Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics

Abstract Improving the initial conditions of short-range numerical weather prediction (NWP) models is one of the main goals of the meteorological community. Development of data assimilation and ensemble forecast systems is essential in any national weather service (NWS). In this sense, the local ensemble transform Kalman filter (LETKF) is a methodology that can satisfy both requirements in an efficient manner. The Weather Research and Forecasting (WRF) Model coupled with the LETKF, developed at the University of Maryland, College Park, have been implemented experimentally at the NWS of Argentina [Servicio Meteorológico Nacional (SMN)], but at a somewhat lower resolution (40 km) than the operational Global Forecast System (GFS) at that time (27 km). The purpose of this work is not to show that the system presented herein is better than the higher-resolution GFS, but that its performance is reasonably comparable, and to provide the basis for a continued improved development of an independent regional data assimilation and forecasting system. The WRF-LETKF system is tested during the spring of 2012, using the prepared or quality controlled data in Binary Universal Form for Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) and lateral boundary conditions from the GFS. To assess the effect of model error, a single-model LETKF system (LETKF-single) is compared with a multischeme implementation (LETKF-multi), which uses different boundary layer and cumulus convection schemes for the generation of the ensemble of forecasts. The performance of both experiments during the test period shows that the LETKF-multi usually outperforms the LETKF-single, evidencing the advantages of the use of the multischeme approach. Both data assimilation systems are slightly worse than the GFS in terms of the synoptic environment representation, as could be expected given their lower resolution. Results from a case study of a strong convective system suggest that the LETKF-multi improves the location of the most intense area of precipitation with respect to the LETKF-single, although both systems show an underestimation of the total accumulated precipitation. These preliminary results encourage continuing the development of an operational data assimilation system based on WRF-LETKF at the SMN.

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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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Impact of lateral boundary and initial conditions in the prediction of Bay of Bengal cyclones using WRF model and its 3D-VAR data assimilation system

Journal of Atmospheric and Solar-Terrestrial Physics ◽

10.1016/j.jastp.2018.05.007 ◽

2018 ◽

Vol 175 ◽

pp. 64-75 ◽

Cited By ~ 6

Author(s):

K.S. Singh ◽

Prasad K. Bhaskaran

Keyword(s):

Data Assimilation ◽

Bay Of Bengal ◽

Initial Conditions ◽

Wrf Model ◽

Lateral Boundary ◽

Data Assimilation System ◽

Assimilation System

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Impact study of scatterometer observations with improved representation error in an Arctic data assimilation system

10.5194/egusphere-egu2020-7012 ◽

2020 ◽

Author(s):

Máté Mile ◽

Roger Randriamampianina ◽

Gert-Jan Marseille

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Weather Prediction ◽

Satellite Observations ◽

The Arctic ◽

Limited Area ◽

Observation Operator ◽

Representation Error ◽

Data Assimilation System ◽

Assimilation System

Nowadays, satellite observations are providing primary information for initial conditions of state-of-the-art numerical weather prediction (NWP) systems and the amount of remote sensing data in the Global Observing System increases rapidly. However, the way such data are assimilated is usually conservative and sub-optimal especially in high resolution limited-area models. Our objective is to improve the use of scatterometer observations from polar-orbiting satellites by taking into account the observation footprint and reducing the observation representation error through the observation operator.&#160;The variational assimilation system (including 3D- and 4D-Var) of HARMONIE-AROME is widely used for research and operational NWP purposes by many European countries. In most cases, the HARMONIE-AROME model and its data assimilation are run on higher resolution (corresponding to around 2.5km grid size or smaller) than the effective resolution of some satellite observations (e.g. the effective resolution of scatterometer instruments). The use of ASCAT scatterometer observations is studied in an Arctic data assimilation system (AROME-Arctic) and a new observation operator (called supermodding) is evaluated in terms of scatterometer representation error. The results are demonstrated through data assimilation diagnostics, observing system experiments and case studies focusing on the challenges of the Arctic weather forecasting as well.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-6-3563-2013 ◽

