• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 89
  • 24
  • 17
  • 14
  • 8
  • 4
  • 3
  • 2
  • 2
  • 2
  • 1
  • 1
  • Tagged with
  • 205
  • 31
  • 24
  • 22
  • 20
  • 20
  • 19
  • 18
  • 18
  • 16
  • 15
  • 14
  • 13
  • 13
  • 12
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Decadal and interdecadal climate variability

Jewson, Stephen P. January 1996 (has links)
No description available.
2

Mesoscale predictability of an extreme warm-season precipitation event

Odins, Andrew Michael 17 February 2005 (has links)
During the period of June 29 through July 6, 2002, an extreme precipitation event occurred over Texas, resulting in catastrophic flooding. Operational forecasts performed poorly, neither predicting the copious amounts of rain nor its longevity. The Penn State University/NCAR Mesoscale Model version 5 (MM5) was used to conduct predictability experiments, which follow closely to the research conducted by Zhang et al. A control simulation initialized at 00Z 1 July is established over a 30-km grid. First, practical predictability experiments are performed by exploring the impacts due to different lead-times, resolution dependence, and different physics parameterizations. Second, intrinsic predictability is investigated by inducing a random temperature perturbation in the initial conditions, followed by numerous simulations with various perturbed initializations. Similar results to those found by Zhang et al. were discovered here: the prominent initial error growth is associated with moist processes leading to convection. Eventually these errors grow from the convective scale to sub-synoptic scale, essentially below 1000 kilometers. This indicates that as the forecast time extends further beyond initialization, the resulting errors will impact forecasts of larger-scale features such as differences in the positioning and intensity of positive PV anomalies and distribution of precipitation from the control simulation.
3

Predictability in the New Zealand Stock Market

Li, Yanhui January 2015 (has links)
Recent financial literature suggests that the variation in the dividend–price ratio is significantly related to the expected returns but not to the expected dividend growth. In other words, stock returns are predictable but dividend growth is not. However, most of this evidence comes from the U.S. at the aggregate level, and there is a lack of research that relates to this topic in the New Zealand stock market. This research examines the predictive power of the dividend–price ratio using New Zealand stock market data from 1931 to 2012. The results confirm the claim in the U.S data that returns are predictable but dividend growth is not in the New Zealand stock market data. This research also investigates whether the return predictability is associated with risk-pricing or mispricing; whether the return predictability is due to the fundamental relationship among the dividend–price ratio, future returns and future dividend growth, or whether it is due to the effects of historical events; whether out-of-sample forecasts will have the same patterns as in-sample predictions; and whether individual company returns are predictable.
4

Linear Diagnostics to Assess the Performance of an Ensemble Forecast System

Satterfield, Elizabeth A. 2010 August 1900 (has links)
The performance of an ensemble prediction system is inherently flow dependent. This dissertation investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model grid point location ℓ. A local error covariance matrix Pℓ is defined for each local region and the diagnostics are applied to the linear space Sℓ defined by the range of the ensemble based estimate of Pℓ. The particular diagnostics are chosen to help investigate the ability of Sℓ to efficiently capture the space of true forecast or analysis uncertainties, accurately predict the magnitude of forecast or analysis uncertainties, and to distinguish between the importance of different state space directions. Additionally, we aim to better understand the roots of the underestimation of the magnitude of uncertainty by the ensemble at longer forecast lead times. Numerical experiments are carried out with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on a reduced (T62L28) resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere are used in these experiments. It is found that (i) paradoxically, the linear space Sℓ provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time, (ii) Sℓ provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth, (iii) the E-dimension is a reliable predictor of the performance of Sℓ in predicting the space of forecast uncertainties, (iv) the ensemble grossly underestimates the forecast error variance in Sℓ, (v) when realistic observation coverage is used, the ensemble typically overestimates the uncertainty in the leading eigen-directions of ˆP ℓ and underestimates the uncertainty in the trailing directions at analysis time and underestimates the uncertainty in all directions by the 120-hr forecast lead time, and (vi) at analysis time, with a constant covariance inflation factor, the ensemble typically underestimates uncertainty in densely observed regions and overestimates the uncertainty in sparsely observed regions.
5

