1. Introduction

Four-dimensional variational data assimilation (4D-Var) is used for generating estimates of model variables, taking into account both information about the dynamics and physics from a numerical model, and available information about the true state of the atmosphere contained in the observations and the background. Usually, a cost function is defined measuring the distance between a model trajectory and the observations over an assimilation time window. 4D-Var is implemented mathematically by minimizing the defined cost function with respect to the control variables. The minimization algorithm used in 4D-Var employs the adjoint equations for the computation of the gradient of the cost function with respect to control variables.

Thus, a successful 4D-Var system hinges upon three main aspects: the use of observations and background information, the accuracy of the assimilation model used, and the efficiency of the minimization algorithm. Theoretically, improvement in any aspect may improve the 4D-Var system. A sizable progress has been accomplished in every one of the above aspects during the last decade. However, due to the enormous size of meteorological problems, such improvement is greatly restricted by presently available computer platforms.

In the present study, we focus on how to use a full physics adjoint model in 4D-Var. The inclusion of the totality of the physical processes in the adjoint model is a required step toward obtaining a better defined cost function. Thus this research aims to address improvements in the second aforementioned aspect, along with a discussion of associated computational CPU requirements. Issues related to model errors are not discussed here.

4D-Var was first applied to simple models (Le Dimet and Talagrand 1986; Lewis and Derber 1985; Courtier and Talagrand 1987; Talagrand and Courtier 1987), before being tested in the context of adiabatic primitive equation models (Thepaut and Courtier 1991; Navon et al. 1992b; Chao and Chang 1992; M. Zupanski 1993). Recently, increasingly sophisticated physical parameterizations have been introduced into 4D-Var (e.g., Zou et al. 1993a; Zou and Kuo 1996; Zou 1997; Tsuyuki 1997; Zupanski and Mesinger 1995; D. Zupanski 1993;Mahfouf and Rabier 1998, manuscript submitted to Quart. J. Roy. Meteor. Soc.). These studies have provided encouraging evidence that the inclusion of physics in the adjoint may improve the performance of 4D-Var.

In the context of an operational environment, Rabier et al. (1997, 1998), Rabier et al. (1998, manuscript submitted to Quart. J. Roy. Meteor. Soc.) and Klinker et al. (1998, manuscript submitted to Quart. J. Roy. Meteor. Soc.) used the European Centre for Medium-Range Weather Forecasts (ECMWF) 4D-Var incremental assimilation system to examine the influence of physical processes. The inclusion of physics was found to have the largest impact on the analysis of humidity fields. A positive impact was also found on the performance of analyses in the Tropics, with a reduction of the spinup of precipitation in the subsequent forecast, and improved wind scores. Also, an improvement in extratropical scores was noted. It should be pointed out that the physics in the ECMWF’s adjoint model consists of a simplified version of the physics package used in the nonlinear forecasting model (Mahfouf et al. 1996; Mahfouf 1999, Mahfouf and Rabier 1998, manuscript submitted to Quart. J. Roy. Meteor. Soc.).

While recent research has shown a beneficial impact of adjoint physics on the quality of assimilated data, the effect of nonlinearities in physical processes on the convergence rate of 4D-Var minimization is still not adequately understood. Theoretically, an adjoint model with full physics should be completely consistent with the nonlinear forecasting model, thus providing exact gradients. However, physical parameterizations display much stronger nonlinearities than the model dynamical part, even when they do not contain discontinuities or on-off switches. Due to the presence of strong nonlinearities in physical parameterizations, the accurate gradient and Hessian matrix may still not provide an effective descent direction for a minimization process.

The 4D-Var experiments in the present research will show that, for an intermediate number of minimization iterations at the early stages of the minimization process, relatively large errors in the assimilation analysis are found to be closely related to precipitation when full physics is included in the adjoint model, and these errors are larger than those present in assimilation analyses where almost no physics is included in the adjoint model. On the other hand, previous studies (e.g., Zou and Kuo 1996; Zou 1997) showed that strong nonlinearities, even on-off switches, may not pose a negative impact on final convergence of the minimization process, and thus the inclusion of physics in the adjoint model has an overall beneficial impact on assimilation results.

