GSI-Based Four-Dimensional Ensemble–Variational (4DEnsVar) Data Assimilation: Formulation and Single-Resolution Experiments with Real Data for NCEP Global Forecast System

2014 ◽  
Vol 142 (9) ◽  
pp. 3303-3325 ◽  
Author(s):  
Xuguang Wang ◽  
Ting Lei
Keyword(s):  
Data Assimilation ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Negative Impact ◽  
Lead Times ◽  
Multiple Time ◽  
Global Forecast System ◽  
Forecast System ◽  
Variational Minimization ◽  
Ncep Global Forecast System

A four-dimensional (4D) ensemble–variational data assimilation (DA) system (4DEnsVar) was developed, building upon the infrastructure of the gridpoint statistical interpolation (GSI)-based hybrid DA system. 4DEnsVar used ensemble perturbations valid at multiple time periods throughout the DA window to estimate 4D error covariances during the variational minimization, avoiding the tangent linear and adjoint of the forecast model. The formulation of its implementation in GSI was described. The performance of the system was investigated by evaluating the global forecasts and hurricane track forecasts produced by the NCEP Global Forecast System (GFS) during the 5-week summer period assimilating operational conventional and satellite data. The newly developed system was used to address a few questions regarding 4DEnsVar. 4DEnsVar in general improved upon its 3D counterpart, 3DEnsVar. At short lead times, the improvement over the Northern Hemisphere extratropics was similar to that over the Southern Hemisphere extratropics. At longer lead times, 4DEnsVar showed more improvement in the Southern Hemisphere than in the Northern Hemisphere. The 4DEnsVar showed less impact over the tropics. The track forecasts of 16 tropical cyclones initialized by 4DEnsVar were more accurate than 3DEnsVar after 1-day forecast lead times. The analysis generated by 4DEnsVar was more balanced than 3DEnsVar. Case studies showed that increments from 4DEnsVar using more frequent ensemble perturbations approximated the increments from direct, nonlinear model propagation better than using less frequent ensemble perturbations. Consistently, the performance of 4DEnsVar including both the forecast accuracy and the balances of analyses was in general degraded when less frequent ensemble perturbations were used. The tangent linear normal mode constraint had positive impact for global forecast but negative impact for TC track forecasts.

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 A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Weizhong Zheng ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Michael Ek
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Prediction Models ◽  
Precipitation Forecast ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Soil Moisture Data ◽  
Data Products ◽  
Ncep Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

scholarly journals GSI 3DVar-Based Ensemble–Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments

2013 ◽  
Vol 141 (11) ◽  
pp. 4098-4117 ◽  
Author(s):  
Xuguang Wang ◽  
David Parrish ◽  
Daryl Kleist ◽  
Jeffrey Whitaker
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Coupled System ◽  
Global Forecast System ◽  
Forecast System ◽  
Outer Loop ◽  
Hybrid Data Assimilation ◽  
Hybrid Data ◽  
The One ◽  
Ncep Global Forecast System

Abstract An ensemble Kalman filter–variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winter period. Both the control and ensemble forecasts were run at the same, reduced resolution. Operational conventional and satellite observations along with an 80-member ensemble were used. Various configurations of the system including one- or two-way couplings, with zero or nonzero weights on the static covariance, were intercompared and compared with the GSI 3DVar system. It was found that the hybrid system produced more skillful forecasts than the GSI 3DVar system. The inclusion of a static component in the background-error covariance and recentering the analysis ensemble around the variational analysis did not improve the forecast skill beyond the one-way coupled system with zero weights on the static covariance. The one-way coupled system with zero static covariances produced more skillful wind forecasts averaged over the globe than the EnKF at the 1–5-day lead times and more skillful temperature forecasts than the EnKF at the 5-day lead time. Sensitivity tests indicated that the difference may be due to the use of the tangent linear normal mode constraint in the variational system. For the first outer loop, the hybrid system showed a slightly slower (faster) convergence rate at early (later) iterations than the GSI 3DVar system. For the second outer loop, the hybrid system showed a faster convergence.

