scholarly journals A variable-resolution stretched-grid general circulation model and data assimilation system with multiple areas of interest: Studying the anomalous regional climate events of 1998

2002 ◽  
Vol 107 (D24) ◽  
Author(s):  
Michael S. Fox-Rabinovitz
Keyword(s):  
Data Assimilation ◽  
General Circulation Model ◽  
General Circulation ◽  
Regional Climate ◽  
Circulation Model ◽  
Variable Resolution ◽  
Areas Of Interest ◽  
Climate Events ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals An ensemble Kalman filter data assimilation system for the whole neutral atmosphere

2020 ◽  
Vol 13 (7) ◽  
pp. 3145-3177
Author(s):  
Dai Koshin ◽  
Kaoru Sato ◽  
Kazuyuki Miyazaki ◽  
Shingo Watanabe
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
General Circulation ◽  
Lower Thermosphere ◽  
Localization Length ◽  
Circulation Model ◽  
Ensemble Member ◽  
Neutral Atmosphere ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system with a four-dimensional local ensemble transform Kalman filter (4D-LETKF) is developed to make a new analysis dataset for the atmosphere up to the lower thermosphere using the Japanese Atmospherics General Circulation model for Upper Atmosphere Research. The time period from 10 January to 20 February 2017, when an international radar network observation campaign was performed, is focused on. The model resolution is T42L124, which can resolve phenomena at synoptic and larger scales. A conventional observation dataset provided by the National Centers for Environmental Prediction, PREPBUFR, and satellite temperature data from the Aura Microwave Limb Sounder (MLS) for the stratosphere and mesosphere are assimilated. First, the performance of the forecast model is improved by modifying the vertical profile of the horizontal diffusion coefficient and modifying the source intensity in the non-orographic gravity wave parameterization by comparing it with radar wind observations in the mesosphere. Second, the MLS observational bias is estimated as a function of the month and latitude and removed before the data assimilation. Third, data assimilation parameters, such as the degree of gross error check, localization length, inflation factor, and assimilation window, are optimized based on a series of sensitivity tests. The effect of increasing the ensemble member size is also examined. The obtained global data are evaluated by comparison with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data covering pressure levels up to 0.1 hPa and by the radar mesospheric observations, which are not assimilated.

scholarly journals Incremental Analysis Update Implementation into a Sequential Ocean Data Assimilation System

2006 ◽  
Vol 23 (12) ◽  
pp. 1729-1744 ◽  
Author(s):  
Y. Ourmières ◽  
J-M. Brankart ◽  
L. Berline ◽  
P. Brasseur ◽  
J. Verron
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
General Circulation ◽  
Circulation Model ◽  
Low Rank ◽  
Incremental Analysis ◽  
High Frequency Oscillations ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This study deals with the enhancement of a sequential assimilation method applied to an ocean general circulation model (OGCM). A major drawback of sequential assimilation methods is the time discontinuity of the solution resulting from intermittent corrections of the model state. The data analysis step can induce shocks in the model restart phase, causing spurious high-frequency oscillations and data rejection. A method called Incremental Analysis Update (IAU) is now recognized to efficiently tackle these problems. In the present work, an IAU-type method is implemented into an intermittent data assimilation system using a low-rank Kalman filter [Singular Evolutive Extended Kalman (SEEK)] in the case of an OGCM with a 1/3° North Atlantic grid. A 1-yr (1993) experiment has been conducted for different setups in order to evaluate the impact of the IAU scheme. Results from all of the different tests are compared with a specific interest in high-frequency output behaviors and solution consistency. The improvements brought up by the IAU implementation, such as the disappearance of spurious high-frequency oscillations and the time continuity of the solution, are shown. An overall assessment of the impact of this new approach on the assimilated runs is discussed. Advantages and drawbacks of the IAU method are pointed out.

scholarly journals An ensemble Kalman filter data assimilation system for the whole neutral atmosphere

