Newspaper clippings on various subjects

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Newspaper clipping regarding presentation of flag by Mrs. Commodore Levy

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Variability of Indian summer monsoon rainfall in daily data from gauge and satellite

It has long been thought that tropical rainfall retrievals from satellites have large errors. Here we show, using a new daily 1 degree gridded rainfall data set based on about 1800 gauges from the India Meteorology Department (IMD), that modern satellite estimates are reasonably close to observed rainfall over the Indian monsoon region. Daily satellite rainfalls from the Global Precipitation Climatology Project (GPCP 1DD) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are available since 1998. The high summer monsoon (June-September) rain over the Western Ghats and Himalayan foothills is captured in TMPA data. Away from hilly regions, the seasonal mean and intraseasonal variability of rainfall (averaged over regions of a few hundred kilometers linear dimension) from both satellite products are about 15% of observations. Satellite data generally underestimate both the mean and variability of rain, but the phase of intraseasonal variations is accurate. On synoptic timescales, TMPA gives reasonable depiction of the pattern and intensity of torrential rain from individual monsoon low-pressure systems and depressions. A pronounced biennial oscillation of seasonal total central India rain is seen in all three data sets, with GPCP 1DD being closest to IMD observations. The new satellite data are a promising resource for the study of tropical rainfall variability.

A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Effects of transit quality of service characteristics on daily bus ridership

A case study of Brisbane, the capital city of Queensland, Australia, explored how explicit measures of transit quality of service (e.g., service frequency, service span, and travel time ratio) and implicit environmental predictors (e.g., topographic grade factor) influenced bus ridership. The primary hypothesis tested was that bus ridership was higher in suburbs with high transit quality of service than in suburbs with limited service quality. Multiple linear regression, used to identify a strong positive relationship between route intensity (bus-km/h-km2) and bus ridership, indicated that both increased service frequency and spatial route density corresponded to higher bus ridership. Additionally, the travel time ratio (i.e., the ratio of in-vehicle transit travel time to in-vehicle automobile travel time) had a significant negative association with suburban ridership: transit use declined as travel time ratio increased. In contrast, topographic grade and service span did not significantly affect suburban bus ridership. The study findings enhance the fundamental understanding of traveler behavior, which is informative to urban transportation policy, planning, and provision.

Nonlinear ensemble prediction of chaotic daily rainfall

The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. Many of these approaches aim at only analyzing the chaotic nature and not its prediction. In the present study, an attempt is made to identify chaos using various techniques and prediction is also done by generating ensembles in order to quantify the uncertainty involved. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha, Mahanadi and All-India for the period 1955-2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series. Correlation dimension method is done on th phase randomized and first derivative of the data series to check whether the saturation of the dimension is due to the inherent linear correlation structure or due to low dimensional dynamics. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996-2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are done from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. (C) 2010 Elsevier Ltd. All rights reserved.

Swedish advertising language in Finland mirrored by the newspaper advertisements of the Stockmanns department store during the 20th century

The advertising business is often said to favour a modern, innovative language use. This is a statement not easily verified. Newspaper ads are in fact the genre of written language that linguists have paid the least attention to. People writing texts for newspaper ads are individuals representing contemporary language use. Advertisements representing different periods therefore diverge not only regarding the change of style and form advertising undergoes over time, but changes in the language itself also reflect the continuous process of alteration in a speech community. Advertisements and marketing material on the whole, are also read by many individuals who otherwise are not accustomed to reading at all. The marketing manager, the copywriter and the Art Director, in other words, produce texts that unconsciously function as language models. Changes are not created by, or urged on by linguistic expertise, but by ordinary users confronting other ordinary users. From a sociolinguistic perspective the widely diffused advertising language is therefore a most influential factor.

Reklamsvenska i Finland speglad genom varuhuset Stockmanns tidningsannonser under det 20e seklet - Swedish Advertising Language in Finland Mirrored by the Newspaper Advertisements of the Stockmanns Department Store during the 20th Century

Reklam sägs använda ett modernt, gärna ett nyskapande språk. Detta är ett påstående som inte så lätt kan verifieras. Tidningsannonsen är troligen den skriftspråksgenre som har fått minst uppmärksamhet av språkforskare. De som skriver texten i en tidningsannons är personer som representerar det samtida språkbruket. Annonser som representerar olika tidsepoker skiljer sig därför från varandra inte bara genom att annonsen förändras i fråga om stil och form. Annonsens språk avspeglar också den språkliga förändringsprocess som kontinuerligt pågår i varje språksamhälle. Annonser, och marknadsföringsmaterial över huvud taget, läses också av många människor som i övrigt läser mycket litet eller kanske inte alls. Marknadsföraren, reklamskribenten (copywriter) och AD:n producerar m.a.o. texter som på ett omedvetet sätt kommer att vara språkmodeller för sina läsare. Förändringar i språket kreeras inte och drivs inte på av språkforskare, utan av vanliga språkbrukare i interaktion med andra språkbrukare. Sett ur ett sociolingvistiskt perspektiv har det vitt spridda reklamspråket därför inflytande på språket i samhället. Syftet med det reklamspråksprojekt som presenteras i föreliggande rapport är att analysera hur och när förändringar i svenskan som uppträder i Sverige dyker upp i annonser som skrivs på svenska i Finland. Reklam på svenska Finland under 1900-talet står i fokus, och tidningsannonser för Stockmanns varuhus i Helsingfors utgör primärmaterialet. Tidningsannonser för varuhuset Nordiska Kompaniet (NK) i Stockholm under motsvarande tid tjänar som jämförelsematerial. I denna rapport presenteras projektets syfte, de uppställda forskningsfrågorna, och resonemanget illustreras med exempel ur projektmaterialet. Rapporten innehåller också en beskrivning av projektets reklamdatabas och basfakta om material och metoder. -

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.