Therefore, the inclusion of full physics in the adjoint model requires a 4D-Var algorithm capable of overcoming the negative effect of strong nonlinearities present in physics at the early stages of the minimization process, while being able to take advantage of the positive aspects resulting from consistency between the forecasting nonlinear model and adjoint model.

Several approaches have been proposed for mitigating the negative effect of strong nonlinearities in physical processes included in the adjoint model by either direct modifications or simplifications to physical parameterizations themselves. Zupanski and Mesinger (1995) and Tsuyuki (1997) showed a beneficial effect when smoothing formulas are used to replace those with discontinuities. This technique is applicable for dealing with various nonlinearities. ECMWF uses simplified physics in the adjoint model (Mahfouf et al. 1996; Rabier et al. 1997).

An alternative approach is to deal with the nonlinear problem of physical processes in the adjoint model in the framework of the minimization procedure. In 4D-Var, physical processes are indeed of secondary importance for large-scale problems, compared with the dynamical processes for short-span assimilation windows (less than 24 h). This fact was validated by the success obtained by 4D-Var at ECMWF, where only simple horizontal and vertical diffusions are the sole physical processes included in the adjoint model used (Rabier et al. 1997, 1998). Further, physical processes are controlled by dynamical processes to a large degree for large-scale dynamics. These features allow us to deal with physical processes in a fashion differing from that used with dynamical processes. This basic view constitutes the foundation of this research. As an immediate consequence of this tenet, we naturally resort to the idea of a progressive inclusion of physical processes in 4D-Var as the minimization process proceeds, an idea first proposed by Courtier et al. (1994).

The progressive inclusion of physical processes can be implemented in 4D-Var using the incremental approach proposed by Courtier et al. (1994). In this approach, the minimization is performed with respect to increments. An increment is defined as a deviation from a background (guess) trajectory. The background trajectory is computed with the full nonlinear forecasting model that has a high-resolution and comprehensive physics. A simplified linear model with low-resolution and/or simplified physics is used for solving the minimization problem in the vicinity of the trajectory. This method is further extended by defining a sequence of cost functions for different stages of the minimization process. Then the approach allows a progressive inclusion of physical processes, for instance, using simplified physics adjoint models during the initial stages and full physics adjoint models toward the final stages of the minimization process. Due to the fact that effects of nonlinearities are weaker for perturbations of smaller sizes, this sequential cost function incremental method offers an alternative approach for alleviating effects of strong nonlinearities present in physics. This assertion will be validated using 4D-Var experiments designed in this research work.

Courtier et al. (1994) mentioned that there is no guarantee for convergence of the incremental approach using a sequence of cost functions. Also, the convergence rate may be negatively affected by using different cost functions in the course of a minimization iteration procedure. Minimization algorithms used in 4D-Var usually belong to limited-memory quasi-Newton methods. The basic motivation behind limited memory quasi-Newton methods is to try obtaining the rapid convergence associated with Newton’s method without explicitly evaluating the Hessian of a cost function at every iteration. This is accomplished by constructing approximations to the inverse Hessian based on information gathered during the descent process prior to the current iteration (Liu and Nocedal 1989). In 4D-Var, the minimization is terminated after a number of iterations much smaller than the dimension of the problem. Using a sequence of cost functions may cause additional difficulties for obtaining a good approximation to the inverse Hessian.

The truncated Newton minimization method has been extensively investigated and applied to various research areas (e.g., Dembo and Steihaug 1983; Nash 1985; Nash and Nocedal 1991; Schlick and Fogelson 1992). It has also been applied to meteorological problems (Navon et al. 1992a; Zou et al. 1993b; Wang et al. 1992, 1995). The standard truncated Newton method consists of nested iterations: an outer iteration and inner iteration. The theoretical framework of truncated Newton methods presents a suitable tool for examining performance of the incremental method using a sequence of cost functions. In fact, the incremental method using a sequence of cost functions can be viewed as an algorithmic variant of the truncated Newton method. As such, we expect to refine the sequential cost function algorithm and better analyze its convergence properties by using the theoretical framework of truncated Newton methods.

