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1.
In this study, the Weather Research and Forecasting (WRF-2.0.3.1) model with three-dimensional variational data assimilation (3DVAR) was utilized to study a heavy rainfall event along the west coast of India with and without the assimilation of GPS occultation refractivity soundings in the monsoon period of 2002. The WRF model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research communities. The Global Positioning System (GPS) radio occultation (RO) refractivity data, processed by UCAR, were obtained from the CHAMP and SAC-C missions. This study investigates the impact of thirteen GPS occultation refractivity soundings only, as assimilated into the WRF model with 3DVAR, on the rainfall prediction over the western coastal mountain of India. The model simulation, with the finest resolution of 10 km, was in good agreement with rainfall observations, up to 72-h forecast. There are some subtle but important differences in predicted rainfalls between the control run CN (without the assimilation of refractivity soundings) and G13 (with the assimilation of thirteen GPS RO soundings). In general, the assimilation run G13 gives a better prediction in terms of both rainfall locations and amounts at later times. The moisture increments were analyzed at the initial and forecast times to assess the impact of GPS RO data assimilation. The results indicate that remote soundings in the forcing region could have significant impacts on distant downstream regions. It is anticipated, based on this study, that considerably occultation soundings available from the six-satellite constellation of FORMOSAT-3/COSMIC would have even more significant impacts on weather prediction in this region.  相似文献   

2.
Three choices of control variables for meteorological variational analysis (3DVAR or 4DVAR) are associated with horizontal wind: (1) streamfunction and velocity potential, (2) eastward and northward velocity, and (3) vorticity and divergence. This study shows theoretical and numerical differences of these variables in practical 3DVAR data assimilation through statistical analysis and numerical experiments. This paper demonstrates that (a) streamfunction and velocity potential could potentially introduce analysis errors; (b) A 3DVAR using velocity or vorticity and divergence provides a natural scale dependent influence radius in addition to the covariance; (c) for a regional analysis, streamfunction and velocity potential are retrieved from the background velocity field with Neumann boundary condition. Improper boundary conditions could result in further analysis errors; (d) a variational data assimilation or an inverse problem using derivatives as control variables yields smoother analyses, for example, a 3DVAR using vorticity and divergence as controls yields smoother wind analyses than those analyses obtained by a 3DVAR using either velocity or streamfunction/velocity potential as control variables; and (e) statistical errors of higher order derivatives of variables are more independent, e.g., the statistical correlation between U and V is smaller than the one between streamfunction and velocity potential, and thus the variables in higher derivatives are more appropriate for a variational system when a cross-correlation between variables is neglected for efficiency or other reasons. In summary, eastward and northward velocity, or vorticity and divergence are preferable control variables for variational systems and the former is more attractive because of its numerical efficiency. Numerical experiments are presented using analytic functions and real atmospheric observations.  相似文献   

3.
A new 3DVAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3DVAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 21-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 420 m reach 0.63℃ and 0.34 psu.  相似文献   

4.
In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30?km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7?mm) event than light rainfall (for thresholds of 1 and 3?mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations.  相似文献   

5.
An explicit four-dimensional variational data assimilation method   总被引：2，自引：0，他引：2  
A new data assimilation method called the explicit four-dimensional variational (4DVAR) method is proposed. In this method, the singular value decomposition (SVD) is used to construct the orthogonal basis vectors from a forecast ensemble in a 4D space. The basis vectors represent not only the spatial structure of the analysis variables but also the temporal evolution. After the analysis variables are ex-pressed by a truncated expansion of the basis vectors in the 4D space, the control variables in the cost function appear explicitly, so that the adjoint model, which is used to derive the gradient of cost func-tion with respect to the control variables, is no longer needed. The new technique significantly simpli-fies the data assimilation process. The advantage of the proposed method is demonstrated by several experiments using a shallow water numerical model and the results are compared with those of the conventional 4DVAR. It is shown that when the observation points are very dense, the conventional 4DVAR is better than the proposed method. However, when the observation points are sparse, the proposed method performs better. The sensitivity of the proposed method with respect to errors in the observations and the numerical model is lower than that of the conventional method.  相似文献   

