scholarly journals Improving Incremental Balance in the GSI 3DVAR Analysis System

2009 ◽  
Vol 137 (3) ◽  
pp. 1046-1060 ◽  
Author(s):  
Daryl T. Kleist ◽  
David F. Parrish ◽  
John C. Derber ◽  
Russ Treadon ◽  
Ronald M. Errico ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Normal Mode ◽  
Three Dimensional ◽  
Substantial Improvement ◽  
Environmental Modeling ◽  
Variational Data Assimilation ◽  
North American Data ◽  
Analysis System ◽  
Environmental Prediction ◽  
Statistical Interpolation

Abstract The gridpoint statistical interpolation (GSI) analysis system is a unified global/regional three-dimensional variational data assimilation (3DVAR) analysis code that has been under development for several years at the National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center. It has recently been implemented into operations at NCEP in both the global and North American data assimilation systems (GDAS and NDAS, respectively). An important aspect of this development has been improving the balance of the analysis produced by GSI. The improved balance between variables has been achieved through the inclusion of a tangent-linear normal-mode constraint (TLNMC). The TLNMC method has proven to be very robust and effective. The TLNMC as part of the global GSI system has resulted in substantial improvement in data assimilation at NCEP.

Predictions of 2010’s Tropical Cyclones Using the GFS and Ensemble-Based Data Assimilation Methods

2011 ◽  
Vol 139 (10) ◽  
pp. 3243-3247 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Daryl T. Kleist ◽  
Michael Fiorino ◽  
Stanley G. Benjamin
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Successful Testing ◽  
Gfs Model ◽  
Environmental Prediction ◽  
Statistical Interpolation

Abstract Experimental ensemble predictions of tropical cyclone (TC) tracks from the ensemble Kalman filter (EnKF) using the Global Forecast System (GFS) model were recently validated for the 2009 Northern Hemisphere hurricane season by Hamill et al. A similar suite of tests is described here for the 2010 season. Two major changes were made this season: 1) a reduction in the resolution of the GFS model, from 2009’s T384L64 (~31 km at 25°N) to 2010’s T254L64 (~47 km at 25°N), and some changes in model physics; and 2) the addition of a limited test of deterministic forecasts initialized from a hybrid three-dimensional variational data assimilation (3D-Var)/EnKF method. The GFS/EnKF ensembles continued to produce reduced track errors relative to operational ensemble forecasts created by the National Centers for Environmental Prediction (NCEP), the Met Office (UKMO), and the Canadian Meteorological Centre (CMC). The GFS/EnKF was not uniformly as skillful as the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. GFS/EnKF track forecasts had slightly higher error than ECMWF at longer leads, especially in the western North Pacific, and exhibited poorer calibration between spread and error than in 2009, perhaps in part because of lower model resolution. Deterministic forecasts from the hybrid were competitive with deterministic EnKF ensemble-mean forecasts and superior in track error to those initialized from the operational variational algorithm, the Gridpoint Statistical Interpolation (GSI). Pending further successful testing, the National Oceanic and Atmospheric Administration (NOAA) intends to implement the global hybrid system operationally for data assimilation.

Introduction of the GSI into the NCEP Global Data Assimilation System

2009 ◽  
Vol 24 (6) ◽  
pp. 1691-1705 ◽  
Author(s):  
Daryl T. Kleist ◽  
David F. Parrish ◽  
John C. Derber ◽  
Russ Treadon ◽  
Wan-Shu Wu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Environmental Modeling ◽  
Analysis Scheme ◽  
Global Data Assimilation System ◽  
Analysis System ◽  
Data Assimilation System ◽  
Global Data ◽  
Statistical Interpolation ◽  
New System ◽  
Assimilation System

Abstract At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains. Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

scholarly journals Four-Dimensional Variational Data Assimilation for the Canadian Regional Deterministic Prediction System

2012 ◽  
Vol 140 (5) ◽  
pp. 1517-1538 ◽  
Author(s):  
Monique Tanguay ◽  
Luc Fillion ◽  
Ervig Lapalme ◽  
Manon Lajoie
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Radiosonde Data ◽  
Second Step ◽  
Lateral Boundary Conditions ◽  
Analysis System ◽  
Horizontal Grid ◽  
Data Assimilation System

Abstract As a second step in the development of the Canadian Regional Data Assimilation System following Fillion et al., this study extends the approach to the four-dimensional variational data assimilation (4D-Var) context. Emphasis is first put on illustrating the importance of controlling lateral boundary conditions (LBCs). The use in the minimization of a horizontal grid over a domain exceeding the horizontal grid of the high-resolution nonlinear model is then proposed. The authors examine the performance of this 4D-Var formulation as an upcoming upgrade to the currently operational regional three-dimensional variational data assimilation (3D-Var) system. Forecast verifications against radiosonde data for 118 winter cases and 118 summer cases were performed. Results indicate a slight positive impact up to 48 h against North American radiosondes, but with a significant positive impact (especially for winds) at mid- and high latitudes during the summer. Accumulated precipitation scores over 24 h, whether during the first or second day of the forecasts, are slightly improved. The regional 4D-Var analysis system described in this study can run within current real-time “regional run” allocation for operations at the Canadian Meteorological Center (CMC). Future improvements of this system are briefly mentioned especially regarding the upcoming computer upgrade at CMC.

