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An expression for the probability density of any distribution of observed values (given background values of known accuracy) is derived from the properties of multivariate normal distributions. This is used in the quality control of observations-'good' and 'bad' observations are assumed to have errors from a normal distribution and from a distribution giving no useful information respectively.Three methods of quality control are presented and compared; two of these are based on the probability density derived above, and the third is based on a related maximum probability analysis. They differ in the optimality principal used: Individual Quality Control finds the most likely quality (i.e. good or bad) for each observation, given information from all the others; Simultaneous Quality Control finds the most likely combination of qualities; while Variational Quality Control is based on a variational analysis which finds the most likely true values. The quality control should be considered as part of the 'analysis' process of using the observations; these approaches to quality control are considered as approximations to a system giving the 'best' analysis, based on minimizing a Bayesian loss function. Approximations are also necessary in their practical implementations; the effect of these on various operational schemes is discussed.The multi-observation framework used includes the 'background' check as a special case, and it is extended to deal with observations with common sources of gross error. Applications to multi-level checks, bias checks and checks for known error patterns are sketched. As a by-product the standard statistical interpolation formulae are derived from the properties of normal distributions, thus demonstrating the implicit dependence of statistical interpolation on the normal distribution.* Corresponding author: Meteorological Office, London Road, Bracknell, Berks RG12 2S2, UK. t By 'objective' we imply more than the automatic application of ad hoe rules, rather that the rules themselves have some theoretical foundation. ( c ) Choice of methodsLet us try to take a subjective view of the examples in Table 1. In (a) and (b) the observations seem fairly consistent, so in (a) SQC and VQC are better than IQC, in (b)
An expression for the probability density of any distribution of observed values (given background values of known accuracy) is derived from the properties of multivariate normal distributions. This is used in the quality control of observations-'good' and 'bad' observations are assumed to have errors from a normal distribution and from a distribution giving no useful information respectively.Three methods of quality control are presented and compared; two of these are based on the probability density derived above, and the third is based on a related maximum probability analysis. They differ in the optimality principal used: Individual Quality Control finds the most likely quality (i.e. good or bad) for each observation, given information from all the others; Simultaneous Quality Control finds the most likely combination of qualities; while Variational Quality Control is based on a variational analysis which finds the most likely true values. The quality control should be considered as part of the 'analysis' process of using the observations; these approaches to quality control are considered as approximations to a system giving the 'best' analysis, based on minimizing a Bayesian loss function. Approximations are also necessary in their practical implementations; the effect of these on various operational schemes is discussed.The multi-observation framework used includes the 'background' check as a special case, and it is extended to deal with observations with common sources of gross error. Applications to multi-level checks, bias checks and checks for known error patterns are sketched. As a by-product the standard statistical interpolation formulae are derived from the properties of normal distributions, thus demonstrating the implicit dependence of statistical interpolation on the normal distribution.* Corresponding author: Meteorological Office, London Road, Bracknell, Berks RG12 2S2, UK. t By 'objective' we imply more than the automatic application of ad hoe rules, rather that the rules themselves have some theoretical foundation. ( c ) Choice of methodsLet us try to take a subjective view of the examples in Table 1. In (a) and (b) the observations seem fairly consistent, so in (a) SQC and VQC are better than IQC, in (b)
This paper gives an account of the global soundings system (GLOSS), which is the new method introduced at the Meteorological Office at Bracknell for the processing of global TOVS radiance data for assimilation into numerical weather prediction (NWP) models. The assimilating NWP models themselves provide the prior information necessary to infer temperature and humidity information from radiances. After a brief summary of the historical background, the GLOSS processing is described, noting in particular the differences from similar work elsewhere. Results are then presented from NWP impact studies of the assimilation of temperature profiles derived from satellite soundings. An advantage is demonstrated for the GLOSS temperature retrievals relative to the retrievals distributed by NESDIS and produced from the same radiance data. The advantage of GLOSS is clear and consistent in the extratropical regions of the northern hemisphere and, especially, of the southern hemisphere. In the tropics the results are more mixed. The paper concludes with a note of additional work required before operational implementation of GLOSS, and with an outline of other expected future developments.
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