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Christopher J. Anderson
,
Raymond W. Arritt
, and
John S. Kain

Abstract

The authors have altered the vertical profile of updraft mass flux detrainment in an implementation of the Kain–Fritsch2 (KF2) convective parameterization within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5). The effect of this modification was to alter the vertical profile of convective parameterization cloud mass (including cloud water and ice) supplied to the host model for explicit simulation by the grid-resolved dynamical equations and parameterized microphysical processes. These modifications and their sensitivity to horizontal resolution in a matrix of experimental simulations of the June–July 1993 flood in the central United States were tested.

The KF2 modifications impacted the diurnal cycle of precipitation by reducing precipitation from the convective parameterization and increasing precipitation from more slowly evolving mesoscale processes. The modified KF2 reduced an afternoon bias of high precipitation rate in both low- and high-resolution simulations but affected mesoscale precipitation processes only in high-resolution simulations. The combination of high-resolution and modified KF2 resulted in more frequent and more realistically clustered propagating, nocturnal mesoscale precipitation events and agreed best with observations of the nocturnal precipitation rate.

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M. Segal
,
R. W. Arritt
,
J. Shen
,
C. Anderson
, and
M. Leuthold

Abstract

In this note the forcing of cumulus cloud clearing over and downwind from lakes during the warm season is evaluated conceptually by modeling and observational approaches. It is suggested that drying by dynamically induced subsidence and suppression of the CBL over the lake mutually contribute to the cloud clearing. The effect of background flow speed and the extent of potential clearing area is illustrated. Various implications of the cloud clearing are discussed.

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David Anderson
,
Kevin I. Hodges
, and
Brian J. Hoskins

Abstract

For the tracking of extrema associated with weather systems to be applied to a broad range of fields it is necessary to remove a background field that represents the slowly varying, large spatial scales. The sensitivity of the tracking analysis to the form of background field removed is explored for the Northern Hemisphere winter storm tracks for three contrasting fields from an integration of the U.K. Met Office's (UKMO) Hadley Centre Climate Model (HadAM3). Several methods are explored for the removal of a background field from the simple subtraction of the climatology, to the more sophisticated removal of the planetary scales. Two temporal filters are also considered in the form of a 2–6-day Lanczos filter and a 20-day high-pass Fourier filter. The analysis indicates that the simple subtraction of the climatology tends to change the nature of the systems to the extent that there is a redistribution of the systems relative to the climatological background resulting in very similar statistical distributions for both positive and negative anomalies. The optimal planetary wave filter removes total wavenumbers less than or equal to a number in the range 5–7, resulting in distributions more easily related to particular types of weather system. For the temporal filters the 2–6-day bandpass filter is found to have a detrimental impact on the individual weather systems, resulting in the storm tracks having a weak waveguide type of behavior. The 20-day high-pass temporal filter is less aggressive than the 2–6-day filter and produces results falling between those of the climatological and 2–6-day filters.

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A. T. Weaver
,
J. Vialard
, and
D. L. T. Anderson

Abstract

Three- and four-dimensional variational assimilation (3DVAR and 4DVAR) systems have been developed for the Océan Parallélisé (OPA) ocean general circulation model (OGCM) of the Laboratoire d'Océanographie Dynamique et de Climatologie. An iterative incremental approach is used to minimize a cost function that measures the statistically weighted squared differences between the observational information and their model equivalent. The control variable of the minimization problem is an increment to the background estimate of the model initial conditions at the beginning of each assimilation window. In 3DVAR, the increment is transported between observation times within the window using a persistence model, while in 4DVAR a dynamical model derived from the tangent linear (TL) of the OGCM is used. Both the persistence and TL models are shown to provide reasonably good descriptions of the evolution of typical errors over the 10- and 30-day widths of the assimilation windows used in the authors' 3DVAR and 4DVAR experiments, respectively.

