Exploiting ground-based measurements of the Global Positioning System for numerical weather prediction

Data assimilation provides an initial atmospheric state, called the analysis, for Numerical Weather Prediction (NWP). This analysis consists of pressure, temperature, wind, and humidity on a three-dimensional NWP model grid. Data assimilation blends meteorological observations with the NWP model in a statistically optimal way. The objective of this thesis is to describe methodological development carried out in order to allow data assimilation of ground-based measurements of the Global Positioning System (GPS) into the High Resolution Limited Area Model (HIRLAM) NWP system. Geodetic processing produces observations of tropospheric delay. These observations can be processed either for vertical columns at each GPS receiver station, or for the individual propagation paths of the microwave signals. These alternative processing methods result in Zenith Total Delay (ZTD) and Slant Delay (SD) observations, respectively. ZTD and SD observations are of use in the analysis of atmospheric humidity. A method is introduced for estimation of the horizontal error covariance of ZTD observations. The method makes use of observation minus model background (OmB) sequences of ZTD and conventional observations. It is demonstrated that the ZTD observation error covariance is relatively large in station separations shorter than 200 km, but non-zero covariances also appear at considerably larger station separations. The relatively low density of radiosonde observing stations limits the ability of the proposed estimation method to resolve the shortest length-scales of error covariance. SD observations are shown to contain a statistically significant signal on the asymmetry of the atmospheric humidity field. However, the asymmetric component of SD is found to be nearly always smaller than the standard deviation of the SD observation error. SD observation modelling is described in detail, and other issues relating to SD data assimilation are also discussed. These include the determination of error statistics, the tuning of observation quality control and allowing the taking into account of local observation error correlation. The experiments made show that the data assimilation system is able to retrieve the asymmetric information content of hypothetical SD observations at a single receiver station. Moreover, the impact of real SD observations on humidity analysis is comparable to that of other observing systems.

Improving GPS time series for geodynamic studies

Accurate and stable time series of geodetic parameters can be used to help in understanding the dynamic Earth and its response to global change. The Global Positioning System, GPS, has proven to be invaluable in modern geodynamic studies. In Fennoscandia the first GPS networks were set up in 1993. These networks form the basis of the national reference frames in the area, but they also provide long and important time series for crustal deformation studies. These time series can be used, for example, to better constrain the ice history of the last ice age and the Earth s structure, via existing glacial isostatic adjustment models. To improve the accuracy and stability of the GPS time series, the possible nuisance parameters and error sources need to be minimized. We have analysed GPS time series to study two phenomena. First, we study the refraction in the neutral atmosphere of the GPS signal, and, second, we study the surface loading of the crust by environmental factors, namely the non-tidal Baltic Sea, atmospheric load and varying continental water reservoirs. We studied the atmospheric effects on the GPS time series by comparing the standard method to slant delays derived from a regional numerical weather model. We have presented a method for correcting the atmospheric delays at the observational level. The results show that both standard atmosphere modelling and the atmospheric delays derived from a numerical weather model by ray-tracing provide a stable solution. The advantage of the latter is that the number of unknowns used in the computation decreases and thus, the computation may become faster and more robust. The computation can also be done with any processing software that allows the atmospheric correction to be turned off. The crustal deformation due to loading was computed by convolving Green s functions with surface load data, that is to say, global hydrology models, global numerical weather models and a local model for the Baltic Sea. The result was that the loading factors can be seen in the GPS coordinate time series. Reducing the computed deformation from the vertical time series of GPS coordinates reduces the scatter of the time series; however, the long term trends are not influenced. We show that global hydrology models and the local sea surface can explain up to 30% of the GPS time series variation. On the other hand atmospheric loading admittance in the GPS time series is low, and different hydrological surface load models could not be validated in the present study. In order to be used for GPS corrections in the future, both atmospheric loading and hydrological models need further analysis and improvements.

