From 647e2d886bcb26eee799f1a36ac925c9941a6afa Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Mon, 15 Jan 2024 22:18:06 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- content/gsi/01-gsi.html | 60 ++++++++++++++++++++++++++++++++++++----- search.json | 4 +-- sitemap.xml | 22 +++++++-------- 4 files changed, 68 insertions(+), 20 deletions(-) diff --git a/.nojekyll b/.nojekyll index d43c2e7..eacaf0f 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -64686bc6 \ No newline at end of file +04ec72d7 \ No newline at end of file diff --git a/content/gsi/01-gsi.html b/content/gsi/01-gsi.html index 04d0be3..48e822f 100644 --- a/content/gsi/01-gsi.html +++ b/content/gsi/01-gsi.html @@ -20,7 +20,26 @@ margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ vertical-align: middle; } - +/* CSS for citations */ +div.csl-bib-body { } +div.csl-entry { + clear: both; +} +.hanging-indent div.csl-entry { + margin-left:2em; + text-indent:-2em; +} +div.csl-left-margin { + min-width:2em; + float:left; +} +div.csl-right-inline { + margin-left:2em; + padding-left:1em; +} +div.csl-indent { + margin-left: 2em; +} @@ -179,11 +198,11 @@

Intro a GSI

The GSI assimilation system

-

The GSI (Gridpoint Statistical Interpolation) System, is a state-of-the-art data assimilation system initially developed by the Environmental Modeling Center at NCEP. It was designed as a traditional 3DVAR system applied in the gridpoint space of models to facilitate the implementation of inhomogeneous anisotropic covariances [@wu2002; @purser2003a; @purser2003]. It is designed to run on various computational platforms, create analyses for different numerical forecast models, and remain flexible enough to handle future scientific developments, such as the use of new observation types, improved data selection, and new state variables [@kleist2009].

-

The- 3DVAR system replaced the NCEP regional grid-point operational analysis system by the North American Mesoscale Prediction System (NAM) in 2006 and the Spectral Statistical Interpolation (SSI) global analysis system used to generate Global Forecast System (GFS) initial conditions in 2007 [@kleist2009]. In recent years, GSI has evolved to include various data assimilation techniques for multiple operational applications, including 2DVAR [e.g., the Real-Time Mesoscale Analysis (RTMA) system; @pondeca2011], the hybrid EnVar technique (e.g., data assimilation systems for the GFS, the Rapid Refresh system (RAP), the NAM, the HWRF, etc. ), and 4DVAR [e.g., the data assimilation system for NASA’s Goddard Earth Observing System, version 5 (GEOS-5); @zhu2008]. GSI also includes a hybrid 4D-EnVar approach that is currently used for GFS generation.

-

In addition to the development of hybrid techniques, GSI allows the use of ensemble assimilation methods. To achieve this, it uses the same observation operator as the variational methods to compare the preliminary field or background with the observations. In this way the exhaustive quality controls developed for variational methods are also applied in ensemble assimilation methods. The EnKF code was developed by the Earth System Research Lab (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) in collaboration with the scientific community. It contains two different algorithms for calculating the analysis increment, the serial Ensemble Square Root Filter [EnSRF, @whitaker2002] and the LETKF [@hunt2007] contributed by Yoichiro Ota of the Japan Meteorological Agency (JMA).

+

The GSI (Gridpoint Statistical Interpolation) System, is a state-of-the-art data assimilation system initially developed by the Environmental Modeling Center at NCEP. It was designed as a traditional 3DVAR system applied in the gridpoint space of models to facilitate the implementation of inhomogeneous anisotropic covariances (Wu, Purser, and Parrish 2002; Purser et al. 2003b, 2003a). It is designed to run on various computational platforms, create analyses for different numerical forecast models, and remain flexible enough to handle future scientific developments, such as the use of new observation types, improved data selection, and new state variables (Kleist et al. 2009).

+

The- 3DVAR system replaced the NCEP regional grid-point operational analysis system by the North American Mesoscale Prediction System (NAM) in 2006 and the Spectral Statistical Interpolation (SSI) global analysis system used to generate Global Forecast System (GFS) initial conditions in 2007 (Kleist et al. 2009). In recent years, GSI has evolved to include various data assimilation techniques for multiple operational applications, including 2DVAR [e.g., the Real-Time Mesoscale Analysis (RTMA) system; Pondeca et al. (2011)], the hybrid EnVar technique (e.g., data assimilation systems for the GFS, the Rapid Refresh system (RAP), the NAM, the HWRF, etc. ), and 4DVAR [e.g., the data assimilation system for NASA’s Goddard Earth Observing System, version 5 (GEOS-5); Zhu and Gelaro (2008)]. GSI also includes a hybrid 4D-EnVar approach that is currently used for GFS generation.

