Search Results

You are looking at 51 - 60 of 2,092 items for :

  • Forecasting techniques x
  • Journal of the Atmospheric Sciences x
  • User-accessible content x
Clear All
B. F. Ceselski

prediction experiments. The technique includes entrainment, moist and drydowndrafts, and environmental mixing. Vertical extent of the model cloud is to one of three possible tropospheric levels. The formulation of the mass flux into cloud base is such that the amount of low-level energyconsumed by the clouds is approximately that supplied by the large-scale flow. The model convection was employed, without change, in real-data primitive equation forecasts of twoseparate tropical disturbances. The first

Full access
Justin Finkel, Dorian S. Abbot, and Jonathan Weare

precipitation ( Baldwin and Dunkerton 2001 ; Thompson et al. 2002 ). Abrupt cold spells severely stress infrastructures, economies and human lives, and every bit of extra prediction lead time is helpful for adaptation. Unfortunately, numerical weather prediction struggles to forecast SSW at any lead time longer than about 2 weeks ( Tripathi et al. 2016 ). Understanding SSW is therefore important for practical forecasting as well as science, but this task remains difficult. Several different geophysical

Free access
Cathy Hohenegger and Christoph Schär

complicate the design of ensemble prediction systems, data assimilation, and targeting techniques, since far remote initial uncertainties may disrupt the skill of a forecast within a few hours. These difficulties are emphasized by the underlying strong nonlinearities, which are likely to have a detrimental impact on the use of the tangent-linear approximation for cloud-resolving NWP. Also, the ensuing extremely short predictability horizons tend to confirm the pessimistic view of Lorenz (1969

Full access
T. P. Barnett and R. W. Preisendorfer

particularly simple stochaster againstwhich we pit the forecasting skills of Methods I, IIand III.5. Skill scores In order to gage the skill of the prediction methodsstudied here, they have been compared against thatof the stochaster mentioned above. It turns out that.we can in this way evaluate our skill on both a localand global level, as will be 0i~tlined below. Our description will be general here, and we will apply thescoring technique to our particular studies in latersection. For a detailed

Full access
Timothy DelSole

with the fewest number of patterns. Similarly, singular value decomposition (SVD) is used to investigate relationships between fields because the patterns capture the most observed covariance with the fewest pairs of patterns. However, EOFs or SVD pairs are not ideal in all cases. For instance in predictability studies, one would like to determine patterns that are the “most predictable” according to some measure of forecast skill. One approach, explored by Renwick and Wallace (1995) , is to

Full access
Ming Cai and Huug M. Van Den Dool

tendency components of the low-frequencyvariations of the 500-rob streamfunction induced by various internal linear-nonlinear interaction processes. Withthe aid of a special composite technique ( "phase-shifting" method) that effectively records the observations ina coordinate system moving with an identifiable low-frequency pattern, the authors are able to separate theinternal interactions that primarily act to make low-frequency waves propagate from those that are mostlyresponsible for development

Full access
Jeroen Oortwijn and Jan Barkmeijer

-Afiantic blocking regime and a Euro-Atlantic strong zonal flow regime. Both regimes arecharacterized by the same anomaly pattern but with opposite sign. Using a three-level quasigeostrophic T21model and its tangent linear and adjoint versions, initial perturbations are computed that have the largest projection on this anomaly pattern at a prescribed forecast time. The tangent linear and adjoint techniques can beused only to describe linear error growth. However, with an iterative procedure, nonlinear error

Full access
Ian Simmonds

then thecomposite set of forecast and observed data 'analyzedinto spectral space. The previous study 'has emphasized the value ofrepeatedly inserting available data. Hence all theassimilation experiments discussed herein have used thisrepeated insertion technique, in which 2 h data (obtained by linearly interpolating the 6. h data) wereinserted every timestep from 1 h before to 1 h afterthe valid time. In all the experiments reported, theassimilation period was six days, from 0000 GMT 4September

Full access
Richard A. Anthes

balancing of thedata. As will be shown in Section 5, it is possible toachieve better 24-hr forecasts from much less information using a dynamic initialization technique. The usual measure of a forecast's accuracy comparedto a control experiment is the root mean square (RMS)error of wind speed. In judging a hurricane model'sperformance, it is also important to consider the errorof the maximum wind speed (or central pressure, sincethe two parameters are highly correlated). Both of thesemeasurements of

Full access
Agnieszka A. Mrowiec, Olivier M. Pauluis, and Fuqing Zhang

important insights on the nature of hurricanes, the actual behavior of real storms is much more complex owing to a wide range of scales involved. For example, interactions between organized convection in spiral rainbands in the outer convective regions and the eyewall can cause intensity variations and possibly play a role in the rapid intensification or the eyewall replacement cycle. In the present study, we utilize the isentropic analysis technique recently developed by Pauluis and Mrowiec (2013

Full access