Implementation of aerosol assimilation in Gridpoint Statistical Interpolation v. 3.2 and WRF-Chem v. 4.3.1

Abstract. Gridpoint Statistical Interpolation (GSI) is an assimilation tool that is used at the National Centers for Environmental Prediction in operational weather forecasting. In this article we describe implementation of an extension to the GSI for assimilating surface measurements of PM2.5, PM10, and MODIS Aerosol Optical Depth at 550 nm with WRF-Chem. We also present illustrations of the results. In the past the aerosol assimilation system has been employed to issue daily PM2.5 forecasts at NOAA/ESRL and, in our belief, is well tested and mature enough to make available for wider use. We provide a package that, in addition to augmented GSI, consists of software for calculating background error covariance statistics and for converting in-situ and satellite data to BUFR format, plus sample input files for an assimilation exercise. Thanks to flexibility in the GSI and coupled meteorology-chemistry of WRF-Chem, assimilating aerosol observations can be carried out simultaneously with meteorological data assimilation. Both GSI and WRF-Chem are well documented with user guides available on-line. This article is primarily intended as a technical note on the implementation of the aerosol assimilation. Its purpose is also to provide guidance for prospective users of the computer code. Limited space is devoted to scientific aspects of aerosol assimilation.

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Implementation of aerosol assimilation in Gridpoint Statistical Interpolation (v. 3.2) and WRF-Chem (v. 3.4.1)

Geoscientific Model Development ◽

10.5194/gmd-7-1621-2014 ◽

2014 ◽

Vol 7 (4) ◽

pp. 1621-1627 ◽

Cited By ~ 17

Author(s):

M. Pagowski ◽

Z. Liu ◽

G. A. Grell ◽

M. Hu ◽

H.-C. Lin ◽

...

Keyword(s):

Weather Forecasting ◽

Meteorological Data ◽

Computer Code ◽

Technical Note ◽

Background Error ◽

Error Covariance ◽

The Usa ◽

Surface Measurements ◽

Environmental Prediction ◽

Statistical Interpolation

Abstract. Gridpoint Statistical Interpolation (GSI) is an assimilation tool that is used at the National Centers for Environmental Prediction (NCEP) in operational weather forecasting in the USA. In this article, we describe implementation of an extension to the GSI for assimilating surface measurements of PM2.5, PM10, and MODIS aerosol optical depth at 550 nm with WRF-Chem (Weather Research and Forecasting model coupled with Chemistry). We also present illustrative results. In the past, the aerosol assimilation system has been employed to issue daily PM2.5 forecasts at NOAA/ESRL (Earth System Research Laboratory) and, we believe, it is well tested and mature enough to be made available for wider use. We provide a package that, in addition to augmented GSI, consists of software for calculating background error covariance statistics and for converting in situ and satellite data to BUFR (Binary Universal Form for the Representation of meteorological data) format, and sample input files for an assimilation exercise. Thanks to flexibility in the GSI and coupled meteorology–chemistry of WRF-Chem, assimilating aerosol observations can be carried out simultaneously with meteorological data assimilation. Both GSI and WRF-Chem are well documented with user guides available online. This article is primarily intended to be a technical note on the implementation of the aerosol assimilation. Its purpose is also to provide guidance for prospective users of the computer code. Scientific aspects of aerosol assimilation are also briefly discussed.

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Scale-Dependent Background Error Covariance Localization: Evaluation in a Global Deterministic Weather Forecasting System

Monthly Weather Review ◽

10.1175/mwr-d-17-0369.1 ◽

2018 ◽

Vol 146 (5) ◽

pp. 1367-1381 ◽

Cited By ~ 10

Author(s):

Jean-François Caron ◽

Mark Buehner

Keyword(s):

Data Assimilation ◽

Weather Forecasting ◽

Spatial Scales ◽

Winter Period ◽

Wind Fields ◽

Horizontal Scale ◽

Background Error ◽

Background Error Covariance ◽

Error Covariance ◽

Forecasting System

Abstract Scale-dependent localization (SDL) consists of applying the appropriate (i.e., different) amount of localization to different ranges of background error covariance spatial scales while simultaneously assimilating all of the available observations. The SDL method proposed by Buehner and Shlyaeva for ensemble–variational (EnVar) data assimilation was tested in a 3D-EnVar version of the Canadian operational global data assimilation system. It is shown that a horizontal-scale-dependent horizontal localization leads to implicit vertical-level-dependent, variable-dependent, and location-dependent horizontal localization. The results from data assimilation cycles show that horizontal-scale-dependent horizontal covariance localization is able to improve the forecasts up to day 5 in the Northern Hemisphere extratropical summer period and up to day 7 in the Southern Hemisphere extratropical winter period. In the tropics, use of SDL results in improvements similar to what can be obtained by increasing the uniform amount of spatial localization. An investigation of the dynamical balance in the resulting analysis increments demonstrates that SDL does not further harm the balance between the mass and the rotational wind fields, as compared to the traditional localization approach. Potential future applications for the SDL method are also discussed.

