Assimilating synthetic GOES-R radiances in cloudy conditions using an ensemble-based method

Zupanski, D.; Zupanski, Milija; Grasso, Lewis D.; Brummer, Renate; Jankov, Isidora; Lindsey, Daniel T.; Sengupta, Manajit; DeMaria, Mark

doi:10.1080/01431161.2011.572094

Cited by 26 publications

(20 citation statements)

References 65 publications

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“…Covariance localization schemes utilize prior or ad-hoc knowledge about the nature of the parameter and data correlations to improve estimates of the various covariance matrices (Houtekamer and Mitchell 2001;Constantinescu et al 2006;Adrian et al 2007;Hamill (2007); Campbell et al 2010;Devegowda et al 2010;Zupanski et al 2010). One way to impose localization in EnKF is to use a decorrelation function with local support that monotonically decrease with distance for each increment (Hamill et al 2001), and apply it to the background covariance (P f ), making the background covariance distance decorrelated.…”

Section: Algorithm Of the Localized Enkf Methodsmentioning

confidence: 99%

Assimilating transient groundwater flow data via a localized ensemble Kalman filter to calibrate a heterogeneous conductivity field

Tong

Yang

2011

Stoch Environ Res Risk Assess

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A localized ensemble Kalman filter (EnKF) method is developed to assimilate transient flow data to calibrate a heterogeneous conductivity field. To update conductivity value at a point in a study domain, instead of assimilating all the measurements in the study domain, only limited measurement data in an area around the point are used for the conductivity updating in the localized EnKF method. The localized EnKF is proposed to solve the problems of the filter divergence usually existing in a data assimilation method without localization. The developed method is applied, in a synthetical two dimensional case, to calibrate a heterogeneous conductivity field by assimilating transient hydraulic head data. The simulations by the data assimilation with and without localized EnKF are compared. The study results indicate that the hydraulic conductivity field can be updated efficiently by the localized EnKF, while it cannot be by the EnKF. The covariance inflation and localization are found to solve the problem of the filter divergence efficiently. In comparison with the EnKF method without localization, the localized EnKF method needs smaller ensemble size to achieve stabilized results. The simulation results by the localized EnKF method are much more sensitive to conductivity correlation length than to the localization radius. The developed localized EnKF method provides an approach to improve EnKF method in conductivity calibration.

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Section: Algorithm Of the Localized Enkf Methodsmentioning

confidence: 99%

Assimilating transient groundwater flow data via a localized ensemble Kalman filter to calibrate a heterogeneous conductivity field

Tong

Yang

2011

Stoch Environ Res Risk Assess

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“…An interesting method for tackling such limitations was developed by Renshaw and Francis (2011). Another approach involves ensemble DA methods, which seem to be less severely affected by this problem (Otkin 2010(Otkin , 2012aZupanski et al 2011).…”

Section: Introductionmentioning

confidence: 98%

Observation Operator for Visible and Near-Infrared Satellite Reflectances

Kostka¹,

Weißmann²,

Buras

et al. 2014

Journal of Atmospheric and Oceanic Technology

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Operational numerical weather prediction systems currently only assimilate infrared and microwave satellite observations, whereas visible and near-infrared reflectances that comprise information on atmospheric clouds are not exploited. One of the reasons for that is the absence of computationally efficient observation operators.To remedy this issue in anticipation of the future regional Kilometer-Scale Ensemble Data Assimilation (KENDA) system of Deutscher Wetterdienst, we have developed a version that is fast enough for investigating the assimilation of cloudy reflectances in a case study approach. The operator solves the radiative transfer equation to simulate visible and near-infrared channels of satellite instruments based on the one-dimensional (1D) discrete ordinate method. As input, model output of the operational limited-area Consortium for SmallScale Modeling (COSMO) model of Deutscher Wetterdienst is used. Assumptions concerning subgrid-scale processes, calculation of in-cloud values of liquid water content, ice water content, and cloud microphysics are summarized, and the accuracy of the 1D simulation is estimated through comparison with three-dimensional (3D) Monte Carlo solver results. In addition, the effects of a parallax correction and horizontal smoothing are quantified. The relative difference between the 1D simulation in ''independent column approximation'' and the 3D calculation is typically less than 9% between 0600 and 1500 UTC, computed from four scenes during one day (with local noon at 1115 UTC). The parallax-corrected version reduces the deviation to less than 6% for reflectance observations with a central wavelength of 810 nm. Horizontal averaging can further reduce the error of the 1D simulation. In all cases, the bias is less than 1% for the model domain.

