Sequential Assimilation Scheme Research Articles

ЧУВСТВИТЕЛЬНОСТЬ РЕЗУЛЬТАТОВ МОДЕЛИРОВАНИЯ ЦИРКУЛЯЦИИ ОКЕАНА К ВЫБОРУ ИСТОЧНИКА ДАННЫХ ОБ АТМОСФЕРНЫХ ВОЗДЕЙСТВИЯХ И СЕТОЧНОГО РАЗРЕШЕНИЯ

A comparison of simulations of the global ocean circulation with the NEMO/SI3 model with two types of atmospheric forcing (DFS5.2 and CFSR/CDAS), as well as with two grid resolutions (1° and 0.25°) is presented. The objective of such comparison is to substantiate a choice of a source of data on atmospheric forcing and a grid resolution for use in an operational system for the ocean state diagnosing and forecasting. It is shown that all considered simulations reproduce main large-scale ocean characteristics. The CFSR/CDAS merged meteorological dataset, which provides better agreement with actual data, is quite suitable for specifying atmospheric forcing in retrospective and operational applications of the NEMO/SI3 model, including its use as a computational core in the data assimilation system within the framework of the analysis-forecast-analysis sequential assimilation scheme.

Estimating alpine snow depth by combining multifrequency passive radiance observations with ensemble snowpack modeling

This paper presents a physically-based snow depth retrieval algorithm adapted for deep mountainous snowpack and airborne multifrequency (10.7, 18.7, 37.0 and 89.0 GHz) passive microwave (PM) radiance observations from a single flight. The algorithm employs a single forecast-analysis cycle of a traditional sequential assimilation scheme. It uses an ensemble of multi-layer snowpack model runs to resolve snow microstructure and melt-refreeze crusts, and microwave radiative transfer models to relate snow properties to microwave measurements. Snow depth was retrieved at a 120 m spatial resolution over three 1 km2 Intensive Study Areas (ISA) within the Rabbit Ears Meso-Cell Study Area (MSA) from the NASA Cold Land Processes Experiment (CLPX) in Colorado (United States) for one date in February 2003. When evaluated against in situ observations, root mean square error (RMSE) of the snow depth from the assimilation was 13.3 cm for areas with low (<5%) forest cover, which was a reduction of 48% in the RMSE compared with the modeled snow depth when the PM observations were not assimilated, indicating a ~5% relative error of the posterior snow depth with respect to the average snow depth (200 cm) measured at these pixels. For pixels with forest cover ranging from 5 to 15% and 15–30%, results were improved (R2 increased from 0.65 to 0.71 and from 0 to 0.38, respectively) by introducing a forest radiative transfer model during the assimilation.

Methods for Observation Data Assimilation in Problems of Physics of Atmosphere and Ocean

This paper presents a review and analysis of approaches to data assimilation in problems of geophysical hydrodynamics, from the simplest sequential assimilation schemes to modern variational methods. Special attention is paid to the study of the problem of variational assimilation in a weak formulation, in particular, to the construction of an optimality system and the estimation of the covariance matrices of the optimal solution errors. This is a new direction of research in which the author has obtained some results.

A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): Twin experiments with static forecast error covariances

We assess and compare four sequential data assimilation methods developed for HYCOM in an identical twin experiment framework. The methods considered are Multi-variate Optimal Interpolation (MVOI), Ensemble Optimal Interpolation (EnOI), the fixed basis version of the Singular Evolutive Extended Kalman Filter (SEEK) and the Ensemble Reduced Order Information Filter (EnROIF). All methods can be classified as statistical interpolation but differ mainly in how the forecast error covariances are modeled. Surface elevation and temperature data sampled from an 1/12° Gulf of Mexico HYCOM simulation designated as the truth are assimilated into an identical model starting from an erroneous initial state, and convergence of assimilative runs towards the truth is tracked. Sensitivity experiments are first performed to evaluate the impact of practical implementation choices such as the state vector structure, initialization procedures, correlation scales, covariance rank and details of handling multivariate datasets, and to identify an effective configuration for each assimilation method. The performance of the methods are then compared by examining the relative convergence of the assimilative runs towards the truth. All four methods show good skill and are able to enhance consistency between the assimilative and truth runs in both observed and unobserved model variables. Prediction errors in observed variables are typically less than the errors specified for the observations, and the differences between the assimilated products are small compared to the observation errors. For unobserved variables, RMS errors are reduced by 50% relative to a non-assimilative run and differ between schemes on average by about 5%. Dynamical consistency between the updated state space variables in the data assimilation algorithm, and the data adequately sampling significant dynamical features are the two crucial components for reliable predictions. The experiments presented here suggest that practical implementation details can have at least as much an impact on the accuracy of the assimilated product as the choice of assimilation technique itself. We also present a discussion of the numerical implementation and the computational requirements for the use of these methods in large scale applications.

