Search Results

You are looking at 61 - 70 of 83 items for

  • Author or Editor: Chris Snyder x
  • Refine by Access: All Content x
Clear All Modify Search
Thomas M. Hamill
,
Chris Snyder
, and
Rebecca E. Morss

Abstract

A perfect model Monte Carlo experiment was conducted to explore the characteristics of analysis error in a quasigeostrophic model. An ensemble of cycled analyses was created, with each member of the ensemble receiving different observations and starting from different forecast states. Observations were created by adding random error (consistent with observational error statistics) to vertical profiles extracted from truth run data. Assimilation of new observations was performed every 12 h using a three-dimensional variational analysis scheme. Three observation densities were examined, a low-density network (one observation ∼ every 202 grid points), a moderate-density network (one observation ∼ every 102 grid points), and a high-density network (∼ every 52 grid points). Error characteristics were diagnosed primarily from a subset of 16 analysis times taken every 10 days from a long time series, with the first sample taken after a 50-day spinup. The goal of this paper is to understand the spatial, temporal, and some dynamical characteristics of analysis errors.

Results suggest a nonlinear relationship between observational data density and analysis error; there was a much greater reduction in error from the low- to moderate-density networks than from moderate to high density. Errors in the analysis reflected both structured errors created by the chaotic dynamics as well as random observational errors. The correction of the background toward the observations reduced the error but also randomized the prior dynamical structure of the errors, though there was a dependence of error structure on observational data density. Generally, the more observations, the more homogeneous the errors were in time and space and the less the analysis errors projected onto the leading backward Lyapunov vectors. Analyses provided more information at higher wavenumbers as data density increased. Errors were largest in the upper troposphere and smallest in the mid- to lower troposphere. Relatively small ensembles were effective in capturing a large percentage of the analysis-error variance, though more members were needed to capture a specified fraction of the variance as observation density increased.

Full access
Chris Snyder
,
Thomas M. Hamill
, and
Stanley B. Trier

Abstract

The characteristics of forecast-error covariances, which are of central interest in both data assimilation and ensemble forecasting, are poorly known. This paper considers the linear dynamics of these covariances and examines their evolution from (nearly) homogeneous and isotropic initial conditions in a turbulent quasigeostrophic flow qualitatively similar to that of the midlatitude troposphere. The experiments use ensembles of 100 solutions to estimate the error covariances. The error covariances evolve on a timescale of O(1 day), comparable to the advective timescale of the reference flow. This timescale also defines an initial period over which the errors develop characteristic features that are insensitive to the chosen initial statistics. These include 1) scales comparable to those of the reference flow, 2) potential vorticity (PV) concentrated where the gradient of the reference-flow PV is large, particularly at the surface and tropopause, and 3) little structure in the interior of the troposphere. In the error covariances, these characteristics are manifest as a strong spatial correlation between the PV variance and the magnitude of the reference-flow PV gradient and as a pronounced enhancement of the error correlations along reference-flow PV contours. The dynamical processes that result in such structure are also explored; the key is the advection of reference-flow PV by the error velocity, rather than the passive advection of the errors by the reference flow.

Full access
Gregory J. Hakim
,
Chris Snyder
, and
David J. Muraki

Abstract

Cyclonic vortices on the tropopause are characterized by compact structure and larger pressure, wind, and temperature perturbations when compared to broader and weaker anticyclones. Neither the origin of these vortices nor the reasons for the preferred asymmetries are completely understood; quasigeostrophic dynamics, in particular, have cyclone–anticyclone symmetry.

In order to explore these and related problems, a novel small Rossby number approximation is introduced to the primitive equations applied to a simple model of the tropopause in continuously stratified fluid. This model resolves dynamics that give rise to vortical asymmetries, while retaining both the conceptual simplicity of quasigeostrophic dynamics and the computational economy of two-dimensional flows. The model contains no depth-independent (barotropic) flow, and thus may provide a useful comparison to two-dimensional flows dominated by this flow component.

