International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California / This paper discusses the evolution of the ground satellite communication systems and the efforts made by the Goddard Space Flight Center's (GSFC) Advanced Architectures and Automation (AAA) branch, Code 588 to bring satellite scientific data to the user’s desktop. Primarily, it describes the next generation desktop system, its architecture and processing capabilities, which provide autonomous high-performance telemetry acquisition at the lowest possible cost. It also discusses the planning processes and the applicability of new technologies for communication needs in the next century. The paper is presented in terms simple for those not very familiar with current space programs to understand.

Photochemical grid models are used to evaluate air pollution control strategies by simulating the physical and chemical processes that influence pollutant concentrations. Their accuracy depends on the accuracy of input data used for anthropogenic and biogenic emissions, land surface characteristics, initial and boundary conditions and meteorological conditions. Evaluation of model performance requires sufficient ambient data. This work develops approaches for applying satellite data to allow more frequent and timely estimates of parameters required to estimate emissions and pollutant removal processes for regional air quality modeling. Land use and land cover (LULC) data prepared from remote sensing satellite data were evaluated for use as inputs to photochemical grid models for estimating dry deposition velocities and biogenic emissions. The results indicated that satellite-based data derived from the Moderate Resolution Imaging Spectroradiometer instrument can be used to provide periodic updates to LULC information used in photochemical models. The sensitivity of predicted ozone concentrations to LULC data used for biogenic emission estimates was examined by comparing the database currently used for modeling in southeastern Texas with a new database prepared from Landsat satellite imagery and field data. The satellite data and image classification techniques provide useful tools for mapping and monitoring changes in LULC. However, field validation is necessary to link species and biomass densities to the classification system needed for accurate biogenic emissions estimates, especially in areas that have dense concentrations of species that emit high levels of biogenic hydrocarbons. The application of NO2 measurements from the Ozone Monitoring Instrument (OMI) to validation of NOx emission estimates and identification of emission sources for regional air quality modeling for Texas was examined. OMI observations can be used to identify regions with changes in emissions over time or where estimates have large uncertainties and to evaluate the effectiveness of emission reduction strategies. For example, in the Dallas-Fort Worth area, observed NO2 column densities from OMI indicate that emission controls are less effective than anticipated due to increased area source emissions. The techniques developed in this work have broad applicability in the advancement of methods for including satellite remote sensing data in regional air quality modeling. / text

Thesis (M.A.)--Boston University / This study is a part of an environmental impact assessment of the Gulf War on the desert and the coastal zones of Kuwait. Due to the appearance of many new surface features, a study was necessary to characterize their spectral signatures as detected by Landsat Thematic Mapper (TM) data. A sophisticated image analysis was applied to the Landsat TM scene. An unsupervised classification technique produced a thematic map of the area. Data was collected on the ground at eighty sites in southeastern Kuwait. A radiometer (SE-590) was used to identify the spectral reflectance of desert surface features. A Global Positioning System (GPS) reading on each site was also recorded to register accurately the field observations on a specific pixel from over 72 million pixels in the lower right scene of Kuwait. Field data were collected on surface feature color, soil grain stze, vegetation types and density, and the amount of oil or soot contamination. Statistical correlation's and companson of Landsat and the SE-590 measurements in the visible and near-infrared bands describe the interaction between radiation and different desert surfaces. The oil lakes class was identified to have the lowest reflectance of all the classes. Brightness values gradually increase as less oil, soot or desert vegetation is found. The highest brightness value belongs to the class which represents active sand.

Gravity waves play a fundamental role in driving the large-scale circulation of the atmosphere. They are influenced both by the variation in their sources and the filtering effects of the winds they encounter as they ascend through the atmosphere. In this thesis we present new evidence that gravity waves play a key role in coupling the troposphere, stratosphere and mesosphere. In particular, we examine the connection of gravity waves to two important large-scale oscillations that occur in the atmosphere, namely the Madden-Julian Oscillation (MJO) in the troposphere and the Mesospheric Semi-Annual Oscillation (MSAO). We present the first ever demonstration that the MJO acts to modulate the global field of gravity waves ascending into the tropical stratosphere. We discover a significant correlation with the MJO zonal-wind anomalies and so suggest that the MJO modulates the stratospheric gravity-wave field through a critical-level wave-filtering mechanism. Strong evidence for this mechanism is provided by consideration of the winds encountered by ascending waves. The Ascension Island meteor radar is used for the first time to measure momentum fluxes over the Island. These measurements are then used to investigate the role of gravity-wave in driving a dramatic and anomalous wind event that was observed to occur during the first westward phase of the MSAO in 2002. Gravity waves are shown to play an important role in driving this event, but the observations presented here also suggest that the current theory of the mechanism describing these anomalous mesospheric wind events is not valid. Both of these studies highlight the critical importance of gravity waves to the dynamics of the atmosphere and highlight the need for further work to truly understand these waves, their processes and their variability.

