Beobachtungsoperator zur Assimilation satellitenbasierter Messungen verschiedener Aerosoltypen in ein Chemie-Transportmodell

In der Wettervorhersage konnte durch die Assimilation von Satellitenbeobachtungen eine erhebliche Verbesserung der Prognosegenauigkeit erzielt werden. Bei der Vorhersage von Aerosolpartikeln mit Chemie-Transport-Modellen herrscht derzeit die Modellierung basierend auf statischen Emissionskatastern vor, mit denen episodische Ereignisse nicht modelliert werden können. Ziel dieser Arbeit ist die Entwicklung eines Beobachtungsoperators für die komponentenweise Assimilation von Satellitenbeobachtungen, die wasserlösliche, wasserunlösliche, russhaltige sowie aus Seesalz und Mineralstaub bestehende Aerosolpartikel getrennt erkennen. Dieser erlaubt den Transfer des Modellhintergrunds aus dem Raum der chemischen Massenkonzentrationen in den Raum der aerosoloptischen Dicke, in dem anschließend ein zweidimensionales, variationelles Assimilationsverfahren angewandt wird. Es konnte für den Zeitraum Juli � November 2003 jeweils für die wasserlöslichen, die rußhaltigen und die mineralstaubhaltigen Komponenten separat eine Reduktion der mittleren Abweichung und dadurch des RMSE in den Analysefeldern nachgewiesen werden. Außerdem konnte die verbesserte Modellierung von Waldbrand- und Sandsturm-Episoden exemplarisch gezeigt werden

ESA - RESGROW: Epansion of the Market for EO Based Information Services in Renewable Energy - Biomass Energy sector

Biomass energy is of growing importance as it is widely recognised, both scientifically and politically, that the increase of atmospheric CO2 has led to an enhanced efficiency of the greenhouse effect and, as such, warrants concern for climate change. It is accepted (IPCC 2011 and just recently in the draft version of the IPCC 2013 report) that climate change is partly induced by humans notably by using fossil fuels. For reducing the use of oil or coal, biomass energy is receiving more and more attention as an additional energy source available regionally in large parts of the world. Effective management of renewable energy resources is critical for the European and the global energy supply system. The future contribution of bioenergy to the energy supply strongly depends on its availability, in other words the biomass potential. Biomass potentials are currently mainly assessed on a national to regional or on a global level, with the bulk biomass potential allocated to the whole country. With certain biomass fractions being of low energy density, transport distances and thus their spatial distribution are crucial economic and ecological factors. For other biomass fractions a super-regional or global market is envisaged. Thus spatial information on biomass potentials is vital for the further expansion of bioenergy use. This study, which is an updated version of a study carried out in 2007 in frame of the ENVISOLAR project, analyses the potential use of Earth Observation data as input for biomass models in order to assessment and manage of the biomass energy resources especially biomass potentials of agricultural and forest areas with high spatial resolution (typical 1km x 1km). In addition to a sorrow review of recent developments in data availability and approaches in comparison to its 2007’ version, this study also includes a review on approaches to directly correlate remote sensing data with biomass estimations. An overview of existing biomass models is given covering models using remote sensing data as input as well as models using only meteorological and/or management data as input. It covers the full life cycle from the planning stage to plant management and operations (Figure 1). Several groups of stakeholders were identified

Solar surface irradiance from new meteorological satellite data

International audienceThis paper presents the first prototype of a new method, for assessing solar surface irradiance, benefiting from advanced products derived from recent Earth Observation missions. This method -called Heliosat-4- is based on the radiative transfer model libRadtran and will provide direct, diffuse components and spectral distribution of solar surface irradiance every 3 km and ¼ h over Europe and Africa. The advantage of the Heliosat-4 method is the simultaneous computation of direct and diffuse irradiances. The outcomes of this prototype of Heliosat-4 method are compared to ground measurements, of direct and global irradiances, made at 4 stations in Europe and Northern Africa. The results show that standard deviation attained by the Heliosat-4 method for global irradiance is fairly similar to that attained by current methods. A significant bias is actually observed and discussed

Quantifying residential PV feed-in power in low voltage grids based on satellite-derived irradiance data with application to power flow calculations

A scheme using satellite-derived irradiance measurements to model the feed-in power of residential photovoltaic (PV) systems in a low voltage distribution grid is described. It is validated against smart meter measurements from a test site with 12 residential PV systems in the city of Ulm, Germany, during May 2013 to December 2014. The PV feed-in power is simulated in a 15-min time resolution based on irradiance data derived from Meteosat Second Generation satellite images by the physically based retrieval scheme Heliosat-4. The PV simulation is based on the nominal power and location of the PV systems as provided by the distribution system operator. Orientation angles are taken from high resolution aerial laser-scan data. The overall average mean error of PV feed-in power is 4.6% and the average root-mean-squared error is 12.3% for the individual systems. Relative values are given with respect to the total installed power of 152.3 kWp. Sensitivity studies discuss the need for knowing the exact orientation angles of each individual PV system or the usefulness of a single ground-based measurement as alternative to satellite observations. As an application of the scheme, the modelling of the effect of the power flow from the residential PV on the load flow of the low voltage distribution grid transformer is described and illustrates the advantage of the discussed approach for distribution system operators