Solutions for random initial conditions (i.e., freely decaying turbulence) exhibit vortical asymmetries typical of tropopause observations, with strong localized cyclones, and weaker diffuse anticyclones. Cyclones cluster around a distinct length scale at a given time, whereas anticyclones do not. These results differ significantly from previous studies of cyclone–anticyclone asymmetry in the shallow-water primitive equations and the periodic balance equations. An important source of asymmetry in the present solutions is divergent flow associated with frontogenesis and the forward cascade of tropopause potential temperature variance. This thermally direct flow changes the mean potential temperature of the tropopause, selectively maintains anticyclonic filaments relative to cyclonic filaments, and appears to promote the merger of anticyclones relative to cyclones.

Full access
Fuqing Zhang
,
Naifang Bei
,
Richard Rotunno
,
Chris Snyder
, and
Craig C. Epifanio

Abstract

A recent study examined the predictability of an idealized baroclinic wave amplifying in a conditionally unstable atmosphere through numerical simulations with parameterized moist convection. It was demonstrated that with the effect of moisture included, the error starting from small random noise is characterized by upscale growth in the short-term (0–36 h) forecast of a growing synoptic-scale disturbance. The current study seeks to explore further the mesoscale error-growth dynamics in idealized moist baroclinic waves through convection-permitting experiments with model grid increments down to 3.3 km. These experiments suggest the following three-stage error-growth model: in the initial stage, the errors grow from small-scale convective instability and then quickly [O(1 h)] saturate at the convective scales. In the second stage, the character of the errors changes from that of convective-scale unbalanced motions to one more closely related to large-scale balanced motions. That is, some of the error from convective scales is retained in the balanced motions, while the rest is radiated away in the form of gravity waves. In the final stage, the large-scale (balanced) components of the errors grow with the background baroclinic instability. Through examination of the error-energy budget, it is found that buoyancy production due mostly to moist convection is comparable to shear production (nonlinear velocity advection). It is found that turning off latent heating not only dramatically decreases buoyancy production, but also reduces shear production to less than 20% of its original amplitude.

Full access
Chris Snyder
,
David J. Muraki
,
Riwal Plougonven
, and
Fuqing Zhang

Abstract

Vortex dipoles provide a simple representation of localized atmospheric jets. Numerical simulations of a synoptic-scale dipole in surface potential temperature are considered in a rotating, stratified fluid with approximately uniform potential vorticity. Following an initial period of adjustment, the dipole propagates along a slightly curved trajectory at a nearly steady rate and with a nearly fixed structure for more than 50 days. Downstream from the jet maximum, the flow also contains smaller-scale, upward-propagating inertia–gravity waves that are embedded within and stationary relative to the dipole. The waves form elongated bows along the leading edge of the dipole. Consistent with propagation in horizontal deformation and vertical shear, the waves’ horizontal scale shrinks and the vertical slope varies as they approach the leading stagnation point in the dipole’s flow. Because the waves persist for tens of days despite explicit dissipation in the numerical model that would otherwise damp the waves on a time scale of a few hours, they must be inherent features of the dipole itself, rather than remnants of imbalances in the initial conditions. The wave amplitude varies with the strength of the dipole, with waves becoming obvious once the maximum vertical vorticity in the dipole is roughly half the Coriolis parameter. Possible mechanisms for the wave generation are spontaneous wave emission and the instability of the underlying balanced dipole.

Full access
Rebecca E. Morss
,
Kerry A. Emanuel
, and
Chris Snyder

Abstract

Adaptive sampling uses information about individual atmospheric situations to identify regions where additional observations are likely to improve weather forecasts of interest. The observation network could be adapted for a wide range of forecasting goals, and it could be adapted either by allocating existing observations differently or by adding observations from programmable platforms to the existing network. In this study, observing strategies are explored in a simulated idealized system with a three-dimensional quasigeostrophic model and a realistic data assimilation scheme. Using simple error norms, idealized adaptive observations are compared to nonadaptive observations for a range of observation densities.

