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between whether a tropical disturbance should be classified as a tropical depression and counted in best track, or is some other tropical storm such as a subtropical or hybrid cyclone, difficult and dependent on subjective forecaster interpretation. Gyakum (2011) states that “there is presently no single set of objective criteria that, if applied operationally, would irrefutably support a forecaster’s analysis of cyclone type (subtropical, hybrid or tropical)” (p. 1.6.23). It is also unclear whether
between whether a tropical disturbance should be classified as a tropical depression and counted in best track, or is some other tropical storm such as a subtropical or hybrid cyclone, difficult and dependent on subjective forecaster interpretation. Gyakum (2011) states that “there is presently no single set of objective criteria that, if applied operationally, would irrefutably support a forecaster’s analysis of cyclone type (subtropical, hybrid or tropical)” (p. 1.6.23). It is also unclear whether
analyzed aerosol fields that are radiatively coupled to the atmosphere. To our knowledge, this is the first multidecadal reanalysis within which meteorological and aerosol observations are jointly assimilated into a global assimilation system, although other operational forecasting centers are actively developing similar capabilities (e.g., Benedetti et al. 2009 ; Sekiyama et al. 2010 ; Lynch et al. 2016 ). Previously, the GMAO had performed an offline aerosol reanalysis (the MERRA Aerosol
analyzed aerosol fields that are radiatively coupled to the atmosphere. To our knowledge, this is the first multidecadal reanalysis within which meteorological and aerosol observations are jointly assimilated into a global assimilation system, although other operational forecasting centers are actively developing similar capabilities (e.g., Benedetti et al. 2009 ; Sekiyama et al. 2010 ; Lynch et al. 2016 ). Previously, the GMAO had performed an offline aerosol reanalysis (the MERRA Aerosol
Assessing Sensitivity of MERRA-2 to AMSU-A in the Upper StratosphereASCAT scatterometer data from MetOp-A are also used in ERA5. Similar to MERRA and MERRA-2, the NCEP Climate System Forecast Reanalysis (CFSR) ( Saha et al. 2010 ) also uses intercalibrated MSU radiance. In MERRA and MERRA-2, both uncorrected (no correction due to reprocessing or intercalibration is applied) and intercalibrated MSU radiances are assimilated. Prior to 1 November 1986, the uncorrected data are assimilated as uncorrected antenna temperatures where antenna pattern corrections to remove
ASCAT scatterometer data from MetOp-A are also used in ERA5. Similar to MERRA and MERRA-2, the NCEP Climate System Forecast Reanalysis (CFSR) ( Saha et al. 2010 ) also uses intercalibrated MSU radiance. In MERRA and MERRA-2, both uncorrected (no correction due to reprocessing or intercalibration is applied) and intercalibrated MSU radiances are assimilated. Prior to 1 November 1986, the uncorrected data are assimilated as uncorrected antenna temperatures where antenna pattern corrections to remove
constraints provided by available observations, the development of data assimilation capabilities can potentially provide a better characterization of aerosols than either a model or observational network alone. Several operational and weather and climate research centers have developed aerosol data assimilation capabilities on a global scale recently ( Tanaka et al. 2003 ; Zhang et al. 2008 ; Benedetti et al. 2009 ; Sekiyama et al. 2010 ; Pérez et al. 2011 ; Buchard et al. 2015 ; Rubin et al. 2016
constraints provided by available observations, the development of data assimilation capabilities can potentially provide a better characterization of aerosols than either a model or observational network alone. Several operational and weather and climate research centers have developed aerosol data assimilation capabilities on a global scale recently ( Tanaka et al. 2003 ; Zhang et al. 2008 ; Benedetti et al. 2009 ; Sekiyama et al. 2010 ; Pérez et al. 2011 ; Buchard et al. 2015 ; Rubin et al. 2016
1. Introduction Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of meteorological observations, typically spanning an extended segment of the historical data record. The process relies on an underlying forecast model to combine disparate observations in a physically consistent manner, enabling production of gridded datasets for a broad range of variables, including ones that are sparsely or not directly observed. As such, and
1. Introduction Reanalysis is the process whereby an unchanging data assimilation system is used to provide a consistent reprocessing of meteorological observations, typically spanning an extended segment of the historical data record. The process relies on an underlying forecast model to combine disparate observations in a physically consistent manner, enabling production of gridded datasets for a broad range of variables, including ones that are sparsely or not directly observed. As such, and