Classifying direct normal irradiance 1-minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations

t Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. Eight variability classes were previously defined for the 1 min resolved direct normal irradiance (DNI) variability inside an hour. In this study spatial structural parameters derived fromsatellite-based cloud observations are used as classifiers in order to detect the associated direct normal irradiance (DNI) variability class in a supervised classification scheme. A neighbourhood of 3×3 to 29×29 satellite pixels is evaluated to derive classifiers describing the actual cloud field better than just using a single satellite pixel at the location of the irradiance observation. These classifiers include cloud fraction in a window around the location of interest, number of cloud/cloud free changes in a binary cloud mask in this window, number of clouds, and a fractal box dimension of the cloud mask within the window. Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well – the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.g. to quantify short-term variability impacts on solar power production. This approach is based on satellite-based cloud observations only and does not require any ground observations of the location of interest

Satellite image-based generation of high frequency solar radiation time series for the assessment of solar energy systems

Solar energy is envisaged as a major pillar of the global transition to a climate-friendly energy system. Variability of solar radiation requires additional balancing measures to ensure a stable and secure energy supply. In order to analyze this issue in detail, solar radiation time series data of appropriate temporal and spatial resolution is necessary. Common weather models and satellites are only delivering solar surface irradiance with temporal resolutions of up to 15 min. Significant short-term variability in irradiances within seconds to minutes however is induced by clouds. Ground-based measurements typically used to capture this variability are costly and only sparsely available. Hence, a method to synthetically generate time series from currently available satellite imagery is of value for researchers, grid operators, and project developers. There are efforts to increase satellite resolution to 1 min, but this is not planned everywhere and will not change the spatial resolution. Therefore, the fundamental question remains if there are alternative strategies to obtain high temporal resolution observations at a pinpoint. This paper presents a method to generate 1 min resolved synthetic time series of global and direct normal irradiances for arbitrary locations. A neural network based on satellite image derived cloud structure parameters enables to classify high-frequency solar radiation variability. Combined with clear-sky radiation data, 1 min time series which reflect the typical variability characteristics of a site are reproduced. Testing and validation against ground observations (BSRN) show that the method can accurately reproduce characteristics such as frequency and ramp distributions. An application case demonstrates the usage in low-voltage grid studies

Evaluating remote sensing methods of solar irradiance methods under the consideration of cloud variability

The accuracy of the irradiance remote sensing methods was evaluated for different classes of cloud variability

User's Guide to the SoDa and SOLEMI Services

The European Earth observation programme GMES (Global Monitoring for Environment and Security) aims at providing environmental information to support policymakers, public authorities and both public and commercial users. A systematic monitoring and forecasting of the state of the Earth's subsystems is currently under development. Six thematic areas are developed: marine, land, atmosphere, emergency, security and climate change. A land monitoring service, a marine monitoring service and an atmosphere monitoring service will contribute directly to the monitoring of climate change and to the assessment of mitigation and adaptation policies. Additional GMES services will address respectively emergency response and security-related aspects. The pre-operational atmosphere service of GMES is currently provided through the FP7 project MACC (Monitoring Atmospheric Composition and Climate). MACC combines state-of-the-art atmospheric modelling with Earth observation data to provide information services covering European Air Quality, Global Atmospheric Composition, Climate, and UV and Solar Energy. Within the radiation subproject (MACC-RAD) existing historical and daily updated databases for monitoring incoming surface solar irradiance are further developed. The service will meet the needs of European and national policy development and the requirements of (commercial) downstream services (e.g. planning, monitoring, efficiency improvements, integration into energy supply grids). The SOLEMI service (operated by MACC partner DLR) and the SoDa service (operated by MACC partner ARMINES and its subsidiary Transvalor) have been specifically developed in several national, European and ESA projects to fulfil the requirements for long-term databases and NRT services. On its transition process from the precursor services SoDa and SOLEMI the following User's Guide intends to summarize existing knowledge, which has been published only in a scattered manner. Part A 'Users' Expectations' describes the communities of users, their expectations and gives an overview of the compliance of the MACC RAD service with those. In Part B 'Creating Databases', the current databases HelioClim and SOLEMI as well as the methods used to convert satellite images into solar surface irradiance are presented. The quality of the retrieved irradiances is discussed. An overview of the operations and workflow is presented for the creation, updating and monitoring of these databases. Part C 'Delivering products' is devoted to the supply of products. The core products are defined. The future MACC-RAD Service is described and a prototype is presented. It is intended to update this User's Guide regularly following the realisation of the MACC RAD service line

