ALBERT

Note: Contents: Preface. - Symbols, units and numerical values. - 1. The nature and scope of meteorology. - 1.1. Meteorology in relation to other sciences. - 1.2. Variations in space and time. - 1.3. Applied meteorology. - 2. Physical properties of the atmosphere. - 2.1. Composition of dry air. - 2.1.1. Mean molecular weight. - 2.1.2. Dissociation and ionization. - 2.1.3. Escape to space of component molecules. - 2.2. Pressure, density and temperature. - 2.2.1. Definition of pressure. - 2.2.2. Values near sea level. - 2.2.3. Variations in the vertical. - 2.2.4. Diurnal fluctuations at upper levels. - 2.2.5. Horizontal pressure gradients. - 2.3. Water vapour. - 2.3.1. Humidity mixing ratio. - 2.3.2. Density of moist air. - 2.3.3. Saturation vapour pressure. - 2.3.4. Paths leading to saturation. - 2.3.5. Measurement of vapour pressure. - 2.3.6. Distribution of water vapour. - 3. Heat transfer. - 3.1. Radiation processes. - 3.1.1. Solar radiation: its energy distribution. - 3.1.2. The solar constant. - 3.1.3. Effect of the atmosphere and earth on solar radiation. - 3.1.4. Radiation from the earth and atmosphere. - 3.2. Convection. - 3.2.1. Adiabatic temperature changes. - 3.2.2. Adiabatic equation. - 3.2.3. Potential temperature: dry adiabatic lapse rate. - 3.2.4 Saturated adiabatic lapse rate. - 3.2.5. Stability and instability. - 3.3. Heat transfer in land and sea. - 3.3.1. Heating and cooling of soil. - 3.3.2. Heating and cooling of water. - 4. Condensation and precipitation. - 4.1. Microphysical processes. - 4.1.1 Condensation nuclei. - 4.1.2. Curvature and solute effects. - 4.1.3. Water-droplet clouds. - 4.1.4. Ice nuclei. - 4.1.5. Ice-crystal clouds. - 4.1.6. Precipitation from water clouds. - 4.1.7. Precipitation from mixed clouds. - 4.1.8. Thunderstorm electricity. - 4.2. Larger-scale processes. - 4.2.1. Surface cooling. - 4.2.2. Evaporation. - 4.2.3. Vertical motion. - 4.3. Cloud observations. - 4.3.1. Cloud genera: their heights and composition. - 4.3.2. Cloud recognition and general features. - 4.3.3. Effects of vertical wind shear. - 4.3.4. Cloud classification for forecasting. - 5. The tephigram. - 5.1. Construction of the diagram. - 5.1.1. Coordinates: area and energy. - 5.1.2. Isobars. - 5.1.3. Saturation mixing ratio lines. - 5.1.4. Saturated adiabatics. - 5.1.5. Height variation. - 5.2. Simple graphical computations. - 5.2.1. Height. - 5.2.2. Humidity elements. - 5.2.3. Condensation levels. - 5.2.4. Föhn effects. - 5.3. Precipitable water and precipitation rate. - 5.3.1. Formula and calculation. - 5.3.2. Precipitation rate. - 5.3.3. Water content of convection clouds. - 5.4. The effects of vertical motion on lapse rate. - 5.4.1. Unsaturated or saturated motion. - 5.4.2. Potential (convective) instability. - 5.5. Tephigram analysis. - 5.5.1. Latent instability. - 5.5.2. Air mass characteristics. - 6. Winds. - 6.1. Laws of motion and the earth's rotation. - 6.1.1. Newton's First and Second Laws. - 6.1.2. Nature of the earth's rotation. - 6.1.3. Effects of the earth's rotation: the Coriolis force. - 6.2. Inertial flow and geostrophic winds. - 6.2.1. Nature of inertial flow. - 6.2.2. Nature of geostrophic flow. - 6.2.3. Geostrophic wind equation. - 6.2.4. Wind and pressure near the equator. - 6.3. Gradient winds. - 6.4. Winds in the friction layer. - 6.5. Thermal winds. - 6.5.1. Vertical shear vector. - 6.5.2. Temperature control of the shear vector. - 6.5.3. Thermal wind equation and thickness charts. - 6.5.4. Hodographs and temperature advection. - 6.5.5. Jet streams. - 7. Instruments and observations. - 7.1. Routine surface observations. - 7.1.1. Pressure. - 7.1.2. Temperature