<p>Visualization is the process of constructing methods, which are able to synthesize interesting and informative images from data sets, to simplify the process of interpreting the data. In this thesis a new approach to construct meteorological visualization methods using neural network technology is described. The methods are trained with examples instead of explicitely designing the appearance of the visualization.</p><p>This approach is exemplified using two applications. In the fist the problem to compute an image of the sky for dynamic weather, that is taking account of the current weather state, is addressed. It is a complicated problem to tie the appearance of the sky to a weather state. The method is trained with weather data sets and images of the sky to be able to synthesize a sky image for arbitrary weather conditions. The method has been trained with various kinds of weather and images data. The results show that this is a possible method to construct weather visaualizations, but more work remains in characterizing the weather state and further refinement is required before the full potential of the method can be explored. This approach would make it possible to synthesize sky images of dynamic weather using a fast and efficient empirical method.</p><p>In the second application the problem of computing synthetic satellite images form numerical forecast data sets is addressed. In this case a mode is trained with preclassified satellite images and forecast data sets to be able to synthesize a satellite image representing arbitrary conditions. The resulting method makes it possible to visualize data sets from numerical weather simulations using synthetic satellite images, but could also be the basis for algorithms based on a preliminary cloud classification.</p> / Report code: LiU-Tek-Lic-2004:66.

Global climate change is considered to be one of the most serious concerns of humankind (United Nations, 1992; United Nations, 2002). Anthropogenic greenhouse gases and aerosols impact considerably the energy balance of the Earth system, possibly provoking adverse effects on social, ecological, and economical equilibria. This is one of the main reasons why the understanding of the Earth’s climate system is of major importance. If better predictions of the response of the climate system to anthropogenic perturbations were available, political decisions against negative impacts could be taken, and social adaptations to changed climate conditions would be possible.

Delta regions are typically characterized by their high population density, low elevation, and risk of flooding. Long term planning and preparation is needed to mitigate the adverse effects of floods. Disaster management planning and flood protection measures require information about urbanization patterns, but this information is lacking in many parts of the world. Global satellite data could potentially aid or replace local urbanization data in such data scarce areas. This master thesis assesses the suitability of two global satellite datasets to serve as proxies for urbanization in flood prone areas: the Global Human Settlement (data for 1975, 1990, 2000 and 2014) and stable Nighttime Lights data series (annual data, 1992-2013). The assessment is performed through comparison of spatial-temporal urbanization trends of the global datasets with a previous study performed in the Netherlands using detailed local data. These spatial-temporal trends involve the share or urban area that is situated in flood prone zones and the average inundation depth. Through analysis based on Geographic Information Systems it was found that the Global Human Settlement data series indicates a stable increase in the percentage of urban area in flood prone zones from 31.60% in 1975 to 36.54% in 2014. Potentially, this increase results from the flood protection measures installed between 1954 and 1997. The Nighttime Lights data series shows values of around 36% throughout its time period, with no clear increase or decrease. These values are on average 15-17% higher over the whole time series than the values found with the use of the local data. The Global Human Settlements dataset shows values for the average inundation depth from 1.47m in 1975 to 1.72m in 2014, similar to the local data. The increase could be explained by the fact that only areas with higher inundation depths are available for urbanization. The Nighttime Lights does not show a clear trend with values ranging from 1.52m to 1.70m and large annual variation. Overall, the suitability of the Global Human Settlement dataset is higher than the stable Nighttime Lights dataset for this study area as it shows values more similar to the local data and does not require prerequisite threshold analysis, which is the case for the Nighttime Lights data.