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The impacts of uncertainties in emissions on aerosol data assimilation and short-term PM<sub>2.5</sub> predictions in CMAQ v5.2.1 over East Asia

10.5194/gmd-2020-116 ◽

2020 ◽

Author(s):

Sojin Lee ◽

Chul Han Song ◽

Kyung Man Han ◽

Daven K. Henze ◽

Kyunghwa Lee ◽

...

Keyword(s):

Air Quality ◽

Data Assimilation ◽

East Asia ◽

Conventional Method ◽

Meteorological Data ◽

Three Dimensional ◽

Short Term ◽

Background Error ◽

Multi Scale ◽

Error Covariance

Abstract. For the purpose of improving PM prediction skills in East Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations that accounts for the uncertainties in anthropogenic emissions. In contrast to the conventional method to estimate the BEC that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over East Asia using Community Multi-scale Air Quality (CMAQ) initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1,535 sites in China were assimilated every six hours during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–14 June 2016). Data assimilation with our new BEC showed better agreement with the surface PM2.5 observations than that with the conventional method. Our method also showed closer agreement with the observations in 24-hour PM2.5 predictions with ~ 44 % fewer negative biases than the conventional method. We conclude that increased standard deviations, together with horizontal and vertical length scales in the new BEC, tend to improve the data assimilation and short-term predictions for the surface PM2.5. This paper also suggests further research efforts devoted to estimating the BEC to improve PM2.5 predictions.

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Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of convective storms

10.5194/gmd-2021-289 ◽

2021 ◽

Author(s):

Ivette H. Banos ◽

Will D. Mayfield ◽

Guoqing Ge ◽

Luiz F. Sapucci ◽

Jacob R. Carley ◽

...

Keyword(s):

Data Assimilation ◽

Current Data ◽

Dew Point ◽

Squall Line ◽

Forecast System ◽

Background Error ◽

Convective Storms ◽

Error Covariance ◽

Convective Scale ◽

Environmental Prediction

Abstract. The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional and convective scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. Results show that a baseline RRFS run without data assimilation is able to represent the observed convection, but with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection and precipitation are overforecast in most forecast hours when using planetary boundary layer pseudo-observations, but the root mean square error and bias of the 2 h forecast of 2 m dew point temperature are reduced by 1.6 K during the afternoon hours. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.

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Refinement of CrIS channel selection for global data assimilation and its impact on the global weather forecast

Weather and Forecasting ◽

10.1175/waf-d-21-0002.1 ◽

2021 ◽

Author(s):

Young-Chan Noh ◽

Hung-Lung Huang ◽

Mitchell D. Goldberg

Keyword(s):

Water Vapor ◽

Data Assimilation ◽

Weather Forecasting ◽

Selection Process ◽

Forecast Error ◽

Weather Forecast ◽

Error Covariance ◽

Environmental Prediction ◽

Cross Track ◽

Global Data

AbstractTo maximize the contribution of the Cross-track Infrared Sounder (CrIS) measurements to the global weather forecasting, we attempt to choose the CrIS channels to be assimilated in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). From pre-selected 431 CrIS channels, 207 channels are newly selected using a one-dimensional variational (1D-Var) approach where the channel score index (CSI) is used as a figure of merit. Newly selected 207 channels consist of 85 temperature, 49 water vapor, and 73 surface channels, respectively. In addition, to examine how the channels are selected if the forecast error covariance is differently defined depending on the latitudinal regions (i.e., Northern and Southern Hemispheres, and tropics), the same selection process is carried out repeatedly using three regional forecast error covariances. From three regional channel sets, two-channel sets are made for the global data assimilation. One channel set is made with 134 channels overlapped between three regional channel sets. Another channel set consists of 277 channels that is the sum of three regional channel sets. In the global trial experiments, the global CrIS 207 channels have a significant positive forecast impact in terms of the improvement of GFS global forecasting, as compared with the forecasts with the operational 100 channels as well as the overlapped 134 and the union 277 channel sets. The improved forecast is mainly due to the additional temperature/water vapor channels of the global CrIS 207 channels that are selected optimally based on the global forecast error of operational GFS.