Place Identification : A Comparative Study

Place identification is the methodology of automatically detecting spatial regions or places that are meaningful to a user by analysing her location traces. Following this approach several algorithms have been proposed in the literature. Most of the algorithms perform well on a particular data set with suitable choice of parameter values. However, tuneable parameters make it difficult for an algorithm to generalise to data sets collected from different geographical locations, different periods of time or containing different activities. This thesis compares the generalisation performance of our proposed DPCluster algorithm along with six state-of-the-art place identification algorithms on twelve location data sets collected using Global Positioning System (GPS). Spatial and temporal variations present in the data help us to identify strengths and weaknesses of the place identification algorithms under study. We begin by discussing the notion of a place and its importance in location-aware computing. Next, we discuss different phases of the place identification process found in the literature followed by a thorough description of seven algorithms. After that, we define evaluation metrics and compare generalisation performance of individual place identification algorithms and report the results. The results indicate that the DPCluster algorithm performs superior to all other algorithms in terms of generalisation performance.

Topics in Spatial Econometrics - With Applications to House Prices

Topics in Spatial Econometrics — With Applications to House Prices Spatial effects in data occur when geographical closeness of observations influences the relation between the observations. When two points on a map are close to each other, the observed values on a variable at those points tend to be similar. The further away the two points are from each other, the less similar the observed values tend to be. Recent technical developments, geographical information systems (GIS) and global positioning systems (GPS) have brought about a renewed interest in spatial matters. For instance, it is possible to observe the exact location of an observation and combine it with other characteristics. Spatial econometrics integrates spatial aspects into econometric models and analysis. The thesis concentrates mainly on methodological issues, but the findings are illustrated by empirical studies on house price data. The thesis consists of an introductory chapter and four essays. The introductory chapter presents an overview of topics and problems in spatial econometrics. It discusses spatial effects, spatial weights matrices, especially k-nearest neighbours weights matrices, and various spatial econometric models, as well as estimation methods and inference. Further, the problem of omitted variables, a few computational and empirical aspects, the bootstrap procedure and the spatial J-test are presented. In addition, a discussion on hedonic house price models is included. In the first essay a comparison is made between spatial econometrics and time series analysis. By restricting the attention to unilateral spatial autoregressive processes, it is shown that a unilateral spatial autoregression, which enjoys similar properties as an autoregression with time series, can be defined. By an empirical study on house price data the second essay shows that it is possible to form coordinate-based, spatially autoregressive variables, which are at least to some extent able to replace the spatial structure in a spatial econometric model. In the third essay a strategy for specifying a k-nearest neighbours weights matrix by applying the spatial J-test is suggested, studied and demonstrated. In the final fourth essay the properties of the asymptotic spatial J-test are further examined. A simulation study shows that the spatial J-test can be used for distinguishing between general spatial models with different k-nearest neighbours weights matrices. A bootstrap spatial J-test is suggested to correct the size of the asymptotic test in small samples.

Estimation of cutting volume with three inventory methods for harvest planning in Canadian boreal forests.

Two methods of pre-harvest inventory were designed and tested on three cutting sites containing a total of 197 500 m3 of wood. These sites were located on flat-ground boreal forests located in northwestern Quebec. Both methods studied involved scaling of trees harvested to clear the road path one year (or more) prior to harvest of adjacent cut-blocks. The first method (ROAD) considers the total road right-of-way volume divided by the total road area cleared. The resulting volume per hectare is then multiplied by the total cut-block area scheduled for harvest during the following year to obtain the total estimated cutting volume. The second method (STRATIFIED) also involves scaling of trees cleared from the road. However, in STRATIFIED, log scaling data are stratified by forest stand location. A volume per hectare is calculated for each stretch of road that crosses a single forest stand. This volume per hectare is then multiplied by the remaining area of the same forest stand scheduled for harvest one year later. The sum of all resulting estimated volumes per stand gives the total estimated cutting-volume for all cut-blocks adjacent to the studied road. A third method (MNR) was also used to estimate cut-volumes of the sites studied. This method represents the actual existing technique for estimating cutting volume in the province of Quebec. It involves summing the cut volume for all forest stands. The cut volume is estimated by multiplying the area of each stand by its estimated volume per hectare obtained from standard stock tables provided by the governement. The resulting total estimated volume per cut-block for all three methods was then compared with the actual measured cut-block volume (MEASURED). This analysis revealed a significant difference between MEASURED and MNR methods with the MNR volume estimate being 30 % higher than MEASURED. However, no significant difference from MEASURED was observed for volume estimates for the ROAD and STRATIFIED methods which respectively had estimated cutting volumes 19 % and 5 % lower than MEASURED. Thus the ROAD and STRATIFIED methods are good ways to estimate cut-block volumes after road right-of-way harvest for conditions similar to those examined in this study.

