3DVAR Technique Research Articles

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation.

Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.

Satellite radiance assimilation using the 3D-var technique for the heavy rainfall over the Indian region

The present study evaluates the impact of satellite radiance assimilation on the predictability of the WRF model. The 3D-VAR system of the WRF model was used to assimilate satellite radiance data at the initial condition. Four thunderstorm cases were considered, which occurred over Kolkata, India. A set of two numerical experiments, namely CNTL (no-assimilation) and SAT (satellite radiance assimilation), were carried out, and the WRF model was integrated for 24 hours. The assimilation of satellite radiance had improved the wind and moisture fields at the initial condition, which led to evolve the conducive atmosphere further. Results showed that even though thunderstorms are a small-scale system, assimilation of satellite radiance had shown significant improvement in the simulation of the location, evolution, and intensity of thunderstorms. Temporal evolution of surface parameters (such as 2-m temperature and rainfall) was closer to observation (AWS) in SAT experiment. Further, the SAT experiments were able to produce stronger updrafts/downdrafts and increased moisture content deep into the atmosphere. These findings suggest that satellite radiance data assimilation significantly impacts initial conditions, providing a conducive atmosphere.

The Utilization of Weather Research Forecasting (WRF) Model of 3DVar (Three Dimensional Variational) and Himawari-8 Satellite Imagery to the Heavy Rain in Palangkaraya (Case Study : April 27, 2018)

<p><em>On April 27, 2018 heavy rain was occurred in Palangkaraya. Based on surface data observations at Tjilik Riwut Meteorological Station, the peak of rain occurred between 18-21 UTC, which 54 mm within 3 hours. As a result, the flood inundated on the following day. This research purposed to discover the cause of heavy rain used the WRF model of 3DVar technique that assimilated with AMSU-A satellite which used the tropical physic suite parameterization scheme and Himawari-8 Satellite (IR-1 data), processed by Python Programming. Based on the results, the WRF of the 3DVar model is not representative enough in total rainfall results. However, several weather disturbances show the potency for severe weather occurrence from WRF 3DVar modeling. These are indicated by the shear line and eddy circulation at 18 and 21 UTC, and the time series of air pressure decreases with a 0.5 Mb tendency between 15 to 18 UTC. Moreover, the cloud top temperature graph from Himawari-8 Satellite data shows a drastic reduction in temperature to -61.4323 at 18.20 UTC, which supports the heavy rain process. The weather analysis above show that WRF 3DVar is not representative enough for total rainfall result, but appropriate for other weather aspects (shear line, eddy, and air pressure). Therefore, the heavy rain is caused by shear line and eddy condition, air pressure and low temperature of the cloud top.</em><em></em></p>

Improvement in WRF model prediction for heavy rain events over North Sumatra region using satellite data assimilation

Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.

A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region

Abstract. We develop a new inversion method which is suitable for linear and nonlinear emission source (ES) modeling, based on the three-dimensional decoupled direct (DDM-3D) sensitivity analysis module in the Community Multiscale Air Quality (CMAQ) model and the three-dimensional variational (3DVAR) data assimilation technique. We established the explicit observation operator matrix between the ES and receptor concentrations and the background error covariance (BEC) matrix of the ES, which can reflect the impacts of uncertainties of the ES on assimilation. Then we constructed the inversion model of the ES by combining the sensitivity analysis with 3DVAR techniques. We performed the simulation experiment using the inversion model for a heavy haze case study in the Beijing–Tianjin–Hebei (BTH) region during 27–30 December 2016. Results show that the spatial distribution of sensitivities of SO2 and NOx ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of SO2 and NO2 during the heavy haze period are remarkably improved, especially for NO2. Spatial distributions of SO2 and NO2 concentrations simulated by the constrained ES were more accurate compared with an a priori ES in the BTH region. The temporal variations in regionally averaged SO2, NO2, and O3 modeled concentrations using an a posteriori inversed ES are consistent with in situ observations at 45 stations over the BTH region, and simulation errors decrease significantly. These results are of great significance for studies on the formation mechanism of heavy haze, the reduction of uncertainties of the ES and its dynamic updating, and the provision of accurate “virtual” emission inventories for air-quality forecasts and decision-making services for optimization control of air pollution.

GPS-ZTD data assimilation and its impact on wintertime haze prediction over North China Plain using WRF 3DVAR and CMAQ modeling system.

