Until January 2013, data from the high‐resolution sounder IASI were assimilated with a diagonal observation‐error covariance matrix within the Met Office 4D‐Var assimilation scheme, assuming no correlation between channels. The errors were inflated to account indirectly for known inter‐channel error correlations. This is sub‐optimal as it artificially down‐weights observations from these instruments. The true nature of these correlations for IASI are estimated here using data from the Met Office 4D‐Var assimilation scheme and a posteriori diagnostics based on analysis and background departures. The diagnosed matrices are symmetrised and reconditioned, to make them suitable for use in the operational assimilation scheme. These matrices have been tested in full assimilation experiments. The results of these experiments show that using the new matrices improves forecast accuracy due to more weight in the assimilation being given to the IASI observations, particularly those from water‐vapour‐sensitive channels.
el; of Silurian soils as a result of pedoturbat i o~~, effectively mcreasing the average depth of soil CO, proiluction.Our results (Table 1) ~m p l y that atmospheric CO-, decllneil 1~y a factor of 10 from the Late S i h r i a n to the Early Permian, closely follow~ng (Fig. 4 ) a decline precllctecl hi; theoretical carbon lnais balance models (1). T h e largest decrease, hetween the Late Sil~lrian a11il Late Devonian. coincides with a of rapid evolution and diversificatlon of the terrestrial ecosystem (18).Estimates of atmospheric C02 levels from separated, time-equivalent ~-7aleosols are consistent, suggertlng that a coherent record of changing atlnospheric chem~stry is yreserl-eii In the ancient soil recorJ.
REFERENCES AND NOTES1 R A Berner Science 261, 68 (1 993) At?? J SCI 294 56 (1 994) 2 T J Crowley and G R North, Paleoc!~~rato!ogy (Oxford UI?I\/ Press, Oxford, 1991) 3 R A Berner and R Ralswell, Geochim. Cosmochlm. Acta 47. 855 11983) L R. I
Using co-locations of three different observation types of sea surface temperatures (SSTs) gives enough information to enable the standard deviation of error on each observation type to be derived. SSTs derived from the Advanced Along-Track Scanning Radiometer (AATSR) and Advanced Microwave Scanning Radiometer (AMSR-E) instruments are used, along with SST observations from buoys. Various assumptions are made within the error theory including that the errors are not correlated, which should be the case for three independent data sources. An attempt is made to show that this assumption is valid and also that the covariances between the observations due to representativity error are negligible. Overall, the AATSR observations are shown to have a very small standard deviation of error of 0.16K, whilst the buoy SSTs have an error of 0.23K and the AMSR-E SST observations have an error of 0.42K.
A practical technique for the assimilation of cloud-affected infrared radiances is presented. The technique is best suited to advanced infrared sounders such as AIRS and IASI. Radiances are first pre-processed by a one-dimensional variational analysis (1D-Var) scheme, where cloud parameters (cloud-top pressure and effective cloud fraction) are retrieved simultaneously with atmospheric profile variables. The retrieved cloud parameters are then passed to a variational data assimilation system, where they are used to constrain the radiative transfer calculation in the assimilation of a reduced set of channels. The channel selection is chosen to reduce the sensitivity to errors in the forward modelling of radiation originating below the cloud top. The performance of this technique is explored by means of a 1D-Var study using simulated measurements. It is demonstrated that the technique has the potential to allow the assimilation of a significant proportion of cloud-affected infrared sounding measurements, possibly bringing valuable benefits to an operational NWP system. Crown
This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.The optimal utilisation of hyper-spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation-error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation-error correlations when the data are processed in the Met Office one-dimensional (1D-Var) and four-dimensional (4D-Var) variational assimilation systems. The method used to calculate the observation errors is a post-analysis diagnostic which utilises the background and analysis departures from the two systems.The results show significant differences in the source and structure of the observation errors when processed in the two different assimilation systems, but also highlight some common features. When the observations are processed in 1D-Var, the diagnosed error variances are approximately half the size of the error variances used in the current operational system and are very close in size to the instrument noise, suggesting that this is the main source of error. The errors contain no consistent correlations, with the exception of a handful of spectrally close channels. When the observations are processed in 4D-Var, we again find that the observation errors are being overestimated operationally, but the overestimation is significantly larger for many channels. In contrast to 1D-Var, the diagnosed error variances are often larger than the instrument noise in 4D-Var. It is postulated that horizontal errors of representation, not seen in 1D-Var, are a significant contributor to the overall error here. Finally, observation errors diagnosed from 4D-Var are found to contain strong, consistent correlation structures for channels sensitive to water vapour and surface properties.
In recent years difficulties have been experienced in exploiting satellite sounding data in numerical weather prediction (NWP) in the form of independently retrieved temperature and humidity profiles. Attention has now focused on methods through which the information in the radiance measurements may be assimilated more directly into the NWP system%. A scheme known as ‘one‐dimensional variational analysis’ (1DVAR) has been developed at the European Centre for Medium‐range Weather Forecasts as a method for extracting information from TIROS Operational Vertical Sounder radiances for use in the operational data‐assimilation system. The 1DVAR scheme is based on variational principles applied to the analysis of the atmospheric profile at a single location, using a forecast profile and its error covariance as constraints. The details of the scheme are presented. Errors in 1DVAR products are correlated with those of the short‐range forecast which serves as a background for the subsequent three‐dimensional analysis. Methods for addressing this aspect of the assimilation problem are discussed. The characteristics of 1DVAR products and their impact on the analysis are described. A series of forecast impact experiments has been conducted and has demonstrated consistent positive impacts on forecast skill in the northern hemisphere.
SUMMARYStructure functions for the 3D-Var assimilation scheme of the European Centre for Medium-Range Weather Forecasts are evaluated from statistics of the differences between two forecasts valid at the same time. Results compare satisfactorily with those reported in the existing literature. Non-separability of the correlation functions is a pervasive feature. Accounting for non-separability in 3D-Var is necessary to reproduce geostrophic characteristics of the statistics, such as the increase of length-scale with height for the horizontal correlation of the mass variable, sharper vertical correlations for wind than for mass and shorter horizontal length-scales for temperature than for mass. In our non-separable 3D-Var, the vertical correlations vary with total wave-number and the horizontal correlation functions vary with vertical level.
ABSTRACT:Observations from the Infrared Atmospheric Sounding Interferometer (IASI), onboard EUMETSAT's MetOp satellite, have been assimilated at the Met Office in global and regional numerical weather-prediction systems since 27 November 2007. Pre-operational trials of IASI assimilation in the global model during the summer of 2007 delivered a positive impact on forecasts approximately twice as large as that shown by the Atmospheric InfraRed Sounder (AIRS) on the EOS-Aqua satellite. A series of observing system experiments confirmed the relative performance of IASI and AIRS, and showed that impact from IASI is equivalent to a single Advanced Microwave Sounding Unit-A (AMSU-A) combined with a single Microwave Humidity Sounder (MHS). The results of an IASI assimilation trial for the winter of 2007 were consistent with those of the summer trial, although the impact was slightly lower overall. The assessment of impact is strongly dependent on the variables and methods chosen for verification: assimilation trials with the regional model showed similar improvements to the large-scale fields (e.g. mean-sea-level pressure and geopotential height) as seen in the global model, but no forecast impact was seen for variables such as visibility and rain-rate.
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