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predictability limit of multiscale midlatitude weather assuming a perfect model with nearly perfect initial conditions? 2) How much longer can the practical predictability be increased by reducing initial-condition uncertainties to different degree of accuracy? a. Model details 1) ECMWF/IFS model The IFS control and ensemble forecasts presented herein uses the latest upgrade (cycle 41r2) of ECMWF, the highest-resolution-ever (~9 km) global operational NWP model. More details of this model upgrade can be
predictability limit of multiscale midlatitude weather assuming a perfect model with nearly perfect initial conditions? 2) How much longer can the practical predictability be increased by reducing initial-condition uncertainties to different degree of accuracy? a. Model details 1) ECMWF/IFS model The IFS control and ensemble forecasts presented herein uses the latest upgrade (cycle 41r2) of ECMWF, the highest-resolution-ever (~9 km) global operational NWP model. More details of this model upgrade can be
first questioned the perturbations used in Z19 , arguing EDA-only type perturbations and the rescaling of these perturbations “do not provide realistic simulations of either the current-day operational, or the future ideal evolution of the forecast errors.” Another major issue they raised is related to the impacts of model error on the growth of forecast uncertainty. They also suggested the use of a parametric model for estimates of the extension of the practical predictability. We would like to
first questioned the perturbations used in Z19 , arguing EDA-only type perturbations and the rescaling of these perturbations “do not provide realistic simulations of either the current-day operational, or the future ideal evolution of the forecast errors.” Another major issue they raised is related to the impacts of model error on the growth of forecast uncertainty. They also suggested the use of a parametric model for estimates of the extension of the practical predictability. We would like to
, during, and after RI have been successfully reproduced with a 72-h (0000 UTC 18 October–0000 UTC 21 October 2005) prediction using the Weather Research and Forecasting (WRF) Model with a quadruply nested (27, 9, 3, and 1 km) grid and initial and lateral boundary conditions that are identical to the Geophysical Fluid Dynamics Laboratory’s then-operational data. Then, Zhang and Chen (2012 , hereafter ZC12 ) used the hydrostatic equation to demonstrate how the warming above the 380-K isentrope in
, during, and after RI have been successfully reproduced with a 72-h (0000 UTC 18 October–0000 UTC 21 October 2005) prediction using the Weather Research and Forecasting (WRF) Model with a quadruply nested (27, 9, 3, and 1 km) grid and initial and lateral boundary conditions that are identical to the Geophysical Fluid Dynamics Laboratory’s then-operational data. Then, Zhang and Chen (2012 , hereafter ZC12 ) used the hydrostatic equation to demonstrate how the warming above the 380-K isentrope in
1. Introduction Zhang et al. (2019 , hereafter Zetal2019 ) investigated the gap between the inherent and current-day practical limit of midlatitude weather predictability by perfect model experiments. Their methodology and conclusions raise a number of questions, which we summarize as follows: 1) the experiments of Zetal2019 do not provide realistic simulations of either the current-day operational, or the future ideal evolution of the forecast errors; 2) estimates of the extension of the
1. Introduction Zhang et al. (2019 , hereafter Zetal2019 ) investigated the gap between the inherent and current-day practical limit of midlatitude weather predictability by perfect model experiments. Their methodology and conclusions raise a number of questions, which we summarize as follows: 1) the experiments of Zetal2019 do not provide realistic simulations of either the current-day operational, or the future ideal evolution of the forecast errors; 2) estimates of the extension of the
JUNE 1958IRVING I. GRINGORTENON THE COMPARISON OF ONE OR MORE SETS OF PROBABILITY FORECASTS By Irving I. Gringorten Geophysics Research Directorate, Air Force Cambridge Research Center(Original manuscript received 16 August 1957 ; revised manuscript received 19 December 1957)ABSTRACTGeneral expressions for the expected score for accuracy, score for skill and for operational value of theforecasts are developed and discussed. The expressions are then applied to the special
JUNE 1958IRVING I. GRINGORTENON THE COMPARISON OF ONE OR MORE SETS OF PROBABILITY FORECASTS By Irving I. Gringorten Geophysics Research Directorate, Air Force Cambridge Research Center(Original manuscript received 16 August 1957 ; revised manuscript received 19 December 1957)ABSTRACTGeneral expressions for the expected score for accuracy, score for skill and for operational value of theforecasts are developed and discussed. The expressions are then applied to the special