Severe haze frequently hits the North China Plain (NCP), especially in winter during recent years. Meteorological factors affect aerosol formation and its optical properties, and accurate meteorological fields are imperative for accurate aerosol simulations. The impacts of Global Positioning System Zenith Total Delay (GPS-ZTD) data assimilation on meteorology and aerosol simulations were evaluated in this study using the WRF-CMAQ (the Weather Research and Forecasting model and Community Multiscale Air Quality) modelling system over the NCP during 01-31 December 2019. After bias correction, GSP-ZTD data were assimilated into the WRF model using the 3DVAR technique. Two sensitivity tests (CTR and ZTD) were conducted. The WRF model had generally acceptable performance for surface and upper air meteorological variables, PM2.5 and visibility. From the aspect of BIAS, STDE, RMSE, and R, the assimilation of ZTD data improved the underestimation of ground relative humidity (RH). The improvement was more pronounced in the first 18 forecast hours. The mean RH BIAS decreased by 8%. Surface pressure was also improved in ZTD. The influence of ZTD data assimilation on ground temperature and wind tended to be neutral. The BIAS of ZTD decreased by 3% after data assimilation while STED or RMSE increased slightly. After ZTD data assimilation, the PM2.5 underestimation decreased by 3.4% over NCP. And station mean BIAS or RMSE of PM2.5 decreased at more than 70% stations. After ZTD data assimilation, the visibility overestimation was reduced by 2.5%. And more than 81% stations over had lower visibility BIAS or RMSE. Station mean PM2.5 mass concentration increased by 1.5% in ZTD. The primary aerosol species increased by approximately 1%, and most secondary aerosol species increased by greater than 2% affected by both aerosol physical and chemical process. Although the improvement of PM2.5 seems marginal from the perspective of regional or temporal average, the contribution of ZTD data assimilation on specific pollution episodes at specific stations can be great. The improvement of PM2.5 troughs was in the range of 1-5 μg/m3, while the overestimation of PM2.5 peaks was reduced by few up to dozens μg/m3. This will contribute to the extreme value prediction during pollution episode.

Evaluating the Performance of Lightning Data Assimilation from BLNET Observations in a 4DVAR-Based Weather Nowcasting Model for a High-Impact Weather over Beijing

The Beijing Broadband Lightning Network (BLNET) was successfully set up in North China and had yielded a considerable detection capability of total lightning (intracloud and cloud to ground) over the regions with complex underlying (plains, mountains, and oceans). This study set up a basic framework for the operational application of assimilating total lightning activities from BLNET and assesses the potential benefits in cloud-scale, very short-term forecast (nowcasting) by modulating the vertical velocity using the 4DVar technique. Nowcast statistics aggregated over 11 cycles show that the nowcasting performances with the assimilation of BLNET lightning datasets outperform RAD and the assimilation of GLD360 (Global Lightning) datasets. The assimilation of BLNET data improves the model's dynamical states in the analysis by enhancing the convergence and updraft in and near the convective system. To better implement of assimilating real-time lightning data, this study also conducts sensitivity experiments to investigate the impact of the horizontal length scale of a distance-weighted interpolation, binning time intervals, and different vertical profile or distance weights prior to the DA. The results indicate that the best forecast performance for assimilating BLNET lightning datasets is obtained in a 4DVar cycle when the lightning accumulation interval is 3 min, the radius of horizontal interpolation is 5 × 5, and the statistically vertical velocity profile and the distance weights obtained from cumulus cloud.

Development and application of a hybrid long-short term memory – three dimensional variational technique for the improvement of PM2.5 forecasting

The current state-of-the-art three-dimensional (3D) numerical model for air quality forecasting is restricted by the uncertainty from the emission inventory, physical/chemical parameterization, and meteorological prediction. Forecasting performance can be improved by using the 3D-variational (3D-VAR) technique for assimilating the observation data, which corrects the initial concentration field. However, errors from the prognostic model cause the correction effects at the first hour to be erased, and the bias of the forecast increases relatively fast as the simulation progresses. As an emerging alternative technique, long short-term memory (LSTM) shows promising performance in air quality forecasting for individual stations and outperforms the traditional persistent statistical models. In this study, a new method was developed to combine a 3D numerical model with 3D-VAR and LSTM techniques. This method integrates the advantage of LSTM, namely its high-accuracy forecasting for a single station and that of the 3D-VAR technique, namely its ability to extend improvement to the whole simulation domain. This hybrid method can effectively improve PM2.5 forecasting for the next 24 h, relative to forecasting with the 3D-VAR technique which uses the initial hour concentration correction. Results showed that the root-mean-square error and normalized mean error were decreased by 29.3% and 33.3% in the validation stations, respectively. The LSTM-3D-VAR method developed in this study can be further applied in other regions to improve the forecasting of PM2.5 and other ambient pollutants.

