 2010-2011, 38
B. Hunt, E. Kostelich, I. Szunyogh (2005)
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filterPhysica D: Nonlinear Phenomena, 230
(2012)
The impact of satellite atmospheric motion vectors in the U.S. Navy Global Data Assimilation System—The superob procedure
Downloaded
Fuqing Zhang, Z. Meng, A. Aksoy (2006)
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model ExperimentsMonthly Weather Review, 134
E. Kalnay, Shu‐Chih Yang (2008)
Accelerating the spin‐up of Ensemble Kalman FilteringQuarterly Journal of the Royal Meteorological Society, 136
D. Stensrud, J. Bao, T. Warner (2000)
Using Initial Condition and Model Physics Perturbations in Short-Range Ensemble Simulations of Mesoscale Convective SystemsMonthly Weather Review, 128
(2015)
Observing system experiment in the CPTEC/INPE 3D-Var data assimilation system
S. Lakshmivarahan, D. Stensrud (2009)
Ensemble Kalman filterIEEE Control Systems, 29
Song‐You Hong, J. Lim (2006)
The WRF Single-Moment 6-Class Microphysics Scheme (WSM6)Asia-pacific Journal of Atmospheric Sciences, 42
T. Miyoshi, Masaru Kunii (2012)
The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real ObservationsPure and Applied Geophysics, 169
Jeffrey Anderson, Stephen Anderson (1999)
A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and ForecastsMonthly Weather Review, 127
E. Amitai, W. Petersen, X. Llort, S. Vasiloff (2012)
Multiplatform Comparisons of Rain Intensity for Extreme Precipitation EventsIEEE Transactions on Geoscience and Remote Sensing, 50
Yong Wang, S. Tascu, F. Weidle, Karin Schmeisser (2012)
Evaluation of the Added Value of Regional Ensemble Forecasts on Global Ensemble ForecastsWeather and Forecasting, 27
M. Hamrud, M. Bonavita, L. Isaksen (2015)
EnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF ImplementationMonthly Weather Review, 143
(2015)
Shlyaeva, 2015: Scale-dependent covariance localization for EnVar data assimilation
J. Verspeek, A. Stoffelen, M. Portabella, H. Bonekamp, C. Anderson, J. Figa-Saldana (2010)
Validation and Calibration of ASCAT Using CMOD5.nIEEE Transactions on Geoscience and Remote Sensing, 48
S. Penny (2014)
The Hybrid Local Ensemble Transform Kalman FilterMonthly Weather Review, 142
P. Salio, M. Nicolini, E. Zipser (2007)
Mesoscale Convective Systems over Southeastern South America and Their Relationship with the South American Low-Level JetMonthly Weather Review, 135
T. Miyoshi, Masaru Kunii (2012)
Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather predictionTellus A: Dynamic Meteorology and Oceanography, 64
(2014)
NCEP regional ensembles: Evolving toward hourly-updated convection-allowing scale and storm-scale predictions within a unified regional modeling system
(2013)
PREPBUFR processing at NCEP
M. Hamrud, M. Bonavita, L. Isaksen (2014)
EnKF and Hybrid Gain Ensemble Data Assimilation
(2013)
The CHUVA field campaign: Overview
Z. Janjic (1994)
The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure SchemesMonthly Weather Review, 122
T. Miyoshi (2011)
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman FilterMonthly Weather Review, 139
S. Cohn, A. Silva, Jing Guo, M. Sienkiewicz, D. Lamich (1998)
Assessing the Effects of Data Selection with the DAO Physical-Space Statistical Analysis System*Monthly Weather Review, 126
L. Gandin (1963)
Objective Analysis of Meteorological Fields
(2002)
O sistema de assimila ç ao de dados atmosf é ricos global do CPTEC/INPE
Unauthenticated | Downloaded 09/17/23
http://data-assimilation.jp/isda2015/program
(2007)
Assimila ç ao de dados no CPTEC/INPE (Data assimilation in CPTEC/INPE)
Etienne Lehmann (2006)
A Search Model of Unemployment and InflationWiley-Blackwell: Scandinavian Journal of Economics
Fuqing Zhang, C. Snyder, Juanzhen Sun (2004)
Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman FilterMonthly Weather Review, 132