2019 ◽  
Author(s):  
Dai Koshin ◽  
Kaoru Sato ◽  
Kazuyuki Miyazaki ◽  
Shingo Watanabe
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
General Circulation ◽  
Lower Thermosphere ◽  
Localization Length ◽  
Circulation Model ◽  
Ensemble Member ◽  
Neutral Atmosphere ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system with a four-dimensional local ensemble transform Kalman filter (4D-LETKF) is developed to make a new analysis data for the atmosphere up to the lower thermosphere using the Japanese Atmospherics General Circulation model for Upper Atmosphere Research. The time period from 10 January 2017 to 20 February 2017, when an international radar network observation campaign was performed, is focused on. The model resolution is T42L124 which can resolve phenomena at synoptic and larger scales. A conventional observation dataset provided by National Centers for Environmental Prediction, PREPBUFR, and satellite temperature data from the Aura Microwave Limb Sounder (MLS) for the stratosphere and mesosphere are assimilated. First, the performance of the forecast model is improved by modifying the vertical profile of the horizontal diffusion coefficient and modifying the source intensity in the non-orographic gravity wave parameterization, by comparing it with radar wind observations in the mesosphere. Second, the MLS observational bias is estimated as a function of the month and latitude and removed before the data assimilation. Third, data assimilation parameters, such as the degree of gross error check, localization length, inflation factor, and assimilation window are optimized based on a series of sensitivity tests. The effect of increasing the ensemble member size is also examined. The obtained global data are evaluated by comparison with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data covering pressure levels up to 0.1 hPa and by the radar mesospheric observations which are not assimilated.

An ensemble Kalman filter data assimilation system for the whole neutral atmosphere

2020 ◽  
Author(s):  
Dai Koshin ◽  
Kaoru Sato ◽  
Kazuyuki Miyazaki ◽  
Shingo Watanabe
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
General Circulation ◽  
Lower Thermosphere ◽  
Localization Length ◽  
Circulation Model ◽  
Ensemble Member ◽  
Neutral Atmosphere ◽  
Data Assimilation System ◽  
Assimilation System

<p>A data assimilation system with a four-dimensional local ensemble transform Kalman filter (4D-LETKF) is developed to make a new analysis data for the atmosphere up to the lower thermosphere using the Japanese Atmospherics General Circulation model for Upper Atmosphere Research. The time period from 10 January 2017 to 20 February 2017, when an international radar network observation campaign was performed, is focused on. The model resolution is T42L124 which can resolve phenomena at synoptic and larger scales. A conventional observation dataset provided by National Centers for Environmental Prediction, PREPBUFR, and satellite temperature data from the Aura Microwave Limb Sounder (MLS) for the stratosphere and mesosphere are assimilated. First, the performance of the forecast model is improved by modifying the vertical profile of the horizontal diffusion coefficient and modifying the source intensity in the non-orographic gravity wave parameterization, by comparing it with radar wind observations in the mesosphere. Second, the MLS observational bias is estimated as a function of the month and latitude and removed before the data assimilation. Third, data assimilation parameters, such as the degree of gross error check, localization length, inflation factor, and assimilation window are optimized based on a series of sensitivity tests. The effect of increasing the ensemble member size is also examined. The obtained global data are evaluated by comparison with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data covering pressure levels up to 0.1 hPa and by the radar mesospheric observations which are not assimilated.</p>

scholarly journals An Optimum Initial Manifold for Improved Skill and Lead in Long-range Forecasting of Monsoon Variability

2021 ◽  
Author(s):  
K C GOUDA ◽  
S Joshi ◽  
Nagaraj Bhat
Keyword(s):  
General Circulation Model ◽  
Long Range ◽  
Present Method ◽  
General Circulation ◽  
Circulation Model ◽  
Ensemble Forecast ◽  
Variable Resolution ◽  
Monsoon Variability ◽  
Initial States ◽  
Scale 8

Abstract Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~ 60km) and lead (2–5 months) considered here the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June-August) scale. 8-member ensemble average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented.

Sign in / Sign up

Export Citation Format

Share Document