Move a Tropical Cyclone with 4D-Var and Vortex Dynamical Initialization in WRF Model

Previous studies showed that 4D-Var technique used for data assimilation could be modified for weather control. This study demonstrates the ability of 4D-Var to influence the future path of a tropical cyclone by calculating perturbations in WRF simulation. Given the background error covariance matrix, the initial field is improved by the vortex dynamic initialization technique. Our results show that 4D-Var can be applied to control the trajectory of simulated tropical cyclones by producing "optimal" perturbations. In the numerical simulation experiment of Typhoon Mitag in 2019, after this kind of weather control similar to data assimilation, the tropical cyclone moved obviously, and the damaging wind over the coastline weakened. The prediction results after the initial field modified by 4D-Var have a great change, and the position of the tropical cyclone moved about 0.5° southeastward after assimilation, which misses the southeast coast of China. Moreover, the damaging wind is also weakened. Since the 4D-Var is premised on the assumption that the model is perfect and does not consider the model error, then the research plan to consider model error and introduce new methods is discussed in the paper.

Improving forecasts of a record-breaking rainstorm in Guangzhou by assimilating every 10-min AHI radiances with WRF 4DVAR

Abstract On 6 May 2017, a record-breaking warm-sector torrential rainfall (WSTR) event initiated over Guangzhou city around 1600 UTC and lasted for 20 h. To replicate this rainfall event, both three-dimensional and four-dimensional variational (3DVAR/4DVAR) data assimilation (DA) strategies were used to initialize a convection-allowing configuration of the Weather Research and Forecasting (WRF) model in an attempt to evaluate precipitation forecasts with or without the assimilation of Advanced Himawari Imager (AHI) radiances from three water vapor (WV) channels. This case study yielded impressions that both the 4DVAR experiments with and without the assimilation of AHI radiances prominently improved convection initiation (CI) forecasts, and replicated the observations accurately in predicting hourly area precipitation totals. Due to the small spatial scale of this event, the impacts of assimilating every 10-min AHI radiances on the convection evolution and hourly precipitation forecasts were subjectively small to be seen, but the equitable threat score (ETS) and fractions skill score (FSS) did show slight improvement for both hourly and 20-h accumulated precipitation. For example, the 4DVAR technique combined with AHI radiances improved the FSS of 20-h accumulated precipitation forecasts by 2%–4.5%, 1%–3%, 6%–20%, and 8%–24% for 5 mm, 20 mm, 50 mm, and 80 mm thresholds, respectively, over those with only conventional observations assimilated. Reasons for these improvements were attributed to better analyses and forecasts of temperature, moisture, and wind.

Prediction of landfalling Bay of Bengal cyclones during 2013 using the high resolution Weather Research and Forecasting model

AbstractThe present study investigated the capability of the Weather Research and Forecasting (WRF) model in the forecast/simulation of five landfalling Bay of Bengal (BoB) cyclones during 2013. A 3D variational (3DVar) method of data assimilation of the WRF model was used to assimilate the Global Telecommunication System and satellite radiances to improve the initial conditions. The results demonstrated that the model has the ability to simulate the cyclone track, intensity and structure. The study also indicates that predicted storm track, landfall and intensity improved with the 3DVar technique compared to without it. This was due to better initial conditions in the 3DVar experiment. The predicted track errors of the five storms were compared to available global and regional forecasting systems and the results show that the model has the capability for prediction of cyclones over the BoB region. The forecasted mean track errors were about 70, 114, 182 and 184 km; minimum centre pressure was about 8, 10, 13, 13 hPa and maximum surface wind was about 6, 10, 9, 8 m·s−1 on day 1 to day 4 forecast respectively. The study also presents some of the important predicted diagnostic parameters such as temperature anomaly, divergence, heating rate, frozen hydrometeors, vorticity field, equivalent potential temperature, maximum reflectivity, accumulated rainfall and horizontal wind fields compared to available observations for the extremely severe cyclonic storm Phailin. The warm core structure of the Phailin cyclone was resolved in the model between 9 and 15 km compared to satellite observations. The statistical analysis, distribution and magnitude of forecasted accumulated rainfall are also compared to the rainfall of 174 automatic weather stations.

