Applications of ISES for vegetation and land use

Remote sensing relative to applications involving vegetation cover and land use is reviewed to consider the potential benefits to the Earth Observing System (Eos) of a proposed Information Sciences Experiment System (ISES). The ISES concept has been proposed as an onboard experiment and computational resource to support advanced experiments and demonstrations in the information and earth sciences. Embedded in the concept is potential for relieving the data glut problem, enhancing capabilities to meet real-time needs of data users and in-situ researchers, and introducing emerging technology to Eos as the technology matures. These potential benefits are examined in the context of state-of-the-art research activities in image/data processing and management

JRC storm surge system: new developments

JRC has developed the first storm surge calculation system for the Tropical Cyclones (TCs) included in the Global Disasters Alert and Coordination System (GDACS) in 2011. The TCs are not the only weather system that can generate a storm surge event, therefore a new Storm Surge Calculation System (SSCS) has been developed in 2013, to simulate the storm surge also in Europe. JRC has recently developed and implemented a new storm surge system, using a new hydrodynamic code and new atmospheric forecasts, creating several new SSCS bulletins and TCs GDACS web pages. This report describes the new procedures developed.JRC.E.1-Disaster Risk Managemen

Science for Disaster Risk Reduction

This thematic report describes JRC's activities in support to disaster management. The JRC develops tools and methodologies to help in all phases of disaster management, from preparedness and risk assessment to recovery and reconstruction through to forecasting and early warning.JRC.A.6-Communicatio

Aeronautics and space report of the President, 1982 activities

Achievements of the space program are summerized in the area of communication, Earth resources, environment, space sciences, transportation, aeronautics, and space energy. Space program activities of the various deprtments and agencies of the Federal Government are discussed in relation to the agencies' goals and policies. Records of U.S. and world spacecraft launchings, successful U.S. launches for 1982, U.S. launched applications and scientific satellites and space probes since 1975, U.S. and Soviet manned spaceflights since 1961, data on U.S. space launch vehicles, and budget summaries are provided. The national space policy and the aeronautical research and technology policy statements are included

Aeronautics and space report of the President, 1980 activities

The year's achievements in the areas of communication, Earth resources, environment, space sciences, transportation, and space energy are summarized and current and planned activities in these areas at the various departments and agencies of the Federal Government are summarized. Tables show U.S. and world spacecraft records, spacecraft launchings for 1980, and scientific payload anf probes launched 1975-1980. Budget data are included

Requirements of a global information system for corn production and distribution

There are no author-identified significant results in this report

Application of machine learning techniques to weather forecasting

Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the interpretation of the information generated by these models. This doctoral thesis explores the use of machine learning methodologies to solve specific problems in meteorology and particularly focuses on the exploration of methodologies to improve the accuracy of numerical weather prediction models using machine learning. The work presented in this manuscript contains two different approaches using machine learning. In the first part, classical methodologies, such as multivariate non-parametric regression and binary trees are explored to perform regression on meteorological data. In this first part, we particularly focus on forecasting wind, where the circular nature of this variable opens interesting challenges for classic machine learning algorithms and techniques. The second part of this thesis, explores the analysis of weather data as a generic structured prediction problem using deep neural networks. Neural networks, such as convolutional and recurrent networks provide a method for capturing the spatial and temporal structure inherent in weather prediction models. This part explores the potential of deep convolutional neural networks in solving difficult problems in meteorology, such as modelling precipitation from basic numerical model fields. The research performed during the completion of this thesis demonstrates that collaboration between the machine learning and meteorology research communities is mutually beneficial and leads to advances in both disciplines. Weather forecasting models and observational data represent unique examples of large (petabytes), structured and high-quality data sets, that the machine learning community demands for developing the next generation of scalable algorithms

Development of the MEGAN3 BVOC Emission Model for Use with the SILAM Chemical Transport Model

This project has aimed to investigate and propose improvements to the methods used in the System for Integrated ModeLing of Atmospheric coMposition (SILAM) model for simulating biogenic volatile organic compound (BVOC) emissions. The goal is to study an option in SILAM to use the Model for Emission of Gases and Aerosols in Nature, Version 3 (MEGAN3) as an alternative to SILAM’s existing BVOC calculation algorithm, which is a more simplified approach. SILAM is an atmospheric chemical transport, dispersion, and deposition modelling system owned and continuously developed by the Finnish Meteorological Institute (FMI). The model’s most well-known use is in forecasting air quality in Europe and southeast Asia. Although traffic and other urban emissions are important when modelling air quality, accurate modelling of biogenic emissions is also very important when developing a comprehensive, high-quality regional and sub-regional scale model. One of the motivations of this project is that if BVOC emission simulation in SILAM were improved, the improvements would be passed into subsequent atmospheric chemistry algorithms which form the molecules responsible to produce secondary organic aerosols (SOA). SOA have significant impacts on local and regional weather, climate, and air quality. The development in this project will therefore offer the potential for future improvement of air quality forecasting in the SILAM model. Because SILAM requires meteorological forecast as input boundary conditions, this study used output generated by the Environment-High Resolution Limited Area Model (Enviro-HIRLAM), developed by the HIRLAM Consortium in collaboration with universities in Denmark, Finland, the Baltic States, Ukraine, Russia, Turkey, Kazakhstan, and Spain. Enviro-HIRLAM includes multiple aerosol modes, which account for the effects of aerosols in the meteorological forecast. Running SILAM with and without the aerosol effects included in the Enviro-HIRLAM meteorological output showed that aerosols likely caused a minor decrease in BVOC emission rate. This project has focused on the boreal forest of Hyytiälä, southern Finland, the site of the Station for Measuring Ecosystem-Atmosphere Relations - II (SMEAR-II, 61.847°N, 24.294°E) during a one day trial on July 14, 2010. After performing a test run over the Hyytiälä region in July 2010 for analysis, it was found that SILAM significantly underestimates BVOC emission rates of both isoprene and monoterpene, likely because of an oversimplified approach used in the model. The current approach in SILAM, called ‘Guenther Modified’, uses only a few equations from MEGAN and can be classified as a strongly simplified MEGAN version, with selected assumptions. It references a land cover classification map and lookup table, taking into account only three parameters (air temperature, month, and solar radiation) when performing the calculations. It does not take into account several other important parameters, which affect the BVOC emission rates. Based on qualitative analysis, this appears to be a simplified but limited approach. Therefore, based on these findings, the next step to improve SILAM simulations is to propose a full implementation of MEGAN as a replacement to the current logic in SILAM, which is to use land classification and a lookup table for BVOC emission estimates. MEGAN, which is a much more comprehensive model for simulating BVOC emissions from terrestrial ecosystems. MEGAN includes additional input parameters, such as Leaf Area Index (LAI), relative humidity, CO2 concentration, land cover, soil moisture, soil type, and canopy height. Furthermore, this study found that in the future, simulations involving BVOCs could also potentially be improved in SILAM by adding modern schemes for chemical reactions and SOA formation in future development of SILAM. After gaining in-depth understanding of the strengths and limitations of BVOC in the SILAM model, as practical result, some recommendations for improvements to the model are proposed