Understanding seasonal climate predictability in the Atlantic sector

Barreiro, Marcelo 17 February 2005 (has links)
This dissertation aims at understanding ocean-atmosphere interactions in the Atlantic basin, and how this coupling may lead to increased climate predictability on seasonal-to-interannual time scales. Two regions are studied: the South Atlantic convergence zone (SACZ), and the tropical Atlantic. We studied the SACZ during austral summer and separated its variability into forced and internal components. This was done by applying a signal-to-noise optimization procedure to an ensemble of integrations of the NCAR Community Climate Model (CCM3)forced with observed Sea Surface Temperature (SST). The analysis yielded two dominant responses: (1) a response to local Atlantic SST consisting of a dipole-like structure in precipitation close to the coast of South America; (2) a response to Pacific SST which manifests mainly in the upper-level circulation consisting of a northeastward shift of the SACZ during El Niño events. The land portion of the SACZ was found to be primarily dominated by internal variability, thereby having limited potential predictability at seasonal time scales. We studied two aspects of tropical Atlantic Variability (TAV). First, we investigated the effect of extratropical variability on the gradient mode. We found that the intensive Southern Hemisphere (SH) winter variability can play a pre-conditioning role in the onset of the interhemispheric anomalies in the deep tropics during boreal spring. This SH influence on TAV is contrasted with its northern counterpart that primarily comes from the North Atlantic Oscillation during boreal winter. Second, we explored the importance of ocean dynamics in the predictability of TAV. We used the CCM3 coupled to a slab ocean as a tier-one prediction system. The ocean processes are included as a statistical correction that parameterizes the heat transport due to anomalous linear ocean dynamics. The role of ocean dynamics was studied by comparing prediction runs with and without the correction. We showed that in the corrected region the corrected model outperforms the non-corrected one particularly at long lead times. Furthermore, when the model was initialized with global initial conditions, tropical Atlantic SST anomalies are skillfully predicted for lead times of up to six months. As result, the corrected model showed high skill in predicting rainfall in the ITCZ during boreal spring.
6

The Dynamics and Predictability of Tropical Cyclones

Sippel, Jason A. 15 January 2010 (has links)
Through methodology unique for tropical cyclones in peer-reviewed literature, this study explores how the dynamics of moist convection affects the predictability of tropical cyclogenesis. Mesoscale models are used to perform short-range ensemble forecasts of a non-developing disturbance in 2004 and Hurricane Humberto in 2007; both of these cases were highly unpredictable. Taking advantage of discrepancies between ensemble members in short-range ensemble forecasts, statistical correlation is used to pinpoint sources of error in forecasts of tropical cyclone formation and intensification. Despite significant differences in methodology, storm environment and development, it is found in both situations that high convective instability (CAPE) and mid-level moisture are two of the most important factors for genesis. In the gulf low, differences in CAPE are related to variance in quasi-geostrophic lift, and in Humberto the differences are related to the degree of interaction between the cyclone and a nearby front. Regardless of the source of CAPE variance, higher CAPE and mid-level moisture combine to yield more active initial convection and more numerous and strong vortical hot towers (VHTs), which incrementally contribute to a stronger vortex. In both cases, strength differences between ensemble members are further amplified by differences in convection that are related to oceanic heat fluxes. Eventually the WISHE mechanism results in even larger ensemble spread, and in the case of Humberto, uncertainty related to the time of landfall drives spread even higher. It is also shown that initial condition differences much smaller than current analysis error can ultimately control whether or not a tropical cyclone forms. Furthermore, even smaller differences govern how the initial vortex is built. Differences in maximum winds and/or vorticity vary nonlinearly with initial condition differences and depend on the timing and intensity of small mesoscale features such as VHTs and cold pools. Finally, the strong sensitivity to initial condition differences in both cases exemplifies the inherent uncertainties in hurricane intensity prediction. This study illustrates the need for implementing advanced data analysis schemes and ensemble prediction systems to provide more accurate and event-dependent probabilistic forecasts.
7

Local and Remote Forcing of the Ocean by the Madden-Julian Oscillation and its Predictability

