A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model

Jones, Chris; Macpherson, Bruce

doi:10.1017/s1350482797000522

Cited by 155 publications

(125 citation statements)

References 17 publications

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“…The most common method in operational use at the time of writing is latent heat nudging (LHN: Jones and Macpherson, 1997;Leuenberger, 2005;Leuenberger and Rossa, 2007;Montmerle et al, 2007;Stephan et al, 2008;Dixon et al, 2009), although a variety of more advanced techniques have been studied in research contexts. Operational experience suggests that the impact of the assimilated radar data is often short-lived, e.g.…”

Section: Radar Data Assimilation: One Avenue To Improving Srnwp Qpfmentioning

confidence: 99%

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

Rossa¹,

Liechti

Zappa

et al. 2011

Atmospheric Research

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Publication informationAtmospheric Research, 100 (2-3): 150-167Publisher Elsevier This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT AbstractQuantifying uncertainty in flood forecasting is a difficult task, given the multiple and strongly nonlinear model components involved in such a system. Much effort has been and is being invested in the quest of dealing with uncertain precipitation observations and forecasts and the propagation of such uncertainties through hydrological and hydraulic models predicting river discharges and risk for inundation. The COST 731 Action is one of these and constitutes a European initiative which deals with the quantification of forecast uncertainty in hydro-meteorological forecast systems.COST 731 addresses three major lines of development: (1) combining meteorological and hydrological models to form a forecast chain, (2) propagating uncertainty information through this chain and make it available to end users in a suitable form, (3)

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Section: Radar Data Assimilation: One Avenue To Improving Srnwp Qpfmentioning

confidence: 99%

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

Rossa¹,

Liechti

Zappa

et al. 2011

Atmospheric Research

View full text Add to dashboard Cite

show abstract

“…The most common method in operational use at the time of writing is latent heat nudging (LHN: Jones and Macpherson, 1997;Leuenberger, 2005;Leuenberger and Rossa, 2007;Stephan et al, 2008;Dixon et al, 2009), although a variety of more advanced techniques have been studied in research contexts (e.g. Jakubiak, 2008;Caumont et al, 2009).…”

Section: Radar Data Assimilation As Promising Avenue For Improved Shomentioning

confidence: 99%

Propagation of uncertainty from observing systems into NWP: COST‐731 Working Group 1

Rossa¹,

Haase²,

Keil³

et al. 2010

Atmospheric Science Letters

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The COST-731 Action is focused on uncertainty propagation in hydrometeorological forecasting chains. The goals and activities of the Action Working Group 1 can be subdivided by (1) describing and studying the impact of imperfect observations, mostly from radar, (2) exploiting radar data assimilation as a promising avenue for improved short-range precipitation forecasts and (3) high-resolution ensemble forecasting. Activities of Working Group 1 are presented along with their possible significance for hydrological applications.

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“…As conventional observations are sparse at the convective scale, radar is an important source of information. The first operational NWP systems with kilometre-scale resolution use simple methods to assimilate radar data, such as latent heat nudging (LHN) (Jones and Macpherson, 1997;Macpherson, 2001;Leuenberger and Rossa, 2007;Stephan et al, 2008), but research is ongoing using more sophisticated techniques. In a perfect model context, simulated observations of convective storms have been assimilated using fourdimensional variational assimilation (4DVAR) Crook, 1997, 1998) and Ensemble Kalman Filter (EnKF) methods (Caya et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

The impact of localization and observation averaging for convective‐scale data assimilation in a simple stochastic model

Craig

Würsch

2012

Quart J Royal Meteoro Soc

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A simple stochastic model, based on a Poisson birth-death process, is proposed as a test bed for convective-scale data assimilation methods. The simple model mimics the extreme nonlinearity and non-Gaussianity associated with rapidly developing and intermittent convective storms. In this framework, we evaluate the Ensemble Transform Kalman Filter (ETKF) and Sequential Importance Resampling (SIR) filter, and assess the impact of two strategies to improve their performance and efficiency: localization and observation averaging. In their basic implementations, both filters perform poorly. The SIR filter rapidly collapses, then very gradually converges to the observations as random perturbations introduced by resampling occasionally improve the analysis. The ETKF rapidly assimilates the correct locations of convective storms, but has large errors due to creation of spurious clouds by nonlinear amplification of small data assimilation increments. Localization, i.e. assimilating only local observations to produce the analysis at a given grid point, dramatically improves the performance of the SIR filter, but does not reduce errors in the ETKF. Observation averaging, i.e. spatially smoothing the observations before assimilation and thus making the distribution more Gaussian, is also effective for the SIR filter, and improves convergence of the ETKF. Copyright c 2012 Royal Meteorological Society Key Words: data assimilation; Ensemble Transform Kalman Filter; particle filter

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A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model

Cited by 155 publications

References 17 publications

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

Propagation of uncertainty from observing systems into NWP: COST‐731 Working Group 1

The impact of localization and observation averaging for convective‐scale data assimilation in a simple stochastic model

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