Stochastic Representations for Model Uncertainty in the Ensemble Data Assimilation System

2021 ◽  
pp. 143-153
Author(s):  
Sujeong Lim ◽  
Seon Ki Park
Keyword(s):  
Data Assimilation ◽  
Model Uncertainty ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Stochastic Representations ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Ensemble Data Assimilation with the NCEP Global Forecast System

2008 ◽  
Vol 136 (2) ◽  
pp. 463-482 ◽  
Author(s):  
Jeffrey S. Whitaker ◽  
Thomas M. Hamill ◽  
Xue Wei ◽  
Yucheng Song ◽  
Zoltan Toth
Keyword(s):  
Data Assimilation ◽  
Southern Hemisphere ◽  
Global Forecast System ◽  
Forecast System ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Triangular Truncation ◽  
Data Assimilation System ◽  
Ncep Global Forecast System ◽  
Assimilation System

Abstract Real-data experiments with an ensemble data assimilation system using the NCEP Global Forecast System model were performed and compared with the NCEP Global Data Assimilation System (GDAS). All observations in the operational data stream were assimilated for the period 1 January–10 February 2004, except satellite radiances. Because of computational resource limitations, the comparison was done at lower resolution (triangular truncation at wavenumber 62 with 28 levels) than the GDAS real-time NCEP operational runs (triangular truncation at wavenumber 254 with 64 levels). The ensemble data assimilation system outperformed the reduced-resolution version of the NCEP three-dimensional variational data assimilation system (3DVAR), with the biggest improvement in data-sparse regions. Ensemble data assimilation analyses yielded a 24-h improvement in forecast skill in the Southern Hemisphere extratropics relative to the NCEP 3DVAR system (the 48-h forecast from the ensemble data assimilation system was as accurate as the 24-h forecast from the 3DVAR system). Improvements in the data-rich Northern Hemisphere, while still statistically significant, were more modest. It remains to be seen whether the improvements seen in the Southern Hemisphere will be retained when satellite radiances are assimilated. Three different parameterizations of background errors unaccounted for in the data assimilation system (including model error) were tested. Adding scaled random differences between adjacent 6-hourly analyses from the NCEP–NCAR reanalysis to each ensemble member (additive inflation) performed slightly better than the other two methods (multiplicative inflation and relaxation-to-prior).

scholarly journals Model Bias in a Continuously Cycled Assimilation System and Its Influence on Convection-Permitting Forecasts

2013 ◽  
Vol 141 (4) ◽  
pp. 1263-1284 ◽  
Author(s):  
Glen S. Romine ◽  
Craig S. Schwartz ◽  
Chris Snyder ◽  
Jeff L. Anderson ◽  
Morris L. Weisman
Keyword(s):  
Data Assimilation ◽  
Weather Research And Forecasting ◽  
Systematic Bias ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Model Configuration ◽  
Fixed Model ◽  
Observation Bias ◽  
Data Assimilation System ◽  
Assimilation System

Abstract During the spring 2011 season, a real-time continuously cycled ensemble data assimilation system using the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed toolkit provided initial and boundary conditions for deterministic convection-permitting forecasts, also using WRF, over the eastern two-thirds of the conterminous United States (CONUS). In this study the authors evaluate the mesoscale assimilation system and the convection-permitting forecasts, at 15- and 3-km grid spacing, respectively. Experiments employing different physics options within the continuously cycled ensemble data assimilation system are shown to lead to differences in the mean mesoscale analysis characteristics. Convection-permitting forecasts with a fixed model configuration are initialized from these physics-varied analyses, as well as control runs from 0.5° Global Forecast System (GFS) analysis. Systematic bias in the analysis background influences the analysis fit to observations, and when this analysis initializes convection-permitting forecasts, the forecast skill is degraded as bias in the analysis background increases. Moreover, differences in mean error characteristics associated with each physical parameterization suite lead to unique errors of spatial, temporal, and intensity aspects of convection-permitting rainfall forecasts. Observation bias by platform type is also shown to impact the analysis quality.

scholarly journals Initial Conditions for Convective-Scale Ensemble Forecasting Provided by Ensemble Data Assimilation

2015 ◽  
Vol 143 (5) ◽  
pp. 1583-1600 ◽  
Author(s):  
Florian Harnisch ◽  
Christian Keil
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Small Scale ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Convective Scale ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A kilometer-scale ensemble data assimilation system (KENDA) based on a local ensemble transform Kalman filter (LETKF) has been developed for the Consortium for Small-Scale Modeling (COSMO) limited-area model. The data assimilation system provides an analysis ensemble that can be used to initialize ensemble forecasts at a horizontal grid resolution of 2.8 km. Convective-scale ensemble forecasts over Germany using ensemble initial conditions derived by the KENDA system are evaluated and compared to operational forecasts with downscaled initial conditions for a short summer period during June 2012. The choice of the inflation method applied in the LETKF significantly affects the ensemble analysis and forecast. Using a multiplicative background covariance inflation does not produce enough spread in the analysis ensemble leading to a degradation of the ensemble forecasts. Inflating the analysis ensemble instead by either multiplicative analysis covariance inflation or relaxation inflation methods enhances the analysis spread and is able to provide initial conditions that produce more consistent ensemble forecasts. The forecast quality for short forecast lead times up to 3 h is improved, and 21-h forecasts also benefit from the increased spread. Doubling the ensemble size has not only a clear positive impact on the analysis but also on the short-term ensemble forecasts, while a simple representation of model error perturbing parameters of the model physics has only a small impact. Precipitation and surface wind speed ensemble forecasts using the high-resolution KENDA-derived initial conditions are competitive compared to the operationally used downscaled initial conditions.

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

2011 ◽  
Vol 12 (1) ◽  
pp. 118-134 ◽  
Author(s):  
Dusanka Zupanski ◽  
Sara Q. Zhang ◽  
Milija Zupanski ◽  
Arthur Y. Hou ◽  
Samson H. Cheung
Keyword(s):  
Data Assimilation ◽  
Satellite Observations ◽  
Observation Error ◽  
Satellite Precipitation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Analysis Scheme ◽  
Data Assimilation System ◽  
Hydrological Applications ◽  
Assimilation System

Abstract 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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