Characterization of mixing errors in a coupled physical biogeochemical model of the North Atlantic: implications for nonlinear estimation using Gaussian anamorphosis

Abstract. In biogeochemical models coupled to ocean circulation models, vertical mixing is an important physical process which governs the nutrient supply and the plankton residence in the euphotic layer. However, vertical mixing is often poorly represented in numerical simulations because of approximate parameterizations of sub-grid scale turbulence, wind forcing errors and other mis-represented processes such as restratification by mesoscale eddies. Getting a sufficient knowledge of the nature and structure of these errors is necessary to implement appropriate data assimilation methods and to evaluate if they can be controlled by a given observation system. In this paper, Monte Carlo simulations are conducted to study mixing errors induced by approximate wind forcings in a three-dimensional coupled physical-biogeochemical model of the North Atlantic with a 1/4° horizontal resolution. An ensemble forecast involving 200 members is performed during the 1998 spring bloom, by prescribing perturbations of the wind forcing to generate mixing errors. The biogeochemical response is shown to be rather complex because of nonlinearities and threshold effects in the coupled model. The response of the surface phytoplankton depends on the region of interest and is particularly sensitive to the local stratification. In addition, the statistical relationships computed between the various physical and biogeochemical variables reflect the signature of the non-Gaussian behaviour of the system. It is shown that significant information on the ecosystem can be retrieved from observations of chlorophyll concentration or sea surface temperature if a simple nonlinear change of variables (anamorphosis) is performed by mapping separately and locally the ensemble percentiles of the distributions of each state variable on the Gaussian percentiles. The results of idealized observational updates (performed with perfect observations and neglecting horizontal correlations) indicate that the implementation of this anamorphosis method into sequential assimilation schemes can substantially improve the accuracy of the estimation with respect to classical computations based on the Gaussian assumption.

Variational Assimilation of GOME Total-Column Ozone Satellite Data in a 2D Latitude–Longitude Tracer-Transport Model

A four-dimensional data-assimilation method is described to derive synoptic ozone fields from total-column ozone satellite measurements. The ozone columns are advected by a 2D tracer-transport model, using ECMWF wind fields at a single pressure level. Special attention is paid to the modeling of the forecast error covariance and quality control. The temporal and spatial dependence of the forecast error is taken into account, resulting in a global error field at any instant in time that provides a local estimate of the accuracy of the assimilated field. The authors discuss the advantages of the 4D-variational (4D-Var) approach over sequential assimilation schemes. One of the attractive features of the 4D-Var technique is its ability to incorporate measurements at later times t . t 0 in the analysis at time t 0, in a way consistent with the time evolution as described by the model. This significantly improves the offline analyzed ozone fields.

A sequential assimilation scheme applied to global wave analysis and prediction

Abstract This paper examines the results of the assimilation of satellite Significant Wave Height (SWH) observations in a global wave prediction model. The model used is the third generation wave model WAM. The assimilation is carried out using a sequential (single time level) scheme. Each assimilation step is split into two parts. The analysed SWH field is built by Optimal Interpolation, and the analysed spectrum is successively derived from it and from the first guess spectrum. The period used in the study is February 1992, and the altimeter data have been provided by the ERS-1 satellite. The results show positive effects of the assimilation on both wave analysis and forecast.