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Growth of Forecast Errors from Covariances Modeled by 4DVAR and ETKF Methods

Monthly Weather Review ◽

10.1175/2010mwr3182.1 ◽

2011 ◽

Vol 139 (5) ◽

pp. 1505-1518 ◽

Cited By ~ 4

Author(s):

Chiara Piccolo

Keyword(s):

Weather Forecasting ◽

Initial Conditions ◽

Forecast Error ◽

Forecast Errors ◽

Model Errors ◽

Background Error ◽

Background Error Covariance ◽

Error Covariance ◽

Numerical Weather Forecasting ◽

Ensemble Transform Kalman Filter

Numerical weather forecasting errors grow with time. Error growth results from the amplification of small perturbations due to atmospheric instability or from model deficiencies during model integration. In current NWP systems, the dimension of the forecast error covariance matrices is far too large for these matrices to be represented explicitly. They must be approximated. This paper focuses on comparing the growth of forecast error from covariances modeled by the Met Office operational four-dimensional variational data assimilation (4DVAR) and ensemble transform Kalman filter (ETKF) methods over a period of 24 h. The growth of forecast errors implied by 4DVAR is estimated by drawing a random sample of initial conditions from a Gaussian distribution with the standard deviations given by the background error covariance matrix and then evolving the sample forward in time using linearized dynamics. The growth of the forecast error modeled by the ETKF is estimated by propagating the full nonlinear model in time starting from initial conditions generated by an ETKF. This method includes model errors in two ways: by using an inflation factor and by adding model perturbations through a stochastic physics scheme. Finally, these results are compared with a benchmark of the climatological error. The forecast error predicted by the implicit evolution of 4DVAR does not grow, regardless of the dataset used to generate the static background error covariance statistics. The forecast error predicted by the ETKF grows more rapidly because the ETKF selects balanced initial perturbations, which project onto rapidly growing modes. Finally, in both cases it is not possible to disentangle the contribution of the initial condition error from the model error.

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

Monthly Weather Review ◽

10.1175/mwr-d-16-0418.1 ◽

2017 ◽

Vol 145 (10) ◽

pp. 4205-4225 ◽

Cited By ~ 16

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.

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Incorporating Ensemble Covariance in the Gridpoint Statistical Interpolation Variational Minimization: A Mathematical Framework

Monthly Weather Review ◽

10.1175/2010mwr3245.1 ◽

2010 ◽

Vol 138 (7) ◽

pp. 2990-2995 ◽

Cited By ~ 81

Author(s):

Xuguang Wang

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Mathematical Framework ◽

Dependent Manner ◽

Error Statistics ◽

Background Error ◽

Background Error Covariance ◽

Error Covariance ◽

Variational Minimization ◽

Statistical Interpolation

Abstract Gridpoint statistical interpolation (GSI), a three-dimensional variational data assimilation method (3DVAR) has been widely used in operations and research in numerical weather prediction. The operational GSI uses a static background error covariance, which does not reflect the flow-dependent error statistics. Incorporating ensemble covariance in GSI provides a natural way to estimate the background error covariance in a flow-dependent manner. Different from other 3DVAR-based hybrid data assimilation systems that are preconditioned on the square root of the background error covariance, commonly used GSI minimization is preconditioned upon the full background error covariance matrix. A mathematical derivation is therefore provided to demonstrate how to incorporate the flow-dependent ensemble covariance in the GSI variational minimization.

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The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA)

Remote Sensing ◽

10.3390/rs13030426 ◽

2021 ◽

Vol 13 (3) ◽

pp. 426

Author(s):

Zheng Qi Wang ◽

Roger Randriamampianina

Keyword(s):

Weather Prediction ◽

Deterministic System ◽

Observation Error ◽

Key Factors ◽

Background Error ◽

Current Configuration ◽

Error Covariance ◽

Regional Reanalysis ◽

The Impact ◽

European Regional

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%.

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Impact of AMSU-A Radiances in a Column Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr-d-18-0093.1 ◽

2018 ◽

Vol 146 (12) ◽

pp. 3949-3976 ◽

Cited By ~ 2

Author(s):

Herschel L. Mitchell ◽

P. L. Houtekamer ◽

Sylvain Heilliette

Keyword(s):

Covariance Matrix ◽

Background Error ◽

Error Covariance Matrix ◽

Background Error Covariance ◽

Error Covariance ◽

Microwave Sounding Unit ◽

The Matrix ◽

Microwave Sounding ◽

Background Temperature ◽

The Difference

Abstract A column EnKF, based on the Canadian global EnKF and using the RTTOV radiative transfer (RT) model, is employed to investigate issues relating to the EnKF assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) radiance measurements. Experiments are performed with large and small ensembles, with and without localization. Three different descriptions of background temperature error are considered: 1) using analytical vertical modes and hypothetical spectra, 2) using the vertical modes and spectrum of a covariance matrix obtained from the global EnKF after 2 weeks of cycling, and 3) using the vertical modes and spectrum of the static background error covariance matrix employed to initiate a global data assimilation cycle. It is found that the EnKF performs well in some of the experiments with background error description 1, and yields modest error reductions with background error description 3. However, the EnKF is virtually unable to reduce the background error (even when using a large ensemble) with background error description 2. To analyze these results, the different background error descriptions are viewed through the prism of the RT model by comparing the trace of the matrix , where is the RT model and is the background error covariance matrix. Indeed, this comparison is found to explain the difference in the results obtained, which relates to the degree to which deep modes are, or are not, present in the different background error covariances. The results suggest that, after 2 weeks of cycling, the global EnKF has virtually eliminated all background error structures that can be “seen” by the AMSU-A radiances.

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