2013 ◽

Vol 6 (12) ◽

pp. 3563-3576 ◽

Cited By ~ 8

Author(s):

S. Federico

Keyword(s):

Relative Humidity ◽

Data Assimilation ◽

Initial Conditions ◽

Weather Prediction ◽

Wind Profiler ◽

Short Term ◽

Background Error ◽

Data Assimilation System ◽

Rams Model ◽

Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). 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 representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is 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 analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement 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 three-hours forecasts 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 3D-Var as initial conditions, then it is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

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Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

Advances in Atmospheric Sciences ◽

10.1007/s00376-021-0326-5 ◽

2021 ◽

Author(s):

Magnus Lindskog ◽

Adam Dybbroe ◽

Roger Randriamampianina

Keyword(s):

Data Assimilation ◽

Positive Impact ◽

Weather Prediction ◽

Implementation Process ◽

Satellite System ◽

Limited Area ◽

Global Navigation Satellite ◽

Microwave Radiances ◽

Data Assimilation System ◽

Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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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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SINGV‐DA: A data assimilation system for convective‐scale numerical weather prediction over Singapore

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.3774 ◽

2020 ◽

Vol 146 (729) ◽

pp. 1923-1938 ◽

Cited By ~ 2

Author(s):

B. C. Peter Heng ◽

Robert Tubbs ◽

Xiang‐Yu Huang ◽

Bruce Macpherson ◽

Dale M. Barker ◽

...

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Numerical Weather ◽

Convective Scale ◽

Data Assimilation System ◽

Assimilation System

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A NLS-4Dvar Assimilation System of Surface PM2.5 with WRF-CMAQ Model : Observing System Simulation Experiments

10.5194/egusphere-egu2020-4019 ◽

2020 ◽

Author(s):

Shan Zhang ◽

Xiangjun Tian ◽

Hongqin Zhang ◽

Xiao Han ◽

Meigen Zhang

Keyword(s):

Data Assimilation ◽

Atmospheric Chemistry ◽

Initial Conditions ◽

System Simulation ◽

Fine Particulate Matter ◽

Wrf Model ◽

Nonlinear Least Squares ◽

Simulation Experiments ◽

Improve Model ◽

Assimilation System

&#160; &#160; &#160; &#160; While complete atmospheric chemical transport models have been developed to understanding the complex interactions of atmospheric chemistry and physics, there are large uncertainties in numerical approaches. Data assimilation is an efficient method to improve model forecast of aerosols with optimized initial conditions. We have developed a new framework for assimilating surface fine particulate matter (PM2.5) observations in coupled Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model, based on nonlinear least squares four-dimensional variational (NLS-4DVar) data assimilation method. The NLS-4DVar approach, which does not require the tangent and adjoint models, has been extensive used in meteorological and environmental areas due to the low computational complexity. Two parallel experiments were designed in the observing system simulation experiments (OSSEs) to evaluate the effectiveness of this system. Hourly PM2.5 observations over China be assimilated in WRF-CMAQ model with 6-h assimilation window, while the background state without data assimilation is conducted as control experiment. The results show that the assimilation significantly reduced the uncertainties of initial conditions (ICs) for WRF-CMAQ model and leads to better forecast. The newly developed PM2.5 data assimilation system can improve PM2.5 prediction effectively and easily. In the future, we expect emission to be optimized together with concentrations, and integrate meteorological assimilation into aerosol assimilation system.

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Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

Weather and Forecasting ◽

10.1175/waf-d-16-0202.1 ◽

2017 ◽

Vol 32 (4) ◽

pp. 1603-1611 ◽

Cited By ~ 3

Author(s):

Brett T. Hoover ◽

David A. Santek ◽

Anne-Sophie Daloz ◽

Yafang Zhong ◽

Richard Dworak ◽

...

Keyword(s):

Water Vapor ◽

Data Assimilation ◽

Positive Impact ◽

Weather Prediction ◽

Warm Season ◽

Precipitation Forecast ◽

Global Data Assimilation System ◽

Data Assimilation System ◽

Global Data ◽

Assimilation System

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

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