<scp>RMetS</scp> Special Interest Group Meeting: high resolution data assimilation

Browne, Philip; Charlton‐Perez, Cristina; Dance, Sarah L.

doi:10.1002/asl2.512

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…To account for this, the data may be thinned (Dando et al, 2007) or 'super-obbed' (Berger and Forsythe, 2004;van Leeuwen, 2015) or the error variances inflated (Hilton et al, 2009). Therefore, accounting for OECs correctly could allow for denser observations to be assimilated, which could be important for high-resolution weather forecasting (Browne et al, 2014;Sun et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

On the interaction of observation and prior error correlations in data assimilation

Fowler

Dance

Waller

2017

Quart J Royal Meteoro Soc

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The importance of prior error correlations in data assimilation has long been known; however, observation-error correlations have typically been neglected. Recent progress has been made in estimating and accounting for observation-error correlations, allowing for the optimal use of denser observations. Given this progress, it is now timely to ask how prior and observation-error correlations interact and how this affects the value of the observations in the analysis. Addressing this question is essential to understanding the optimal design of future observation networks for high-resolution numerical weather prediction. This article presents new results, which unify and advance upon previous studies on this topic.The interaction of the prior and observation-error correlations is illustrated with a series of two-variable experiments in which the mapping between the state and observed variables (the observation operator) is allowed to vary. In an optimal system, the reduction in the analysis-error variance and spread of information is shown to be greatest when the observation and prior errors have complementary statistics: for example, in the case of direct observations, when the correlations between the observation and prior errors have opposite signs. This can be explained in terms of the relative uncertainty of the observations and prior on different spatial scales. The results from these simple two-variable experiments are used to inform the optimal observation density for observations of a circular domain (with 32 grid points). It is found that dense observations are most beneficial when they provide a more accurate estimate of the state at smaller scales than the prior estimate. In the case of second-order auto-regressive correlation functions, this is achieved when the length-scales of the observation-error correlations are greater than those of the prior estimate and the observations are direct measurements of the state variables.

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Section: Introductionmentioning

confidence: 99%

On the interaction of observation and prior error correlations in data assimilation

Fowler

Dance

Waller

2017

Quart J Royal Meteoro Soc

Self Cite

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“…In the same manner, an academic researcher will want the flexibility to test novel DA techniques with state of the art models (Browne et al, 2014). Such ongoing changes to the DA system are used as proof-of-concept tests and as such do not seek maximum performance and efficiency.…”

Section: Introductionmentioning

confidence: 99%

A simple method for integrating a complex model into an ensemble data assimilation system using MPI

Browne

Wilson

2015

Environmental Modelling & Software

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a b s t r a c tThis paper details a strategy for modifying the source code of a complex model so that the model may be used in a data assimilation context, and gives the standards for implementing a data assimilation code to use such a model. The strategy relies on keeping the model separate from any data assimilation code, and coupling the two through the use of Message Passing Interface (MPI) functionality. This strategy limits the changes necessary to the model and as such is rapid to program, at the expense of ultimate performance. The implementation technique is applied in different models with state dimension up to .2.7 Â 10 8 The overheads added by using this implementation strategy in a coupled ocean-atmosphere climate model are shown to be an order of magnitude smaller than the addition of correlated stochastic random errors necessary for some nonlinear data assimilation techniques.

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RMetS Special Interest Group Meeting: high resolution data assimilation

Cited by 2 publications

References 32 publications

On the interaction of observation and prior error correlations in data assimilation

On the interaction of observation and prior error correlations in data assimilation

A simple method for integrating a complex model into an ensemble data assimilation system using MPI

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