Global climate change is considered to be one of the most serious concerns of humankind (United Nations, 1992; United Nations, 2002). Anthropogenic greenhouse gases and aerosols impact considerably the energy balance of the Earth system, possibly provoking adverse effects on social, ecological, and economical equilibria. This is one of the main reasons why the understanding of the Earth’s climate system is of major importance. If better predictions of the response of the climate system to anthropogenic perturbations were available, political decisions against negative impacts could be taken, and social adaptations to changed climate conditions would be possible.

Clouds have a large impact on the Earth’s radiation and energy budget and play consequently a crucial role in prediction of climate change. At the same time, they are highly variable in time and space. To study and distinguish the different influences of clouds on the climate system it is useful to separate clouds into individual cloud regimes. In this thesis a new cloud classification and their response to disturbed cloud droplet number concentration is presented. Liquid water clouds at cloud scale are separated on the basis of cloud properties derived from combined A-Train satellite measurements involving the MODIS measurements onboard Aqua, the CloudSat cloud radar and the CALIPSO cloud lidar. Using the combined MODIS and CALIPSO cloud-top phase discrimination, liquid water clouds are identified. The high resolved vertical measurements of the CALIPSO lidar provide three cloud base height classes and the spatial variability of cloud top height within a 20 km footprint as an inhomogeneity parameter from which two cloud inhomogeneity classes are defined resulting in a total of six liquid cloud classes. The classification smoothly disentangles marine and continental clouds as well as stratiform and cumuliform clouds in different latitudes at the high spatial resolution of about 20 km. Analyzing the cloud droplet effective radius reff , cloud optical thickness τc , adiabatic liquid water path Lad, adiabatic cloud droplet number concentration Nc,ad and cloud geometrical thickness Hthick,CC derived from collocated combined MODIS, CloudSat and CALIPSO measurements shows a useful discrimination between cloud regimes. Further separations between non precipitating and precipitating clouds using the CloudSat precipitation flag as well as between three classes of free tropospheric relative humidity from a meteorological reanalysis above cloud top are made to investigate adjustments to aerosol-cloud interactions for individual cloud regimes. For this, the cloud liquid water path response, cloud thickness response, and cloud fraction response to perturbed cloud droplet concentration is analyzed. All sensitivities depend on the chosen cloud or environmental parameter indicating the importance of analyzing aerosol-cloud interactions for particular cloud regimes since similar clouds with similar cloud parameter responses are grouped together helping to identify individual behavior of these cloud regimes to perturbations in cloud droplet number concentration.

The practical handling and analysis of satellite data is outlined using the programming language Python. The limb sounding technique of the SABER instrument on board of the TIMED satellite delivers vertical profiles of kinematic temperature from the stratosphere (∼30 km) up to the lower thermosphere (∼120 km). The procedure may be summarised as follow: In the first step the level 2 data for one month are extracted from the netCDF format and arranged into a new altitude-latitude grid for the ascending and descending orbits. The longitudinal structure is rearranged applying the decomposition into zonal harmonics. Various cross sections of the data give a good overview of the thermal structure and dynamics of the atmosphere up to 120 km. The monthly values of the zonal averaged temperature are compared to the available data from stratospheric reanalyses up to 60 km as well as the initialized background climatology of general circulation models for the middle atmosphere. / In diesem Artikel soll der praktische Umgang mit Satellitendaten und deren Auswertung unter Verwendung der Programmiersprache Python skizziert werden. Auf der Basis der Horizontsondierungen des SABER Instruments auf dem TIMED Satelliten werden vertikale Profile wie die kinetischen Temperatur von der Stratosphäre (∼30 km) bis zur unteren Thermosphäre (∼120 km) gewonnen. Die Arbeitsschritte bei der Analyse lassen sich wie folgt gliedern: Als erstes werden die Level 2 Produkte eines Monats aus dem netCDF Format extrahiert und an ein neues Höhen-Breiten Gitter für jeden auf- und absteigenden Orbit angepasst. Die Längenstruktur wird mit Hilfe einer Zerlegung in harmonische Funktionen regularisiert. Diverse Querschnitte der Daten geben ein guten Überblick über die thermischen Struktur und Dynamik der Atmosphäre bis 120 km. Die Monatswerte des Zonalmittels der Temperatur werden mit denen aus operationellen Reanalysedaten (∼60 km) sowie der Hintergrundklimatologie von Zirkulationsmodellen der mittleren Atmosphäre verglichen.