6.
A coupled ocean–atmosphere mesoscale ensemble prediction system has been developed by the Naval Research Laboratory. This paper describes the components and implementation of the system and presents baseline results from coupled ensemble simulations for two tropical cyclones. The system is designed to take into account major sources of uncertainty in: (1) non-deterministic dynamics, (2) model error, and (3) initial states. The purpose of the system is to provide mesoscale ensemble forecasts for use in probabilistic products, such as reliability and frequency of occurrence, and in risk management applications. The system components include COAMPS® (Coupled Ocean/Atmosphere Mesoscale Prediction System) and NCOM (Navy Coastal Ocean Model) for atmosphere and ocean forecasting and NAVDAS (NRL Atmospheric Variational Data Assimilation System) and NCODA (Navy Coupled Ocean Data Assimilation) for atmosphere and ocean data assimilation. NAVDAS and NCODA are 3D-variational (3DVAR) analysis schemes. The ensembles are generated using separate applications of the Ensemble Transform (ET) technique in both the atmosphere (for moving or non-moving nests) and the ocean. The atmospheric ET is computed using wind, temperature, and moisture variables, while the oceanographic ET is derived from ocean current, temperature, and salinity variables. Estimates of analysis error covariance, which is used as a constraint in the ET, are provided by the ocean and atmosphere 3DVAR assimilation systems. The newly developed system has been successfully tested for a variety of configurations, including differing model resolution, number of members, forecast length, and moving and fixed nest options. Results from relatively coarse resolution (～27-km) ensemble simulations of Hurricanes Hanna and Ike demonstrate that the ensemble can provide valuable uncertainty information about the storm track and intensity, though the ensemble mean provides only a small amount of improved predictive skill compared to the deterministic control member.  相似文献   

7.
An attempt is made to evaluate the impact of Doppler Weather Radar (DWR) radial velocity and reflectivity in Weather Research and Forecasting (WRF)-3D variational data assimilation (3DVAR) system for prediction of Bay of Bengal (BoB) monsoon depressions (MDs). Few numerical experiments are carried out to examine the individual impact of the DWR radial velocity and the reflectivity as well as collectively along with Global Telecommunication System (GTS) observations over the Indian monsoon region. The averaged 12 and 24 h forecast errors for wind, temperature and moisture at different pressure levels are analyzed. This evidently explains that the assimilation of radial velocity and reflectivity collectively enhanced the performance of the WRF-3DVAR system over the Indian region. After identifying the optimal combination of DWR data, this study has also investigated the impact of assimilation of Indian DWR radial velocity and reflectivity data on simulation of the four different summer MDs that occurred over BoB. For this study, three numerical experiments (control no assimilation, with GTS and GTS along with DWR) are carried out to evaluate the impact of DWR data on simulation of MDs. The results of the study indicate that the assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. The simulated meteorological parameters and tracks of the MDs are reasonably improved after assimilation of DWR observations as compared to the other experiments. The root mean square errors (RMSE) of wind fields at different pressure levels, equitable skill score and frequency bias are significantly improved in the assimilation experiments mainly in DWR assimilation experiment for all MD cases. The mean Vector Displacement Errors (VDEs) are significantly decreased due to the assimilation of DWR observations as compared to the CNTL and 3DV_GTS experiments. The study clearly suggests that the performance of the model simulation for the intense convective system which influences the large scale monsoonal flow is significantly improved after assimilation of the Indian DWR data from even one coastal locale within the MDs track.  相似文献   

8.
An attempt is made to evaluate the impact of the three dimensional variational (3DVAR) data assimilation within the Weather Research Forecasting (WRF) modeling system to simulate two heavy rainfall events which occured on 26–27 July 2005 and 27–30 July 2006. During the 26–27 July 2005 event, the unprecedented localized intense rainfall 90–100 cm was recorded over the northeast parts of Mumbai city; however, southern parts received only 10 cm. Model simulation with the data assimilation experiment is reasonably well predicted for the rainfall intensity (800 mm) in 24 h and with accurate location over Mumbai agreeing with observation. Divergence, vorticity, vertical velocity and moisture parameters are evaluated during the various stages of the event. It is noticed that maximum convergence and vorticity during the mature stage; at the same time the vertical velocity also follows a similar trend during the period in the assimilation experiment. Vorticity budget terms over the location of heavy rainfall revealed that the contribution of the positive tilting term produced positive vorticity which triggered the convection and negative contribution to vorticity from the tilting term to precede the dissipation of the system. Model simulations from the second rain event, the off-shore trough at sea level along the west coast of India, is well represented after assimilation of observations during day-1 and day-2 as compared to the control simulations; the orientation of the off-shore trough is well matched with that of the observed. The intensity and spatial distribution of the rainfall has considerably improved in the assimilation simulation. The statistical skill scores also revealed that the precipitation forecast during the period has appreciably improved due to assimilation of observations. The results of this study indicate a positive impact of the 3DVAR assimilation on the simulation of heavy rainfall events.  相似文献   