The aforementioned effort requires a specific and detailed understanding of the impact of physical processes in the adjoint model on 4D-Var. Here we use an adjoint model with full physics based on The Florida State University global spectral model (FSUGSM). This model has been successfully applied to carry out both 4D-Var and optimal parameter estimation by Zhu and Navon (1998, 1999) and Tsuyuki (1997, 1996). Having a full physics adjoint model at our disposal, we can use it as a benchmark with which to compare other adjoint models with partial physical processes. Carrying out such comparisons enables us to examine how physical processes in the adjoint model impact on the minimization process in 4D-Var.

The outline of this paper is as follows. In section 2, we briefly summarize the essential features of the FSUGSM model and its full physics adjoint model. Section 3 presents a description of a standard 4D-Var approach using the full physics adjoint model and an incremental method that involves very simple physics in the adjoint. Section 4 details comparisons between results obtained via the incremental 4D-Var and those obtained using the standard 4D-Var, followed by discussions on the effect of physical processes on 4D-Var. Section 5 examines the incremental method with a sequence of cost functions, and a new truncated Newton-like incremental method is presented and tested. Finally, section 6 discusses and summarizes the numerical results obtained in this study.

2. A brief description of FSUGSM and its adjoint

FSUGSM has been used in numerical weather forecasts for operational purposes for more than a decade. Forecasts using this model especially emphasize tropical aspects such as monsoon and tropical storms (e.g., Krishnamurti et al. 1991).

The model has a comprehensive advanced physical parameterization package. The main physical parameterizations include a fourth-order horizontal diffusion (Kanamitsu et al. 1983), a modified Kuo-type convective scheme (Krishnamurti et al. 1993), dry convective adjustment, large-scale condensation (Kanamitsu et al. 1983), surface flux via similarity theory (Businger et al. 1971), vertical distribution of fluxes utilizing diffusive formulation where the exchange coefficients are functions of the Richardson number (Louis 1979), longwave and shortwave radiative fluxes based on a band model (Harshvardan and Corsetti 1984; Lacis and Hansen 1974), computation of low, middle, and high clouds based on threshold relative humidity for radiative calculation, and surface energy balance coupled to the similarity theory (Krishnamurti et al. 1991).

The adjoint system of FSUGSM has been developed as a result of several years efforts. The adjoint system includes the dynamic core (Wang 1993) and all abovementioned physical parameterizations (Tsuyuki 1996; Zhu and Navon 1997). To improve the performance of the corresponding linearized model, based on which the adjoint model was derived, a number of smoothing techniques were introduced to remove discontinuities in some physical parameterization formulations prior to derivation of the adjoint model (Tsuyuki 1996, 1997; Zhu and Navon 1997).

The dependent variables of the forecasting model as well as the adjoint model include vorticity, divergence, the logarithm of surface pressure, temperature, and dewpoint depression that is defined as the difference between temperature and dewpoint temperature. In the following variational data assimilation experiments, the spectral expansion of the model variables is triangularly truncated at the wavenumber 42 (T42). A sigma (σ) coordinate is used in the vertical and the vertical resolution consists of 12 layers roughly between 100 and 1000 hPa.

3. Formulation descriptions

a. Standard 4D-Var

The standard 4D-Var minimizes the cost (objective) function J that measures the weighted sum of squares of distances to the background state x^b and to the observations y^o distributed over a time interval [t₀, t_n] (Ide et al. 1997; Lorenc 1986),

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where x is the control variable vector, and y_i = H_i[x(t_i)]. Here H_i is an observation operator that maps model state variables to the observed quantity. If the state variables are observed directly, then H_i is just an interpolation operator. Here B is the error-covariance matrix of x^b, and R the error-covariance matrix of y^o. The minimization of the cost function (3.1) is carried out with respect to the initial state x(t₀).

The evaluation of J involves time integrations of a nonlinear forecasting model from t₀ to t_i,

xt_iMt_it₀xt₀(3.2)

where M is a nonlinear operator. Efficient algorithms available for performing minimization of J require availability of its gradient with respect to x(t₀). Efficient computation of the gradient requires integrating the adjoint model backward in time from t_i to t₀ (e.g., see Le Dimet and Talagrand 1986; Talagrand and Courtier 1987).

