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Dive into the research topics where David G. H. Tan is active.

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Featured researches published by David G. H. Tan.


Journal of Climate | 2016

ERA-20C: An Atmospheric Reanalysis of the Twentieth Century

Paul Poli; Hans Hersbach; Dick Dee; Paul Berrisford; A. J. Simmons; F. Vitart; Patrick Laloyaux; David G. H. Tan; Carole Peubey; Jean-Noël Thépaut; Yannick Trémolet; E. Hólm; Massimo Bonavita; Lars Isaksen; Michael Fisher

AbstractThe ECMWF twentieth century reanalysis (ERA-20C; 1900–2010) assimilates surface pressure and marine wind observations. The reanalysis is single-member, and the background errors are spatiotemporally varying, derived from an ensemble. The atmospheric general circulation model uses the same configuration as the control member of the ERA-20CM ensemble, forced by observationally based analyses of sea surface temperature, sea ice cover, atmospheric composition changes, and solar forcing. The resulting climate trend estimations resemble ERA-20CM for temperature and the water cycle. The ERA-20C water cycle features stable precipitation minus evaporation global averages and no spurious jumps or trends. The assimilation of observations adds realism on synoptic time scales as compared to ERA-20CM in regions that are sufficiently well observed. Comparing to nighttime ship observations, ERA-20C air temperatures are 1 K colder. Generally, the synoptic quality of the product and the agreement in terms of climat...


Tellus A | 2008

The ADM-Aeolus wind retrieval algorithms

David G. H. Tan; Erik Andersson; Jos de Kloe; Gert-Jan Marseille; Ad Stoffelen; Paul Poli; Marie-Laure Denneulin; Alain Dabas; Dorit Huber; Oliver Reitebuch; Pierre H. Flamant; Olivier Le Rille; Herbert Nett

The ADM-Aeolus is primarily a research and demonstration mission flying the first Doppler wind lidar in space. Flexible data processing tools are being developed for use in the operational ground segment and by the meteorological community. We present the algorithms developed to retrieve accurate and representative wind profiles, suitable for assimilation in numerical weather prediction. The algorithms provide a flexible framework for classification and weighting of measurement-scale (1–10 km) data into aggregated, observation-scale (50 km) wind profiles for assimilation. The algorithms account for temperature and pressure effects in the molecular backscatter signal, and so the main remaining scientific challenge is to produce representative winds in inhomogeneous atmospheric conditions, such as strong wind shear, broken clouds, and aerosol layers. The Aeolus instrument provides separate measurements in Rayleigh and Mie channels, representing molecular (clear air) and particulate (aerosol and clouds) backscatter, respectively. The combining of information from the two channels offers possibilities to detect and flag difficult, inhomogeneous conditions. The functionality of a baseline version of the developed software has been demonstrated based on simulation of idealized cases.


Journal of Applied Meteorology | 2005

Comments on “The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis”

Ad Stoffelen; Gert-Jan Marseille; Erik Andersson; David G. H. Tan

The paper by Riishojgaard et al. (2004) investigates the assimilation and impact of prospective Doppler wind lidar (DWL) line-of-sight (LOS) single-perspective winds in meteorological analysis. It is argued that single-component wind observations are far less effective in reducing wind analysis error than vector wind information. This work has relevance because the prospects are good that space-based DWL instruments will provide accurate wind profiles of single-perspective LOS wind profile measurements in the future. Riishojgaard et al. rightly argue that the usefulness of such winds needs to be well addressed in the design phase of space missions. The forthcoming European Space Agency Atmospheric Dynamics Mission (ADM), called Aeolus, is referred to in this context. The Riishojgaard et al. study is carried out in an idealized and very simplified framework. Our concerns are 1) that the simple framework poorly represents the characteristics of a state-of-the-art global data assimilation system for numerical weather prediction (NWP) and 2) that the DWL scenarios that are discussed have abundant and unrealistic coverage and quality. As such, their conclusions may be misleading for, and contribute little toward, the critical design considerations for an affordable space-based DWL. The results (and the quality of the analyzed wind fields) could be far more realistic and, in our view, far more favorable for LOS winds in a more carefully designed experiment. The NWP analysis problem would be severely underdetermined if it were based on the observations alone. To overcome this problem, data assimilation typically combines the information provided by the relatively sparse observations with a short-range forecast on a dense grid (Daley 1991). Because the NWP model state is poorly observed, it is critical that local observation increments are carefully distributed spatially in a wider area. This process is done based on statistical knowledge of the background error structures. In a fourdimensional variational data assimilation (4DVAR) analysis system, information on the temporal evolution of the model state is also exploited. Around any local observation, information on the multivariate spatial correlation of the background errors, as represented in the background-error covariance matrix B, is used to provide a spatially coherent update of the model atmospheric state. For LOS wind analysis, the B covariance structures are crucial in both spatially interpolating the observed wind component and inferring the spatial pattern of the unobserved component of wind as well as the associated temperature and pressure increments. The design of the B matrix and the sampling strategy of the DWL space mission are the two most important factors that determine the impact of the data, both in real application and within the simplified framework of Riishojgaard et al. In the case in which B is poor, this would generally result in spatially poor analyses, especially when the observations are sparse or when one or several analysis variables are unobserved. In a relatively dense observation network, on the other hand, the multivariate spatial structures associated with many observations will overlap and the effect of an imperfect B will diminish (by oversampling). Our specific comments are in two areas. The first is that the Riishojgaard et al. paper uses a synthetic vortex Corresponding author address: Dr. Ad Stoffelen, Royal Netherlands Meteorological Institute, Postbus 201, 3730 AE de Bilt, Netherlands. E-mail: [email protected] 1276 J O U R N A L O F A P P L I E D M E T E O R O L O G Y VOLUME 44