analyses that are especially appropriate for QBO studies, as reanalyses lack the possibly disruptive upgrades of archived operational numerical weather prediction systems. Since the observations are not uniformly distributed in the stratosphere, improvements in the model dynamics to better represent the QBO along with improved algorithms for weighting and blending observations should lead to improved equatorial zonal mean winds and temperatures. Another consideration when using reanalyses to study the
analyses that are especially appropriate for QBO studies, as reanalyses lack the possibly disruptive upgrades of archived operational numerical weather prediction systems. Since the observations are not uniformly distributed in the stratosphere, improvements in the model dynamics to better represent the QBO along with improved algorithms for weighting and blending observations should lead to improved equatorial zonal mean winds and temperatures. Another consideration when using reanalyses to study the
; Kobayashi et al. 2015 ) that blend diverse measurements of wind, moisture, and temperature as well as other observations with first-guess estimates from model short-term forecasts. While reanalyses effectively reconcile observations with physically based dynamical models, there are a number of practical problems that result in moisture transport fields typically having substantial systematic time-dependent biases ( Trenberth et al. 2011 ; Robertson et al. 2011 ; Lorenz and Kunstmann 2012 ; Trenberth
; Kobayashi et al. 2015 ) that blend diverse measurements of wind, moisture, and temperature as well as other observations with first-guess estimates from model short-term forecasts. While reanalyses effectively reconcile observations with physically based dynamical models, there are a number of practical problems that result in moisture transport fields typically having substantial systematic time-dependent biases ( Trenberth et al. 2011 ; Robertson et al. 2011 ; Lorenz and Kunstmann 2012 ; Trenberth
. Ideally these two components related to stability should be formulated as one term. APPENDIX B Acronyms 20CR NOAA Twentieth Century Reanalysis AIRS Atmospheric Infrared Sounder AMIP Atmospheric Model Intercomparison Project (prescribed SST) AMSU Advanced Microwave Sounding Unit ATOVS Advanced TIROS Operational Vertical Sounder C-C Clausius–Clapeyron CFSR Climate Forecast System Reanalysis CPCU NOAA Climate Prediction Center (CPC) unified precipitation dataset ECMWF European Centre for Medium
. Ideally these two components related to stability should be formulated as one term. APPENDIX B Acronyms 20CR NOAA Twentieth Century Reanalysis AIRS Atmospheric Infrared Sounder AMIP Atmospheric Model Intercomparison Project (prescribed SST) AMSU Advanced Microwave Sounding Unit ATOVS Advanced TIROS Operational Vertical Sounder C-C Clausius–Clapeyron CFSR Climate Forecast System Reanalysis CPCU NOAA Climate Prediction Center (CPC) unified precipitation dataset ECMWF European Centre for Medium
analyses provide an opportunity for comparing their representation of the tropical zonal mean momentum budget during the QBO disruption. Here, we use a ±5° latitude average and examine the same momentum budget terms for MERRA-2 as presented in Osprey et al. (2016 , their Fig. 2b) for the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis. Four terms of the 40-hPa zonal mean momentum budget for November 2015 through April 2016 are plotted in Fig. 4 . They consist of the
analyses provide an opportunity for comparing their representation of the tropical zonal mean momentum budget during the QBO disruption. Here, we use a ±5° latitude average and examine the same momentum budget terms for MERRA-2 as presented in Osprey et al. (2016 , their Fig. 2b) for the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis. Four terms of the 40-hPa zonal mean momentum budget for November 2015 through April 2016 are plotted in Fig. 4 . They consist of the
.1029/95JC00382 . 10.1029/95JC00382 Kim , D. , and Coauthors , 2009 : Application of MJO simulation diagnostics to climate models . J. Climate , 22 , 6413 – 6436 , doi: 10.1175/2009JCLI3063.1 . 10.1175/2009JCLI3063.1 Kim , H.-M. , P. J. Webster , V. E. Toma , and D. Kim , 2014 : Predictability and prediction skill of the MJO in two operational forecasting systems . J. Climate , 27 , 5364 – 5378 , doi: 10.1175/JCLI-D-13-00480.1 . 10.1175/JCLI-D-13-00480.1 Kug , J.-S. , F.-F. Jin
.1029/95JC00382 . 10.1029/95JC00382 Kim , D. , and Coauthors , 2009 : Application of MJO simulation diagnostics to climate models . J. Climate , 22 , 6413 – 6436 , doi: 10.1175/2009JCLI3063.1 . 10.1175/2009JCLI3063.1 Kim , H.-M. , P. J. Webster , V. E. Toma , and D. Kim , 2014 : Predictability and prediction skill of the MJO in two operational forecasting systems . J. Climate , 27 , 5364 – 5378 , doi: 10.1175/JCLI-D-13-00480.1 . 10.1175/JCLI-D-13-00480.1 Kug , J.-S. , F.-F. Jin