The present system relies on a univariate formulation of the background-error covariance matrix. In practice, the background-error covariances are specified implicitly within a change of control variable designed to improve the conditioning of the minimization problem. Horizontal and vertical correlation functions are modeled using a filter based on a numerical integration of a diffusion equation. The background-error variances are geographically dependent and specified from the model climatology. Single observation experiments are presented to illustrate how the TL dynamics act to modify these variances in a flow-dependent way by diminishing their values in the mixed layer and by displacing the maximum value of the variance to the level of the background thermocline.

The 3DVAR and 4DVAR systems have been applied to a tropical Pacific version of OPA and cycled over the period 1993–98 using in situ temperature observations from the Global Temperature and Salinity Pilot Programme. The overall effect of the data assimilation is to reduce a large bias in the thermal field, which was present in the control. The fit to the data in 4DVAR is better than in 3DVAR, and within the specified observation-error standard deviation. Intermittent updating of the linearization state of the TL model is shown to be an important feature of the incremental 4DVAR algorithm and contributes significantly to improving the fit to the data.

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F. Vitart
,
J. L. Anderson
, and
W. F. Stern

Abstract

The present study examines the simulation of the number of tropical storms produced in GCM integrations with a prescribed SST. A 9-member ensemble of 10-yr integrations (1979–88) of a T42 atmospheric model forced by observed SSTs has been produced; each ensemble member differs only in the initial atmospheric conditions. An objective procedure for tracking-model-generated tropical storms is applied to this ensemble during the last 9 yr of the integrations (1980–88). The seasonal and monthly variations of tropical storm numbers are compared with observations for each ocean basin.

Statistical tools such as the Chi-square test, the F test, and the t test are applied to the ensemble number of tropical storms, leading to the conclusion that the potential predictability is particularly strong over the western North Pacific and the eastern North Pacific, and to a lesser extent over the western North Atlantic. A set of tools including the joint probability distribution and the ranked probability score are used to evaluate the simulation skill of this ensemble simulation. The simulation skill over the western North Atlantic basin appears to be exceptionally high, particularly during years of strong potential predictability.

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J. Segschneider
,
D. L. T. Anderson
, and
T. N. Stockdale

Abstract

The TOPEX/Poseidon and ERS-1/2 satellites have now been observing sea level anomalies for a continuous time span of more than 6 yr. These sea level observations are first compared with tide gauge data and then assimilated into an ocean model that is used to initialize coupled ocean–atmosphere forecasts with a lead time of 6 months. Ocean analyses in which altimeter data are assimilated are compared with those from a no-assimilation experiment and with analyses in which subsurface temperature observations are assimilated. Analyses with altimeter data show variations of upper-ocean heat content similar to analyses using subsurface observations, whereas the ocean model has large errors when no data are assimilated. However, obtaining good results from the assimilation of altimeter data is not straightforward: it is essential to add a good mean sea level to the observed anomalies, to filter the sea level observations appropriately, to start the analyses from realistic initial temperature and salinity fields, and to assign appropriate weights for the analyzed increments.

To assess the impact of altimeter data assimilation on the coupled system, ensemble hindcasts are initialized from ocean analyses in which either no data, subsurface temperatures, or sea level observations were assimilated. For each kind of ocean analysis, a five-member ensemble is started every 3 months from January 1993 to October 1997, adding up to 100 forecasts for each type. The predicted SST anomalies for the equatorial Pacific are intercompared between the experiments and against observations. The predicted anomalies are on average closer to observed values when forecasts are initialized from the ocean analysis using altimeter data than when initialized from the no-assimilation ocean analysis, and forecast errors appear to be only slightly larger than for forecasts initialized from ocean analyses using subsurface temperatures. However, even based on 100 coupled forecasts, the distinction between the two experiments that benefit from data assimilation is barely statistically significant. The verification should still be considered preliminary, because the period covered by the forecasts is only 5 yr, which is too short properly to sample ENSO variability. It is, nonetheless, encouraging that altimeter assimilation can improve the forecast skill to a level comparable to that obtained from using Tropical Ocean Atmosphere–expendable bathythermograph data.

