Atmospheric Data Assimilation System Research Articles

An Adaptive Ensemble Kalman Filter

Abstract To the extent that model error is nonnegligible in numerical models of the atmosphere, it must be accounted for in 4D atmospheric data assimilation systems. In this study, a method of estimating and accounting for model error in the context of an ensemble Kalman filter technique is developed. The method involves parameterizing the model error and using innovations to estimate the model-error parameters. The estimation algorithm is based on a maximum likelihood approach and the study is performed in an idealized environment using a three-level, quasigeostrophic, T21 model and simulated observations and model error. The use of a limited number of ensemble members gives rise to a rank problem in the estimate of the covariance matrix of the innovations. The effect of this problem on the two terms of the log-likelihood function is that the variance term is underestimated, while the χ2 term is overestimated. To permit the use of relatively small ensembles, a number of strategies are developed to deal w...

Quasi‐stationary wave variability in NSCAT winds

Planetary and interseasonal properties of the NASA scatterometer (NSCAT) winds are explored in the context of the quasi‐stationary waves (QSWs) of the southern hemisphere. The QSWs are examined by means of zonal asymmetries of 3‐month running averages of the meridional velocity derived from NSCAT. The study period spans the entire NSCAT record from September 15, 1996, through June 29, 1997. Meridional winds from the European Space Agency ERS 2 scatterometer are used to augment 3‐month averages centered on August 1996 through May 1997. The time period corresponds to the transition from year −1 to 0 of the 1997 warm event in the Southern Oscillation. Comparisons are made with QSW signals in geopotential height anomalies from the National Centers for Environmental Prediction/National Center for Atmospheric Research Climate Data Assimilation System. The zonal anomalies of meridional wind from NSCAT are shown to be in approximate geostrophic balance with zonal gradients in the zonal anomalies of geopotential height at 500 and 1000 hPa.

A study on assimilating potential vorticity data

The correlation that exists between the potential vorticity (PV) field and the distribution ofchemical tracers such as ozone suggests the possibility of using tracer observations as proxyPV data in atmospheric data assimilation systems. Especially in the stratosphere, there areplentiful tracer observations but a general lack of reliable wind observations, and the correlationis most pronounced. The issue investigated in this study is how model dynamics would respondto the assimilation of PV data. First, numerical experiments of identical-twin type were conductedwith a simple univariate nuding algorithm and a global shallow water model based onPV and divergence (PV-D model). All model fields are successfully reconstructed through theinsertion of complete PV data alone if an appropriate value for the nudging coefficient is used. A simple linear analysis suggests that slow modes are recovered rapidly, at a rate nearly independentof spatial scale. In a more realistic experiment, appropriately scaled total ozone datafrom the NIMBUS-7 TOMS instrument were assimilated as proxy PV data into the PV-Dmodel over a 10-day period. The resulting model PV field matches the observed total ozonefield relatively well on large spatial scales, and the PV, geopotential and divergence fields aredynamically consistent. These results indicate the potential usefulness that tracer observations, as proxy PV data, may offer in a data assimilation system.

The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS)

Abstract The three-dimensional Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) has been developed by the Naval Research Laboratory. COAMPS consists of an atmospheric data assimilation system comprising data quality control, analysis, initialization, and nonhydrostatic forecast model components, as well as a hydrostatic ocean model. The models can be integrated simultaneously so that the surface fluxes of heat, momentum, and moisture are exchanged across the air–water interface every time step. Optionally, either the atmospheric or ocean model can be used as a stand-alone system. The atmospheric component of COAMPS was used for operational support for the America3 team in the 1995 America’s Cup races. Results of these forecasts indicated the necessity of data assimilation to reduce model spinup in the first 6 h of the forecast. Accurate forecasts of the low-level wind in the coastal race area was accomplished by utilizing triply nested grids to attain the necessary high resolution to resolve ...

The Effect of Serially Correlated Observation and Model Error on Atmospheric Data Assimilation

Abstract Observation error statistics are required in most atmospheric data assimilation systems. While observation errors are often assumed to be spatially correlated, serial correlations have received virtually no attention. In this article, the effect of serially correlated observation error is examined in the context of Kalman filter theory. It is shown that for spatially uncorrelated observation errors, serial correlations will only be detrimental for rapid-sampling instruments or low-flow regimes. In standard Kalman filter theory, it is assumed that the model error is not serially correlated. This assumption has been questioned in the past. In this article, certain types of serially correlated model errors are shown to have a serious detrimental effect on atmospheric data assimilation. It is also suggested that certain performance diagnostics may be capable of detecting serial correlations.