We carry out experiments with the standard 4D-Var using the full physics adjoint model, that is, the adjoint model is consistent with the forecasting model. The result serves as a benchmark for comparison with results obtained from simplified forms of 4D-Var. One purpose of this research is to examine the impact of physical processes on minimization processes and on errors in resulting assimilation analyses at different stages of minimization processes.

To examine errors in the assimilation analyses, the experiments are designed in an identical twin framework. Thus assimilation errors are just the differences between assimilation analyses and observations. A reference forecast is carried out from t₀ = 0 to t₁ = 6 h using the full physics forecasting model, and the initial field and the results of the forecast at t₁ = 6 h are used as “observations”. The 4D-Var is carried out to recover the state at time t₀. The initial condition of the reference forecast is the initialized analysis valid at 0000 UTC 3 September 1996. The guess initial condition (or the background field) is constructed by adding random perturbations with a mean-root-square error of 2.5 × 10⁻⁵ s⁻¹ for vorticity, 1.6 × 10⁻⁵ s⁻¹ for divergence, 4.0 K for dewpoint depression, and 2.1 K for temperature.

In the identical twin framework, the cost function (3.1) is simplified to read as

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Here y^o_i are all model variables defined on all model Gaussian grid points. The covariance matrix of observation error is defined as a diagonal matrix, the diagonal elements of which correspond to the variances of random errors used to construct the guess initial condition. In such a case, the exact minimization solution is y^o_i, while J_s[x(t₀)] = 0. The experiments have been performed using the limited memory quasi-Newton minimization algorithm (L-BFGS) described by Liu and Nocedal (1989), which is equivalent to the M1QN3 algorithm used at ECMWF (Gilbert and Lemarechal 1989).

b. Incremental 4D-Var

The incremental method proposed by Courtier et al. (1994) allows us to perform 4D-Var using a simplified adjoint model. In the incremental method, an increment is defined by

δxt₀xt₀x^bt₀(3.4)

Here x^b(t₀) is the background (guess) field at the initial time t₀. Then the incremental 4D-Var is designed to find the analysis increment δx^a(t₀) that minimizes a cost function. This cost function is defined as the function of δx(t₀), instead of x(t₀) as in the cost function (3.1) for the standard 4D-Var (Courtier et al. 1994; see also Veerse and Thepaut 1998). The assimilation analysis is obtained by adding the analysis increment δx^a(t₀) to the background

x^at₀x^bt₀δx^at₀(3.5)

The idea central to the incremental method is to use a simplified and linearized version of the forward forecasting model to compute the evolution of the increment δx(t₀), while the evolution of the background is predicted with the full forward forecasting model. The impact of the simplification and linearization is thus considerably reduced.

In accordance with the cost function (3.3) in the standard 4D-Var, we define the incremental cost function as

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where δx̃(t_i) = x̃(t_i − x̃^b(t_i), and the innovation vectors d_i = y^o_i − x^b(t_i). Here x̃(t_i) and x̃^b(t_i) are governed by the simplified model

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Here M̃(t_i, t₀) is the nonlinear operator representing the time integration of the simplified forecasting model from time t₀ to time t_i. It is worth emphasizing that x^b(t_i) is predicted by the full forecasting model starting from x^b(t₀). In this way, the nonlinearity in the full forecasting model is partially taken into account. Note that we do not use the linearized model to compute δx̃(t_i), but use the difference between x̃(t_i) and x̃^b(t_i) as done by Courtier et al. (1994).

The definition of (3.6) corresponds to the one (referred to as P^′′′′_4D) in Courtier et al. (1994). As mentioned by Courtier et al. (1994), the practical advantage of this formulation is that there is little technical development required once the standard 4D-Var problem has already been implemented. Another advantage is that (3.6) becomes identical to the cost function used in the standard 4D-Var when we switch from the simplified model to the full model.