9.
Application of altimetry data assimilation on mesoscale eddies simulation   总被引：3，自引：0，他引：3  
Mesoscale eddy plays an important role in the ocean circulation. In order to improve the simulation accuracy of the mesoscale eddies, a three-dimensional variation (3DVAR) data assimilation system called Ocean Variational Analysis System (OVALS) is coupled with a POM model to simulate the mesoscale eddies in the Northwest Pacific Ocean. In this system, the sea surface height anomaly (SSHA) data by satellite altimeters are assimilated and translated into pseudo temperature and salinity (T-S) profile data. Then, these profile data are taken as observation data to be assimilated again and produce the three-dimensional analysis T-S field. According to the characteristics of mesoscale eddy, the most appropriate assimilation parameters are set up and testified in this system. A ten years mesoscale eddies simulation and comparison experiment is made, which includes two schemes: assimilation and non-assimilation. The results of comparison between two schemes and the observation show that the simulation accuracy of the assimilation scheme is much better than that of non-assimilation, which verified that the altimetry data assimilation method can improve the simulation accuracy of the mesoscale dramatically and indicates that it is possible to use this system on the forecast of mesoscale eddies in the future.  相似文献   

10.
A three-dimensional variational(3DVAR) data assimilation(DA) system is presented here based on a size-resolved sectional aerosol model, the Model for Simulating Aerosol Interactions and Chemistry(MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry(WRF-Chem) model. The use of this approach means that both gaseous pollutants such as SO_2, NO_2, CO, and O_3 as well as particulate matter(PM_(2.5), PM_(10)) observational data can be assimilated simultaneously.Two one-month parallel simulation experiments were conducted, one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre(CNEMC) and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts. Results show that, in the first place, use of the DA system can provide a more accurate model initial field. The root-mean-square error of PM_(2.5), PM_(10), SO_2, NO_2, CO, and O_3 mass concentrations in analysis field fell by 29.27 μg m~(-3)(53.5%), 34.5 μg m~(-3)(50.9%),30.36 μg m~(-3)(64.2%), 8.91 μg m~(-3)(39.5%), 0.46 mg m~(-3)(47.4%), and 15.11 μg m~(-3)(51.0%), respectively, compared to a background field without assimilation. At the same time, mean fraction error was reduced by 42.6%, 53.1%, 45.2%, 43.1%,69.9%, and 48.8%, respectively, while the correlation coefficient increased by 0.51, 0.55, 0.48, 0.38, 0.47, 0.65, respectively.Secondly, the results of this analysis reveal variable benefits from assimilation on different pollutants. DA significantly improves PM_(2.5), PM_(10), and CO forecasts leading to positive effects that last more than 48 h. The positive effects of DA on SO_2 and O_3 forecasts last up to 8 h but that remains relatively poor for NO_2 forecasts. Thirdly, the influence of assimilation varies in different areas. It is possible that the positive effects of DA on PM_(2.5) and PM_(10) forecasts can last more than 48 h across most regions of China. Indeed, DA significantly improves SO_2 forecasts within 48 h over north China, and much longer CO assimilation benefits(48 h) are found in most regions apart from north and east China and across the Sichuan Basin. DA is able to improve O_3 forecasts within 48 h across China with the exception of southwest and northwest regions and the O_3 DA benefits in southern China are more evident, while from a spatial distribution perspective, NO_2 DA benefits remain relatively poor.  相似文献   

