Changes in the Systematic Errors of Global Reforecasts due to an Evolving Data Assimilation System

A global reforecast dataset was recently created for the National Centers for Environmental Prediction’s Global Ensemble Forecast System (GEFS). This reforecast dataset consists of retrospective and real-time ensemble forecasts produced for the GEFS from 1985 to present day. An 11-member ensemble was produced once daily to +15-day lead time from 0000 UTC initial conditions. While the forecast model was stable during the production of this dataset, in 2011 and several times thereafter, there were significant changes to the forecast model that was used in the data assimilation system itself, as well as changes to the assimilation system and the observations that were assimilated. These changes resulted in substantial changes in the statistical characteristics of the reforecast dataset. Such changes make it challenging to uncritically use reforecasts for statistical postprocessing, which commonly assume that forecast error and bias are approximately consistent from one year to the next. Ensuring the consistency in the statistical characteristics of past and present initial conditions is desirable but can be in tension with the expectation that prediction centers upgrade their forecast systems rapidly.

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

Monthly Weather Review ◽

10.1175/mwr-d-14-00209.1 ◽

2015 ◽

Vol 143 (5) ◽

pp. 1583-1600 ◽

Cited By ~ 25

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.

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Evaluation of the Four-dimensional Ensemble-Variational Hybrid Data Assimilation with Self-consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

10.20944/preprints202008.0305.v1 ◽

2020 ◽

Author(s):

Shixuan Zhang ◽

Zhaoxia Pu

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Forecast Errors ◽

Hurricane Intensity ◽

Ensemble Forecasts ◽

Background Error ◽

Hybrid Data Assimilation ◽

Self Consistent ◽

Data Assimilation System ◽

Assimilation System

The feasibility of a hurricane initialization framework based on the GSI-4DEnVar data assimilation system for the HWRF model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-3DEnVar data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the NCEP GFS ensemble Kalman filtering data assimilation system. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, MSLP, and MSW forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts.

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Regional Ensemble–Variational Data Assimilation Using Global Ensemble Forecasts

Weather and Forecasting ◽

10.1175/waf-d-16-0045.1 ◽

2017 ◽

Vol 32 (1) ◽

pp. 83-96 ◽

Cited By ~ 19

Author(s):

Wan-Shu Wu ◽

David F. Parrish ◽

Eric Rogers ◽

Ying Lin

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Positive Impact ◽

Relative Weight ◽

Variational Data Assimilation ◽

Forecast System ◽

Ensemble Forecasts ◽

Background Error ◽

Data Assimilation System ◽

Assimilation System

Abstract At the National Centers for Environmental Prediction, the global ensemble forecasts from the ensemble Kalman filter scheme in the Global Forecast System are applied in a regional three-dimensional (3D) and a four dimensional (4D) ensemble–variational (EnVar) data assimilation system. The application is a one-way variational method using hybrid static and ensemble error covariances. To enhance impact, three new features have been added to the existing EnVar system in the Gridpoint Statistical Interpolation (GSI). First, the constant coefficients that assign relative weight between the ensemble and static background error are now allowed to vary in the vertical. Second, a new formulation is introduced for the ensemble contribution to the analysis surface pressure. Finally, in order to make use of the information in the ensemble mean that is disregarded in the existing EnVar in GSI, the trajectory correction, a novel approach, is introduced. Relative to the application of a 3D variational data assimilation algorithm, a clear positive impact on 1–3-day forecasts is realized when applying 3DEnVar analyses in the North American Mesoscale Forecast System (NAM). The 3DEnVar DA system was operationally implemented in the NAM Data Assimilation System in August 2014. Application of a 4DEnVar algorithm is shown to further improve forecast accuracy relative to the 3DEnVar. The approach described in this paper effectively combines contributions from both the regional and the global forecast systems to produce the initial conditions for the regional NAM system.

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Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-3581-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 3581-3610

Author(s):

S. Federico

Keyword(s):

Relative Humidity ◽

Data Assimilation ◽

Initial Conditions ◽

Weather Prediction ◽

Wind Profiler ◽

Short Term ◽

Recursive Filters ◽

Data Assimilation System ◽

Rams Model ◽

Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

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Simulation of Monsoon Depressions Using WRF-VAR: Impact of Different Background Error Statistics and Lateral Boundary Conditions

Monthly Weather Review ◽

10.1175/mwr-d-13-00285.1 ◽

2014 ◽

Vol 142 (10) ◽

pp. 3586-3613 ◽

Cited By ~ 20

Author(s):

A. Routray ◽

S. C. Kar ◽

P. Mali ◽

K. Sowjanya

Keyword(s):