Quarterly Journal of the Royal Meteorological Society | 2015

Signatures of naturally induced variability in the atmosphere using multiple reanalysis datasets

Dann M Mitchell; Lesley J. Gray; Masatomo Fujiwara; T. Hibino; James Anstey; Wesley Ebisuzaki; Yayoi Harada; Craig S. Long; Stergios Misios; Peter A. Stott; David G. H. Tan


Atmospheric Chemistry and Physics | 2016

Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems

Masatomo Fujiwara; Jonathon S. Wright; G. L. Manney; Lesley J. Gray; James Anstey; Thomas Birner; Sean M. Davis; Edwin P. Gerber; V. Lynn Harvey; M. I. Hegglin; Cameron R. Homeyer; John A. Knox; Kirstin Krüger; Alyn Lambert; Craig S. Long; Patrick Martineau; Andrea Molod; B. M. Monge-Sanz; Michelle L. Santee; Susann Tegtmeier; Simon Chabrillat; David G. H. Tan; D. R. Jackson; Saroja Polavarapu; Gilbert P. Compo; Rossana Dragani; Wesley Ebisuzaki; Yayoi Harada; Chiaki Kobayashi; Will McCarty


Quarterly Journal of the Royal Meteorological Society | 2007

Observing‐system impact assessment using a data assimilation ensemble technique: application to the ADM–Aeolus wind profiling mission

David G. H. Tan; Erik Andersson; Michael Fisher; Lars Isaksen


Quarterly Journal of the Royal Meteorological Society | 2005

Simulation of the yield and accuracy of wind profile measurements from the Atmospheric Dynamics Mission (ADM‐Aeolus)

David G. H. Tan; Erik Andersson


Quarterly Journal of the Royal Meteorological Society | 2013

Balance properties of the short‐range forecast errors in the ECMWF 4D‐Var ensemble

Nedjeljka Žagar; Lars Isaksen; David G. H. Tan; Joseph Tribbia


Quarterly Journal of the Royal Meteorological Society | 2015

Linearity aspects of the ensemble of data assimilations technique

Linda Megner; David G. H. Tan; Heiner Körnich; Lars Isaksen; András Horányi; Ad Stoffelen; Gert-Jan Marseille


Archive | 2007

ADM-Aeolus Level-2B Wind Retrieval Algorithms

David G. H. Tan; Erik Andersson; Jos de Kloe; Gert-Jan Marseille; Ad Stoffelen; Paul Poli; Marie-Laure Denneulin; Alain Dabas; Dorit Huber; Oliver Reitebuch; Pierre H. Flamant; Olivier Le Rille; Anne-Grete Straume; Herbert Nett

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Ad Stoffelen

Royal Netherlands Meteorological Institute

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Gert-Jan Marseille

Royal Netherlands Meteorological Institute

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Erik Andersson

European Centre for Medium-Range Weather Forecasts

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Paul Poli

European Centre for Medium-Range Weather Forecasts

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Herbert Nett

European Space Research and Technology Centre

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Dorit Huber

German Aerospace Center

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Lars Isaksen

European Centre for Medium-Range Weather Forecasts

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Jos de Kloe

Royal Netherlands Meteorological Institute

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