+

In addition to the development of hybrid techniques, GSI allows the use of ensemble assimilation methods. To achieve this, it uses the same observation operator as the variational methods to compare the preliminary field or background with the observations. In this way the exhaustive quality controls developed for variational methods are also applied in ensemble assimilation methods. The EnKF code was developed by the Earth System Research Lab (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) in collaboration with the scientific community. It contains two different algorithms for calculating the analysis increment, the serial Ensemble Square Root Filter (EnSRF, Whitaker and Hamill 2002) and the LETKF (Hunt, Kostelich, and Szunyogh 2007) contributed by Yoichiro Ota of the Japan Meteorological Agency (JMA).

To reduce the impact of spurious covariances on the increment applied to the analysis, ensemble systems apply a localization to the covariance matrix of the errors of the observations \(R\) in both the horizontal and vertical directions. GSI uses a polynomial of order 5 to reduce the impact of each observation gradually until a limiting distance is reached at which the impact is zero. The vertical location scale is defined in terms of the logarithm of the pressure and the horizontal scale is usually defined in kilometers. These parameters are important in obtaining a good analysis and depend on factors such as the size of the ensemble and the resolution of the model.

-

GSI uses the Community Radiative Transfer Model [CRTM, @liu2008] as an operator for the radiance observations that calculates the brightness temperature simulated by the model in order to compare it with satellite sensor observations. GSI also implements a bias correction algorithm for the satellite radiance observations. The preliminary field estimate with the CRMT is compared with the radiance observations to obtain the innovation. This innovation is then used to calculate a bias that is applied to an updated innovation. This process can be repeated several times until the innovation and the bias correction coefficients converge.

+

GSI uses the Community Radiative Transfer Model (CRTM, Liu et al. 2008) as an operator for the radiance observations that calculates the brightness temperature simulated by the model in order to compare it with satellite sensor observations. GSI also implements a bias correction algorithm for the satellite radiance observations. The preliminary field estimate with the CRMT is compared with the radiance observations to obtain the innovation. This innovation is then used to calculate a bias that is applied to an updated innovation. This process can be repeated several times until the innovation and the bias correction coefficients converge.

Available observations for assimilation

@@ -350,10 +369,39 @@

Running GSI

+
-

Footnotes

+

References

+
+Hunt, Brian R., Eric J. Kostelich, and Istvan Szunyogh. 2007. “Efficient Data Assimilation for Spatiotemporal Chaos: A Local Ensemble Transform Kalman Filter.” Physica D: Nonlinear Phenomena 230 (1-2): 112–26. https://doi.org/10.1016/j.physd.2006.11.008. +
+
+Kleist, Daryl T., David F. Parrish, John C. Derber, Russ Treadon, Wan-Shu Wu, and Stephen Lord. 2009. “Introduction of the GSI into the NCEP Global Data Assimilation System.” Weather and Forecasting 24 (6): 1691–1705. https://doi.org/10.1175/2009WAF2222201.1. +
+
+Liu, Quanhua, Fuzhong Weng, Yong Han, and Paul van Delst. 2008. “Community Radiative Transfer Model for Scattering Transfer and Applications.” In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 4:IV - 1193-IV - 1196. https://doi.org/10.1109/IGARSS.2008.4779942. +
+
+Pondeca, Manuel S. F. V. De, Geoffrey S. Manikin, Geoff DiMego, Stanley G. Benjamin, David F. Parrish, R. James Purser, Wan-Shu Wu, et al. 2011. “The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development.” Weather and Forecasting 26 (5): 593–612. https://doi.org/10.1175/WAF-D-10-05037.1. +
+
+Purser, R. James, Wan-Shu Wu, David F. Parrish, and Nigel M. Roberts. 2003a. “Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part I: Spatially Homogeneous and Isotropic Gaussian Covariances.” Monthly Weather Review 131 (8): 1524–35. https://doi.org/10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2. +
+
+———. 2003b. “Numerical Aspects of the Application of Recursive Filters to Variational Statistical Analysis. Part II: Spatially Inhomogeneous and Anisotropic General Covariances.” Monthly Weather Review 131 (8): 1536–48. https://doi.org/10.1175//2543.1. +
+
+Whitaker, Jeffrey S., and Thomas M. Hamill. 2002. “Ensemble Data Assimilation Without Perturbed Observations.” Monthly Weather Review 130 (7): 1913–24. https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2. +
+
+Wu, Wan-Shu, R. James Purser, and David F. Parrish. 2002. “Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances.” Monthly Weather Review 130 (12): 2905–16. https://doi.org/10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2. +
+
+Zhu, Yanqiu, and Ronald Gelaro. 2008. “Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System.” Monthly Weather Review 136 (1): 335–51. https://doi.org/10.1175/MWR3525.1. +
+

