Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I: Experiments with Synoptic-Scale Data

Stauffer, D.; Seaman, Nelson L.

doi:10.1175/1520-0493(1990)118<1250:uofdda>2.0.co;2

Cited by 637 publications

(425 citation statements)

References 0 publications

Supporting

Mentioning

413

Contrasting

Unclassified

Order By: Relevance

“…The FDDA technique used in the current simulations is based on Newtonian relaxation (or nudging), as described by Stauffer and Seaman (1990), Seaman et al (1995), and Shafran et al (2000). Nudging is a continuous form of FDDA that ''relaxes'' model results toward the ''correct solution'' by artificial tendency terms in the prognostic equations.…”

Section: Methodsmentioning

confidence: 98%

MM5 simulations of a SCOS97-NARSTO episode

Boucouvala

Bornstein

Wilkinson

et al. 2003

Atmospheric Environment

View full text Add to dashboard Cite

MM5 simulations of the 4-7 August SCOS97-NARSTO ozone episode in the SoCAB showed that a relatively rare easterly upper-level background flow influenced surface wind flow directions at in-land sites. An inland-moving surface convergence zone resulted where the offshore background flow met an inland moving westerly (sea breeze plus thermal upslope) onshore flow. Maximum inland penetration of the zone was to the San Gabriel Mountains, where it produced peak observed ozone concentrations. MM5 reproduced the main qualitative features of the evolution of the diurnal sea breeze cycle, as it reproduced the opposing easterly flow, inland movement of the sea breeze front, and retreat of the land breeze front. The accuracy of predicted surface winds and temperatures were improved by modifications of MM5 and/or its input parameters, e.g., by use of the PSU MBLI scheme, analysis nudging with NCAR/NCEP model output, accurate specification of deep-soil temperature, observational nudging with SCOS97-NARSTO surface and upper level winds and temperatures, and use of updated urban land-use patterns. r

show abstract

Section: Methodsmentioning

confidence: 98%

MM5 simulations of a SCOS97-NARSTO episode

Boucouvala

Bornstein

Wilkinson

et al. 2003

Atmospheric Environment

View full text Add to dashboard Cite

show abstract

“…This relaxation is performed by adding a term to the prognostic equation that is proportional to the difference between the two states. These small forcing terms gradually correct the model fields, which are assumed to remain in approximate equilibrium at each time step [Stauffer and Seaman, 1990]. In this way the model can be nudged toward regularly spaced observations, or toward randomly spaced observations during a period of time and space.…”

Section: Newtonian Nudging Assimilation Methodsmentioning

confidence: 99%

Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

Houser¹,

Shuttleworth²,

Famiglietti³

et al. 1998

Water Resources Research

425

310

View full text Add to dashboard Cite

Abstract. The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km 2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surfacesubsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time. IntroductionSoil moisture is most often described as the water in the root zone that can interact with the atmosphere through evapotranspiration and precipitation. Because soil moisture links the hydrologic cycle and the energy budget of land surfaces by regulating latent heat fluxes, accurate assessment of the spatial and temporal variation of soil moisture is important for the study, understanding, and management of surface biogeophysical processes. Given the crucial role of soil moisture in land surface processes, it should be monitored with the same accuracy and frequency as other important environmental variables. However, because in situ soil moisture measurements are generally expensive and often problematic, no large-area soil moisture networks exist to measure soil moisture at the high frequency, multiple depths, and fine spatial resolution that is required for various applications. Remote sensing of soil moisture is limited by errors introduced by soil type, landscape roughness, vegetation cover, and inadequate coverage in both space and time. Alternatively, many reliable hydrologic models are available for calculating soil moisture, but these are prone to error in both structure and parameterization. It has been suggested [Wei, 1995] In the "statistical correction" data assimilation technique, = F(O, x, t) + GoWo(x, t)•o(x)(O•,-O) OtThe model's forcing terms are represented by F, 0•, is the observation at the model grid, and t is time. G 0 is the nudging factor that determines the magnitude of the nudging term relative to all other model processes, while the fourdimensional weighting function Wo specifies its spatial and temporal variation. The analysis quality factor • varies between 0 and 1 and is based on the quality and distribution of the observations. Equation (6) is implemented for all three TOPLATS soil layers, with the weighting factor decreasing with depth and time using...

show abstract

“…The term 'indiscrimnate nudging' is thus not as commonly used as 'spectral nudging'. Salameh et al(2010) use the term 'indiscriminate nudging', Anthes (1974), Hoke and Anthes (1976) and Stauffer and Seaman (1990) refer to 'data assimilation', and other synonymous terminologies exist such as 'dynamical relaxation' (Davies and Turner, 1977) or 'grid' or 'analysis nudging'. In the following we will use 'indiscriminate nudging'.…”

Section: Introductionmentioning

confidence: 99%

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

Omrani

Drobinski

Dubos

2011

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

In this work, we consider the effect of indiscriminate nudging time on an idealized high-resolution global model (GM) and limited-area model (LAM) simulations. The model used is a two-layer quasi-geostrophic model on the beta-plane.The effect of nudging is studied as a function of the predictability time, following a 'Big Brother' experimental approach: a high-resolution 'global' model is used to generate a 'reference run'. These fields are filtered afterwards to remove small scales and provide the coarse-resolution fields which are used to drive the high-resolution GM and the LAM. Comparison of the reference fields and the high-resolution runs over the same region allows the estimation of the ability of the high-resolution GM and LAM to regenerate the removed small scales. This fully nonlinear set-up mimics the configuration used for regional high-resolution atmospheric modelling.For the high-resolution GM, the results show that the behaviour of the nudged model depends primarily on the ratio of the nudging time to the predictability time. When the nudging time is very small compared to the predictability time, the model reproduces the large scale used to drive the model. On the other hand, if the nudging time is close to or larger than the predictability time, the nudging effect is weak and both large and small scales are poorly reproduced compared to the reference fields. The best result is obtained with a nudging time close to half the predictability time. This technique clearly improves the model capacity to reproduce the reference fields.For the high-resolution LAM, our results show that for a sufficiently small domain the simulation is largely controlled by the lateral boundary conditions (LBCs) and is quasi-insensitive to nudging. However, if the domain size exceeds a few Rossby radii, the high-resolution LAM becomes sensitive to initial conditions and the control exerted by LBCs becomes insufficient to prevent a divergence from the driving fields. Although the reconstructed fine scales are significantly damped, they are surprisingly well correlated to their reference values in a deterministic sense, not a statistical sense. Copyright c 2011 Royal Meteorological Society Key Words: downscaling; uncertainties; large/small-scale perturbations; potential vorticity

show abstract

Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I: Experiments with Synoptic-Scale Data

Cited by 637 publications

References 0 publications

MM5 simulations of a SCOS97-NARSTO episode

MM5 simulations of a SCOS97-NARSTO episode

Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

Investigation of indiscriminate nudging and predictability in a nested quasi‐geostrophic model

Contact Info

Product

Resources

About