Results Of Assimilation Experiments Research Articles

Assimilation of AIRS Radiances Affected by Mid- to Low-Level Clouds

Abstract An approach to make use of Atmospheric Infrared Sounder (AIRS) cloud-affected infrared radiances has been developed at Météo-France in the context of the global numerical weather prediction model. The method is based on (i) the detection and the characterization of clouds by the CO2-slicing algorithm and (ii) the identification of clear–cloudy channels using the ECMWF cloud-detection scheme. Once a hypothetical cloud-affected pixel is detected by the CO2-slicing scheme, the cloud-top pressure and the effective cloud fraction are provided to the radiative transfer model simultaneously with other atmospheric variables to simulate cloud-affected radiances. Furthermore, the ECMWF scheme flags each channel of the pixel as clear or cloudy. In the current configuration of the assimilation scheme, channels affected by clouds whose cloud-top pressure ranges between 600 and 950 hPa are assimilated over sea in addition to clear channels. Results of assimilation experiments are presented. On average, 3.5% of additional pixels are assimilated over the globe but additional assimilated channels are much more numerous for mid- to high latitudes (10% of additional assimilated channels on average). Encouraging results are found in the quality of the analyses: background departures of AIRS observations are reduced, especially for surface channels, which are globally 4 times smaller, and the analysis better fits some conventional and satellite data. Global forecasts are slightly improved for the geopotential field. These improvements are significant up to the 72-h forecast range. Predictability improvements have been obtained for a case study: a low pressure system that affected the southeastern part of Italy in September 2006. The trajectory, intensity, and the whole development of the cyclogenesis are better predicted, whatever the forecast range, for this case study.

The assimilation of AIRS radiance data at ECMWF

AbstractThe development of an assimilation system for radiance data from the Atmospheric InfraRed Sounder (AIRS) is described, in particular the identification of cloud contamination, bias correction and the characterization of errors in the measured radiances and radiative‐transfer model. The results of assimilation experiments are presented. These show that a conservative use of AIRS radiance data (in a system already extensively observed with other satellite data) results in a small, but consistent, improvement in the quality of analyses and forecasts. Larger impacts of AIRS are found in hypothetical experiments that test the use of radiances from only a single sounding instrument. In these, the use of AIRS is found to outperform the use of data either from a single Advanced Microwave Sounding Unit‐A (AMSU‐A) or from a single High‐resolution InfraRed Sounder (HIRS). In this hypothetical context the relative forecast performance of each sensor is found to correlate with the size and vertical scale of increments caused by the assimilation of the radiances. Copyright © 2006 Royal Meteorological Society.

SST data assimilation experiments using an adaptive varia-tional method

An adaptive variational data assimilation method is proposed by Zhu and Kamachi[1]. This method can adaptively adjust the model state without knowing explicitly the model error covariance matrix. The method enables very flexible ways to form some reduced order problems. A proper reduced order problem not only reduces computational burden but also leads to corrections that are more consistent with the model dynamics that trends to produce better forecast. These features make the adaptive variational method a good candidate for SST data assimilation because the model error of an ocean model is usually difficult to estimate. We applied this method to an SST data assimilation problem using the LOTUS data sets and an ocean mixed layer model (Mellor-Yamada level 2.5). Results of assimilation experiments showed good skill of improvement subsurface temperatures by assimilating surface observation alone.

Data Assimilation in a Quasi-geostrophic Open-Ocean Model of the Gulf Stream Region Using the Adjoint Method

The method of adjoint data assimilation is applied in a quasi-geostrophic (QG) open-ocean model of the Gulf Stream region. The results of data assimilation experiments are presented in which simulated AXBT and satellite altimeter observations are assimilated into the QG model. The adjoint data assimilation scheme has the ability to correct for large errors in the speed and position of the Gulf Stream jet. These experiments provide valuable insight into the dynamics of the assimilation procedure, and highlight the limitations of the adjoint method in the Gulf Stream region. The adjoint variables of the linearized QG model are approximations of the Green's functions of the adjoint equations. By examining maps of the adjoint variables, one can determine the domain of influence of individual observations. The results of assimilation experiments in which GEOSAT sea surface height observations are assimilated into the QG model are also presented. The results of the GEOSAT assimilation experiments can be interpreted using the insight gained from the simulated data assimilation experiments. It is found that not only can the adjoint method correct the position of the Gulf Stream axis, but it can also reconstruct observed features present in the real ocean (such as Gulf Stream rings) which were originally absent in the model.