Footnotes

  1. This files need to be downloaded separately as they are to big to be part of the GSI repository. Also the coefficient files can be updated with better approximations over time.↩︎

    2. diff --git a/search.json b/search.json index 6ca0d7e..50ff352 100644 --- a/search.json +++ b/search.json @@ -67,14 +67,14 @@ "href": "content/gsi/01-gsi.html", "title": "Intro a GSI", "section": "", - "text": "The GSI (Gridpoint Statistical Interpolation) System, is a state-of-the-art data assimilation system initially developed by the Environmental Modeling Center at NCEP. It was designed as a traditional 3DVAR system applied in the gridpoint space of models to facilitate the implementation of inhomogeneous anisotropic covariances [@wu2002; @purser2003a; @purser2003]. It is designed to run on various computational platforms, create analyses for different numerical forecast models, and remain flexible enough to handle future scientific developments, such as the use of new observation types, improved data selection, and new state variables [@kleist2009].\nThe- 3DVAR system replaced the NCEP regional grid-point operational analysis system by the North American Mesoscale Prediction System (NAM) in 2006 and the Spectral Statistical Interpolation (SSI) global analysis system used to generate Global Forecast System (GFS) initial conditions in 2007 [@kleist2009]. In recent years, GSI has evolved to include various data assimilation techniques for multiple operational applications, including 2DVAR [e.g., the Real-Time Mesoscale Analysis (RTMA) system; @pondeca2011], the hybrid EnVar technique (e.g., data assimilation systems for the GFS, the Rapid Refresh system (RAP), the NAM, the HWRF, etc. ), and 4DVAR [e.g., the data assimilation system for NASA’s Goddard Earth Observing System, version 5 (GEOS-5); @zhu2008]. GSI also includes a hybrid 4D-EnVar approach that is currently used for GFS generation.\nIn addition to the development of hybrid techniques, GSI allows the use of ensemble assimilation methods. To achieve this, it uses the same observation operator as the variational methods to compare the preliminary field or background with the observations. In this way the exhaustive quality controls developed for variational methods are also applied in ensemble assimilation methods. The EnKF code was developed by the Earth System Research Lab (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) in collaboration with the scientific community. It contains two different algorithms for calculating the analysis increment, the serial Ensemble Square Root Filter [EnSRF, @whitaker2002] and the LETKF [@hunt2007] contributed by Yoichiro Ota of the Japan Meteorological Agency (JMA).\nTo reduce the impact of spurious covariances on the increment applied to the analysis, ensemble systems apply a localization to the covariance matrix of the errors of the observations \\(R\\) in both the horizontal and vertical directions. GSI uses a polynomial of order 5 to reduce the impact of each observation gradually until a limiting distance is reached at which the impact is zero. The vertical location scale is defined in terms of the logarithm of the pressure and the horizontal scale is usually defined in kilometers. These parameters are important in obtaining a good analysis and depend on factors such as the size of the ensemble and the resolution of the model.\nGSI uses the Community Radiative Transfer Model [CRTM, @liu2008] as an operator for the radiance observations that calculates the brightness temperature simulated by the model in order to compare it with satellite sensor observations. GSI also implements a bias correction algorithm for the satellite radiance observations. The preliminary field estimate with the CRMT is compared with the radiance observations to obtain the innovation. This innovation is then used to calculate a bias that is applied to an updated innovation. This process can be repeated several times until the innovation and the bias correction coefficients converge." + "text": "The GSI (Gridpoint Statistical Interpolation) System, is a state-of-the-art data assimilation system initially developed by the Environmental Modeling Center at NCEP. It was designed as a traditional 3DVAR system applied in the gridpoint space of models to facilitate the implementation of inhomogeneous anisotropic covariances (Wu, Purser, and Parrish 2002; Purser et al. 2003b, 2003a). It is designed to run on various computational platforms, create analyses for different numerical forecast models, and remain flexible enough to handle future scientific developments, such as the use of new observation types, improved data selection, and new state variables (Kleist et al. 2009).