Application of a WRF Mesoscale Data Assimilation System to Springtime Severe Weather Events 2007–09

Abstract An ensemble-based data assimilation system using the Weather Research and Forecasting Model (WRF) has been used to initialize forecasts of prolific severe weather events from springs 2007 to 2009. These experiments build on previous work that has shown the ability of ensemble Kalman filter (EnKF) data assimilation to produce realistic mesoscale features, such as drylines and convectively driven cold pools, which often play an important role in future convective development. For each event in this study, severe weather parameters are calculated from an experimental ensemble forecast started from EnKF analyses, and then compared to a control ensemble forecast in which no ensemble-based data assimilation is performed. Root-mean-square errors for surface observations averaged across all events are generally smaller for the experimental ensemble over the 0–6-h forecast period. At model grid points nearest to tornado reports, the ensemble-mean significant tornado parameter (STP) and the probability that STP > 1 are often greater in the experimental 0–6-h ensemble forecasts than in the control forecasts. Likewise, the probability of mesoscale convective system (MCS) maintenance probability (MMP) is often greater with the experimental ensemble at model grid points nearest to wind reports. Severe weather forecasts can be sharpened by coupling the respective severe weather parameter with the probability of measurable rainfall at model grid points. The differences between the two ensembles are found to be significant at the 95% level, suggesting that even a short period of ensemble data assimilation can yield improved forecast guidance for severe weather events.

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The Heavy Rainfall Mechanism Revealed by a Terrain-Resolving 4DVar Data Assimilation System—A Case Study

Monthly Weather Review ◽

10.1175/mwr-d-19-0244.1 ◽

2020 ◽

Vol 148 (6) ◽

pp. 2307-2330

Author(s):

Sheng-Lun Tai ◽

Yu-Chieng Liou ◽

Shao-Fan Chang ◽

Juanzhen Sun

Keyword(s):

Data Assimilation ◽

Nonlinear Interaction ◽

Evaporative Cooling ◽

Precipitation Event ◽

Dominant Factor ◽

Immersed Boundary ◽

Convective System ◽

Convergence Zone ◽

Data Assimilation System ◽

Assimilation System

Abstract In this research a newly developed terrain-resolving four-dimensional variational (4DVar)-based data assimilation system, Immersed Boundary Method_Variational Doppler Radar Analysis System (IBM_VDRAS), is applied to investigate the mechanisms leading to a heavy precipitation event that occurred in Taiwan during the Southwesterly Monsoon Experiment (SoWMEX) conducted in 2008. The multivariate analyses using IBM_VDRAS and surface observations reveal that the warm and moist southwesterly flow from the ocean decelerates after making landfall, forming a surface convergence zone along the coast, which is further strengthened during the passage of a prefrontal rainband. The flow ascends as it advances inland until reaching the mountains, producing persistent precipitation and the enhancement of evaporative cooling as well as a widespread high pressure zone. A very shallow (<0.4 km) layer of offshore flow can be identified over the southwestern plain, which helps to generate a quasi-stationary convergence zone near the coast. Sensitivity studies are carried out to quantify the relative importance of the contributions made by topographic blockage, evaporative cooling, and their nonlinear interaction, to the evolution of this type of convective system. The influence of the topography is identified as the dominant factor in modulating the flow structure of the rainfall system. However, it is the nonlinear interaction between terrain and evaporation that determines the distribution of the temperature and pressure fields.

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

Weather and Forecasting ◽

10.1175/waf-d-14-00157.1 ◽

2016 ◽

Vol 31 (1) ◽

pp. 217-236 ◽

Cited By ~ 5

Author(s):

María E. Dillon ◽

Yanina García Skabar ◽

Juan Ruiz ◽

Eugenia Kalnay ◽

Estela A. Collini ◽

...

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Meteorological Data ◽

Weather Prediction ◽

Wrf Model ◽

Convective System ◽

Efficient Manner ◽

Maryland College ◽

Data Assimilation System ◽

Assimilation System

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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Predictability of Record-Breaking Rainfall in Japan in July 2018: Ensemble Forecast Experiments with the Near-Real-Time Global Atmospheric Data Assimilation System NEXRA

SOLA ◽

10.2151/sola.15a-001 ◽

2019 ◽

Vol 15A (0) ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Shunji Kotsuki ◽

Koji Terasaki ◽

Kaya Kanemaru ◽

Masaki Satoh ◽

Takuji Kubota ◽

...