Oliver, Eric Curtis John 24 August 2011 (has links)
The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in the tropical atmosphere and provides global predictability on timescales that bridge the gap between weather and climate. The influence of the MJO on the ocean is explored with a combination of statistical analysis of observations using multivariate time series techniques, dynamical theory, and general circulation models with realistic forcing and bathymetry. The MJO is shown to have a significant and predictable influence on global sea level. Three main regions of influence are identified: (i) the equatorial Pacific and the west coast of the Americas, (ii) the Gulf of of Carpentaria, and (iii) the northeastern Indian Ocean. In the equatorial Pacific, equatorially trapped Kelvin waves are forced by MJO-related surface winds in the western Pacific and propagate eastward. These remotely forced waves then transform into coastal trapped waves that propagate poleward along the west coast of the Americas (consistent with previous work). By way of contrast, in the Gulf of Carpentaria it is shown that the connection with the MJO is due to local wind forcing through simple set-up of sea level. In the northeastern Indian Ocean, a complex sea level pattern involving equatorially trapped Kelvin waves, coastal trapped waves along Sumatra, Java and the Bay of Bengal, and reflected Rossby waves along 5.5$^\circ$N is shown to be caused by a combination of local and remote forcing by MJO-related surface winds. To examine the predictability of the MJO, and the stability of MJO variability on multidecadal time scales, the MJO index is reconstructed over the last century. The reconstructed index is verified by comparing it with independently observed environmental variables. Three predictability time scales are proposed and estimated from the MJO index. A simple forced damped harmonic oscillator model is used to explain the complex relationship amongst the predictability time scales and also gain insight into the predictability of the MJO.
8

A nonlinear internal tide on the Portuguese Shelf

Jeans, Gus January 1998 (has links)
No description available.
9

Quantifying the Predictability of Evolution at the Genomic Level in Lycaeides Butterflies

Chaturvedi, Samridhi 01 August 2019 (has links)
Stephen Jay Gould, a great scientist and evolutionary biologists, suggested that if we could replay the tape of life, we would not have observed similar course of events because evolution is stochastic and if affected by several events. Since then, the possibility that evolution is repeatable or predictable has been debated. Studies using large-scale evolution experiments, long-term data for individual populations, and controlled experiments in nature, have demonstrated phenotypic and genetic convergence in several taxa. These studies suggest that despite some randomness, predictable evolutionary patterns can emerge on a large temporal and spatial scale. However, a few cases also exist where evolution is unpredictable and stochastic. One way to understand evolutionary predictability better can be to have quantitative estimates of predictability at different heirarchical levels (mutations, genetic, phenotypic). This can help better understand if evolution is predictable and the extent to which it is predictable. My dissertation uses Lycaeides butterflies to identify and quantify evolutionary predictability in different contexts such as on a geographic scale, temporal scale and genomic scale. I accomplished this by sequencing and annotating the genomes of these butterflies across a vast geographic range and on a temporal scale and by comparing natural and experimental populations. My results show that different mechanisms can assist evolution of organisms to adapt to novel environmental challenges, and that the evolutionary changes can be somewhat predictable. Through this work I demonstrate three main findings: first, quantitative estimates of evolutionary predictability indicate that degree of predictability is variable and is highly context-dependent. Second, we can predict evolutionary patterns on a spatial as well as temporal scale, and can predict patterns in nature by controlled laboratory experiments. Additionally, genomic changes underlying repeatability vary across the genome. Lastly, the approach of quantifying predictability can help us better understand the mechanisms which drive evolution and how organisms will evolve in response to similar environmental pressures. These results suggest that evolution can be constrained and if we actually replay the tape of life, we could see a considerably similar outcome in biodiversity compared to what Gould predicted.
10

Likvidita a prediktabilita kryptoaktiv / Liquidity and Predictability of Cryptoassets

Mjartanová, Viktória January 2021 (has links)
The relationship between liquidity and return predictability may be an im- portant aspect to consider when investing in cryptoassets. We examine this relation using both cross-sectional as well as panel data. First, we calculate a set of predictability measures and aggregate the results into four variables. We then regress the predictability variables on a set of controls and two measures of liquidity, specifically the Amihud illiquidity ratio and the Corwin-Schultz spread estimate. The other independent variables include the logarithm of volume, turnover ratio and Garman-Klass volatility. Results from the cross- sectional analysis indicate that liquidity negatively impacts the degree of return predictability. Moreover, findings from a subset of panel data, including only 50 cryptoassets with the largest market capitalization, provide some evidence in favor of this relationship. Results from full panel data, however, present contradictory evidence. For these regressions, liquidity is found to be either in- significant or to possess a positive impact on the degree of return predictability. Altogether, we obtain mixed evidence about the effect of cryptoasset liquidity on return predictability. JEL Classification C53, C58, G14 Keywords Cryptoassets, Predictability, Liquidity, Panel data Title...

Page generated in 0.0879 seconds