Use and impact of Arctic observations in the ECMWF Numerical Weather Prediction system

[1]  S. Serrar,et al.  Arctic influence on subseasonal midlatitude prediction , 2014 .

[2]  Alan J. Geer,et al.  Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies , 2014 .

[3]  S. English,et al.  Use of the ocean surface wind direction signal in microwave radiance assimilation , 2015 .

[4]  R. Eresmaa.  Imager‐assisted cloud detection for assimilation of Infrared Atmospheric Sounding Interferometer radiances , 2014 .

[5]  R. Errico,et al.  Examination of various-order adjoint-based approximations of observation impact , 2007 .

[6]  Christopher W. Fairall,et al.  Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales , 2016 .

[7]  R. Errico.  Interpretations of an adjoint-derived observational impact measure , 2007 .

[8]  J. Curry,et al.  Surface Heat Budget of the Arctic Ocean , 2002 .

[9]  Massimo Bonavita,et al.  On the use of EDA background error variances in the ECMWF 4D‐Var , 2012 .

[10]  Catherine Prigent,et al.  The Assimilation of Observations from the Advanced Microwave Sounding Unit over Sea Ice in the French Global Numerical Weather Prediction System , 2014 .

[11]  R. Todling,et al.  Adjoint Estimation of the Variation in Model Functional Output due to the Assimilation of Data , 2009 .

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

[13]  N. Bond,et al.  Regional Variation of Winter Temperatures in the Arctic , 1997 .

[14]  S. Vavrus,et al.  Evidence linking Arctic amplification to extreme weather in mid‐latitudes , 2012 .

[15]  R. Barry,et al.  The Arctic Climate System: List of Abbreviations , 2005 .

[16]  Quanhua Liu,et al.  An Improved Fast Microwave Water Emissivity Model , 2011, IEEE Transactions on Geoscience and Remote Sensing.

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

[18]  M. Matricardi,et al.  An improved fast radiative transfer model for assimilation of satellite radiance observations , 1999 .

[19]  Jean-Noël Thépaut,et al.  Impact of MODIS polar winds in ECMWF's 4DVAR data assimilation system , 2004 .

[20]  Manfred Wendisch,et al.  Role of air-mass transformations in exchange between the Arctic and mid-latitudes , 2018, Nature Geoscience.

[21]  F. Bouttier,et al.  Observing‐system experiments in the ECMWF 4D‐Var data assimilation system , 2001 .

[22]  Florence Rabier,et al.  Toward a Better Modeling of Surface Emissivity to Improve AMSU Data Assimilation Over Antarctica , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[23]  P. Bauer,et al.  A Revised Cloud Overlap Scheme for Fast Microwave Radiative Transfer in Rain and Cloud , 2009 .

[24]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[25]  Niels Bormann,et al.  An update on the RTTOV fast radiative transfer model (currently at version 12) , 2018, Geoscientific Model Development.

[26]  Roger G. Barry,et al.  The Arctic Climate System by Mark C. Serreze , 2005 .

[27]  N. Bormann,et al.  The growing impact of satellite observations sensitive to humidity, cloud and precipitation , 2017 .

[28]  A. Simmons,et al.  The ECMWF operational implementation of four‐dimensional variational assimilation. I: Experimental results with simplified physics , 2007 .

[29]  Takeshi Enomoto,et al.  The impact of radiosonde data over the ice‐free Arctic Ocean on the atmospheric circulation in the Northern Hemisphere , 2013 .

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

[31]  P. Bauer,et al.  Direct 4D‐Var assimilation of all‐sky radiances. Part II: Assessment , 2010 .

[32]  A. McNally.  The direct assimilation of cloud‐affected satellite infrared radiances in the ECMWF 4D‐Var , 2009 .

[33]  Klaus Dethloff,et al.  Improved forecasts of winter weather extremes over midlatitudes with extra Arctic observations , 2017 .

[34]  Mats Hamrud,et al.  A new grid for the IFS , 2016 .

[35]  Seong-Joong Kim,et al.  The Melting Arctic and Midlatitude Weather Patterns: Are They Connected?* , 2015 .

[36]  J. Thepaut,et al.  Aspects of ECMWF model performance in polar areas , 2016 .

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

[38]  Alan J. Geer,et al.  Assimilation of SSMIS humidity‐sounding channels in all‐sky conditions over land using a dynamic emissivity retrieval , 2016 .

[39]  Catherine Prigent,et al.  Microwave land emissivity calculations using AMSU measurements , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Ronald Gelaro,et al.  Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models , 2009 .

[41]  Dick Dee,et al.  Adaptive bias correction for satellite data in a numerical weather prediction system , 2007 .

[42]  Dara Entekhabi,et al.  Recent Arctic amplification and extreme mid-latitude weather , 2014 .

[43]  Roger Daley,et al.  Observation and background adjoint sensitivity in the adaptive observation‐targeting problem , 2007 .

[44]  Qifeng Lu,et al.  Evaluation and Assimilation of the Microwave Sounder MWHS-2 Onboard FY-3C in the ECMWF Numerical Weather Prediction System , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Philippe Courtier,et al.  Sensitivity of forecast errors to initial conditions , 1996 .

[46]  M. Rodwell,et al.  Increased Arctic influence on the midlatitude flow during Scandinavian Blocking episodes , 2019, Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society.

[47]  S. Healy,et al.  The combined impact of future space‐based atmospheric sounding instruments on numerical weather‐prediction analysis fields: A simulation study , 2003 .

[48]  Peter Bauer,et al.  Direct 4D‐Var assimilation of all‐sky radiances. Part I: Implementation , 2010 .

[49]  Peter Bauer,et al.  Multiple‐scattering microwave radiative transfer for data assimilation applications , 2006 .