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“…To investigate the ensemble representation of precipitation observation operators, an ensemble smoother was developed and assimilated rain retrievals from multiple microwave instruments into the Goddard GEOS-5 general circulation model . At mesoscales, a recent study by Zupanski et al (2010) applied an ensemble filter to assimilate synthetic cloudy IR radiances from the next-generation series of GOES-R Advanced Baseline Imager (ABI) instruments. The study also demonstrated that the forecast error covariance, updated by ensemble forecasts, is reflecting the occurring storm environment, which allows for maximizing information extracted from observations in the storm areas.…”

Section: Introductionmentioning

confidence: 99%

A Prototype WRF-Based Ensemble Data Assimilation System for Dynamically Downscaling Satellite Precipitation Observations

Zupanski

Zhang

Zupanski

et al. 2011

Journal of Hydrometeorology

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In the near future, the Global Precipitation Measurement (GPM) mission will provide precipitation observations with unprecedented accuracy and spatial/temporal coverage of the globe. For hydrological applications, the satellite observations need to be downscaled to the required finer-resolution precipitation fields. This paper explores a dynamic downscaling method using ensemble data assimilation techniques and cloud-resolving models. A prototype ensemble data assimilation system using the Weather Research and Forecasting Model (WRF) has been developed. A high-resolution regional WRF with multiple nesting grids is used to provide the first-guess and ensemble forecasts. An ensemble assimilation algorithm based on the maximum likelihood ensemble filter (MLEF) is used to perform the analysis. The forward observation operators from NOAA-NCEP's gridpoint statistical interpolation (GSI) are incorporated for using NOAA-NCEP operational datastream, including conventional data and clear-sky satellite observations. Precipitation observation operators are developed with a combination of the cloud-resolving physics from NASA Goddard cumulus ensemble (GCE) model and the radiance transfer schemes from NASA Satellite Data Simulation Unit (SDSU). The prototype of the system is used as a test bed to optimally combine observations and model information to produce a dynamically downscaled precipitation analysis. A case study on Tropical Storm Erin (2007) is presented to investigate the ability of the prototype of the WRF Ensemble Data Assimilation System (WRF-EDAS) to ingest information from in situ and satellite observations including precipitation-affected radiance. The results show that the analyses and forecasts produced by the WRF-EDAS system are comparable to or better than those obtained with the WRF-GSI analysis scheme using the same set of observations. An experiment was also performed to examine how the analyses and short-term forecasts of microphysical variables and dynamical fields are influenced by the assimilation of precipitation-affected radiances. The results highlight critical issues to be addressed in the next stage of development such as model-predicted hydrometeor control variables and associated background error covariance, bias estimation, and correction in radiance space, as well as the observation error statistics. While further work is needed to optimize the performance of WRF-EDAS, this study establishes the viability of developing a cloud-scale ensemble data assimilation system that has the potential to provide a useful vehicle for downscaling satellite precipitation information to finer scales suitable for hydrological applications.

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Assimilating synthetic GOES-R radiances in cloudy conditions using an ensemble-based method

Cited by 26 publications

References 65 publications

Assimilating transient groundwater flow data via a localized ensemble Kalman filter to calibrate a heterogeneous conductivity field

Assimilating transient groundwater flow data via a localized ensemble Kalman filter to calibrate a heterogeneous conductivity field

Observation Operator for Visible and Near-Infrared Satellite Reflectances

A Prototype WRF-Based Ensemble Data Assimilation System for Dynamically Downscaling Satellite Precipitation Observations

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