In (3.6), x̃^b(t_i) and d_i are kept constant during the minimization process. Only x̃(t_i), associated with x̃(t₀), is changed as the minimization iteration proceeds. Correspondingly, only the adjoint model associated with M̃(t_i, t₀) is required.

In incremental 4D-Var experiments, the simplified model does not include physical processes except for horizontal diffusion and a simple surface drag scheme as the one used in Tsuyuki (1996, 1997), while all other aspects remain the same as in the full forecasting model. Accordingly, the adjoint model includes only the dynamic core, the horizontal diffusion, and the simple surface drag scheme.

4. Comparisons between the standard and incremental 4D-Var

The incremental method provides us with a powerful tool to examine the impact of inclusion of physical processes in the adjoint model on 4D-Var. In the incremental 4D-Var setting, we may define the simplified model by using a lower model resolution and/or by using only part of physical processes or their simplified forms (e.g., Courtier et al. 1994; Rabier et al. 1997; Veerse and Thepaut 1998). In this section, we are only concerned with the incremental 4D-Var without almost any physical processes in the adjoint model. With the standard 4D-Var analysis described in the previous section, we can compare the difference between the standard 4D-Var and the incremental method. This difference is due only to the physics package being absent in the adjoint model. Only when the performance of the standard 4D-Var is superior to that of the incremental 4D-Var, does the inclusion of the full physics package in the adjoint model have a beneficial impact, and vice versa.

Previous results of some experiments have shown that 4D-Var displays a different behavior in the tropics than in the extratropics (e.g., Rabier et al. 1997). It is often assumed that this originates in differences in convective activities. We analyze the differences between the tropics and extratropics in several aspects of 4D-Var. Figure 1 presents the 3-h accumulated precipitation of the reference forecast (observations) over the last 3 h within the assimilation window. The major precipitation events occur between 30°S and 30°N. Thus in what follows we represent the tropics by the region between 30°S and 30°N, and the extratropics by all the other regions.

5. A 4D-Var strategy using full physics adjoint

As mentioned previously, inclusion of physical processes into the adjoint model sizably increases the computational cost of 4D-Var. In this model, the standard 4D-Var requires an amount of CPU time twice as large as that required by the incremental method that does not include almost any physics in the adjoint model. This ratio of CPU time between the standard 4D-Var and the incremental method is representative of operational models as estimated by Courtier et al. (1994) and Rabier et al. (1997). The increase in the CPU time when the full physics package is included into adjoint models may preclude the operational implementation of 4D-Var.

We have shown that the inclusion of the full physics into the adjoint model presents a negative impact on the assimilation analyses over precipitation regions at the early stages of the minimization process. Thus, when full physics is included in the adjoint model, an adequate 4D-Var strategy is necessary in order to circumvent the above-mentioned negative impact while taking advantage of the positive impact on the final assimilation analyses.

As mentioned in the introduction, the sequential cost function incremental approach proposed by Courtier et al. (1994) provides a possible method for alleviating the effect of nonlinearities and reducing the computational load due to the inclusion of physics into the adjoint model. We will discuss this algorithm by comparing it with the algorithmic features of the truncated Newton method, and then introduce and test a new algorithm suitable for dealing with physical processes in the adjoint model.

a. The standard truncated Newton method and its variants

The truncated-Newton method is used to solve the problem

Jx(5.1)

where x is a vector. This method computes a search direction by finding an approximate solution p to the Newton equation

H_kp∇Jx_k(5.2)

using some iterative method, usually the conjugate-gradient method or the Lanczos method (Dembo and Steihaug 1983). Here H_k is the Hessian of J(x) at x_k. The iterative method is stopped (truncated) prior to attaining the exact solution to the Newton equation. When the exact solution is obtained, the search direction is just a Newton direction.

Thus, the standard truncated Newton method consists of nested iterations. There is an outer iteration (loop) that corresponds to a general optimization method. At each outer iteration (loop) we compute a search direction and perform a line search. The computation of the search direction uses an inner iteration corresponding to the iterative method used to solve the Newton equations.

