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Richard A. Anthes and David P. Baumhefner

In operational numerical weather prediction systems, both observations and numerical models contribute to the skill of the forecast. A simple diagram representing the relative contributions of observations and models to the current level of forecast skill and to the ultimate predictability of atmospheric phenomena is interpreted in this note. The forecast skill of 500 mb heights and an estimate of the ultimate predictability of this variable are used in a quantitative illustration of the diagram.

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Thomas W. Bettge and David P. Baumhefner

Abstract

The total and systematic errors in the 500 mb geopotential height forecasts from the NMC grid-point and spectral operational models are compared and contrasted for two recent winters. The spectral model is shown to be an improvement in the forecasts through a more skillful prediction of the planetary-scale (zonal wavenumbers 1-2) quasi-stationary wave amplitudes, and through the elimination of the grid-point model's large systematic error at low latitudes.

In agreement with estimates from related studies, the systematic error in the NMC spectral model accounts for 15-20% of the total error variance. Approximately one-half of the total systematic error resides in the planetary scales.

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Thomas W. Bettge and David P. Baumhefner

Abstract

The design of a digital filter is outlined and its application as a band-pass filter to separate various scales from an atmospheric field in a limited domain is discussed. The accuracy of the filter is demonstrated by decomposing both a function with specified wave components and a 500 mb geopotential field within a 90°longitudinal area of the globe. The boundary effects of the non-periodic domain are not negligible, but tests using various boundary conditions show that little contamination exists inside 7–10 grid points from the boundaries. The suitability of the technique to examine the spatial wavenumber characteristics of a geopotential field within a limited domain is demonstrated.

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David P. Baumhefner and Richard C. J. Somerville

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No abstract available.

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David P. Baumhefner and Paul R. Julian

Abstract

The error produced by an observational system of remotely sensed temperature profiles is partially simulated with aid of an operational retrieval scheme. A given temperature distribution is converted to radiances and then back to temperatures. A comparison is made between the retrieved temperatures and the original values.

The sensitivity of the retrieval scheme to various input parameters, such as guess profiles, the statistical coefficients used in the retrieval scheme, and cloudiness, is examined. Experiments with the placement of a reference level from which to integrate the geopotential field are performed.

The relative growth rate of the simulated initial error is examined by forecasting with two initial states, one case with the observational error and one central case. Error growths are calculated for different reference levels, clear, and cloudy cases.

The results show that the generated error fields do not seriously contaminate forecasts of the large-scale baroclinic waves for periods up to one week, providing the retrievals are free of the effects of clouds and an accurate guess is used. In general, the results support the need for reference-level information, and if forecast error in the low troposphere is to be a minimum, placing of the reference level at sea level.

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Steven L. Mullen and David P. Baumhefner

Abstract

The impact of initial condition uncertainty on short-range (up to 48 h) forecasts of large-scale explosive cyclogenesis is examined. Predictability experiments are conducted on 11 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global, high-resolution, spectral model. Results are derived from the 11-case ensemble average. The perturbation used to represent the initial condition error in this study has a magnitude and spatial distribution that closely matches estimates of global analysis error. Results from the predictability experiments are compared to a set of physics sensitivity experiments which are used to represent an estimate of a “typical” modeling, error.

Compared to the control simulations, the inclusion of initial error produces a composite cyclone with maximum deepening rate that is slightly reduced and a 24 h period of most rapid deepening that is somewhat delayed. The absolute position error in the surface cyclone is approximately 100 km the first +36 h of the forecast then abruptly increases to 300 km by +48 h. We estimate that, on the average, the forecast error due to initial condition uncertainty is as large as that due to the modeling error associated with today's best operational models, whereas five years ago modeling error was much more important.

The relative importance of initial condition uncertainty for explosive cyclogenesis is compared to that for the entire midlatitude flow in general. Error growth rates in an explosive cyclogenetic environment are 50% greater in the upper troposphere (500 mb and above) and two times faster near the surface (850 mb and below). The rapid growth rates indicate that short-range forecasts of explosive cyclogenesis are much wore sensitive to initial error than those for ordinary flows.

The case-to-case variability exhibited by the 11-member ensemble is examined. Noteworthy departure from the aggregate results are evident In individual cases, initial condition error can lead to short-range forecast differences which can be either greater than those due to a typical modeling error or much less. This variability implies a strong sensitivity to initial condition perturbation location and structure.

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Steven L. Mullen and David P. Baumhefner

Abstract

The relative importance of different parameterized physical process and baroclinic dynamics in numerical simulations of explosive oceanic cyclogenesis is examined. The numerical simulations are derived from a global spectral model having nine vertical levels and a rhomboidal 31 truncation. Eleven cases of rapid cyclogenesis over the North Pacific Ocean that occurred in a 150-day simulation of perpetual January conditions are used as initial conditions for model sensitivity experiments. Statistical techniques based on predictability theory are employed to estimate the relative importance of the sensitivity experiments.