that the same could be true for the whole tropical planetary-scale circulations. Such a new perspective may have an immediate impact on global model initialization strategy over the tropics: the current basic strategy is an initialization based on a linear equatorial wave decomposition (cf. Žagar et al. 2005 ). The proposed MJO–modon theory suggests a nonlinear balance initialization ( Baer and Tribbia 1977 ; Kasahara 1982 ; Tribbia 1984b ) as the key for a successful MJO forecast. From a
that the same could be true for the whole tropical planetary-scale circulations. Such a new perspective may have an immediate impact on global model initialization strategy over the tropics: the current basic strategy is an initialization based on a linear equatorial wave decomposition (cf. Žagar et al. 2005 ). The proposed MJO–modon theory suggests a nonlinear balance initialization ( Baer and Tribbia 1977 ; Kasahara 1982 ; Tribbia 1984b ) as the key for a successful MJO forecast. From a
JuLY 1972 I A N D. R U T H E R F O R D 809Data Assimilation by Statistical Interpolation of Forecast Error Fields IAN D. RUTHERFORDAtmospheric Environment Service, Dorval, Quebec, Canada(Manuscript received 7 September 1971, in revised form 10 March 1972)ABSTRACT An operational method of combining observations with short-period forecasts of the same quantities isdescribed. The
JuLY 1972 I A N D. R U T H E R F O R D 809Data Assimilation by Statistical Interpolation of Forecast Error Fields IAN D. RUTHERFORDAtmospheric Environment Service, Dorval, Quebec, Canada(Manuscript received 7 September 1971, in revised form 10 March 1972)ABSTRACT An operational method of combining observations with short-period forecasts of the same quantities isdescribed. The
1. Introduction Toward the end of September 2002, the cold polar vortex in the Southern Hemisphere stratosphere elongated and split in a manner similar to that seen every few years in the Northern Hemisphere stratosphere but never before observed in the Southern Hemisphere. Despite its rarity, the event was predicted accurately up to about a week in advance by the operational forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF). Although the forecasts were
1. Introduction Toward the end of September 2002, the cold polar vortex in the Southern Hemisphere stratosphere elongated and split in a manner similar to that seen every few years in the Northern Hemisphere stratosphere but never before observed in the Southern Hemisphere. Despite its rarity, the event was predicted accurately up to about a week in advance by the operational forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF). Although the forecasts were
1. Introduction Since 1997, the National Oceanic and Atmospheric Administration (NOAA) has conducted operational synoptic surveillance missions with its Gulfstream-IV (G-IV) jet aircraft around storms expected to impact the United States ( Aberson and Franklin 1999 ). During the first two years of such missions, the impact on operational dynamical track forecast models was less than 10% ( Aberson 2002 ), and this was attributed to suboptimal sampling and data assimilation procedures
1. Introduction Since 1997, the National Oceanic and Atmospheric Administration (NOAA) has conducted operational synoptic surveillance missions with its Gulfstream-IV (G-IV) jet aircraft around storms expected to impact the United States ( Aberson and Franklin 1999 ). During the first two years of such missions, the impact on operational dynamical track forecast models was less than 10% ( Aberson 2002 ), and this was attributed to suboptimal sampling and data assimilation procedures
are described. The results of a pilot forecast are presented, and the implications of this work for both research and operational applications are discussed.1. IntroductionIn the past few years, research in numerical forecasting has gained much impetus. Efforts to integratenumerically a variety of non-linear dynamical modelsof the atmosphere have been made by a number ofgroups, such as the efforts reported by Charney(1954), Charney and Phillips (1953), Charney et al(1950), Staff Members of the
are described. The results of a pilot forecast are presented, and the implications of this work for both research and operational applications are discussed.1. IntroductionIn the past few years, research in numerical forecasting has gained much impetus. Efforts to integratenumerically a variety of non-linear dynamical modelsof the atmosphere have been made by a number ofgroups, such as the efforts reported by Charney(1954), Charney and Phillips (1953), Charney et al(1950), Staff Members of the
