Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

[1]  Thomas A. Jones,et al.  Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments , 2015 .

[2]  Kazuo Saito,et al.  A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area , 2011 .

[3]  Fuqing Zhang,et al.  Adaptive Observation Error Inflation for Assimilating All-Sky Satellite Radiance , 2017 .

[4]  Takemasa Miyoshi,et al.  Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008) , 2013 .

[5]  G. Mellor,et al.  A Hierarchy of Turbulence Closure Models for Planetary Boundary Layers. , 1974 .

[6]  M. Kazumori.  Satellite Radiance Assimilation in the JMA Operational Mesoscale 4DVAR System , 2014 .

[7]  T. Ushio,et al.  Relationship between thunderstorm electrification and storm kinetics revealed by phased array weather radar , 2017 .

[8]  T. Miyoshi,et al.  Data Assimilation with Error-Correlated and Non-Orthogonal Observations: Experiments with the Lorenz-96 Model , 2014 .

[9]  Paul Poli,et al.  Diagnosis of observation, background and analysis‐error statistics in observation space , 2005 .

[10]  Timothy J. Schmit,et al.  A Closer Look at the ABI on the GOES-R Series , 2017 .

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

[12]  Eugenia Kalnay,et al.  Accelerating the spin‐up of Ensemble Kalman Filtering , 2008, 0806.0180.

[13]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[14]  Chris Snyder,et al.  A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation , 2005 .

[15]  Louis J. Wicker,et al.  Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses , 2011 .

[16]  Ralf Bennartz,et al.  Retrieval of two‐layer cloud properties from multispectral observations using optimal estimation , 2011 .

[17]  Adrian M. Tompkins,et al.  A cloud scheme for data assimilation: Description and initial tests , 2004 .

[18]  Roland Potthast,et al.  Nonlinear Bias Correction for Satellite Data Assimilation Using Taylor Series Polynomials , 2017 .

[19]  Corey K. Potvin,et al.  Progress and challenges with Warn-on-Forecast , 2012 .

[20]  Thomas A. Jones,et al.  Assimilation of GOES-13 imager clear-sky water vapor (6.5 μm) radiances into a Warn-on-Forecast system , 2018 .

[21]  Jason A. Otkin,et al.  Assimilation of water vapor sensitive infrared brightness temperature observations during a high impact weather event , 2012 .

[22]  Fuqing Zhang,et al.  An adaptive background error inflation method for assimilating all‐sky radiances , 2019, Quarterly Journal of the Royal Meteorological Society.

[23]  Tomoo Ushio,et al.  “Big Data Assimilation” Revolutionizing Severe Weather Prediction , 2016 .

[24]  Bernhard Mayer,et al.  Observation Operator for Visible and Near-Infrared Satellite Reflectances , 2014 .

[25]  Eric Maddy,et al.  Impact Assessment of Himawari-8 AHI Data Assimilation in NCEP GDAS/GFS with GSI , 2017 .

[26]  William Bell,et al.  Progress towards the assimilation of all‐sky infrared radiances: an evaluation of cloud effects , 2014 .

[27]  M. Xue,et al.  Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data , 2011 .

[28]  Franco Marenco,et al.  A self‐consistent scattering model for cirrus. II: The high and low frequencies , 2014 .

[29]  Kazuo Saito,et al.  The Operational JMA Nonhydrostatic Mesoscale Model , 2006 .

[30]  Fuqing Zhang,et al.  Potential impacts of assimilating all‐sky infrared satellite radiances from GOES‐R on convection‐permitting analysis and prediction of tropical cyclones , 2016 .

[31]  K. Okamoto,et al.  Assimilation of Himawari‐8 All‐Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction , 2018 .

[32]  E. Kalnay,et al.  The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model , 2013 .

[33]  Y. Sawada,et al.  Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system , 2019, Quarterly Journal of the Royal Meteorological Society.

[34]  Geir Evensen,et al.  Analysis of iterative ensemble smoothers for solving inverse problems , 2018, Computational Geosciences.

[35]  Fuqing Zhang,et al.  Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation , 2016 .

[36]  K. Okamoto.  Evaluation of IR radiance simulation for all‐sky assimilation of Himawari‐8/AHI in a mesoscale NWP system , 2017 .

[37]  Peter Bauer,et al.  Observation errors in all‐sky data assimilation , 2011 .

[38]  M. Kazumori.  Assimilation of Himawari-8 Clear Sky Radiance Data in JMA's Global and Mesoscale NWP Systems , 2018 .

[39]  David J. Stensrud,et al.  Assimilating All-Sky Infrared Radiances from GOES-16 ABI Using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction , 2018, Monthly Weather Review.

[40]  Sho Yokota,et al.  The Tornadic Supercell on the Kanto Plain on 6 May 2012: Polarimetric Radar and Surface Data Assimilation with EnKF and Ensemble-Based Sensitivity Analysis , 2016 .

[41]  Sho Yokota,et al.  Ensemble experiments using a nested LETKF system to reproduce intense vortices associated with tornadoes of 6 May 2012 in Japan , 2015, Progress in Earth and Planetary Science.

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

[43]  Takemasa Miyoshi,et al.  Estimating and including observation-error correlations in data assimilation , 2013 .

[44]  Fuqing Zhang,et al.  Intrinsic versus Practical Limits of Atmospheric Predictability and the Significance of the Butterfly Effect , 2016 .

[45]  Takemasa Miyoshi,et al.  Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM) , 2006 .

[46]  J. Otkin,et al.  Assimilation of Synthetic GOES-R ABI Infrared Brightness Temperatures and WSR-88D Radar Observations in a High-Resolution OSSE , 2016 .

[47]  Martin Weissmann,et al.  Error model for the assimilation of cloud‐affected infrared satellite observations in an ensemble data assimilation system , 2016 .

[48]  Jason A. Otkin,et al.  Clear and cloudy sky infrared brightness temperature assimilation using an ensemble Kalman filter , 2010 .

[49]  John S. Kain,et al.  Convective parameterization for mesoscale models : The Kain-Fritsch Scheme , 1993 .

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

[51]  A. Okuyama,et al.  An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites , 2016 .

[52]  E. Kalnay,et al.  Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model , 2012 .

[53]  Takumi Honda,et al.  Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015) , 2018 .

[54]  Ensemble Data Assimilation and Forecast Experiments for the September 2015 Heavy Rainfall Event in Kanto and Tohoku Regions with Atmospheric Motion Vectors from Himawari-8 , 2016 .

[55]  J. Otkin,et al.  Assimilation of Satellite Infrared Radiances and Doppler Radar Observations during a Cool Season Observing System Simulation Experiment , 2013 .

[56]  Anthony J. Baran,et al.  A new ice cloud parameterization for infrared radiative transfer simulation of cloudy radiances: Evaluation and optimization with IIR observations and ice cloud profile retrieval products , 2015 .

[57]  Tomoo Ushio,et al.  “Big Data Assimilation” Toward Post-Petascale Severe Weather Prediction: An Overview and Progress , 2016, Proceedings of the IEEE.