Four-Dimensional Variational Data Assimilation of Heterogeneous Mesoscale Observations for a Strong Convective Case (original) (raw)
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Quarterly Journal of the Royal Meteorological Society, 1998
Results of four-dimensional variational assimilations, 4D-Var, in cycling mode, over a few two-week assimilation periods are presented. 4D-Var is implemented in its incremental formulation, with a high-resolution model with the full physical parametrization package to compare the atmospheric states with the Observations, and a lowresolution model with simplified physics to minimize the cost-function. The comparison of 4D-Var using several assimilation windows (6, 12 and 24 hours) with 3D-Var (the equivalent of 4D-Var with no time-dimension) over a two-week period shows a clear benefit from using 4D-Var over a 6 or 12-hour window compared to the static 3D-Var scheme. It also exhibits some problems with the forecasts started using 4D-Var over a 24-hour window. The poorer performance of 4D-Var over a relatively long assimilation window can be partly explained by the fact that, in these experiments, the tangent-linear and adjoint models used in the minimization are only approximations of the assimilating model (having lower resolution and crude physics). The error these approximations introduce in the time evolution of a perturbation affects the convergence of the incremental 4D-Var, with larger discontinuities in the values of the cost-function when going from low to high resolution for longer assimilation windows. Additional experiments are performed comparing 4D-Var using a 6-hour window with the 3D-Var system. Two additional 2-week periods show a consistent improvement in extratropical forecast scores with the 4D-Var system. The main 4D-Var improvements occur in areas where the 3D-Var errors were the largest. Local improvement can be as large as 35% for the root-mean-square of the 5-day-forecast error, averaged over a two-week period. A comparison of key analysis errors shows that, indeed, 4D-Var using a 6-hour window is able to reduce substantially the amplitude of its fast-growing error components. The overall fit to observations of analyses and short-range forecasts from 3D-Var and 4D-Var is comparable. In active baroclinic areas, the fit of the background to the data is considerably better for the 4D-Var system, resulting in smaller increments. It appears that in these areas (and in particular over the west Atlantic), 4D-Var is able to better use the information contained in the observations. The ability of 4D-Var to extrapolate some aircraft data in the vertical with a baroclinic tilt is illustrated. Problems exist in the tropics and mountainous areas due partly to a lack of physics in the tangent-linear model. Possible improvements to the system (the introduction of more physics; better behaviour of the incremental approach owing to a line search at high resolution) are also discussed.
Monthly Weather Review, 2007
A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.
Four-Dimensional Variational Assimilation of Precipitation Data
Monthly Weather Review, 1995
This paper studies the impact of assimilating rain-derived information in the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational (4DVAR) system. The approach is based on a one-dimensional variational (1DVAR) method. First, model temperature and humidity profiles are adjusted by assimilating observed surface rain rates in 1DVAR. Second, 1DVAR total column water vapor (TCWV) estimates are assimilated in 4DVAR. Observations used are Tropical Rainfall Measuring Mission (TRMM) surface rainrate estimates from the TRMM Microwave Imager. Two assimilation experiments making use of 1DVAR TCWV were run for a 15-day period. The ''Rain-1'' experiment only assimilates 1DVAR retrievals where the observed rain rate is nonzero while the ''Rain-2'' experiment assimilates all 1DVAR TCWV estimates. The period selected includes Hurricane Bonnie, which was well sampled by TRMM (late August 1998). Results show a positive impact on the humidity analysis of assimilating 1DVAR TCWV in 4DVAR. The model rain rates at the analysis time are closer to the TRMM observations showing a posteriori the consistency of the two-step approach chosen to assimilate rain-rate information in 4DVAR. The modification of the humidity analysis induces changes in the wind and pressure analysis. In particular the analysis of the track of Hurricane Bonnie is noticeably improved for the early stage of the storm development for both the Rain-1 and Rain-2 experiments. When Bonnie is in a mature stage the influence of the 1DVAR TCWV assimilation is to intensify the hurricane. Comparison with Clouds and the Earth's Radiant Energy System (CERES) measurements also show a neutral impact on the radiative fluxes at the top-of-the atmosphere when using 1DVAR TCWV estimates. The impact on the forecasts is a slight reduction of the model precipitation spindown over tropical oceans. Objective scores for the Tropics are improved, particularly for wind and for upper-tropospheric temperature. Analysis and forecast results are generally better for the Rain-2 experiment compared to Rain-1, implying that the 1DVAR TCWV estimates retrieved where no rain is observed provide useful information to 4DVAR.
Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada
Monthly Weather Review, 2007
On 15 March 2005, the Meteorological Service of Canada (MSC) proceeded to the implementation of a four-dimensional variational data assimilation (4DVAR) system, which led to significant improvements in the quality of global forecasts. This paper describes the different elements of MSC's 4DVAR assimilation system, discusses some issues encountered during the development, and reports on the overall results from the 4DVAR implementation tests. The 4DVAR system adopted an incremental approach with two outer iterations. The simplified model used in the minimization has a horizontal resolution of 170 km and its simplified physics includes vertical diffusion, surface drag, orographic blocking, stratiform condensation, and convection. One important element of the design is its modularity, which has permitted continued progress on the three-dimensional variational data assimilation (3DVAR) component (e.g., addition of new observation types) and the model (e.g., computational and numerical changes). This paper discusses some numerical problems that occur in the vicinity of the Poles where the semi-Lagrangian scheme becomes unstable when there is a simultaneous occurrence of converging meridians and strong wind gradients. These could be removed by filtering the winds in the zonal direction before they are used to estimate the upstream position in the semi-Lagrangian scheme. The results show improvements in all aspects of the forecasts over all regions. The impact is particularly significant in the Southern Hemisphere where 4DVAR is able to extract more information from satellite data. In the Northern Hemisphere, 4DVAR accepts more asynoptic data, in particular coming from profilers and aircrafts. The impact noted is also positive and the short-term forecasts are particularly improved over the west coast of North America. Finally, the dynamical consistency of the 4DVAR global analyses leads to a significant impact on regional forecasts. Experimentation has shown that regional forecasts initiated directly from a 4DVAR global analysis are improved with respect to the regional forecasts resulting from the regional 3DVAR analysis.