11.
In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region of the Indian Ocean using a three-dimensional variational data assimilation (3DVAR) technique. The Weather Research and Forecasting (WRF)-Advanced Research WRF (ARW) mesoscale model is used to simulate the severe cyclone JAL: 5–8 November 2010 and the very severe cyclone THANE: 27–30 December 2011 with a double nested domain configuration and with a horizontal resolution of 27 × 9 km. Five numerical experiments are conducted for each cyclone. In the control run (CTL) the National Centers for Environmental Prediction global forecast system analysis and forecasts available at 50 km resolution were used for the initial and boundary conditions. In the second (VARAWS), third (VARSCAT), fourth (VARMODIS) and fifth (VARALL) experiments, the conventional surface observations, Oceansat-2 ocean surface wind vectors, temperature and humidity profiles of MODIS, and all observations were respectively used for assimilation. Results indicate meager impact with surface observations, and relatively higher impact with scatterometer wind data in the case of the JAL cyclone, and with MODIS temperature and humidity profiles in the case of THANE for the simulation of intensity and track parameters. These relative impacts are related to the area coverage of scatterometer winds and MODIS profiles in the respective storms, and are confirmed by the overall better results obtained with assimilation of all observations in both the cases. The improvements in track prediction are mainly contributed by the assimilation of scatterometer wind vector data, which reduced errors in the initial position and size of the cyclone vortices. The errors are reduced by 25, 21, 38 % in vector track position, and by 57, 36, 39 % in intensity, at 24, 48, 72 h predictions, respectively, for the two cases using assimilation of all observations. Simulated rainfall estimates indicate that while the assimilation of scatterometer wind data improves the location of the rainfall, the assimilation of MODIS profiles produces a realistic pattern and amount of rainfall, close to the observational estimates.  相似文献   

12.
Variational data assimilation methods optimize the match between an observed and a predicted field. These methods normally require information on error variances of both the analysis and the observations, which are sometimes difficult to obtain for transport and dispersion problems. Here, the variational problem is set up as a minimization problem that directly minimizes the root mean squared error of the difference between the observations and the prediction. In the context of atmospheric transport and dispersion, the solution of this optimization problem requires a robust technique. A genetic algorithm (GA) is used here for that solution, forming the GA-Variational (GA-Var) technique. The philosophy and formulation of the technique is described here. An advantage of the technique includes that it does not require observation or analysis error covariances nor information about any variables that are not directly assimilated. It can be employed in the context of either a forward assimilation problem or used to retrieve unknown source or meteorological information by solving the inverse problem. The details of the method are reviewed. As an example application, GA-Var is demonstrated for predicting the plume from a volcanic eruption. First the technique is employed to retrieve the unknown emission rate and the steering winds of the volcanic plume. Then that information is assimilated into a forward prediction of its transport and dispersion. Concentration data are derived from satellite data to determine the observed ash concentrations. A case study is made of the March 2009 eruption of Mount Redoubt in Alaska. The GA-Var technique is able to determine a wind speed and direction that matches the observations well and a reasonable emission rate.  相似文献   

13.
The Mediterranean Forecasting System (MFS) has been operational for a decade, and is continuously providing forecasts and analyses for the region. These forecasts comprise local- and basin-scale information of the environmental state of the sea and can be useful for tracking oil spills and supporting search-and-rescue missions. Data assimilation is a widely used method to improve the forecast skill of operational models and, in this study, the three-dimensional variational (OceanVar) scheme has been extended to include Argo float trajectories, with the objective of constraining and ameliorating the numerical output primarily in terms of the intermediate velocity fields at 350 m depth. When adding new datasets, it is furthermore crucial to ensure that the extended OceanVar scheme does not decrease the performance of the assimilation of other observations, e.g., sea-level anomalies, temperature, and salinity. Numerical experiments were undertaken for a 3-year period (2005–2007), and it was concluded that the Argo float trajectory assimilation improves the quality of the forecasted trajectories with ~15%, thus, increasing the realism of the model. Furthermore, the MFS proved to maintain the forecast quality of the sea-surface height and mass fields after the extended assimilation scheme had been introduced. A comparison between the modeled velocity fields and independent surface drifter observations suggested that assimilating trajectories at intermediate depth could yield improved forecasts of the upper ocean currents.  相似文献   

14.
Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR) system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005. In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out. The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of these convective storms can be improved.  相似文献   