J. daÂngela, T. Shanks, S. Croom, P. Weilbacher, Robert Brunner, W. Couch, L. Miller, Adam Myers, R. Nichol, K. Pimbblet, R. Propris, G. Richards, N. Ross, D. Schneider, D. Wake (2006)
The 2dF-SDSS LRG and QSO survey: QSO clustering and the L-z degeneracyMonthly Notices of the Royal Astronomical Society, 383
Yoichiro Ota, J. Derber, E. Kalnay, T. Miyoshi (2013)
Ensemble-based observation impact estimates using the NCEP GFSTellus A: Dynamic Meteorology and Oceanography, 65
T. Miyoshi, Yoshiaki Sato, T. Kadowaki (2010)
Ensemble Kalman Filter and 4D-Var Intercomparison with the Japanese Operational Global Analysis and Prediction SystemMonthly Weather Review, 138
(2013)
Satellite Division and Environmental Systems/CPTEC
(2007)
Assimilaçao de dados
G. Grell, D. Dévényi (2002)
A generalized approach to parameterizing convection combining ensemble and data assimilation techniquesGeophysical Research Letters, 29
Eugenia Kalnay (2002)
Atmospheric Modeling, Data Assimilation and Predictability
J. Ruiz, C. Saulo, Julia Nogués-Paegle (2010)
WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface VariablesMonthly Weather Review, 138
E. Mlawer, Steven Taubman, P. Brown, M. Iacono, S. Clough (1997)
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwaveJournal of Geophysical Research, 102
Song‐You Hong, Y. Noh, J. Dudhia (2006)
A New Vertical Diffusion Package with an Explicit Treatment of Entrainment ProcessesMonthly Weather Review, 134
S. CohnData (1994)
Assessing the Eeects of Data Selection with Dao's Physical-space Statistical Analysis System
G. Huffman, D. Bolvin, E. Nelkin, D. Wolff, R. Adler, G. Gu, Y. Hong, K. Bowman, E. Stocker (2007)
The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine ScalesJournal of Hydrometeorology, 8
C. Köpken, G. Kelly, J. Thepaut (2004)
Assimilation of Meteosat radiance data within the 4D‐Var system at ECMWF: Assimilation experiments and forecast impactQuarterly Journal of the Royal Meteorological Society, 130
T. Jones, D. Stensrud (2012)
Assimilating AIRS Temperature and Mixing Ratio Profiles Using an Ensemble Kalman Filter Approach for Convective-Scale ForecastsWeather and Forecasting, 27
G. Cressman (1959)
AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEMMonthly Weather Review, 87
P. Houtekamer, Xingxiu Deng, H. Mitchell, Seung-Jong Baek, N. Gagnon (2014)
Higher Resolution in an Operational Ensemble Kalman FilterMonthly Weather Review, 142
J. Kain (2004)
The Kain–Fritsch Convective Parameterization: An UpdateJournal of Applied Meteorology, 43
Xuguang Wang, D. Barker, C. Snyder, T. Hamill (2008)
A Hybrid ETKF-3DVAR Data Assimilation Scheme for the WRF Model. Part I: Observing System Simulation ExperimentMonthly Weather Review, 136
(2014)
Sensitivity experiments to design a regional assimilation system combining the LETKF and the WRF Model. Abstracts, The World Weather Open Science Conf
S. Sukoriansky, B. Galperin, V. Perov (2005)
‘Application of a New Spectral Theory of Stably Stratified Turbulence to the Atmospheric Boundary Layer over Sea Ice’Boundary-Layer Meteorology, 117
(2013)
Bolvin, 2013: TRMM and other data
G. Huffman, D. Bolvin (2015)
TRMM and Other Data Precipitation Data Set Documentation
D. Kleist (2012)
An Evaluation of Hybrid variational-Ensemble Data Assimilation for the NCEP GFS
Anderson (1999)
A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecastsMon. Wea. Rev., 127
Fei Chen, J. Dudhia (2001)
Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and SensitivityMonthly Weather Review, 129
Y. Ying, Fuqing Zhang (2015)
An adaptive covariance relaxation method for ensemble data assimilationQuarterly Journal of the Royal Meteorological Society, 141
A. Lorenc (2003)
Modelling of error covariances by 4D‐Var data assimilationQuarterly Journal of the Royal Meteorological Society, 129
W. Skamarock, J. Klemp, J. Dudhia, Dave Gill, D. Barker, M. Duda, Xiang-Yu Huang, Wei Wang, Jordan Powers (2008)
A Description of the Advanced Research WRF Version 3, 113
(2015)
