Using the GPS observations to reconstruct the ionosphere three-dimensionally with an ionospheric data assimilation and analysis system (IDAAS)

Abstract The differences in the balance characteristics between dry and precipitation areas in estimated short-term forecast error fields are investigated. The motivation is to see if dry and precipitation areas need to be treated differently in atmospheric data assimilation systems. Using an ensemble of lagged forecast differences, it is shown that perturbations are, on average, farther away from geostrophic balance over precipitation areas than over dry areas and that the deviation from geostrophic balance is proportional to the intensity of precipitation. Following these results, the authors investigate whether some improvements in the coupling between mass and rotational wind increments over precipitation areas can be achieved by using only the precipitation points within an ensemble of estimated forecast errors to construct a so-called diabatic balance operator by linear regression. Comparisons with a traditional approach to construct balance operators by linear regression show that the new approach leads to a gradually significant improvement (related to the intensity of the diabatic processes) of the accuracy of the coupling over precipitation areas as judged from an ensemble of lagged forecast differences. Results from a series of simplified data assimilation experiments show that the new balance operators can produce analysis increments that are substantially different from those associated with the traditional balance operator, particularly for observations located in the lower atmosphere. Issues concerning the implementation of this new approach in a full-fledged analysis system are briefly discussed but their investigations are left for a following study.

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The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis

Journal of Applied Meteorology ◽

10.1175/2091.1 ◽

2004 ◽

Vol 43 (5) ◽

pp. 810-820 ◽

Cited By ~ 15

Author(s):

L. P. Riishøjgaard ◽

R. Atlas ◽

G. D. Emmitt

Keyword(s):

Data Assimilation ◽

Weighted Average ◽

Horizontal Wind ◽

Atmospheric Flow ◽

Single Level ◽

Error Covariance ◽

Analysis System ◽

Background Fields ◽

The Impact ◽

Meteorological Analysis

Abstract Through the use of observation operators, modern data assimilation systems have the capability to ingest observations of quantities that are not themselves model variables but are mathematically related to those variables. An example of this is the so-called line-of-sight (LOS) winds that a spaceborne Doppler wind lidar (DWL) instrument would provide. The model or data assimilation system ideally would need information about both components of the horizontal wind vectors, whereas the observations in this case would provide only the projection of the wind vector onto a given direction. The estimated or analyzed value is then calculated essentially as a weighted average of the observation itself and the model-simulated value of the observed quantity. To assess the expected impact of a DWL, it is important to examine the extent to which a meteorological analysis can be constrained by the LOS winds. The answer to this question depends on the fundamental character of the atmospheric flow fields that are analyzed, but, just as important, it also depends on the real and assumed error covariance characteristics of these fields. A single-level wind analysis system designed to explore these issues has been built at the NASA Data Assimilation Office. In this system, simulated wind observations can be evaluated in terms of their impact on the analysis quality under various assumptions about their spatial distribution and error characteristics and about the error covariance of the background fields. The basic design of the system and experimental results obtained with it are presented. The experiments were designed to illustrate how such a system may be used in the instrument concept definition phase.

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Can a Moderate-Resolution Limited-Area Data Assimilation System Add Value to the Global Analysis of Tropical Cyclones?

Monthly Weather Review ◽

10.1175/mwr-d-12-00093.1 ◽

2013 ◽

Vol 141 (6) ◽

pp. 1866-1883 ◽

Cited By ~ 2

Author(s):

Christina R. Holt ◽

Istvan Szunyogh ◽

Gyorgyi Gyarmati

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Global Analysis ◽

Flow Analysis ◽

Horizontal Resolution ◽

Limited Area ◽

Northwest Pacific ◽

Accurate Analysis ◽

Analysis System ◽

Area Data

Abstract This study investigates the benefits of employing a limited-area data assimilation (DA) system to enhance lower-resolution global analyses in the northwest Pacific tropical cyclone (TC) basin. Numerical experiments are carried out with a global analysis system at horizontal resolution T62 and a limited-area analysis system at resolutions from 200 to 36 km. The global and limited-area DA systems, which are both based on the local ensemble transform Kalman filter algorithm, are implemented using a unique configuration, in which the global DA system provides information about the large-scale analysis and background uncertainty to the limited-area DA system. The limited-area analyses of the storm locations are, on average, more accurate than those from the global analyses, but increasing the resolution of the limited-area system beyond 100 km has little benefit. Two factors contribute to the higher accuracy of the limited-area analyses. First, the limited-area system improves the accuracy of the location estimates for strong storms, which is introduced when the background is updated by the global assimilation. Second, it improves the accuracy of the background estimate of the storm locations for moderate and weak storms. Improvements in the steering flow analysis as a result of increased resolution are modest and short lived in the forecasts. Limited-area track forecasts are more accurate, on average, than global forecasts, independently of the strength of the storms up to five days. This forecast improvement is due to the more accurate analysis of the initial position of storms and the better representation of the interactions between the storms and their immediate environment.

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Introduction of the GSI into the NCEP Global Data Assimilation System

Weather and Forecasting ◽

10.1175/2009waf2222201.1 ◽

2009 ◽

Vol 24 (6) ◽

pp. 1691-1705 ◽

Cited By ~ 258

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.

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Ionospheric data assimilation: recovery of strong mid-latitudinal density gradients

Journal of Atmospheric and Solar-Terrestrial Physics ◽

10.1016/j.jastp.2003.07.004 ◽

2003 ◽

Vol 65 (10) ◽

pp. 1087-1097 ◽

Cited By ~ 8

Author(s):

Jan J. Sojka ◽

Donald C. Thompson ◽

Robert W. Schunk ◽

J.Vincent Eccles ◽

Jonathan J. Makela ◽

...

Keyword(s):

Data Assimilation ◽

Density Gradients ◽

Ionospheric Data Assimilation

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

Monthly Weather Review ◽

10.1175/mwr-d-11-00160.1 ◽

2012 ◽

Vol 140 (5) ◽

pp. 1517-1538 ◽

Cited By ~ 10

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.

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