Assessments of Assimilation of TEMPEST-D into the NCEP Global Forecast System

2020 ◽  
Author(s):  
Ting-Chi Wu ◽  
Milija Zupanski ◽  
Lewis Grasso ◽  
James Fluke ◽  
Heather Cronk ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Control Experiment ◽  
Weather Forecasting ◽  
Cost Effective ◽  
Global Forecast System ◽  
Forecast System ◽  
Research Project ◽  
Microwave Radiometers ◽  
The Impact ◽  
Ncep Global Forecast System

<p>Unlike large, expensive, and high-risk operational satellites, small/cube satellites (SmallSats) are a small, inexpensive, and a low-risk type of satellite. As a NOAA Cooperative Institute with specialties in satellite data processing and data assimilation, CIRA is funded by a Technology Maturation Program (TMP) research project to help NOAA exploit upcoming constellation of SmallSats to be considered for use in operations. In this research, a CSU-led technology demonstration mission entitled “the Temporal Experiment for Storms and Tropical System - Demonstration (TEMPTEST-D)” is used as an example to explore quick and agile methodologies to entrain SmallSats into the NOAA processing stream. Specifically, a workflow that enables TEMPEST-D data for assimilation into the NCEP Global Forecast System (GFS) with Finite-Volume Cube-Sphered (FV3) dycore (FV3GFS) under the Gridpoint Statistical Interpolation (GSI) based hybrid 4DEnVar system is established.</p><p>One objective of this TMP research project is to assess the impact of SmallSat data on NOAA modeling and assimilation systems used in operations. We begin by asking whether the use of TEMPEST-D data is as good as the use of those obtained from well-established operational satellite sensors. Since the radiometric specification of TEMPEST-D is similar to the Microwave Humidity Sounder (MHS), we address the above question by directly comparing the following three cycled FV3GFS data assimilation and forecasting experiments: 1) the control experiment, which includes all routinely assimilated observations, but only assimilates MHS from the NOAA-19 and MetOp-B platforms, 2) the AddMHS experiment, which is the control plus MHS from the MetOp-A platform, and 3) the AddTEMPEST experiment, which is the control plus TEMPEST-D.</p><p>By differentiating the AddMHS and AddTEMPEST experiments against the control experiment, we will be able to demonstrate that a cost-effective TEMPEST-D is as beneficial as a well-established operational satellite like MHS, in terms of aiding operational global weather forecasting. In addition, results from this research offers implications of the utility of a constellation of SmallSats microwave radiometers for global weather forecasting.  </p>

Evaluation of Precipitation Forecast from Global Forecast System Over Transboundary Rivers in Africa

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael
Keyword(s):  
Correlation Coefficients ◽  
Precipitation Forecast ◽  
Nile River ◽  
Lead Times ◽  
General Tendency ◽  
Global Forecast System ◽  
Forecast System ◽  
Transboundary River Basins ◽  
Unbiased Estimates ◽  
Good Agreement

<p>This study evaluates the short-to-medium range precipitation forecasts from Global Forecast System for 14 major transboundary river basins in Africa against GPM IMERG “Early”, IMERG “Final”, and CHIRPSv2 products. Daily precipitation forecasts with lead times of 1-day, 5-day, 10-day, and 15-day and accumulated precipitation forecasts with periods of 1-day, 5-day, 10-day, and 15-day are investigated. The 14 selected basins are (1) Senegal; (2) Volta; (3) Niger; (4) Chad; (5) Nile; (6) Awash; (7) Congo; (8) Omo Gibe; (9) Tana; (10) Pangani; (11) Zambezi; (12) Okavango; (13) Limpopo and (14) Orange. For each basin, several sub-basins are defined by the major dams in the basin. Our preliminary results in the Nile river basin show that in terms of temporal variability, there was a good agreement between the forecasted and observed accumulated precipitation on a 15-day basis. When compared to IMERG “Final”, the correlation coefficients of accumulated GFS forecasts scored as high as 0.75. Thus, GFS products provide relatively reliable accumulated precipitation forecasts. However, the precipitation forecasts were mostly biased: they tend to overpredict rainfall for the eastern part of the Nile river, underestimate rainfall for the northern part of the Nile river and produce almost unbiased estimates for the southern part of the river. Additionally, GFS forecasts have a general tendency to underpredict the area of precipitation across the Nile basin. Although the performance of GFS varies at different locations, the GFS precipitation forecasts can be a good reference to dam operators in Africa. </p>