and humidity. - 7.1.3. Precipitation and evaporation. - 7.1.4. Wind. - 7.1.5. Clouds and visibility. - 7.1.6. Sunshine and radiation. - 7.1.7. Ship observations. - 7.2. Upper air observations. - 7.2.1. Historical. - 7.2.2. The radiosonde: radar winds. - 7.2.3. Ozone measurements. - 7.3. World Weather Watch. - 7.4. Experiments in observation and interpretation. - 7.4.1. Pressure. - 7.4.2. Temperature and humidity. - 7.4.3. Evaporation and rainfall. - 7.4.4. Wind. - 7.4.5. Radiation. - 7.4.6. Topographical influences. - 8. Synoptic Meteorology. - 8.1. The surface weather map: an introduction. - 8.1.1. The plotting code. - 8.1.2. Pressure systems and features. - 8.1.3. Air masses. - 8.1.4. Fronts. - 8.2. Air mass characteristics. - 8.2.1. Classification. - 8.2.2. Modifications. - 8.2.3. Air masses over the British Isles. - 8.3. Frontal characteristics. - 8.3.1. The stability of a frontal surface. - 8.3.2. Equilibrium slope of a frontal surface. - 8.3.3. Frontal structure. - 8.4. Frontal depressions. - 8.4.1. The life cycle of a frontal depression. - 8.4.2. Cold front waves; depression families. - 8.4.3. Warm front waves. - 8.4.4. Secondaries at points of occlusion. - 8.5. Non-frontal depressions. - 8.5.1. Heat lows. - 8.5.2. Polar lows. - 8.5.3. Orographic lows. - 8.5.4. Tropical cyclones. - 8.5.5. Tornadoes. - 8.6. Anticyclones. - 8.6.1. General characteristics. - 8.6.2. Cold and warm anticyclones. - 8.7 Synoptic development. - 8.7.1. Convergence, divergence and vertical motion. - 8.7.2. Convergence and vorticity. - 8.7.3 Long waves. - 8.7.4. Circulation indices: blocking. - 8.8. Surface analysis. - 8.8.1. General. - 8.8.2. Representativeness of observations. - 8.8.3. METMAPS. - 9. Micrometeorology. - 9.1. The nature of airflow near the ground. - 9.1.1. Wind speeds over a uniform level surface. - 9.1.2. Flow within a fluid boundary layer. - 9.1.3. Shearing stress via the mixing length concept. - 9.1.4. The friction velocity u*. - 9.1.5. Interpretation of the mixing length concept. - 9.1.6. The wind profile equation in complete form. - 9.2. The influence of surface roughness on the wind. - 9.2.1. Roughness in the aerodynamic sense. - 9.2.2. Roughness in relation to shearing stress and mean wind speed. - 9.2.3. The drag coefficient CD. - 9.2.4. CD as a transfer coefficient. - 9.2.5. Effect of a change in surface roughness. - 9.3. Vertical transport by turbulence. - 9.3.1. Flux equations; use of electrical analogy. - 9.3.2. Heat flux and other calculations. - 9.3.3. Vertical temperature gradients in relation to turbulent exchange. - 10. The general circulation. - 10.1. General characteristics. - 10.1.1. Genesis and interactions. - 10.1.2. Time fluctuations. - 10.2. Observations. - 10.2.1. Time- and space-averaging. - 10.2.2. Tracers. - 10.3. Experiment and theory. - 10.3.1. The rotating vessel experiment. - 10.3.2. Conservation principles. - 10.3.3. Cellular models. - 10.4. Climatic zones. - 11. Weather forecasting. - 11.1. Historical survey. - 11.1.1. 1860-1920. - 11.1.2. 1920-1945. - 11.1.3. 1945-1960. - 11.1.4. 1960 onwards. - 11.2. Conventional forecasting. - 11.2.1. Pressure tendency. - 11.2.2. Making the forecast. - 11.3. Long-range forecasting. - 11.3.1. Statistical methods. - 11.3.2. Synoptic methods. - 11.3.3. Analogues. - 11.4. Numerical forecasting. - 11.4.1. The barotropic model. - 11.4.2. Later developments. - 11.5. Predictability and control. - 11.5.1. Short-range predictability. - 11.5.2. Medium-range predictability. - 11.5.3. Long-range predictability: climatic trends. - 11.5.4. Weather and climate modification. - Answers to Problems. - Subject Index. - The Wykeham Series.