User's Guide to the MACC-RAD Services on solar energy radiation resources

The European Earth observation programme GMES (Global Monitoring for Environment and Security), now Copernicus (the European Earth Observation Programme) since December 2012, aims at providing environmental information to support policymakers, public authorities and both public and commercial users. A systematic monitoring and forecasting of the state of the Earth's subsystems is currently under development. Six thematic areas are developed: marine, land, atmosphere, emergency, security and climate change. A land monitoring service, a marine monitoring service and an atmosphere monitoring service will contribute directly to the monitoring of climate change and to the assessment of mitigation and adaptation policies. Additional GMES services will address respectively emergency response and security-related aspects. The pre-operational atmosphere service of GMES is currently provided through the FP7 projects MACC and MACC-II (Monitoring Atmospheric Composition and Climate). MACC combines state-of-the-art atmospheric modelling with Earth observation data to provide information services covering European Air Quality, Global Atmospheric Composition, Climate, and UV and Solar Energy. Within the radiation subproject (MACC-RAD) existing historical and daily updated databases for monitoring incoming surface solar irradiance are further developed. The service will meet the needs of European and national policy development and the requirements of (commercial) downstream services (e.g. planning, monitoring, efficiency improvements, integration into energy supply grids). The SOLEMI and the HelioClim 3 databases operated by respectively DLR and ARMINES and its subsidiary Transvalor have been specifically developed in several national, European and ESA projects to fulfil the requirements for long-term databases and NRT services. On its transition process from the precursor services HelioClim and SOLEMI the following User's Guide intends to summarize existing knowledge, which has been published only in a scattered manner. Part A 'Users' Expectations' describes the communities of users, their expectations and gives an overview of the compliance of the MACC RAD service with those. In Part B 'The legacy HelioClim 3 and SOLEMI databases', the current databases HelioClim 3 and SOLEMI as well as the methods used to convert satellite images into solar surface irradiance are presented. The quality of the retrieved irradiances is discussed. An overview of the operations and workflow is presented for the creation, updating and monitoring of these databases. Part C 'The new HelioClim 4 database' describes the new Heliosat 4 method and the new HelioClim 4 database and provides an overview of the operations and the workflow. Part D 'Quality control of estimates of irradiance' discusses the means to control the quality of the elaboration of the products and to assess the uncertainty of the estimates of irradiance. Part E 'Delivering products' is devoted to the supply of HelioClim 4 products. The products are defined. A prototype of a means to access the HelioClim 4 products is presented. It is intended to update this User's Guide regularly following the realisation of the MACC RAD service line

On quality control procedures for solar radiation and meteorological measures, from subhourly to montly average time periods

International audienceMeteorological data measured by ground stations are often a key element in the development and validation of methods exploiting satellite images. These data are considered as a reference against which satellite-derived estimates are compared. Long-term radiation and meteorological measurements are available from a large number of measuring sta- tions. However, close examination of the data often reveals a lack of quality, often for extended periods of time. This lack of quality has been the reason, in many cases, of the rejection of large amount of available data. The quality data must be checked before their use in order to guarantee the inputs for the methods used in modelling, monitoring, forecast, etc. To control their quality, data should be submitted to several conditions or tests. After this checking, data that are not flagged by any of the test is released as a plausible data. In this work, it has been performed a bibliographical research of quality control tests for the common meteoro- logical variables (ambient temperature, relative humidity and wind speed) and for the usual solar radiometrical variables (horizontal global and diffuse components of the solar radiation and the beam normal component). The different tests have been grouped according to the variable and the average time period (sub-hourly, hourly, daily and monthly averages). The quality test may be classified as follows: * Range checks: test that verify values are within a specific range. There are two types of range checks, those based on extrema and those based on rare observations. * Step check: test aimed at detecting unrealistic jumps or stagnation in the time series. * Consistency checks: test that verify the relationship between two or more time series. The gathered quality tests are applicable for all latitudes as they have not been optimized regionally nor seasonably with the aim of being generic. They have been applied to ground measurements in several geographic locations, what result in the detection of some control tests that are no longer adequate, due to different reasons. After the modification of some test, based in our experience, a set of quality control tests is now presented, updated according to technology advances and classified. The presented set of quality tests allows radiation and meteorological data to be tested in order to know their plausibility to be used as inputs in theoretical or empirical methods for scientific research. The research leading to those results has partly receive funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement no. 262892 (ENDORSE projec