Quasi-biennial oscillation disrupted by abnormal Southern Hemisphere stratosphere

2020 ◽  
Author(s):  
James Anstey ◽  
Tim Banyard ◽  
Neal Butchart ◽  
Lawrence Coy ◽  
Paul Newman ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Boreal Winter ◽  
Lead Times ◽  
Climate Projections ◽  
Intergovernmental Panel ◽  
Quasi Biennial Oscillation ◽  
Reliable Source ◽  
Biennial Oscillation ◽  
Wave Momentum

Abstract The quasi-biennial oscillation (QBO) is a repeating cycle of tropical stratosphere winds reversing direction from eastward to westward roughly every 14 months. Discovered independently by British and American scientists the QBO continued uninterrupted for 27 cycles from 1953 until February 2016 when a westward jet unexpectedly formed in the lower stratosphere during the eastward phase. This disruption is attributed to unusually high wave-momentum fluxes from the Northern Hemisphere. A second, similar, QBO disruption occurred during the 2019/2020 northern winter though wave fluxes from the Northern Hemisphere were weak. Here we show that this latest disruption to the regular QBO cycling was stronger than that seen in 2016 and resulted from horizontal momentum transport from the Southern Hemisphere during abnormal winter conditions. In both disruptions the normal downward progression of the QBO halts and the eastward shear zone above the disruption moves upward assisted by stronger tropical upwelling during the boreal winter. The predictable signal associated with the QBO's quasi-regular phase progression is permanently lost during disruptions and the oscillation reemerges after a few months significantly shifted in phase from what would be expected if the phase had progressed uninterrupted. We infer from an increased wave-momentum flux into equatorial latitudes seen in model climate projections supporting the latest Intergovernmental Panel on Climate Change (IPCC) assessment that disruptions to the QBO are likely to be more common in future. Consequently, we anticipate that in future the QBO will be a less reliable source of predictability on lead times extending out to several years than it currently is.

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
The Impact

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

Assimilation of TRMM Multisatellite Precipitation Analysis with a Low-Resolution NCEP Global Forecast System

2016 ◽  
Vol 144 (2) ◽  
pp. 643-661 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Takemasa Miyoshi ◽  
Eugenia Kalnay
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Cumulative Distribution ◽  
Precipitation Data ◽  
Global Forecast System ◽  
Low Resolution ◽  
Forecast System ◽  
Original Solution ◽  
Precipitation Analysis ◽  
Ncep Global Forecast System

Abstract Current methods of assimilation of precipitation into numerical weather prediction models are able to make the model precipitation become similar to the observed precipitation during the assimilation, but the model forecasts tend to return to their original solution after a few hours. To facilitate the precipitation assimilation, a logarithm transformation has been used in several past studies. Lien et al. proposed instead to assimilate precipitation using the local ensemble transform Kalman filter (LETKF) with a Gaussian transformation technique and succeeded in improving the model forecasts in perfect-model observing system simulation experiments (OSSEs). In this study, the method of Lien et al. is tested within a more realistic configuration: the TRMM Multisatellite Precipitation Analysis (TMPA) data are assimilated into a low-resolution version of the NCEP Global Forecast System (GFS). With guidance from a statistical study comparing the GFS model background precipitation and the TMPA data, some modifications of the assimilation methods proposed in Lien et al. are made, including 1) applying separate Gaussian transformations to model and to observational precipitation based on their own cumulative distribution functions; 2) adopting a quality control criterion based on the correlation between the long-term model and observed precipitation data at the observation location; and 3) proposing a new method to define the transformation of zero precipitation that takes into account the zero precipitation probability in the background ensemble rather than the climatology. With these modifications, the assimilation of the TMPA precipitation data improves both the analysis and 5-day model forecasts when compared with a control experiment assimilating only rawinsonde data.

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