Keyword(s):

Data Assimilation ◽

Real Time ◽

Ensemble Forecast ◽

Atmospheric Data ◽

Data Assimilation System ◽

Assimilation System

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A pre-operational three Dimensional variational data assimilation system in the North/Baltic Sea

Ocean Science ◽

10.5194/os-7-771-2011 ◽

2011 ◽

Vol 7 (6) ◽

pp. 771-781 ◽

Cited By ~ 14

Author(s):

S. Y. Zhuang ◽

W. W. Fu ◽

J. She

Keyword(s):

Data Assimilation ◽

Baltic Sea ◽

Model Simulation ◽

Three Dimensional ◽

Empirical Orthogonal Functions ◽

Initial State ◽

The North ◽

Mean Square Errors ◽

Data Assimilation System ◽

Assimilation System

Abstract. This paper describes the implementation and evaluation of a pre-operational three dimensional variational (3DVAR) data assimilation system for the North/Baltic Sea. Univariate analysis for both temperature and salinity is applied in a 3DVAR scheme in which the horizontal component of the background error covariance is modeled by an isotropic recursive filter (IRF) and the vertical component is represented by dominant Empirical Orthogonal Functions (EOFs). Observations of temperature and salinity (T/S) profiles in the North/Baltic Sea are assimilated in the year of 2005. Effect of the 3DVAR scheme is assessed by a comparison between data assimilation run and control run. The statistical analysis indicates that the model simulation is significantly improved with the 3DVAR scheme. On average, the root mean square errors (RMSE) of temperature and salinity are reduced by 0.2 °C and 0.25 psu in the North/Baltic Sea. In addition, the bias of temperature and salinity is also decreased by 0.1 °C and 0.2 psu, respectively. Starting from an analyzed initial state, one month simulation without assimilation is carried out with the aim of examining the persistence of the initial impact. It is shown that the assimilated initial state can impact the model simulation for nearly two weeks. The influence on salinity is more pronounced than temperature.

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Mesoscale Data Assimilation for a Local Severe Rainfall Event with the NHM–LETKF System

Weather and Forecasting ◽

10.1175/waf-d-13-00032.1 ◽

2014 ◽

Vol 29 (5) ◽

pp. 1093-1105 ◽

Cited By ~ 35

Author(s):

Masaru Kunii

Keyword(s):

Data Assimilation ◽

Weather Conditions ◽

Severe Weather ◽

Rainfall Event ◽

Ensemble Forecasting ◽

Spatial Covariance ◽

Localization Scheme ◽

Weather Events ◽

Probabilistic Forecasts ◽

Mean Square Errors

Abstract This study seeks to improve forecasts of local severe weather events through data assimilation and ensemble forecasting approaches using the local ensemble transform Kalman filter (LETKF) implemented with the Japan Meteorological Agency’s nonhydrostatic model (NHM). The newly developed NHM–LETKF contains an adaptive inflation scheme and a spatial covariance localization scheme with physical distance, and it permits a one-way nested analysis in which a finer-resolution LETKF is conducted by using the output of an outer model. These new features enhance the potential of the LETKF for convective-scale events. The NHM–LETKF was applied to a local severe rainfall event in Japan during 2012. Comparison of the root-mean-square errors between the model first guess and analysis showed that the system assimilated observations appropriately. Analysis ensemble spreads indicated a significant increase around the time torrential rainfall occurred, implying an increase in the uncertainty of environmental fields. Forecasts initialized with LETKF analyses successfully captured intense rainfalls, suggesting that the system could work effectively for local severe weather events. Investigation of probabilistic forecasts by ensemble forecasting indicated that this could become a reliable data source for decision making in the future. A one-way nested data assimilation scheme was also tested. The results demonstrated that assimilation with a finer-resolution model improved the precipitation forecasting of local severe weather conditions.

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“Big Data Assimilation” Revolutionizing Severe Weather Prediction

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-15-00144.1 ◽

2016 ◽

Vol 97 (8) ◽

pp. 1347-1354 ◽

Cited By ~ 36

Author(s):

Takemasa Miyoshi ◽

Masaru Kunii ◽

Juan Ruiz ◽

Guo-Yuan Lien ◽

Shinsuke Satoh ◽

...