Finding an approximate solution to the Newton equations corresponds to the computation of the approximate minimizer of the cost function

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obtained by approximating J(x_k + p) by a quadratic approximation consisting of the first three terms in Taylor expansion, where p = x − x_k. We refer to it as an inner cost function. The inner cost function has gradient and Hessian identical to those used in the outer iteration (we will refer to it as the outer cost function).

Here we do not use (5.3) directly but define instead another inner cost function Ĵ(x) approximating J̃(x). This inner cost function Ĵ(x) is required to have the gradients and Hessian that are an approximation to the outer cost function; that is,

H⁻¹_k∇Jx_kĤ⁻¹_k∇Ĵx(5.4)

where Ĥ_k is the Hessian of Ĵ(x) at x_k. Equation (5.4) guarantees that the Newton descent direction of Ĵ(x) is also a descent direction of J(x). We refer to a truncated Newton method as the truncated Newton-like method whenever Ĵ(x) is used to find a search direction instead of (5.3).

6. Summary and discussions

In this paper, we presented results of several 4D-Var experiments. These experiments included both the standard 4D-Var where full physics was used in the adjoint model, and versions of incremental 4D-Var where only selected physical processes were used. The comparison between these experiments provided us a detailed understanding of how physical processes act on a 4D-Var procedure as the minimization process proceeds.

Results obtained showed that at the early stages of the minimization process, the analysis errors at the standard 4D-Var were overall larger than those in the incremental 4D-Var analyses where almost no physics was included in the adjoint model. The major assimilation analysis errors in the standard 4D-Var were found to be located over intense precipitation regions. On the contrary, for the incremental 4D-Var analyses without almost any physics in the adjoint model, the major errors did not correlate with precipitation, and the analysis errors over the large precipitation regions were smaller than those in the standard 4D-Var analyses. After an intermediate number (about 50 for the experiments in this research) of minimization iterations, the errors over some precipitation regions gradually become larger in the incremental 4D-Var analyses than those in the standard 4D-Var. Thus, the inclusion of precipitation physics in the adjoint model appeared to be detrimental to convergence rates of the minimization process at the early stages of the minimization process and then gradually turned out to become beneficial. Interestingly, the 4D-Var with full physics in the adjoint model did indeed speedup the precipitation spinup compared with the incremental 4D-Var without almost any physics in the adjoint model after iteration 70. However, the reduction of the precipitation spinup period cannot be solely attributed to the inclusion of the precipitation physics in the adjoint model, but may be the result of the combined impact of several physical processes.

In the incremental 4D-Var where almost no physics were involved in the adjoint model, major assimilation analysis errors were found to be primarily located in the lower troposphere, and exhibited a correlation with some special orographic features. Especially, the errors in the dewpoint depression and temperature analyses could not be further reduced after 20 minimization iterations. These large errors can be primarily attributed to the absence of the boundary layer physics in the adjoint model. Further, the major assimilation analysis errors were always associated with the absence of the boundary layer physics during the entire minimization procedure. Unlike the precipitation physics, the boundary layer physics in the adjoint model impacted positively the 4D-Var during the entire minimization procedure. The importance of including the boundary layer physics in the adjoint model has been recognized in several studies (e.g., Buizza 1994; Rabier et al. 1997).

The influence of precipitation physics warrants a detailed discussion. In the experiments, assimilation errors in the standard 4D-Var using full physics in the adjoint model did not exhibit a reduction over precipitation regions at least prior to iteration 21. The reduction in assimilation errors over precipitation regions occurred at the latest stages of the minimization process. A further explanation can be provided in terms of the eigenvalue spectrum of the Hessian of the cost function. We conjecture that precipitation physics is related to eigenvectors of the Hessian associated with smaller eigenvalues. Since the minimization process generally deals with smaller eigenvalues at later stages of the minimization process in the framework of the L-BFGS minimization algorithm, assimilation errors can be reduced only at the later stages of the minimization. This also means that if this mechanism prevails for the 4D-Var with full physics, then in order to beneficially extract the additional information provided by the full physics 4D-Var, a larger number of minimization iterations must be carried out than in the adiabatic case. To alleviate this situation, an efficient preconditioning approach is mandatory.

