Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

Abstract. We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of observations in conjunction with models.

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Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

10.5194/acp-2017-888 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yun Liu ◽

Eugenia Kalnay ◽

Ning Zeng ◽

Ghassem Asrar ◽

Zhaohui Chen ◽

...

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Grid Point ◽

Carbon Fluxes ◽

Transfer Model ◽

Observation Window ◽

Surface Carbon ◽

Ensemble Transform Kalman Filter ◽

Ensemble Transform ◽

Assimilation System

Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. [2011, 2012], who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as variable localization, and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the Running-in-Place (RIP) method used to accelerate the spin-up of EnKF data assimilation [Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014]. Like RIP, the new assimilation system uses the no-cost smoothing algorithm for the LETKF [Kalnay et al., 2007b], which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long observation window (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.

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Direct Assimilation of Radar Data With Ensemble Kalman Filter and Hybrid Ensemble‐Variational Method in the National Weather Service Operational Data Assimilation System GSI for the Stand‐Alone Regional FV3 Model at a Convection‐Allowing Resolution

Geophysical Research Letters ◽

10.1029/2020gl090179 ◽

2020 ◽

Vol 47 (19) ◽

Author(s):

Chong‐Chi Tong ◽

Youngsun Jung ◽

Ming Xue ◽

Chengsi Liu

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Variational Method ◽

Ensemble Kalman Filter ◽

Radar Data ◽

National Weather Service ◽

Operational Data ◽

Data Assimilation System ◽

Assimilation System ◽

Weather Service

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A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1300.1 ◽

2010 ◽

Vol 27 (7) ◽

pp. 1140-1152 ◽

Cited By ~ 16

Author(s):

Eunha Lim ◽

Juanzhen Sun

Keyword(s):

Data Assimilation ◽

Doppler Radar ◽

Weather Prediction ◽

Radar Data ◽

Objective Analysis ◽

Doppler Velocity ◽

Horizontal Shear ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the algorithm was effective in dealiasing large areas of aliased velocities when the wind from the objective analysis was used as the reference and that more accurate dealiasing was achieved by using the continuously cycled VDRAS analysis.

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