Search Results

You are looking at 1 - 10 of 8,489 items for :

  • Subgrid-scale processes x
  • Refine by Access: All Content x
Clear All
Lisa Bengtsson
,
Heiner Körnich
,
Erland Källén
, and
Gunilla Svensson

1. Introduction Physical processes that are not explicitly resolved by the model grid in numerical weather prediction (NWP) models are parameterized. To address model errors associated with parameterization schemes and subgrid-scale variability, development of stochastic representations of atmospheric processes is becoming more frequent (e.g., Lin and Neelin 2002 ; Shutts 2005 ; Teixeira and Reynolds 2008 ; Plant and Craig 2008 ; Berner et al. 2008 ). In the context of ensemble prediction

Full access
Tiffany A. Shaw
and
Theodore G. Shepherd

parameterization of processes that occur on scales smaller than can be represented by the discrete model grid. Physical processes occurring on these subgrid scales are important to the resolved energy and momentum budgets and necessarily respect the same conservation principles. Self-consistency of conservation properties in subgrid-scale parameterization is an important issue because parameterizations of subgrid-scale processes can lead to spurious long-term trends if energy and momentum conservation are not

Full access
Phillip J. Smith

by statistical inter- polation of forecase error fields. J. Atmos. Sci., 29, 809-815. Sasaki, Yoshikazu, 1971: A theoretical interpretation of ani sotropically weighted smoothing on the basis of numerical variational analysis. Mort. Wea. R~v., 99, 698-707.The Net Generation of Large-Scale Available Potential Energy by Subgrid-Scale Processes Pm~m J. S~a~National Center for Atmospheric Research? Boulder, Colo. 8030~ 6 April 1973 and 8 August 1973ABSTRACT A

Full access
Mikael K. Witte
,
Hugh Morrison
,
Jørgen B. Jensen
,
Aaron Bansemer
, and
Andrew Gettelman

m. But as the horizontal grid dimension increases toward what is feasible for a general circulation model (GCM), say tens to hundreds of kilometers, neglecting subgrid-scale variability leads to systematic process rate biases because of small-scale heterogeneity and the nonlinearity of the parameterized process rate equations ( Pincus and Klein 2000 ; Rotstayn 2000 ). Since the realization that neglecting small-scale variability is a cause of bias in GCM microphysics, a number of

Full access
Daan Crommelin
and
Eric Vanden-Eijnden

1. Introduction: Stochastic parameterization of subgrid-scale processes The parameterization of subgrid-scale processes in models of atmospheric flow has drawn a lot of research attention recently. To get beyond the limitations of parameterizations with deterministic functions, the focus of various recent investigations has been on the potential of stochastic methods for the parameterization of processes that cannot be resolved because they fall below the model grid scale (e.g., Majda et al

Full access
Rebecca L. Gianotti
and
Elfatih A. B. Eltahir

and the tuned version of the default Emanuel method. The generalized form presented here, where n = 1 is not assumed a priori, removes the need to tune a grid-mean value. The new formulation for autoconversion presented here has significant advantages over the default forms that exist within RegCM3: 1) it explicitly recognizes subgrid variability in CLW and how that variability affects the grid-scale conversion process; 2) only two parameters have to be specified, one dependent upon the other

Full access
Anning Cheng
and
Kuan-Man Xu

1. Introduction It is well known that variability in cloud microphysical and macrophysical properties occurs at very fine spatial and temporal scales. If subgrid-scale (SGS) variability in cloud properties is crudely represented in numerical models, potentially substantial biases in microphysical process rates can result from inadequate consideration of SGS variability ( Pincus and Klein 2000 ; Larson et al. 2001a ; Woods et al. 2002 ). A potential benefit for representing SGS variability

Full access
Kamal Kant Chandrakar
,
Hugh Morrison
,
Wojciech W. Grabowski
,
George H. Bryan
, and
Raymond A. Shaw

Chamber provides a controlled condition for studying cloud–turbulence interactions and is well suited for developing and testing subgrid-scale (SGS) models. MacMillan et al. (2022) performed the first DNS of the Pi Chamber with droplets. However, the Rayleigh number for these simulations was three orders of magnitude smaller than the real Pi Chamber, and periodic sidewall boundaries were used. Thus, the turbulence and moisture statistics were idealized, and different from those in the Pi Chamber. In

Full access
Julia Jeworrek
,
Gregory West
, and
Roland Stull

example, is reached at a coarser resolution than the gray zone for turbulence [planetary boundary layer (PBL) parameterizations]. Traditional parameterization schemes were designed to represent the subgrid-scale processes that are not explicitly resolved because they are spatially or temporally too small scale, too complex and expensive, or not well understood; but nonetheless affect the atmospheric state at the resolved scale. However, when decreasing the grid spacing below a certain value

Open access
Christopher A. Jeffery
,
Jon M. Reisner
, and
Miroslaw Andrejczuk

constant. In particular, an indirect aerosol effect is observed with N D increasing linearly with N c at fixed σ 2 . Overall, the estimates of Fig. 5 indicate that the impact of S ′ variability on volume-averaged quantities is significant over a wide range of temperatures, droplet concentrations and spatial scales; this result provides strong theoretical support for pursuing stochastic condensation as a subgrid cloud model. 6. Early-time enhanced growth The analysis of sections 5c and 5d

Full access