Canopy processes, fluxes and microclimate in a pine forest

Interaction between forests and the atmosphere occurs by radiative and turbulent transport. The fluxes of energy and mass between surface and the atmosphere directly influence the properties of the lower atmosphere and in longer time scales the global climate. Boreal forest ecosystems are central in the global climate system, and its responses to human activities, because they are significant sources and sinks of greenhouse gases and of aerosol particles. The aim of the present work was to improve our understanding on the existing interplay between biologically active canopy, microenvironment and turbulent flow and quantify. In specific, the aim was to quantify the contribution of different canopy layers to whole forest fluxes. For this purpose, long-term micrometeorological and ecological measurements made in a Scots pine (Pinus sylvestris) forest at SMEAR II research station in Southern Finland were used. The properties of turbulent flow are strongly modified by the interaction between the canopy elements: momentum is efficiently absorbed in the upper layers of the canopy, mean wind speed and turbulence intensities decrease rapidly towards the forest floor and power spectra is modulated by spectral short-cut . In the relative open forest, diabatic stability above the canopy explained much of the changes in velocity statistics within the canopy except in strongly stable stratification. Large eddies, ranging from tens to hundred meters in size, were responsible for the major fraction of turbulent transport between a forest and the atmosphere. Because of this, the eddy-covariance (EC) method proved to be successful for measuring energy and mass exchange inside a forest canopy with exception of strongly stable conditions. Vertical variations of within canopy microclimate, light attenuation in particular, affect strongly the assimilation and transpiration rates. According to model simulations, assimilation rate decreases with height more rapidly than stomatal conductance (gs) and transpiration and, consequently, the vertical source-sink distributions for carbon dioxide (CO2) and water vapor (H2O) diverge. Upscaling from a shoot scale to canopy scale was found to be sensitive to chosen stomatal control description. The upscaled canopy level CO2 fluxes can vary as much as 15 % and H2O fluxes 30 % even if the gs models are calibrated against same leaf-level dataset. A pine forest has distinct overstory and understory layers, which both contribute significantly to canopy scale fluxes. The forest floor vegetation and soil accounted between 18 and 25 % of evapotranspiration and between 10 and 20 % of sensible heat exchange. Forest floor was also an important deposition surface for aerosol particles; between 10 and 35 % of dry deposition of particles within size range 10 30 nm occurred there. Because of the northern latitudes, seasonal cycle of climatic factors strongly influence the surface fluxes. Besides the seasonal constraints, partitioning of available energy to sensible and latent heat depends, through stomatal control, on the physiological state of the vegetation. In spring, available energy is consumed mainly as sensible heat and latent heat flux peaked about two months later, in July August. On the other hand, annual evapotranspiration remains rather stable over range of environmental conditions and thus any increase of accumulated radiation affects primarily the sensible heat exchange. Finally, autumn temperature had strong effect on ecosystem respiration but its influence on photosynthetic CO2 uptake was restricted by low radiation levels. Therefore, the projected autumn warming in the coming decades will presumably reduce the positive effects of earlier spring recovery in terms of carbon uptake potential of boreal forests.