Impact of vortex size and Initialization on prediction of landfalling tropical cyclones over Bay of Bengal

This study focuses on improving the vortex of Tropical Cyclone (TC) using different initialization techniques and its subsequent impact on the performance of Advanced Research Weather Research and Forecasting (ARW) mesoscale model. The Initialization of TC vortex from global analyses (CNTL) is found to be poor due to coarse horizontal and vertical structure, and is improved by assimilating available observations using 3DVAR technique (3DV). In another experiment (VAR_VI), vortex is corrected using vortex initialization and relocation procedures to correspond with India Meteorological Department (IMD) ‘observed’ position and intensity estimates, and then assimilation of additional observations is undertaken.The initial vortex improvement in terms of horizontal and vertical structure is noted in both experiments with the VAR_VI results being better than for the 3DV experiment. Simulations following the VAR_VI experiment showed remarkable improvement in simulating track, intensity and structure of the two TCs it was evaluated for (TC Giri and TC Jal). The large scale dynamical and thermo-dynamical fields such as steering flow, vertical wind shear and warm core structure were also improved. For TC Giri, the VAR_VI could reproduce the observed intensification rate of 10 ms−1 within 12 h, while for TC Jal, the VAR_VI run could simulate the inland rainfall due to sheared convective clouds. Results suggested that the improvements would likely to be more for stronger TCs. This study highlights the continued need and value of improvement of TC vortex using initialization and in-situ and remote sensing observations over the Bay of Bengal region.

Coupling between the lower and middle atmosphere observed during a very severe cyclonic storm ‘Madi’

Synoptic-scale systems like cyclones can generate broad spectrum of waves, which propagate from its source to the middle atmosphere. Coupling between the lower and middle atmosphere over Tirupati (13.6°N, 79.4°E) is studied during a very severe cyclonic storm ‘Madi’ (06–13 December 2013) using Weather Research and Forecast (WRF) model assimilated fields and simultaneous meteor radar observations. Since high temporal and spatial measurements are difficult to obtain during these disturbances, WRF model simulations are obtained by assimilating conventional and satellite observations using 3DVAR technique. The obtained outputs are validated for their consistency in predicting cyclone track and vertical structure by comparing them with independent observations. The good agreement between the assimilated outputs and independent observations prompted us to use the model outputs to investigate the gravity waves (GWs) and tides over Tirupati. GWs with the periods 1–5 h are observed with clear downward phase propagation in the lower stratosphere. These upward propagating waves obtained from the model are also noticed in the meteor radar horizontal wind observations in the MLT region (70–110 km). Interestingly, enhancement in the tidal activity in both the zonal and meridional winds in the mesosphere and lower thermosphere (MLT) region is noticed during the peak cyclonic activity except the suppression of semi-diurnal tide in meridional wind. A very good agreement in the tidal activity is also observed in the horizontal winds in the troposphere and lower stratosphere from the WRF model outputs and ERA5. These results thus provide evidence on the vertical coupling of lower and middle atmosphere induced by the tropical cyclone.

Impact of Aquarius Sea-Surface Salinity Assimilation in Improving the Ocean Analysis Over Indian Ocean

ABSTRACTThe distribution of ocean salinity controls the density field and thereby plays a major role in influencing the ocean dynamics. It has been a challenging task to understand the variability of salinity structure in the regions of large fresh water discharge and high precipitation such as Bay of Bengal (BoB). Recent advancement in satellite technology has made possible the measurement of sea surface salinity (SSS). Aquarius is the satellite which measured the global SSS for the period 2011 to 2015. In the present study, we assimilated Aquarius SSS in the Global Ocean Data Assimilation System based on 3DVAR technique. The assimilation of Aquarius SSS resulted in reduced biases in salinity not only at the surface, but also in the vertical distribution of salinity and better captured the temporal variations of salinity structure in sensitive regions, such as the Bay of Bengal. In addition, the assimilation of SSS showed marginal improvement in ocean thermal structure over data sparse regions of Indian Ocean. It is also shown that the assimilation of Aquarius SSS has improved the stratification in the upper Ocean which is the key factor in the observed improvement in ocean analysis.