Journal of Geophysical …, 2008
1] A four-dimensional variational (4D-VAR) data assimilation system using a coupled ocean-atmosphere global model has been successfully developed with the aim of better defining the dynamical states of the global climate on seasonal to interannual scales. The application of this system to state estimations of climate processes during the 1996-1998 period shows, in particular, that the representations of structures associated with several key events in the tropical Pacific and Indian Ocean sector (such as the El Niño, the Indian Ocean dipole, and the Asian summer monsoon) are significantly improved. This fact suggests that our 4D-VAR coupled data assimilation (CDA) approach has the potential to correct the initial location of the model climate attractor on the basis of observational data. In addition, the coupling parameters that control the air-sea exchange fluxes of mass, momentum, and heat become well adjusted. Such an initialization using the 4D-VAR CDA approach allows us to make a roughly 1.5-year lead time prediction of the 1997-1998 El Niño event. These results demonstrate that our 4D-VAR CDA system has the ability to enhance forecast potential for seasonal to interannual phenomena. Citation: Sugiura, N., T. Awaji, S. Masuda, T. Mochizuki, T. Toyoda, T. Miyama, H. Igarashi, and Y. Ishikawa (2008), Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations,
Four-dimensional data assimilation and numerical weather prediction
2003
All forecast models, whether they represent the state of the weather, the spread of a disease, or levels of economic activity, contain unknown parameters. These parameters may be the model's initial conditions, its boundary conditions, or other tunable parameters which have to be determined. Four dimensional variational data assimilation (4D-Var) is a method of estimating this set of parameters by optimizing the fit between the solution of the model and a set of observations which the model is meant to predict. The four dimensional nature of 4D-Var reflects the fact that the observation set spans not only three dimensional space, but also a time domain. Although the method of 4D-Var described in this report is not restricted to any particular system, the application described here has a Numerical Weather Prediction (NWP) model at its core, and the parameters to be determined are the initial conditions of the model. The purpose of this report is to give a survey covering assimilation of Doppler radar wind data into a high-resolution NWP model. Some associated problems, such as sensitivity to small variations in the initial conditions or due to small changes in the background variables, and biases due to nonlinearity are also studied.
Development of an efficient regional four-dimensional variational data assimilation system for WRF
This paper presents the development of a single executable four-dimensional variational data assimilation (4D-Var) system based on the Weather Research and Forecasting (WRF) Model through coupling the variational data assimilation algorithm (WRF-VAR) with the newly developed WRF tangent linear and adjoint model (WRFPLUS). Compared to the predecessor Multiple Program Multiple Data version, the new WRF 4D-Var system achieves major improvements in that all processing cores are able to participate in the computation and all information exchanges between WRF-VAR and WRFPLUS are moved directly from disk to memory. The single executable 4D-Var system demonstrates desirable acceleration and scalability in terms of the computational performance, as demonstrated through a series of benchmarking data assimilation experiments carried out over a continental U.S. domain. To take into account the nonlinear processes with the linearized minimization algorithm and to further decrease the computational cost of the 4D-Var minimization, a multi-incremental minimization that uses multiple horizontal resolutions for the inner loop has been developed. The method calculates the innovations with a high-resolution grid and minimizes the cost function with a lowerresolution grid. The details regarding the transition between the high-resolution outer loop and the low-resolution inner loop are introduced. Performance of the multi-incremental configuration is found to be comparable to that with the full-resolution 4D-Var in terms of 24-h forecast accuracy in the week-long analysis and forecast experiment over the continental U.S. domain. Moreover, the capability of the newly developed multi-incremental 4D-Var system is further demonstrated in the convection-permitting analysis and forecast experiment for Hurricane Sandy (2012), which was hardly computationally feasible with the predecessor WRF 4D-Var system.
Four-Dimensional Variational Assimilation of Total Column Water Vapor in Rainy Areas
Monthly Weather Review, 2002
This paper studies the impact of assimilating rain-derived information in the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational (4DVAR) system. The approach is based on a one-dimensional variational (1DVAR) method. First, model temperature and humidity profiles are adjusted by assimilating observed surface rain rates in 1DVAR. Second, 1DVAR total column water vapor (TCWV) estimates are assimilated in 4DVAR. Observations used are Tropical Rainfall Measuring Mission (TRMM) surface rainrate estimates from the TRMM Microwave Imager.
Use of cloud-cleared radiances in three/four-dimensional variational data assimilation
Quarterly Journal of the Royal Meteorological Society, 1994
The direct use of TOVS (TIROS Operational Vertical Sounder) cloud-cleared radiances in a three/fourdimensional variational data assimilation scheme is described. This scheme uses a fast radiative transfer model and its adjoint. Radiances are used together with all the other observational data. Global spectral fields of mass, wind and humidity are analysed simultaneously under certain mass/wind balance constraints which control the degree to which gravity waves enter into the analysis. In this way the need for a subsequent initialization is avoided. The scheme thus combines retrieval, analysis and initialization in one step and makes it possible to achieve a more optimal combination of the information contained in the radiances, the conventional data and the background (a six-hour forecast).