Results from the simulation comparisons indicate that the total diabatic heating accounts for about one-half of the cyclone's deepening rate, with baroclinic dynamics accounting for the remaining part. The absence of diabatic heating also leads to a systematic error in the position of the cyclone. Surface fluxes of sensible heat are responsible for about one-half of the deepening rate due to diabatic processes, while latent heating due to grid-scale resolvable precipitation in conjunction with surface latent heat flux accounts for most of the remaining half. An increase in the surface drag over the ocean to its larger land value was found to be of comparable importance to both surface sensible heat flux and latent beat release, but was only half as important as the total diabatic heating. These changes in the model were judged to produce highly significant response especially at low levels. The addition of radiative heating, the substitution of a Kuo-type cumulus parameterization scheme for the model's default moist convective adjustment scheme, and a factor of 4 increase in the horizontal diffusion did not produce significant responses.

The case-to-case variability exhibited by the 11-member ensemble is examined. The potential danger in attempting to generalize results from a single case of explosive cyclogenesis as being representative of those for the ensemble average is illustrated.

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Byron A. Boville and David P. Baumhefner

Abstract

The error growth associated with the usual upper boundary formulation (and location) in numerical weather prediction (NWP) and general circulation models (GCMs) is studied. The experimental strategy is to look at the growth of differences between the equilibrated climate simulation of a control simulation and three ensembles of 30 day simulations branching off of the control. The control simulation is a seasonal integration of a medium horizontal-resolution GCM with 30 levels extending from the surface to the upper mesosphere. Each ensemble consists of nine cases with initial conditions taken at 10-day intervals from the control. The main experiment uses a model identical to that of the control except that only the bottom 15 levels (below 10 mb) are retained. The additional experiments either perturb the initial conditions or alter the physical parameterizations (horizontal diffusion) to obtain information on the significance of the results of the main experiment.

It is found that random error growth rate in the troposphere for the case of the altered upper boundary is slightly faster than that for initial-condition uncertainty alone. However, this is not likely to make a significant impact in operational forecast models at present because the uncertainty in the initial conditions is so large.

Systematic errors in the troposphere due to the upper boundary are relatively small prior to day 20. However, the ten day mean errors from days 20–30 are about the same magnitude as the anomalies that extended range forecasts are attempting to predict. The upper boundary treatment is likely to cause significant systematic errors in such forecasts. In the lower stratosphere, the errors are substantial within the first few days, particularly in the winter hemisphere.

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Joseph J. Tribbia and David P. Baumhefner

Abstract

This paper presents the results of an ensemble of 20 predictability experiments derived from the NCAR Community Climate Model (CCM). Particular emphasis is placed on the question of the predictability of dynamically driven low-frequency components of the model atmosphere. The conclusion drawn, using time averaging alone as a means of isolating low-frequency variability, is that in the ensemble mean there is little skill in a 30-day mean forecast. Examination of the variability of skill among the ensemble members indicates that approximately 40 percent of the perturbed monthly mean forecasts would be useful. Examples of skillful and poor monthly mean predictions am shown and conclusions are drawn as to the implications of the results with regard to the likelihood of success of extended range deterministic forecasts.

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David P. Baumhefner and Paul R. Julian

Abstract

A reference level is defined as a level of known altitude at which temperature, pressure, and perhaps wind are specified as functions of time. This study is concerned with the optimum location of a reference level without wind information. Experiments were performed with the NCAR six-layer general circulation model to compare the usefulness of a surface reference level with an upper tropospheric reference level. We first performed a control integration using real atmospheric initial data. We then ran several comparison runs with initial conditions differing from those of the control run. The initial pressure distribution at the reference level was kept the same as the control run. The distribution of temperature pseudo-error employed in calculating the initial pressure distributions at the other levels was chosen to simulate possible error patterns in temperatures radiometrically derived from satellites. The initial conditions in all cases were in hydrostatic and geostrophic balance. Three data sets were used and the experiments were integrated to five or seven days. In addition, two horizontal distributions of initial temperature pseudo-error and two horizontal mesh lengths of the model were used for one of the three data sets. The results were examined using an rms difference of the distribution of pressure and meridional wind normalized (in the vertical) by the difference statistics derived from randomly chosen states of the model.

It appears that pseudo-error growth rates are nearly independent of the location of a reference level, but details of the pseudo-error patterns depend on the initial synoptic conditions. Pseudo-error growth rates differed depending on the manner in which the horizontal pseudo-error was initially distributed (but did not differ with the location of the reference level). The most significant change in the pseudo-error growth rates was observed when the mesh length was changed; halving the mesh length produced much faster growth rates, particularly in the lower layers.

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