scholarly journals GSI Three-Dimensional Ensemble–Variational Hybrid Data Assimilation Using a Global Ensemble for the Regional Rapid Refresh Model

2017 ◽  
Vol 145 (10) ◽  
pp. 4205-4225 ◽  
Author(s):  
Ming Hu ◽  
Stanley G. Benjamin ◽  
Therese T. Ladwig ◽  
David C. Dowell ◽  
Stephen S. Weygandt ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Three Dimensional ◽  
Covariance Structure ◽  
Winter Period ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Perturbation Data ◽  
Analysis System ◽  
Environmental Prediction

The Rapid Refresh (RAP) is an hourly updated regional meteorological data assimilation/short-range model forecast system running operationally at NOAA/National Centers for Environmental Prediction (NCEP) using the community Gridpoint Statistical Interpolation analysis system (GSI). This paper documents the application of the GSI three-dimensional hybrid ensemble–variational assimilation option to the RAP high-resolution, hourly cycling system and shows the skill improvements of 1–12-h forecasts of upper-air wind, moisture, and temperature over the purely three-dimensional variational analysis system. Use of perturbation data from an independent global ensemble, the Global Data Assimilation System (GDAS), is demonstrated to be very effective for the regional RAP hybrid assimilation. In this paper, application of the GSI-hybrid assimilation for the RAP is explained. Results from sensitivity experiments are shown to define configurations for the operational RAP version 2, the ratio of static and ensemble background error covariance, and vertical and horizontal localization scales for the operational RAP version 3. Finally, a 1-week RAP experiment from a summer period was performed using a global ensemble from a winter period, suggesting that a significant component of its multivariate covariance structure from the ensemble is independent of time matching between analysis time and ensemble valid time.

scholarly journals An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part I: System Description and 3D-Hybrid Results

2015 ◽  
Vol 143 (2) ◽  
pp. 433-451 ◽  
Author(s):  
Daryl T. Kleist ◽  
Kayo Ide
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Forecast Errors ◽  
Weather Forecasts ◽  
Global Data Assimilation System ◽  
Analysis Error ◽  
Environmental Prediction ◽  
The Impact

Abstract An observing system simulation experiment (OSSE) has been carried out to evaluate the impact of a hybrid ensemble–variational data assimilation algorithm for use with the National Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE provides a controlled framework for evaluating analysis and forecast errors since a truth is known. In this case, the nature run was generated and provided by the European Centre for Medium-Range Weather Forecasts as part of the international Joint OSSE project. The assimilation and forecast impact studies are carried out using a model that is different than the nature run model, thereby accounting for model error and avoiding issues with the so-called identical-twin experiments. It is found that the quality of analysis is improved substantially when going from three-dimensional variational data assimilation (3DVar) to a hybrid 3D ensemble–variational (EnVar)-based algorithm. This is especially true in terms of the analysis error reduction for wind and moisture, most notably in the tropics. Forecast impact experiments show that the hybrid-initialized forecasts improve upon the 3DVar-based forecasts for most metrics, lead times, variables, and levels. An additional experiment that utilizes 3DEnVar (100% ensemble) demonstrates that the use of a 25% static error covariance contribution does not alter the quality of hybrid analysis when utilizing the tangent-linear normal mode constraint on the total hybrid increment.

scholarly journals The Influence of Assimilating Dropsonde Data on Typhoon Track and Midlatitude Forecasts

2011 ◽  
Vol 139 (3) ◽  
pp. 908-920 ◽  
Author(s):  
Martin Weissmann ◽  
Florian Harnisch ◽  
Chun-Chieh Wu ◽  
Po-Hsiung Lin ◽  
Yoichiro Ohta ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Variational Data Assimilation ◽  
Japan Meteorological Agency ◽  
Limited Area ◽  
Track Forecast ◽  
Forecast Errors ◽  
Typhoon Track ◽  
Environmental Prediction

Abstract A unique dataset of targeted dropsonde observations was collected during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in the autumn of 2008. The campaign was supplemented by an enhancement of the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. For the first time, up to four different aircraft were available for typhoon observations and over 1500 additional soundings were collected. This study investigates the influence of assimilating additional observations during the two major typhoon events of T-PARC on the typhoon track forecast by the global models of the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction (NCEP), and the limited-area Weather Research and Forecasting (WRF) model. Additionally, the influence of T-PARC observations on ECMWF midlatitude forecasts is investigated. All models show an improving tendency of typhoon track forecasts, but the degree of improvement varied from about 20% to 40% in NCEP and WRF to a comparably low influence in ECMWF and JMA. This is likely related to lower track forecast errors without dropsondes in the latter two models, presumably caused by a more extensive use of satellite data and four-dimensional variational data assimilation (4D-Var) of ECMWF and JMA compared to three-dimensional variational data assimilation (3D-Var) of NCEP and WRF. The different behavior of the models emphasizes that the benefit gained strongly depends on the quality of the first-guess field and the assimilation system.

scholarly journals A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

2018 ◽  
Vol XLII-3 ◽  
pp. 2215-2218
Author(s):  
Z. Zang ◽  
X. Pan ◽  
W. You ◽  
Y. Liang
Keyword(s):  
Data Assimilation ◽  
Local Time ◽  
Three Dimensional ◽  
Surface Concentration ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Aircraft Observations ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

Sign in / Sign up

Export Citation Format

Share Document