Antarctic ice shelf melting and its impact on the global sea ice-ocean system

Earth s ice shelves are mainly located in Antarctica. They cover about 44% of the Antarctic coastline and are a salient feature of the continent. Antarctic ice shelf melting (AISM) removes heat from and inputs freshwater into the adjacent Southern Ocean. Although playing an important role in the global climate, AISM is one of the most important components currently absent in the IPCC climate model. In this study, AISM is introduced into a global sea ice-ocean climate model ORCA2-LIM, following the approach of Beckmann and Goosse (2003; BG03) for the thermodynamic interaction between the ice shelf and ocean. This forms the model ORCA2-LIM-ISP (ISP: ice shelf parameterization), in which not only all the major Antarctic ice shelves but also a number of minor ice shelves are included. Using these two models, ORCA2-LIM and ORCA2-LIM-ISP, the impact of addition of AISM and increasing AISM have been investigated. Using the ORCA2-LIM model, numerical experiments are performed to investigate the sensitivity of the polar sea ice cover and the Antarctic Circumpolar Current (ACC) transport through Drake Passage (DP) to the variations of three sea ice parameters, namely the thickness of newly formed ice in leads (h0), the compressive strength of ice (P*), and the turning angle in the oceanic boundary layer beneath sea ice (θ). It is found that the magnitudes of h0 and P* have little impact on the seasonal sea ice extent, but lead to large changes in the seasonal sea ice volume. The variation in turning angle has little impact on the sea ice extent and volume in the Arctic but tends to reduce them in the Antarctica when ignored. The magnitude of P* has the least impact on the DP transport, while the other two parameters have much larger influences. Numerical results from ORCA2-LIM and ORCA2-LIM-ISP are analyzed to investigate how the inclusion of AISM affects the representation of the Southern Ocean hydrography. Comparisons with data from the World Ocean Circulation Experiment (WOCE) show that the addition of AISM significantly improves the simulated hydrography. It not only warms and freshens the originally too cold and too saline bottom water (AABW), but also warms and enriches the salinity of the originally too cold and too fresh warm deep water (WDW). Addition of AISM also improves the simulated stratification. The close agreement between the simulation with AISM and the observations suggests that the applied parameterization is an adequate way to include the effect of AISM in a global sea ice-ocean climate model. We also investigate the models capability to represent the sea ice-ocean system in the North Atlantic Ocean and the Arctic regions. Our study shows both models (with and without AISM) can successfully reproduce the main features of the sea ice-ocean system. However, both tend to overestimate the ice flux through the Nares Strait, produce a lower temperature and salinity in the Hudson Bay, Baffin Bay and Davis Strait, and miss the deep convection in the Labrador Sea. These deficiencies are mainly attributed to the artificial enlargement of the Nares Strait in the model. In this study, the impact of increasing AISM on the global sea ice-ocean system is thoroughly investigated. This provides a first idea regarding changes induced by increasing AISM. It is shown that the impact of increasing AISM is global and most significant in the Southern Ocean. There, increasing AISM tends to freshen the surface water, to warm the intermediate and deep waters, and to freshen and warm the bottom water. In addition, increasing AISM also leads to changes in the mixed layer depths (MLD) in the deep convection sites in the Southern Ocean, deepening in the Antarctic continental shelf while shoaling in the ACC region. Furthermore, increasing AISM influences the current system in the Southern Ocean. It tends to weaken the ACC, and strengthen the Antarctic coastal current (ACoC) as well as the Weddell Gyre and the Ross Gyre. In addition to the ocean system, increasing AISM also has a notable impact on the Antarctic sea ice cover. Due to the cooling of seawater, sea ice concentration and thickness generally become higher. In austral winter, noticeable increases in sea ice concentration mainly take place near the ice edge. In regards with sea ice thickness, large increases are mainly found along the coast of the Weddell Sea, the Bellingshausen and Amundsen Seas, and the Ross Sea. The overall thickening of sea ice leads to a larger volume of sea ice in Antarctica. In the North Atlantic, increasing AISM leads to remarkable changes in temperature, salinity and density. The water generally becomes warmer, more saline and denser. The most significant warming occurs in the subsurface layer. In contrast, the maximum salinity increase is found at the surface. In addition, the MLD becomes larger along the Greenland-Scotland-Iceland ridge. Global teleconnections due to AISM are studied. The AISM signal is transported with the surface current: the additional freshwater from AISM tends to enhance the northward spreading of the surface water. As a result, more warm and saline water is transported from the tropical region to the North Atlantic Ocean, resulting in warming and salt enrichment there. It would take about 30 40 years to establish a systematic noticeable change in temperature, salinity and MLD in the North Atlantic Ocean according to this study. The changes in hydrography due to increasing AISM are compared with observations. Consistency suggests that increasing AISM is highly likely a major contributor to the recent observed changes in the Southern Ocean. In addition, the AISM might contribute to the salinity contrast between the North Atlantic and North Pacific, which is important for the global thermohaline circulation.