Study of the impact of satellite data, radiosondes and GPS in a SACZ episode using G3DVar
Zhiyong Meng (2007)
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part II: Imperfect Model Experiments
H. Seko, T. Miyoshi, Y. Shoji, Kazuo Saito (2011)
Data assimilation experiments of precipitable water vapour using the LETKF system: intense rainfall event over Japan 28 July 2008Tellus A: Dynamic Meteorology and Oceanography, 63
Yanina Skabar, M. Nicolini (2009)
Enriched Analyses with Assimilation of SALLJEX DataJournal of Applied Meteorology and Climatology, 48
J. Dudhia (1989)
Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional ModelJournal of the Atmospheric Sciences, 46
P. Salio, M. Hobouchian, Yanina Skabar, D. Vila (2015)
Evaluation of high-resolution satellite precipitation estimates over southern South America using a dense rain gauge networkAtmospheric Research, 163
T. Miyoshi, S. Yamane, Takeshi Enomoto (2007)
Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF)Sola, 3
C. Vera, J. Báez, M. Douglas, C. Emmanuel, J. Marengo, J. Meitín, M. Nicolini, Julia Nogués-Paegle, J. Paegle, O. Penalba, P. Salio, C. Saulo, M. Dias, P. Dias, E. Zipser (2006)
THE SOUTH AMERICAN LOW-LEVEL JET EXPERIMENTBulletin of the American Meteorological Society, 87
J. Whitaker, T. Hamill (2012)
Evaluating Methods to Account for System Errors in Ensemble Data AssimilationMonthly Weather Review, 140
C. Schwartz, Zhiquan Liu, Yongsheng Chen, Xiangyu Huang (2012)
Impact of Assimilating Microwave Radiances with a Limited-Area Ensemble Data Assimilation System on Forecasts of Typhoon MorakotWeather and Forecasting, 27
Dongmei Xu, Zhiquan Liu, Xiangyu Huang, J. Min, Hongli Wang (2013)
Impact of assimilating IASI radiance observations on forecasts of two tropical cyclonesMeteorology and Atmospheric Physics, 122
Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that can satisfy both requirements in an efficient manner. The Weather Research and Forecasting (WRF) Model coupled with the LETKF, developed at the University of Maryland, College Park, have been implemented experimentally at the NWS of Argentina (Servicio Meteorológico Nacional (SMN)), but at a somewhat lower resolution (40 km) than the operational Global Forecast System (GFS) at that time (27 km). The purpose of this work is not to show that the system presented herein is better than the higher-resolution GFS, but that its performance is reasonably comparable, and to provide the basis for a continued improved development of an independent regional data assimilation and forecasting system. The WRF-LETKF system is tested during the spring of 2012, using the prepared or quality controlled data in Binary Universal Form for Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) and lateral boundary conditions from the GFS. To assess the effect of model error, a single-model LETKF system (LETKF-single) is compared with a multischeme implementation (LETKF-multi), which uses different boundary layer and cumulus convection schemes for the generation of the ensemble of forecasts. The performance of both experiments during the test period shows that the LETKF-multi usually outperforms the LETKF-single, evidencing the advantages of the use of the multischeme approach. Both data assimilation systems are slightly worse than the GFS in terms of the synoptic environment representation, as could be expected given their lower resolution. Results from a case study of a strong convective system suggest that the LETKF-multi improves the location of the most intense area of precipitation with respect to the LETKF-single, although both systems show an underestimation of the total accumulated precipitation. These preliminary results encourage continuing the development of an operational data assimilation system based on WRF-LETKF at the SMN.
Weather and Forecasting – American Meteorological Society
Published: Nov 26, 2014
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