Improvement of atmospheric dispersion simulation using an advanced meteorological data assimilation method to reconstruct the spatiotemporal distribution of radioactive materials released during the Fukushima Daiichi Nuclear Power Station accident

To improve the atmospheric dispersion simulations of radioactive materials released due to the Fukushima Daiichi Nuclear Power Station (FNPS1) accident, we adopted four-dimensional variational data assimilation (4D-Var) of the data assimilation system (WRFDA) and confirmed the effectiveness of the existing 4D-Var technique for the reproducibility of dispersion simulation during the FNPS1 accident. The simulation was performed by the community meteorological model (WRF) and our atmospheric dispersion model (GEARN). The accuracy of simulated 137Cs deposition patterns in the area closed to FNPS1 and the Ibaraki, Tochigi, and Fukushima Prefectures was increased due to improvements in wind and rain fields in 4D-Var calculations. The results demonstrated that 4D-Var was effective for improving local- and regional-scale atmospheric dispersion simulations.

Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

Abstract. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-mean-square differences between the two sources are of the order of 1 and 2 m s−1, respectively, and time–height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s−1. The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. The results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.

Analyses and forecasts of a tornadic supercell outbreak using a 3DVAR system ensemble

As part of NOAA’s “Warn-On-Forecast” initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

Analysis and very short range forecast of cyclone “AILA” with radar data assimilation with rapid intermittent cycle using ARPS 3DVAR and cloud analysis techniques

In this study, both reflectivity and radial velocity are assimilated into the Weather Research and Forecasting (WRF) model using ARPS 3DVAR technique and cloud analysis procedure for analysis and very short range forecast of cyclone AILA. Doppler weather radar (DWR) data from Kolkata radar are assimilated for numerical simulation of landfalling tropical cyclone. Results show that the structure of cyclone AILA has significantly improved when radar data is assimilated. Radar reflectivity data assimilation has strong influence on hydrometeor structures of the initial vortex and precipitation pattern and relatively less influence is observed on the wind fields. Divergence/convergence conditions over cyclone inner-core area in the low-to-middle troposphere (600–900 hPa) are significantly improved when wind data are assimilated. However, less impact is observed on the moisture field. Analysed minimum sea level pressure (SLP) is improved significantly when both reflectivity and wind data assimilated simultaneously (RAD-ZVr experiment), using ARPS 3DVAR technique. In this experiment, the centre of cyclone is relocated very close to the observed position and the system maintains its intensity for longer duration. As compared to other experiments track errors are much reduced and predicted track is very much closer to the best track in RAD-ZVr experiment. Rainfall pattern and amount of rainfall are better captured in this experiment. The study also reveals that cyclone structure, intensification, direction of movement, speed and location of cyclone are significantly improved and different stages of system are best captured when both radar reflectivity and wind data are assimilated using ARPS 3DVAR technique and cloud analysis procedure. Thus optimal impact of radar data is realized in RAD-ZVr experiment. The impact of DWR data reduces after 12 h forecast and it is due to the dominance of the flow from large-scale global forecast system model. Successful coupling of data assimilation package ARPS 3DVAR with WRF model for Indian DWR data is also demonstrated.

Expanded four-dimensional variatiaonal data assimilation method to optimize model physical parameters

A critical error in numerical simulation stems from the physical parameters in the model. To better assess the accuracy of the numerical stimulation, a mthod of impoving physical parameters is urgently desired. By modifying the four-dimensional variatiaonal data assimilation 4DVAR technique, in this paper a new method is proposed based on the use of observational data to optimize initial field and subsequent physical model. Ekman boundary layer model and Lorenz model are taken for example to conduct numerical experiment. The results show that through the variations in observational data, physical parameters and initial field are improved, thus effectively enhancing the accuracy of the model. This method improves the numerical model and physical parameters.

3DVAR versus Traditional Dual-Doppler Wind Retrievals of a Simulated Supercell Thunderstorm

Abstract Use of the three-dimensional variational data assimilation (3DVAR) framework in dual-Doppler wind analysis (DDA) offers several advantages over traditional techniques. Perhaps the most important is that the errors that result from explicit integration of the mass continuity equation in traditional methods are avoided. In this study, observing system simulation experiments (OSSEs) are used to compare supercell thunderstorm wind retrievals from a 3DVAR DDA technique and three traditional DDA methods. The 3DVAR technique produces better wind retrievals near the top of the storm than the traditional methods in the experiments. This is largely attributed to the occurrence of severe errors aloft in the traditional retrievals whether the continuity equation integration proceeds upward (due to vertically accumulating errors), downward (due to severe boundary condition errors arising from uncertainty in the horizontal divergence field aloft), or in both directions. Smaller, but statistically significant, improvement occurs near the ground using the 3DVAR method. When lack of upper-level observations prevents application of a top boundary condition in the traditional DDA framework, the 3DVAR approach produces better analyses at all levels. These results strongly suggest the 3DVAR DDA framework is generally preferable to traditional formulations.