15.
Coupled assimilation for an intermediated coupled ENSO prediction model   总被引：4，自引：0，他引：4  
Fei Zheng  Jiang Zhu 《Ocean Dynamics》2010,60(5):1061-1073
The value of coupled assimilation is discussed using an intermediate coupled model in which the wind stress is the only atmospheric state which is slavery to model sea surface temperature (SST). In the coupled assimilation analysis, based on the coupled wind–ocean state covariance calculated from the coupled state ensemble, the ocean state is adjusted by assimilating wind data using the ensemble Kalman filter. As revealed by a series of assimilation experiments using simulated observations, the coupled assimilation of wind observations yields better results than the assimilation of SST observations. Specifically, the coupled assimilation of wind observations can help to improve the accuracy of the surface and subsurface currents because the correlation between the wind and ocean currents is stronger than that between SST and ocean currents in the equatorial Pacific. Thus, the coupled assimilation of wind data can decrease the initial condition errors in the surface/subsurface currents that can significantly contribute to SST forecast errors. The value of the coupled assimilation of wind observations is further demonstrated by comparing the prediction skills of three 12-year (1997–2008) hindcast experiments initialized by the ocean-only assimilation scheme that assimilates SST observations, the coupled assimilation scheme that assimilates wind observations, and a nudging scheme that nudges the observed wind stress data, respectively. The prediction skills of two assimilation schemes are significantly better than those of the nudging scheme. The prediction skills of assimilating wind observations are better than assimilating SST observations. Assimilating wind observations for the 2007/2008 La Niña event triggers better predictions, while assimilating SST observations fails to provide an early warning for that event.  相似文献   

16.
High-resolution models and realistic boundary conditions are necessary to reproduce the mesoscale dynamics of the Gulf of Mexico (GOM). In order to achieve this, we use a nested configuration of the Hybrid Coordinate Ocean Model (HYCOM), where the Atlantic TOPAZ system provides lateral boundary conditions to a high-resolution (5 km) model of the GOM . However, such models cannot provide accurate forecasts of mesoscale variability, such as eddy shedding event, without data assimilation. Eddy shedding events involve the rapid growth of nonlinear instabilities that are difficult to forecast. The known sources of error are the initial state, the atmospheric condition, and the lateral boundary condition. We present here the benefit of using a small ensemble forecast (10 members) for providing confidence indices for the prediction, while using a data assimilation scheme based on optimal interpolation. Our set of initial states is provided by using different values of a data assimilation parameter, while the atmospheric and lateral boundary conditions are perturbed randomly. Changes in the data assimilation parameter appear to control the main position of the large features of the GOM in the initial state, whereas changes in the boundary conditions (lateral and atmospheric) appears to control the propagation of cyclonic eddies at their boundary. The ensemble forecast is tested for the shedding of Eddy Yankee (2006). The Loop Current and eddy fronts observed from ocean color and altimetry are almost always within the estimated positions from the ensemble forecast. The ensemble spread is correlated both in space and time to the forecast error, which implies that confidence indices can be provided in addition to the forecast. Finally, the ensemble forecast permits the optimization of a data assimilation parameter for best performance at a given forecast horizon.  相似文献   

17.
This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed.  相似文献   

18.
An innovative way to take the large-scale circulation influence into account in coastal primitive-equation models is explored by an inverse modelling approach. Restricted to barotropic external forcing, this work is a first step in the development of a four-dimensional variational (4DVAR) data-assimilation approach to estimate the best initial and open-boundary conditions that force a coastal model according to interior observations. This development is founded on the OPA modelling system which representation of barotropic coastal dynamics is restricted to motions of long time scales ( a day) due to its rigid lid approximation. Twin experiments are performed in an academic configuration of the Gulf of Lions (located in the northwestern Mediterranean Sea) to study the sensitivity of a remote barotropic forcing to different observational networks measuring surface currents deployed in this area. Three monitoring designs are tested for a large-scale barotropic perturbation in the hindcast mode. It is shown that the space and time distribution of observations acts on the efficiency of the 4DVAR method and then allows coarser datasets.Responsible Editor: Phil Dyke  相似文献   

19.
Ocean Dynamics - DIVAnd (Data-Interpolating Variational Analysis, in n-dimensions) is a tool to interpolate observations on a regular grid using the variational inverse method. We have extended...  相似文献   

20.
Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance) as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July 2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture, due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model.  相似文献   

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