Keyword(s):

Big Data ◽

Data Assimilation ◽

Weather Prediction ◽

Severe Weather ◽

Weather System ◽

3 Dimensional ◽

Parallel Simulations ◽

Convective Weather ◽

Data Assimilation System ◽

Assimilation System

Abstract Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

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Radar Data Assimilation with WRF 4D-Var. Part I: System Development and Preliminary Testing

Monthly Weather Review ◽

10.1175/mwr-d-12-00168.1 ◽

2013 ◽

Vol 141 (7) ◽

pp. 2224-2244 ◽

Cited By ~ 85

Author(s):

Hongli Wang ◽

Juanzhen Sun ◽

Xin Zhang ◽

Xiang-Yu Huang ◽

Thomas Auligné

Keyword(s):

Data Assimilation ◽

Radar Data ◽

Variational Data Assimilation ◽

Squall Line ◽

Convective System ◽

Single Observation ◽

Preliminary Testing ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Assimilation System

Abstract The major goal of this two-part study is to assimilate radar data into the high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF) for the improvement of short-term quantitative precipitation forecasting (QPF) using a four-dimensional variational data assimilation (4D-Var) technique. In Part I the development of a radar data assimilation scheme within the WRF 4D-Var system (WRF 4D-Var) and the preliminary testing of the scheme are described. In Part II the performance of the enhanced WRF 4D-Var system is examined by comparing it with the three-dimensional variational data assimilation system (WRF 3D-Var) for a convective system over the U.S. Great Plains. The WRF 4D-Var radar data assimilation system has been developed with the existing framework of an incremental formulation. The new development for radar data assimilation includes the tangent-linear and adjoint models of a Kessler warm-rain microphysics scheme and the new control variables of cloud water, rainwater, and vertical velocity and their error statistics. An ensemble forecast with 80 members is used to produce background error covariance. The preliminary testing presented in this paper includes single-observation experiments as well as real data assimilation experiments on a squall line with assimilation windows of 5, 15, and 30 min. The results indicate that the system is able to obtain anisotropic multivariate analyses at the convective scale and improve precipitation forecasts. The results also suggest that the incremental approach with successive basic-state updates works well at the convection-permitting scale for radar data assimilation with the selected assimilation windows.

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

10.5194/egusphere-egu21-13005 ◽

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

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 10 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) 15 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.&#160;

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Assimilating AMSU-A Radiances in the TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010)

Monthly Weather Review ◽

10.1175/mwr-d-12-00340.1 ◽

2013 ◽

Vol 141 (11) ◽

pp. 3889-3907 ◽

Cited By ~ 20

Author(s):

Man Zhang ◽

Milija Zupanski ◽

Min-Jeong Kim ◽

John A. Knaff

Keyword(s):

Data Assimilation ◽

Core Area ◽

Transfer Model ◽

Hybrid Data Assimilation ◽

Radiance Assimilation ◽

Hybrid Data ◽

The Impact ◽

Mean Square Errors ◽

Data Assimilation System ◽

Assimilation System

Abstract A regional hybrid variational–ensemble data assimilation system (HVEDAS), the maximum likelihood ensemble filter (MLEF), is applied to the 2011 version of the NOAA operational Hurricane Weather Research and Forecasting (HWRF) model to evaluate the impact of direct assimilation of cloud-affected Advanced Microwave Sounding Unit-A (AMSU-A) radiances in tropical cyclone (TC) core areas. The forward components of both the gridpoint statistical interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to process and simulate satellite radiances. The central strategies to allow the use of cloud-affected radiances are (i) to augment the control variables to include clouds and (ii) to add the model cloud representations in the observation forward models to simulate the microwave radiances. The cloudy AMSU-A radiance assimilation in Hurricane Danielle's (2010) core area has produced encouraging results with respect to the operational cloud-cleared radiance preprocessing procedures used in this study. Through the use of the HVEDAS, ensemble covariance statistics for a pseudo-AMSU-A observation in Danielle's core area show physically meaningful error covariances and statistical couplings with hydrometeor variables (i.e., the total-column condensate in Ferrier microphysics). The cloudy radiance assimilation in the TC core region (i.e., ASR experiment) consistently reduced the root-mean-square errors of the background departures, and also generally improved the forecasts of Danielle's intensity as well as the quantitative cloud analysis and prediction. It is also indicated that an entropy-based information content quantification process provides a useful metric for evaluating the utility of satellite observations in hybrid data assimilation.

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