Evaluation of the NCEP/NCAR Reanalysis in Terms of Synoptic Scale Phenomea: A Case Study from the Midwestern USA

We evaluate the ability of the National Centers for Environmental Prediction (NCEP)–National Center for Atmosphere Research (NCAR) reanalysis to represent the synoptic-scale climate of the Midwestern USA relative to radiosonde data. Independent, automated synoptic classifications, based on rotated principal component analysis (PCA) of 500 hPa geopotential heights, 850 hPa air temperatures, and 200 hPa wind speeds and a two-step clustering algorithm, result in a 15-type NCEP–NCAR synoptic classification and a 14-type radiosonde classification. The classifications are examined in terms of similarities and differences in the modes of variance manifest in the PCA solutions, the spatial patterns and variability of input variables within each weather type, and the temporal variability of the occurrence of each weather type. The classifications are then compared in terms of these characteristics and the degree of mutual class occupancy. Although the classifications identify a number of the same weather types (in terms of the input data, PCA solution, and mutual occupancy), the correspondence is imperfect. To assess whether the differences in the classifications are due to errant assignment of data to clusters or to differences in the fundamental modes present in the data sets as represented by the PC loadings and scores, a third targeted classification is undertaken that categorizes the NCEP–NCAR reanalysis data according to the radiosonde PCA solution. This classification exhibits a higher degree of similarity to that derived using the radiosonde data (in terms of both interpretability and mutual class occupancy), but the solutions still exhibit considerable differences. It is probable that the discrepancies are partly a function of the differing data structures and densities, but they may also reflect differences in the intensity of synoptic-scale phenomena as manifest in the data sets

Downscaling Temperature and Precipitation: A Comparison of Regression-Based Methods and Artificial Neural Networks

A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs). Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34–0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements

An Evaluation of Two GCMs: Simulation of North American Teleconnection Indices and Synoptic Phenomena

We evaluate the ability of two coupled atmospheric–oceanic GCMs – the Hadley Center’s third generation coupled climate model (HadCM3) and the Canadian Center for Climate Modeling and Analysis second-generation coupled model (CGCM2) – to simulate the North Atlantic Oscillation (NAO), the Pacific North American teleconnection pattern (PNA), and map patterns in the Midwest region of the United States, relative to NCEP/NCAR reanalysis (NNR) data. The observed (NNR-derived) and GCM-derived probability distributions and temporal behavior of the daily teleconnection indices exhibit agreement over the 1990–2001 reference period, and both GCMs successfully reproduce the range of 500-hPa map patterns over the study region. During the reference period, observed and modeled map patterns are similar in terms of frequency, coherence, persistence, and progression, although the most common map pattern occurs too often in HadCM3 relative to NNR and CGCM2-derived map patterns generally exhibit closer agreement with those derived from NNR data. Despite the relatively high degree of correspondence between the observed and simulated teleconnection indices and map patterns in the study area, differences between the GCM and NNR-derived map-pattern frequencies in the reference period are greater than either (1) recent historical changes in map-pattern frequencies or (2) changes in the mappattern frequencies as derived from twenty-first century GCM simulations, indicating that changes in these phenomena over recent and approaching decades are of insufficient magnitude relative to model uncertainty to be definitively identified

Comparison of two methods for describing the strain profiles in quantum dots

The electronic structure of interfaces between lattice-mismatched semiconductor is sensitive to the strain. We compare two approaches for calculating such inhomogeneous strain -- continuum elasticity (CE, treated as a finite difference problem) and atomistic elasticity (AE). While for small strain the two methods must agree, for the large strains that exist between lattice-mismatched III-V semiconductors (e.g. 7% for InAs/GaAs outside the linearity regime of CE) there are discrepancies. We compare the strain profile obtained by both approaches (including the approximation of the correct C_2 symmetry by the C_4 symmetry in the CE method), when applied to C_2-symmetric InAs pyramidal dots capped by GaAs.Comment: To appear in J. Appl. Physic

Eight-band calculations of strained InAs/GaAs quantum dots compared with one, four, and six-band approximations

The electronic structure of pyramidal shaped InAs/GaAs quantum dots is calculated using an eight-band strain dependent $\bf k\cdot p$ Hamiltonian. The influence of strain on band energies and the conduction-band effective mass are examined. Single particle bound-state energies and exciton binding energies are computed as functions of island size. The eight-band results are compared with those for one, four and six bands, and with results from a one-band approximation in which m(r) is determined by the local value of the strain. The eight-band model predicts a lower ground state energy and a larger number of excited states than the other approximations.Comment: 8 pages, 7 figures, revtex, eps

Absence of correlation between built-in electric dipole moment and quantum Stark effect in InAs/GaAs self-assembled quantum dots

We report significant deviations from the usual quadratic dependence of the ground state interband transition energy on applied electric fields in InAs/GaAs self-assembled quantum dots. In particular, we show that conventional second-order perturbation theory fails to correctly describe the Stark shift for electric field below $F = 10$ kV/cm in high dots. Eight-band ${\bf k}\cdot{\bf p}$ calculations demonstrate this effect is predominantly due to the three-dimensional strain field distribution which for various dot shapes and stoichiometric compositions drastically affects the hole ground state. Our conclusions are supported by two independent experiments.Comment: 4 pages, 4 figure

Hall Conductivity for Two Dimensional Magnetic Systems

A Kubo inspired formalism is proposed to compute the longitudinal and transverse dynamical conductivities of an electron in a plane (or a gas of electrons at zero temperature) coupled to the potential vector of an external local magnetic field, with the additional coupling of the spin degree of freedom of the electron to the local magnetic field (Pauli Hamiltonian). As an example, the homogeneous magnetic field Hall conductivity is rederived. The case of the vortex at the origin is worked out in detail. This system happens to display a transverse Hall conductivity ($P$ breaking effect) which is subleading in volume compared to the homogeneous field case, but diverging at small frequency like $1/\omega^2$. A perturbative analysis is proposed for the conductivity in the random magnetic impurity problem (Poissonian vortices in the plane). At first order in perturbation theory, the Hall conductivity displays oscillations close to the classical straight line conductivity of the mean magnetic field.Comment: 28 pages, latex, 2 figure