Studies on orographic effects in a numerical weather prediction model

Numerical models, used for atmospheric research, weather prediction and climate simulation, describe the state of the atmosphere over the heterogeneous surface of the Earth. Several fundamental properties of atmospheric models depend on orography, i.e. on the average elevation of land over a model area. The higher is the models' resolution, the more the details of orography directly influence the simulated atmospheric processes. This sets new requirements for the accuracy of the model formulations with respect to the spatially varying orography. Orography is always averaged, representing the surface elevation within the horizontal resolution of the model. In order to remove the smallest scales and steepest slopes, the continuous spectrum of orography is normally filtered (truncated) even more, typically beyond a few gridlengths of the model. This means, that in the numerical weather prediction (NWP) models, there will always be subgridscale orography effects, which cannot be explicitly resolved by numerical integration of the basic equations, but require parametrization. In the subgrid-scale, different physical processes contribute in different scales. The parametrized processes interact with the resolved-scale processes and with each other. This study contributes to building of a consistent, scale-dependent system of orography-related parametrizations for the High Resolution Limited Area Model (HIRLAM). The system comprises schemes for handling the effects of mesoscale (MSO) and small-scale (SSO) orographic effects on the simulated flow and a scheme of orographic effects on the surface-level radiation fluxes. Representation of orography, scale-dependencies of the simulated processes and interactions between the parametrized and resolved processes are discussed. From the high-resolution digital elevation data, orographic parameters are derived for both momentum and radiation flux parametrizations. Tools for diagnostics and validation are developed and presented. The parametrization schemes applied, developed and validated in this study, are currently being implemented into the reference version of HIRLAM.

Not in all Walks of Life? : Social Differences in Suicide Mortality

In Finland, the suicide mortality trend has been decreasing during the last decade and a half, yet suicide was the fourth most common cause of death among both Finnish men and women aged 15 64 years in 2006. However, suicide does not occur equally among population sub-groups. Two notable social factors that position people at different risk of suicide are socioeconomic and employment status: those with low education, employed in manual occupations, having low income and those who are unemployed have been found to have an elevated suicide risk. The purpose of this study was to provide a systematic analysis of these social differences in suicide mortality in Finland. Besides studying socioeconomic trends and differences in suicide according to age and sex, different indicators for socioeconomic status were used simultaneously, taking account of their pathways and mutual associations while also paying attention to confounding and mediatory effects of living arrangements and employment status. Register data obtained from Statistics Finland were used in this study. In some analyses suicides were divided into two groups according to contributory causes of death: the first group consisted of suicide deaths that had alcohol intoxication as one of the contributory causes, and the other group is comprised of all other suicide deaths. Methods included Poisson and Cox regression models. Despite the decrease in suicide mortality trend, social differences still exist. Low occupation-based social class proved to be an important determinant of suicide risk among both men and women, but the strong independent effect of education on alcohol-associated suicide indicates that the roots of these differences are probably established in early adulthood when educational qualifications are obtained and health-behavioural patterns set. High relative suicide mortality among the unemployed during times of economic boom suggests that selective processes may be responsible for some of the employment status differences in suicide. However, long-term unemployment seems to have causal effects on suicide, which, especially among men, partly stem from low income. In conclusion, the results in this study suggest that education, occupation-based social class and employment status have causal effects on suicide risk, but to some extent selection into low education and unemployment are also involved in the explanations for excess suicide mortality among the socially deprived. It is also conceivable that alcohol use is to some extent behind social differences in suicide. In addition to those with low education, manual workers and the unemployed, young people, whose health-related behaviour is still to be adopted, would most probably benefit from suicide prevention programmes.