\nThe- 3DVAR system replaced the NCEP regional grid-point operational analysis system by the North American Mesoscale Prediction System (NAM) in 2006 and the Spectral Statistical Interpolation (SSI) global analysis system used to generate Global Forecast System (GFS) initial conditions in 2007 (Kleist et al. 2009). In recent years, GSI has evolved to include various data assimilation techniques for multiple operational applications, including 2DVAR [e.g., the Real-Time Mesoscale Analysis (RTMA) system; Pondeca et al. (2011)], the hybrid EnVar technique (e.g., data assimilation systems for the GFS, the Rapid Refresh system (RAP), the NAM, the HWRF, etc. ), and 4DVAR [e.g., the data assimilation system for NASA’s Goddard Earth Observing System, version 5 (GEOS-5); Zhu and Gelaro (2008)]. GSI also includes a hybrid 4D-EnVar approach that is currently used for GFS generation.\nIn addition to the development of hybrid techniques, GSI allows the use of ensemble assimilation methods. To achieve this, it uses the same observation operator as the variational methods to compare the preliminary field or background with the observations. In this way the exhaustive quality controls developed for variational methods are also applied in ensemble assimilation methods. The EnKF code was developed by the Earth System Research Lab (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) in collaboration with the scientific community. It contains two different algorithms for calculating the analysis increment, the serial Ensemble Square Root Filter (EnSRF, Whitaker and Hamill 2002) and the LETKF (Hunt, Kostelich, and Szunyogh 2007) contributed by Yoichiro Ota of the Japan Meteorological Agency (JMA).\nTo reduce the impact of spurious covariances on the increment applied to the analysis, ensemble systems apply a localization to the covariance matrix of the errors of the observations \\(R\\) in both the horizontal and vertical directions. GSI uses a polynomial of order 5 to reduce the impact of each observation gradually until a limiting distance is reached at which the impact is zero. The vertical location scale is defined in terms of the logarithm of the pressure and the horizontal scale is usually defined in kilometers. These parameters are important in obtaining a good analysis and depend on factors such as the size of the ensemble and the resolution of the model.\nGSI uses the Community Radiative Transfer Model (CRTM, Liu et al. 2008) as an operator for the radiance observations that calculates the brightness temperature simulated by the model in order to compare it with satellite sensor observations. GSI also implements a bias correction algorithm for the satellite radiance observations. The preliminary field estimate with the CRMT is compared with the radiance observations to obtain the innovation. This innovation is then used to calculate a bias that is applied to an updated innovation. This process can be repeated several times until the innovation and the bias correction coefficients converge." }, { "objectID": "content/gsi/01-gsi.html#the-gsi-assimilation-system", "href": "content/gsi/01-gsi.html#the-gsi-assimilation-system", "title": "Intro a GSI", "section": "", - "text": "The GSI (Gridpoint Statistical Interpolation) System, is a state-of-the-art data assimilation system initially developed by the Environmental Modeling Center at NCEP. It was designed as a traditional 3DVAR system applied in the gridpoint space of models to facilitate the implementation of inhomogeneous anisotropic covariances [@wu2002; @purser2003a; @purser2003]. It is designed to run on various computational platforms, create analyses for different numerical forecast models, and remain flexible enough to handle future scientific developments, such as the use of new observation types, improved data selection, and new state variables [@kleist2009].\nThe- 3DVAR system replaced the NCEP regional grid-point operational analysis system by the North American Mesoscale Prediction System (NAM) in 2006 and the Spectral Statistical Interpolation (SSI) global analysis system used to generate Global Forecast System (GFS) initial conditions in 2007 [@kleist2009]. In recent years, GSI has evolved to include various data assimilation techniques for multiple operational applications, including 2DVAR [e.g., the Real-Time Mesoscale Analysis (RTMA) system; @pondeca2011], the hybrid EnVar technique (e.g., data assimilation systems for the GFS, the Rapid Refresh system (RAP), the NAM, the HWRF, etc. ), and 4DVAR [e.g., the data assimilation system for NASA’s Goddard Earth Observing System, version 5 (GEOS-5); @zhu2008]. GSI also includes a hybrid 4D-EnVar approach that is currently used for GFS generation.\nIn addition to the development of hybrid techniques, GSI allows the use of ensemble assimilation methods. To achieve this, it uses the same observation operator as the variational methods to compare the preliminary field or background with the observations. In this way the exhaustive quality controls developed for variational methods are also applied in ensemble assimilation methods. The EnKF code was developed by the Earth System Research Lab (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) in collaboration with the scientific community. It contains two different algorithms for calculating the analysis increment, the serial Ensemble Square Root Filter [EnSRF, @whitaker2002] and the LETKF [@hunt2007] contributed by Yoichiro Ota of the Japan Meteorological Agency (JMA).