The results presented show that in this simulated system, the influence of both adaptive and nonadaptive observations depends strongly on the observation density. For sparse observation networks, the simple adaptive strategies tested are beneficial: adaptive observations can, on average, reduce analysis and forecast errors more than the same number of nonadaptive observations, and they can reduce errors by a given amount using fewer observational resources. In contrast, for dense observation networks it is much more difficult to benefit from adapting observations, at least for the data assimilation method used here. The results suggest that the adaptive strategies tested are most effective when the observations are adapted regularly and frequently, giving the data assimilation system as many opportunities as possible to reduce errors as they evolve. They also indicate that ensemble-based estimates of initial condition errors may be useful for adaptive observations. Further study is needed to understand the extent to which the results from this idealized study apply to more complex, more realistic systems.

Full access
Zhe-Min Tan
,
Fuqing Zhang
,
Richard Rotunno
, and
Chris Snyder
Full access
Lotte Bierdel
,
Chris Snyder
,
Sang-Hun Park
, and
William C. Skamarock

Abstract

Under assumptions of horizontal homogeneity and isotropy, one may derive relations between rotational or divergent kinetic energy spectra and velocities along one-dimensional tracks, such as might be measured by aircraft. Two recent studies, differing in details of their implementation, have applied these relations to the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) dataset and reached different conclusions with regard to the mesoscale ratio of divergent to rotational kinetic energy. In this study the accuracy of the method is assessed using global atmospheric simulations performed with the Model for Prediction Across Scales, where the exact decomposition of the horizontal winds into divergent and rotational components may be easily computed. For data from the global simulations, the two approaches yield similar and very accurate results. Errors are largest for the divergent component on synoptic scales, which is shown to be related to a very dominant rotational mode. The errors are, in particular, sufficiently small so that the mesoscale ratio of divergent to rotational kinetic energy can be derived correctly. The proposed technique thus provides a strong observational check of model results with existing large commercial aircraft datasets. The results do, however, show a significant dependence on the height and latitude ranges considered, and the disparate conclusions drawn from previous applications to MOZAIC data may result from the use of different subsets of the data.

Full access
Zhe-Min Tan
,
Fuqing Zhang
,
Richard Rotunno
, and
Chris Snyder

Abstract

Recent papers by the authors demonstrated the possible influence of initial errors of small amplitude and scale on the numerical prediction of the “surprise” snowstorm of 24–25 January 2000. They found that initial errors grew rapidly at scales below 200 km, and that the rapid error growth was dependent on moist processes. In an attempt to generalize these results from a single case study, the present paper studies the error growth in an idealized baroclinic wave amplifying in a conditionally unstable atmosphere. The present results show that without the effects of moisture, there is little error growth in the short-term (0–36 h) forecast error (starting from random noise), even though the basic jet used here produces a rapidly growing synoptic-scale disturbance. With the effect of moisture included, the error is characterized by upscale growth, basically as found by the authors in their study of the numerical prediction of the surprise snowstorm.

Full access
Valérian Jewtoukoff
,
Riwal Plougonven
,
Albert Hertzog
,
Chris Snyder
, and
Glen Romine

Abstract

Safety compliance issues for operational studies of the atmosphere with balloons require quantifying risks associated with descent and developing strategies to reduce the uncertainties at the location of the touchdown point. Trajectory forecasts are typically computed from weather forecasts produced by an operational center, for example, the European Centre for Medium-Range Weather Forecasts. This study uses past experiments to investigate strategies for improving these forecasts. Trajectories for open stratospheric balloon (OSB) short-term flights are computed using mesoscale simulations with the Weather and Research Forecasting (WRF) Model initialized with ECMWF operational forecasts and are assimilated with radio soundings using the Data Assimilation Research Testbed (DART) ensemble Kalman filter, for three case studies during the Strapolété 2009 campaign in Sweden. The results are very variable: in one case, the error in the final simulated position is reduced by 90% relative to the forecast using the ECMWF winds, while in another case the forecast is hardly improved. Nonetheless, they reveal the main source of forecasting error: during the ceiling phase, errors due to unresolved inertia–gravity waves accumulate as the balloon continuously experiences one phase of a wave for a few hours, whereas they essentially average out during the ascent and descent phases, when the balloon rapidly samples through whole wave packets. This sensitivity to wind during the ceiling phase raises issues regarding the feasibility of such forecasts and the observations that would be needed. The ensemble spread is also analyzed, and it is noted that the initial ensemble perturbations should probably be improved in the future for better forecasts.

Full access