Development of Tsunami Forecasting System Based on Offshore Tsunami Data Assimilation

Abstract A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

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Data assimilation of dust aerosol observations for CUACE/Dust forecasting system

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-7-8309-2007 ◽

2007 ◽

Vol 7 (3) ◽

pp. 8309-8332 ◽

Cited By ~ 16

Author(s):

T. Niu ◽

S. L. Gong ◽

G. F. Zhu ◽

H. L. Liu ◽

X. Q. Hu ◽

...

Keyword(s):

Data Assimilation ◽

Atmospheric Chemistry ◽

Three Dimensional ◽

System A ◽

Source Regions ◽

Operational Forecasts ◽

Forecasting System ◽

Dust Loading ◽

Data Assimilation System ◽

Assimilation System

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust loading retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in forecasting the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The assimilation results usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.

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Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation

Renewable Energy ◽

10.1016/j.renene.2017.02.014 ◽

2017 ◽

Vol 107 ◽

pp. 340-351 ◽

Cited By ~ 48

Author(s):

William Y.Y. Cheng ◽

Yubao Liu ◽

Alfred J. Bourgeois ◽

Yonghui Wu ◽

Sue Ellen Haupt

Keyword(s):

Data Assimilation ◽

Wind Turbine ◽

Weather Forecasting ◽

Short Term ◽

Forecasting System

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Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Nonlinear Processes in Geophysics ◽

10.5194/npg-21-1027-2014 ◽

2014 ◽

Vol 21 (5) ◽

pp. 1027-1041 ◽

Cited By ~ 6

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

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