\nTo reduce the impact of spurious covariances on the increment applied to the analysis, ensemble systems apply a localization to the covariance matrix of the errors of the observations \\(R\\) in both the horizontal and vertical directions. GSI uses a polynomial of order 5 to reduce the impact of each observation gradually until a limiting distance is reached at which the impact is zero. The vertical location scale is defined in terms of the logarithm of the pressure and the horizontal scale is usually defined in kilometers. These parameters are important in obtaining a good analysis and depend on factors such as the size of the ensemble and the resolution of the model.\nGSI uses the Community Radiative Transfer Model [CRTM, @liu2008] as an operator for the radiance observations that calculates the brightness temperature simulated by the model in order to compare it with satellite sensor observations. GSI also implements a bias correction algorithm for the satellite radiance observations. The preliminary field estimate with the CRMT is compared with the radiance observations to obtain the innovation. This innovation is then used to calculate a bias that is applied to an updated innovation. This process can be repeated several times until the innovation and the bias correction coefficients converge." + "text": "The GSI (Gridpoint Statistical Interpolation) System, is a state-of-the-art data assimilation system initially developed by the Environmental Modeling Center at NCEP. It was designed as a traditional 3DVAR system applied in the gridpoint space of models to facilitate the implementation of inhomogeneous anisotropic covariances (Wu, Purser, and Parrish 2002; Purser et al. 2003b, 2003a). It is designed to run on various computational platforms, create analyses for different numerical forecast models, and remain flexible enough to handle future scientific developments, such as the use of new observation types, improved data selection, and new state variables (Kleist et al. 2009).\nThe- 3DVAR system replaced the NCEP regional grid-point operational analysis system by the North American Mesoscale Prediction System (NAM) in 2006 and the Spectral Statistical Interpolation (SSI) global analysis system used to generate Global Forecast System (GFS) initial conditions in 2007 (Kleist et al. 2009). In recent years, GSI has evolved to include various data assimilation techniques for multiple operational applications, including 2DVAR [e.g., the Real-Time Mesoscale Analysis (RTMA) system; Pondeca et al. (2011)], the hybrid EnVar technique (e.g., data assimilation systems for the GFS, the Rapid Refresh system (RAP), the NAM, the HWRF, etc. ), and 4DVAR [e.g., the data assimilation system for NASA’s Goddard Earth Observing System, version 5 (GEOS-5); Zhu and Gelaro (2008)]. GSI also includes a hybrid 4D-EnVar approach that is currently used for GFS generation.\nIn addition to the development of hybrid techniques, GSI allows the use of ensemble assimilation methods. To achieve this, it uses the same observation operator as the variational methods to compare the preliminary field or background with the observations. In this way the exhaustive quality controls developed for variational methods are also applied in ensemble assimilation methods. The EnKF code was developed by the Earth System Research Lab (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) in collaboration with the scientific community. It contains two different algorithms for calculating the analysis increment, the serial Ensemble Square Root Filter (EnSRF, Whitaker and Hamill 2002) and the LETKF (Hunt, Kostelich, and Szunyogh 2007) contributed by Yoichiro Ota of the Japan Meteorological Agency (JMA).\nTo reduce the impact of spurious covariances on the increment applied to the analysis, ensemble systems apply a localization to the covariance matrix of the errors of the observations \\(R\\) in both the horizontal and vertical directions. GSI uses a polynomial of order 5 to reduce the impact of each observation gradually until a limiting distance is reached at which the impact is zero. The vertical location scale is defined in terms of the logarithm of the pressure and the horizontal scale is usually defined in kilometers. These parameters are important in obtaining a good analysis and depend on factors such as the size of the ensemble and the resolution of the model.\nGSI uses the Community Radiative Transfer Model (CRTM, Liu et al. 2008) as an operator for the radiance observations that calculates the brightness temperature simulated by the model in order to compare it with satellite sensor observations. GSI also implements a bias correction algorithm for the satellite radiance observations. The preliminary field estimate with the CRMT is compared with the radiance observations to obtain the innovation. This innovation is then used to calculate a bias that is applied to an updated innovation. 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