The overall influence of the physical processes in the adjoint model on 4D-Var was found to exhibit different behavior between the early and later stages of the minimization process. This different behavior was related to the presence of strong nonlinearities in the precipitation physics. As the minimization process proceeded, the assimilation analysis became closer to the minimization solution. The effect of nonlinearities in physics on the minimization process tended to weaken. At this stage the advantage of the consistency between the nonlinear forecasting model and the adjoint model thus started playing a dominant role. Interestingly, the boundary layer physics in the adjoint model turned out to be beneficial during the entire minimization process. This may be attributed in part to the relatively weak nonlinearities present in the boundary physics.

The results obtained suggest some issues that should be taken into account in order to maximize the beneficial impact when 4D-Var uses full physics in the adjoint model. At the early stages of the minimization process, the 4D-Var would be better implemented using the incremental method that does not include full physics in the adjoint model. 4D-Var experiments have shown that a minimization process generally acts first on the larger scales (Thepaut and Courtier 1991b; Navon et al. 1992b;Tanguay et al. 1995). Thus the small scales can be dealt with only during the last stage of the minimization process as in Veerse and Thepaut (1998). We suggest avoiding using full physics in the adjoint model at the early stages of the minimization process in order to avoid the detrimental effect of the strong nonlinearities, which does not mean that physics have little impact on the 4D-Var at this stage. To take into account the impact of full physics, we should use full physics in the adjoint model repeatedly after the early stages of the minimization. Here we have proposed to avoid using continuously the full physics package in the adjoint model during the entire minimization process, a suggestion justified theoretically by the recognition that physical processes are controlled to a large degree by dynamical processes. This observation is validated by the numerical experiments presented in this research.

The above-mentioned requirements have been fulfilled using the new truncated Newton-like incremental method. The algorithm is a variant of the incremental method using a sequence of cost functions. It consists of an inner loop and an outer loop. The incremental method including almost no physics in the adjoint model comprises the inner loop, while the outer loop consists of the standard 4D-Var using the full physics adjoint model. In the standard truncated Newton method a line search is performed when the minimization process moves from the inner loop to the outer loop, while preconditioning is used in the inner loop. We used the L-BFGS algorithm for both the outer and inner loops. This allowed updating the Hessian continuously at every minimization iteration in both the outer and inner loops. A two-cycle truncated Newton-like incremental experiment was performed. In the first cycle, 25 minimization iterations were conducted in the inner loop using the adjoint model without almost any physics and 10 minimization iterations in outer loop using the full physics adjoint model. In the second cycle, 20 minimization iterations were conducted in the inner loop and 15 minimization iterations in outer loop. The results obtained showed the quality of the assimilation analyses to be better than that obtained from either the standard 4D-Var or the incremental 4D-Var after 70 iterations in all the aspects examined. This two-cycle truncated Newton-like incremental method accomplishes the aim of alleviating the negative effects of the nonlinearities in the physics along with satisfactorily taking into account the impact of physical processes. The CPU time required increased by only 35% compared with that required by the incremental method that involves almost no physics in the adjoint model.

We recognize that the way in which various physical mechanisms operate is highly model dependent. Some of the conclusions derived in this study may be restricted to the particular model used here, namely the FSUGSM. There are still many other open problems to consider. We used only a simple scaling method to precondition the minimization. Better preconditioning methods are available such as those of Courtier et al. (1994), Yang et al. (1996), and Zupanski (1996) to cite but a few. We do not know if better preconditioning methods available will reduce the detrimental effect of the strong nonlinearities present in the physics at the early stages of the minimization process. In this research, we used only observations of wind, temperature, dewpoint depression, and surface pressure fields in a twin experiment frame. Observations sensitive to physics were not used, such as precipitation amount and total precipitable water, or other observations from satellite platforms. Especially, benefits of precipitation data have been demonstrated in 4D-Var (Zupanski and Mesinger 1995; Zou and Kuo 1996; Tsuyuki 1996), but the adjoint precipitation physics is necessary when precipitation data are assimilated. It may be more interesting to investigate possibilities that observational data sensitive to physics be appropriately assimilated, when they are introduced only at some stages of the minimization process. These problems require a more in-depth investigation.