Note: Kaiserslautern, Technische Universität, Dissertation, 2003 (Nicht für den Austausch)

Note: Contents: Preface. - 1. Introduction and data manipulation. - 1.1. Why ordination?. - 1.2. Terminology. - 1.3. Types of analyses. - 1.4. Response variables. - 1.5. Explanatory variables. - 1.6. Handling missing values in data. - 1.7. Importing data from spreadsheets - WCanoImp program. - 1.8. Transformation of species data. - 1.9. Transformation of explanatory variables. - 2. Experimental design. - 2.1. Completely randomized design. - 2.2. Randomized complete blocks. - 2.3. Latin square design. - 2.4. Most frequent errors - pseudoreplications. - 2.5. Combining more than one factor. - 2.6. Following the development of objects in time - repeated observations. - 2.7. Experimental and observational data. - 3. Basics of gradient analysis. - 3.1. Techniques of gradient analysis. - 3.2. Models of species response to environmental gradients. - 3.3. Estimating species optima by the weighted averaging method. - 3.4. Calibration. - 3.5. Ordination. - 3.6. Constrained ordination. - 3.7. Basic ordination techniques. - 3.8. Ordination diagrams. - 3.9. Two approaches. - 3.10. Testing significance of the relation with environmental variables. - 3.11. Monte Carlo permutation tests for the significance of regression. - 4. Using the Canoco for Windows 4.5 package. - 4.1. Overview of the package. - 4.2. Typical flow-chart of data analysis with Canoco for Windows. - 4.3. Deciding on the ordination method: unimodal or linear?. - 4.4. PCA or RDA ordination: centring and standardizing. - 4.5. DCA ordination: detrending. - 4.6. Scaling of ordination scores. - 4.7. Running CanoDraw for Windows 4.0. - 4.8. New analyses providing new views of our data sets. - 5. Constrained ordination and permutation tests. - 5.1. Linear multiple regression model. - 5.2. Constrained ordination model. - 5.3. RDA: constrained PCA. - 5.4. Monte Carlo permutation test: an introduction. - 5.5. Null hypothesis model. - 5.6. Test statistics. - 5.7. Spatial and temporal constraints. - 5.8. Split-plot constraints. - 5.9. Stepwise selection of the model. - 5.10. Variance partitioning procedure. - 6. Similarity measures. - 6.1. Similarity measures for presence-absence data. - 6.2. Similarity measures for quantitative data. - 6.3. Similarity of samples versus similarity of communities. - 6.4. Principal coordinates analysis. - 6.5. Non-metric multidimensional scaling. - 6.6. Constrained principal coordinates analysis (db-RDA). - 6.7. Mantel test. - 7. Classification methods. - 7.1. Sample data set. - 7.2. Non-hierarchical classification (K-means clustering). - 7.3. Hierarchical classifications. - 7.4. TWINSPAN. - 8. Regression methods . - 8.1. Regression models in general. - 8.2. General linear model: terms. - 8.3. Generalized linear models (GLM). - 8.4. Loess smoother. - 8.5. Generalized additive models (GAM). - 8.6. Classification and regression trees. - 8.7. Modelling species response curves with CanoDraw. - 9. Advanced use of ordination. - 9.1. Testing the significance of individual constrained ordination axes. - 9.2. Hierarchical analysis of community variation. - 9.3. Principal response curves (PRC) method. - 9.4. Linear discriminant analysis. - 10. Visualizing multivariate data. - 10.1. What we can infer from ordination diagrams: linear methods. - 10.2. What we can infer from ordination diagrams: unimodal methods. - 10.3. Visualizing ordination results with statistical models. - 10.4. Ordination diagnostics. - 10.5. t-value biplot interpretation. - 11. Case study 1: Variation in forest bird assemblages. - 11.1. Data manipulation. - 11.2. Deciding between linear and unimodal ordination. - 11.3. Indirect analysis: portraying variation in bird community. - 11.4. Direct gradient analysis: effect of altitude. - 11.5.Direct gradient analysis: additional effect of other habitat characteristics. - 12. Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows. - 12.1. The unconstrained ordination. - 12.2. Constrained ordinations. - 12.3. Classification. - 12.4. Suggestions for additional analyses. - 13. Case study 3: Separating the effects of explanatory variables. - 13.1. Introduction. - 13.2. Data. - 13.3. Data analysis. - 14. Case study 4: Evaluation of experiments in randomized complete blocks. - 14.1. Introduction. - 14.2. Data. - 14.3. Data analysis. - 15. Case study 5: Analysis of repeated observations of species composition from a factorial experiment. - 15.1. Introduction. - 15.2. Experimental design. - 15.3. Sampling. - 15.4. Data analysis. - 15.5. Univariate analyses. - 15.6. Constrained ordinations. - 15.7. Further use of ordination results. - 15.8. Principal response curves. - 16. Case study 6: Hierarchical analysis of crayfish community variation. - 16.1. Data and design. - 16.2. Differences among sampling locations. - 16.3. Hierarchical decomposition'of community variation. - 17. Case study 7: Differentiating two species and their hybrids with discriminant analysis. - 17.1. Data. - 17.2. Stepwise selection of discriminating variables. - 17.3. Adjusting the discriminating variables. - 17.4. Displaying results. - Appendix A: Sample datasets and projects. - Appendix B: Vocabulary. - Appendix C: Overview of available software. - References. - Index.

Note: Contents: 1 Introduction ; 2 Income Taxation ; 3 Models of Optimal Lifetime Income Taxation with Time-Consistent Preferences ; 4 Models of Optimal Lifetime Income Taxation with Time-Inconsistent Preferences ; 5 Incomplete Markets and Social Security ; 6 Models of Optimal Retirement Incentives with Varying Disutility of Labor ; 7 Models of Optimal Retirement Incentives with Varying Life Expectancy ; 8 Pension Insurance Reform with a Focus onGermany ; 9 Theory and Policy

Note: Zugl.: Cambridge, Univ., Diss