On the properties of ensemble forecast sensitivity to observations

[1]  Jean-Noël Thépaut,et al.  The value of observations. I: Data denial experiments for the Atlantic and the Pacific , 2007 .

[2]  Misako Kachi,et al.  Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Z. Kawasaki,et al.  A Kalman Filter Approach to the Global Satellite Mapping of Precipitation (GSMaP) from Combined Passive Microwave and Infrared Radiometric Data , 2009 .

[4]  Roland Potthast,et al.  Kilometre‐scale ensemble data assimilation for the COSMO model (KENDA) , 2016 .

[5]  Koji Terasaki,et al.  Multi-Year Analysis Using the NICAM-LETKF Data Assimilation System , 2019 .

[6]  C. Cardinali Forecast sensitivity observation impact with an observation‐only based objective function , 2018, Quarterly Journal of the Royal Meteorological Society.

[7]  Matthias Sommer,et al.  Ensemble-based approximation of observation impact using an observation-based verification metric , 2016 .

[8]  K. Emanuel,et al.  Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model , 1998 .

[9]  Ricardo Todling,et al.  Comparing Two Approaches for Assessing Observation Impact , 2013 .

[10]  M. Weissmann,et al.  The importance of appropriate verification metrics for the assessment of observation impact in a convection‐permitting modelling system , 2018, Quarterly Journal of the Royal Meteorological Society.

[11]  Shunji Kotsuki,et al.  Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere , 2017 .

[12]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[13]  Takemasa Miyoshi,et al.  Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation. , 2016, Monthly weather review.

[14]  A. Arakawa,et al.  Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I , 1974 .

[15]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[16]  J. Whitaker,et al.  Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter , 2001 .

[17]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[18]  Koji Terasaki,et al.  Assimilating the global satellite mapping of precipitation data with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) , 2017 .

[19]  E. Berry,et al.  Cloud Droplet Growth by Collection , 1967 .

[20]  Takemasa Miyoshi,et al.  A simpler formulation of forecast sensitivity to observations: application to ensemble Kalman filters , 2012 .

[21]  Junjie Liu,et al.  Correction of ‘Estimating observation impact without adjoint model in an ensemble Kalman filter’ , 2010 .

[22]  T. Miyoshi,et al.  Assimilating AMSU-A Radiances with the NICAM-LETKF , 2017 .

[23]  Eugenia Kalnay,et al.  Accelerating assimilation development for new observing systems using EFSO , 2017 .

[24]  Masaki Satoh,et al.  Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations , 2008, J. Comput. Phys..

[25]  Takemasa Miyoshi,et al.  The Non-hydrostatic Icosahedral Atmospheric Model: description and development , 2014, Progress in Earth and Planetary Science.

[26]  Richard T. Marriott,et al.  Forecast sensitivity to observations in the Met Office Global numerical weather prediction system , 2014 .

[27]  Koji Terasaki,et al.  Local Ensemble Transform Kalman Filter Experiments with the Nonhydrostatic Icosahedral Atmospheric Model NICAM , 2015 .

[28]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[29]  E. Kalnay,et al.  Estimating observation impact without adjoint model in an ensemble Kalman filter , 2008 .

[30]  Xuguang Wang,et al.  Adaptive Localization for the Ensemble-Based Observation Impact Estimate Using Regression Confidence Factors , 2015 .

[31]  Takemasa Miyoshi,et al.  Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution , 2007 .

[32]  C. Cardinali Monitoring the observation impact on the short‐range forecast , 2009 .

[33]  Hirofumi Tomita,et al.  A new dynamical framework of nonhydrostatic global model using the icosahedral grid , 2004 .

[34]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[35]  E. Kalnay,et al.  Effective assimilation of global precipitation: simulation experiments , 2013 .

[36]  Juanzhen Sun,et al.  Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter , 2004 .

[37]  Shunji Kotsuki,et al.  Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model , 2017 .

[38]  Koji Terasaki,et al.  Online Model Parameter Estimation With Ensemble Data Assimilation in the Real Global Atmosphere: A Case With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data , 2018, Journal of Geophysical Research: Atmospheres.

[39]  B. Hunt,et al.  A comparative study of 4D-VAR and a 4D Ensemble Kalman Filter: perfect model simulations with Lorenz-96 , 2007 .

[40]  J. Whitaker,et al.  Evaluating Methods to Account for System Errors in Ensemble Data Assimilation , 2012 .

[41]  Niels Bormann,et al.  Estimates of spatial and interchannel observation‐error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data , 2010 .

[42]  Takemasa Miyoshi,et al.  Ensemble-based observation impact estimates using the NCEP GFS , 2013 .

[43]  Takemasa Miyoshi,et al.  Ensemble Kalman Filter and 4D-Var Intercomparison with the Japanese Operational Global Analysis and Prediction System , 2010 .

[44]  Takemasa Miyoshi,et al.  Proactive QC: A Fully Flow-Dependent Quality Control Scheme Based on EFSO , 2017 .

[45]  Takemasa Miyoshi,et al.  Assimilation of TRMM Multisatellite Precipitation Analysis with a Low-Resolution NCEP Global Forecast System , 2016 .

[46]  Alan J. Geer,et al.  Significance of changes in medium-range forecast scores , 2016 .

[47]  Mark Buehner,et al.  A New Approach for Estimating the Observation Impact in Ensemble-Variational Data Assimilation , 2017 .

[48]  R. Gelaro,et al.  Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System , 2008 .

[49]  T. Miyoshi,et al.  Predictability of Record-Breaking Rainfall in Japan in July 2018: Ensemble Forecast Experiments with the Near-Real-Time Global Atmospheric Data Assimilation System NEXRA , 2019, SOLA.