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Daniel J. Short Gianotti
,
Bruce T. Anderson
, and
Guido D. Salvucci

Abstract

Using weather station data, the parameters of a stationary stochastic weather model (SSWM) for daily precipitation over the contiguous United States are estimated. By construct, the model exactly captures the variance component of seasonal precipitation characteristics (intensity, occurrence, and total amount) arising from high-frequency variance. By comparing the variance of the lower-frequency accumulations (on the order of months) between the SSWM and the original measurements, potential predictability (PP) is estimated. Decomposing the variability into contributions from occurrence and intensity allows one to establish two contributing sources of total PP. Aggregated occurrence is found to have higher PP than either intensity or the seasonal total precipitation, and occurrence and intensity are found to interfere destructively when convolved into seasonal totals. It is recommended that efforts aimed at forecasting seasonal precipitation or attributing climate variability to particular processes should analyze occurrence and intensity separately to maximize signal-to-noise ratios. Significant geographical and seasonal variations exist in all PP components.

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Donald E. Hagen
,
Rodney J. Anderson
, and
James L. Kassner Jr.

Abstract

Experimental data on ice nucleation, presented in an earlier paper, are analyzed to yield information about the homogeneous nucleation rate of ice from supercooled liquid and the heights of energy barriers to that nucleation. The experiment consisted of using an expansion cloud chamber to nucleate from the vapor a cloud of supercooled pure water drops and the observation of the fraction of drops which subsequently froze. The analysis employed standard classical homogeneous nucleation theory. The data are used to extract the first experimental measurement (albeit indirect) of the activation energy for the transfer of a water molecule across the liquid-ice interface at temperatures near −40°C. The results provide further evidence that the local liquid structure becomes more icelike as the temperature is lowered.

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L. R. Wyatt
,
L. J. Ledgard
, and
C. W. Anderson

Abstract

The maximum-likelihood method is used to extract parameters of two-parameter models of the directional spreading of short wind waves from the power spectrum of high-frequency (HF) radar backscatter. The wind waves have a wavelength of half the radio wavelength that, for the data presented here, is at a frequency of 0.53 Hz. The parameters are short-wave direction, which at this frequency can be identified with wind direction, and the directional spread angle, the parameterization of which is model dependent. For the data presented here, the results suggest that the Donelan directional spreading model provides a better description of directional spreading than the cos s model. The HF radar and wave buoy measurements are compared and show good agreement. Measurements are presented that show the temporal and spatial structure of the short-wave field responding to the passage of a frontal system.

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G. Mastrantonio
,
J. Naithani
,
P. S. Anderson
,
S. Argentini
, and
I. Petenko

Abstract

While some people involved in the acoustic remote sensing field are aware of the possibility of receiving dot echoes from nonatmospheric targets, most of the papers available in the scientific literature dealing with this phenomenon associate them to atmospheric targets, such as clusters of water vapor inhomogeneity, thermodynamical processes of condensation and reevaporation of water vapor, anisotropic irregularities localized in thin layers, etc. At present, dot echoes are defined by their appearance on the echogram and are not differentiated by causative processes. As such, they share similar characteristics, such as being randomly distributed and having a time length that is similar to the time length of the emitted tone. In this paper dot echoes conforming to this definition are investigated through the analysis of the signal in both the time and frequency domain. The timescale of a dot signature along with the configuration of the sodar system provide an upper limit to the size of the targets producing these echoes. The spectral characteristics and the first and second momenta of the echoes are compared with clear-air echoes as well as with echoes produced by pilot balloons released from nearby sodar antennas. The conclusion is that the dot echoes analyzed in this paper are reflections from birds and are not due to atmospheric effects.

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