Molecular features of colorectal cancer predisposition and progression

Both inherited genetic variations and somatically acquired mutations drive cancer development. The aim of this thesis was to gain insight into the molecular mechanisms underlying colorectal cancer (CRC) predisposition and tumor progression. Whereas one-third of CRC may develop in the context of hereditary predisposition, the known highly penetrant syndromes only explain a small fraction of all cases. Genome-wide association studies have shown that ten common single nucleotide polymorphisms (SNPs) modestly predispose to CRC. Our population-based sample series of around thousand CRC cases and healthy controls was genotyped for these SNPs. Tumors of heterozygous patients were analyzed for allelic imbalance, in an attempt to reveal the role of these SNPs in somatic tumor progression. The risk allele of rs6983267 at 8q24 was favored in the tumors significantly more often than the neutral allele, indicating that this germline variant is somatically selected for. No imbalance targeting the risk allele was observed in the remaining loci, suggesting that most of the low-penetrance CRC SNPs mainly play a role in the early stages of the neoplastic process. The ten SNPs were further analyzed in 788 CRC cases, 97 of which had a family history of CRC, to evaluate their combined contribution. A significant association appeared between the overall number of risk alleles and familial CRC and these ten SNPs seem to explain around 9% of the familial clustering of CRC. Finding more CRC susceptibility alleles may facilitate individualized risk prediction and cancer prevention in the future. Microsatellite instability (MSI), resulting from defective mismatch repair function, is a hallmark of Lynch syndrome and observed in a subset of all CRCs. Our aim was to identify microsatellite frameshift mutations that inactivate tumor suppressor genes in MSI CRCs. By sequencing microsatellite repeats of underexpressed genes we found six novel MSI target genes that were frequently mutated in 100 MSI CRCs: 51% in GLYR1, 47% in ABCC5, 43% in WDTC1, 33% in ROCK1, 30% in OR51E2, and 28% in TCEB3. Immunohistochemical staining of GLYR1 revealed defective protein expression in homozygously mutated tumors, providing further support for the loss of function hypothesis. Another mutation screening effort sought to identify MSI target genes with putative oncogenic functions. Microsatellites were similarly sequenced in genes that were overexpressed and, upon mutation, predicted to avoid nonsense-mediated mRNA decay. The mitotic checkpoint kinase TTK harbored protein-elongating mutations in 59% of MSI CRCs and the mutant protein was detected in heterozygous MSI CRC cells. No checkpoint dysregulation or defective protein localization was observable however, and the biological relevance of this mutation may hence be related to other mechanisms. In conclusion, these two large-scale and unbiased efforts identified frequently mutated genes that are likely to contribute to the development of this cancer type and may be utilized in developing diagnostic and therapeutic applications.

Essays on the Modeling and Prediction of Volatility and Higher Moments of Stock Returns (summary section only)

One of the most fundamental and widely accepted ideas in finance is that investors are compensated through higher returns for taking on non-diversifiable risk. Hence the quantification, modeling and prediction of risk have been, and still are one of the most prolific research areas in financial economics. It was recognized early on that there are predictable patterns in the variance of speculative prices. Later research has shown that there may also be systematic variation in the skewness and kurtosis of financial returns. Lacking in the literature so far, is an out-of-sample forecast evaluation of the potential benefits of these new more complicated models with time-varying higher moments. Such an evaluation is the topic of this dissertation. Essay 1 investigates the forecast performance of the GARCH (1,1) model when estimated with 9 different error distributions on Standard and Poor’s 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of variance from intra-day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. In Essay 2, by using 20 years of daily Standard and Poor 500 index returns, it is found that density forecasts are much improved by allowing for constant excess kurtosis but not improved by allowing for skewness. By allowing the kurtosis and skewness to be time varying the density forecasts are not further improved but on the contrary made slightly worse. In Essay 3 a new model incorporating conditional variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously used NIG models are evaluated by their Value at Risk (VaR) forecasts on a long series of daily Standard and Poor’s 500 returns. The results show that only the new model produces satisfactory VaR forecasts for both 1% and 5% VaR Taken together the results of the thesis show that kurtosis appears not to exhibit predictable time variation, whereas there is found some predictability in the skewness. However, the dynamic properties of the skewness are not completely captured by any of the models.