Data Assimilation ◽

Boundary Conditions ◽

Initial Conditions ◽

Variational Data Assimilation ◽

Error Statistics ◽

Background Error ◽

Single Observation ◽

Monsoon Depressions ◽

Data Assimilation System ◽

Assimilation System

Abstract In a variational data assimilation system, background error statistics (BES) spread the influence of the observations in space and filter analysis increments through dynamic balance or statistical relationships. In a data-sparse region such as the Bay of Bengal, BES play an important role in defining the location and structure of monsoon depressions (MDs). In this study, the Indian-region-specific BES have been computed for the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system. A comparative study using single observation tests is carried out using the computed BES and global BES within the WRF system. Both sets of BES are used in the assimilation cycles and forecast runs for simulating the meteorological features associated with the MDs. Numerical experiments have been conducted to assess the relative impact of various BES in the analysis and simulations of the MDs. The results show that use of regional BES in the assimilation cycle has a positive impact on the prediction of the location, propagation, and development of rainbands associated with the MDs. The track errors of MDs are smaller when domain-specific BES are used in the assimilation cycle. Additional experiments have been conducted using data from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) as initial and boundary conditions (IBCs) in the assimilation cycle. The results indicate that the use of domain-dependent BES and high-resolution ERA-I data as IBCs further improved the initial conditions for the model leading to better forecasts of the MDs.

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A Hybrid Global Ocean Data Assimilation System at NCEP

Monthly Weather Review ◽

10.1175/mwr-d-14-00376.1 ◽

2015 ◽

Vol 143 (11) ◽

pp. 4660-4677 ◽

Cited By ~ 35

Author(s):

Stephen G. Penny ◽

David W. Behringer ◽

James A. Carton ◽

Eugenia Kalnay

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Global Ocean ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Ocean Data Assimilation ◽

Data Assimilation System ◽

Ncep Climate Forecast System ◽

Assimilation System

Abstract Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR). The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

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CIRA/CSU Four-Dimensional Variational Data Assimilation System

Monthly Weather Review ◽

10.1175/mwr2891.1 ◽

2005 ◽

Vol 133 (4) ◽

pp. 829-843 ◽

Cited By ~ 39

Author(s):

Milija Zupanski ◽

Dusanka Zupanski ◽

Tomislava Vukicevic ◽

Kenneth Eis ◽

Thomas Vonder Haar

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Control Variable ◽

Three Dimensional ◽

Atmospheric Modeling ◽

Variational Data Assimilation ◽

Regional Atmospheric Modeling ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

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An update on the 4D-LETKF data assimilation system for the whole neutral atmosphere

10.5194/gmd-2020-381 ◽

2021 ◽

Author(s):

Dai Koshin ◽

Kaoru Sato ◽

Masashi Kohma ◽

Shingo Watanabe

Keyword(s):

Data Assimilation ◽

Lower Thermosphere ◽

Mean Field ◽

Forecast Model ◽

Small Scale ◽

Neutral Atmosphere ◽

Broadband Emission ◽

Short Period ◽

Data Assimilation System ◽

Assimilation System

Abstract. The four-dimensional local ensemble transform Kalman filter (4D-LETKF) data assimilation system for the whole neutral atmosphere is updated to better represent disturbances with wave periods shorter than 1 day in the mesosphere and lower thermosphere (MLT) region. First, incremental analysis update (IAU) filtering is introduced to reduce the generation of spurious waves arising from the insertion of the analysis updates. The IAU is better than other filtering methods, and also is commonly used for the middle atmospheric data assimilation. Second, the horizontal diffusion in the forecast model is modified to reproduce the more realistic tidal amplitudes that were observed by satellites. Third, the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) and Special Sensor Microwave Imager/Sounder (SSMIS) observations in the stratosphere and mesosphere also are assimilated. The performance of the resultant analyses is evaluated by comparing them with the mesospheric winds from meteor radars, which are not assimilated. The representation of assimilation products is greatly improved not only for the zonal mean field but also for short-period and/or horizontally small-scale disturbances.

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From Monitoring to Forecasting the Land Surface Condition Using a Land Data Assimilation System: Application over the Contiguous United States

10.5194/egusphere-egu2020-4460 ◽

2020 ◽

Author(s):

Anthony Mucia ◽

Clément Albergel ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

Jean-Christophe Calvet

Keyword(s):

United States ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Initial Conditions ◽

Global Land ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System

LDAS-Monde is a global Land Data Assimilation System developed in the research department of M&#233;t&#233;o-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2o x 0.2o spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.

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

Atmospheric Chemistry and Physics ◽

10.5194/acp-8-3473-2008 ◽

2008 ◽

Vol 8 (13) ◽

pp. 3473-3482 ◽

Cited By ~ 57

Author(s):

T. Niu ◽

S. L. Gong ◽

G. F. Zhu ◽

H. L. Liu ◽

X. Q. Hu ◽

...

Keyword(s):

Data Assimilation ◽

Atmospheric Chemistry ◽

Three Dimensional ◽

Forecast Model ◽

System A ◽

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 (phenomena) and dust loading retrieval from the Chinese geostationary satellite FY-2C. By a number of case studies, the DAS was found to provide corrections to both under- and over-estimates of SDS, presenting a major improvement to the forecasting capability of CUACE/Dust in the short-term variability in the spatial distribution and intensity of dust concentrations in both source regions and downwind areas. 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 forecast results with DAS 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 by the unification of observation and numerical model to improve the performance of forecast model.

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