Identify surface features
Irene Schicker taks about wind forecasts needed to efficiently operate wind turbines.
With the increase in wind energy production being fed into the power grid accurate high frequency predictions of the estimate d power for the next hours and days ahead are needed to schedule feed-in rates and secure power grid stability. To achieve this a combination of different kinds of information and data sets are needed. Here, statistical and machine learning methods proved to be a suitable tool. However, a thorough selection of input data is needed as well as considering extreme events (upper and lower tails) in model training and avoiding smoothed forecasts.
A brief introduction into post-processing for wind energy applications using statistics and machine learning, including useful tools/methods/data, will be given.
Sabine Zerobin recaps the steps necessary to plan, construct and operate wind turbines.
A reliable wind measurement is the basis for the successful accomplishment of wind power projects and the profitable operation of wind farms. Therefore, the current presentation gives an overview about the steps that are necessary to develop a wind power project from the green field, starting with a well-defined measurement campaign. Besides constraints originating from the conditions on site, pros and cons of different measurement techniques as well as the corresponding technical standards have to be kept in mind.
Reaching the measurement target therefore means, that sufficient data in a good quality is available to be used for further evaluations andassessments of the regarded site, which are then used to determine whether a project can be realised in terms of profit as we ll as from the technical point of view.
Even after a successful realisation of a project, wind measurements still play an important role, especially when it comes to the verification of the plant performance.
Gernot Zenkl presents the types of meteorological situation that can lead to critical avalanche situations in the eastern Alps.
Length: 45 minutes.
In this presentation one could see a short overview of the interesting and challenging work as an avalanche forecaster. Gernot Zenkl has shown which types of meteorological situation can lead to critical avalanche situations in the eastern Alps. Furthermore he explained the methods we use to inform and warn the people.
Zuhal Akyurek reports on snow reflectance characteristics that have to be considered in snow detection from satellite data.
Snow on the ground differs from most of the Earth surfaces by its high reflectance or albedo in the visible and near-infrared wavelengths (0.350 to 1 μm). For longer, the snow’s reflectance decreases significantly. In these wavelengths,snow is even less reflective than certain types of vegetation. Most of the incident radiation in these wavelengths is absorbed in the snowpack. These unique spectral characteristics are used in optical remote sensing to distinguish between snow and other types of surfaces. The numerous validation studies indicate that the satellite snow products have large snow mapping accuracy with respect to ground snow observations for cloud-free conditions, which varies between 69 and 94% in the winter seasons. The main limitation of existing optical platforms operating at a daily timescale is cloud coverage, which significantly reduces the availability of snow cover information.
In this session, algorithms used to retrieve HSAF snow products; snow mask (H31, H34) and effective snow cover area (H32, H35) from opticalsatellite data are presented. The challenges and the opportunities in retrieving snow cover mapsfrom optical data are discussed.
Ali Nadir Arslan gave an overall introduction of the H SAF products with a focus on snow detection.
The operational goal of H SAF highlights the need to provide products with a reliable measure of their accuracy, so the potential users are made aware of the advantages and drawbacks of the use of the H SAF products in their operational activities. With this aim, within the H SAF, three Validation Groups have been established: one for precipitation, one for soil moisture and one for snow products.
An overview of existing and future satellite-derived snow products will be provided.
Silvia Puca gave an overall introduction of the H SAF products.
In the first part of the session an introduction to the EUMETSAT HSAF project is made. The EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) started in 2005 and aims to provide remote sensing estimates of relevant hydrological parameters: instantaneous rain rate and cumulated rainfall, soil moisture at surface and in the root zone, snow cover and water equivalent. The project involves experts from 12 national meteorological and hydrological European Institutes of Austria, Belgium, Bulgaria, Finland, France, Germany, Hungary, Italy, Poland, Romania, Slovakia and Turkey, and from the European Centre for Medium-range Weather Forecast (ECMWF).
Kenan Bolat reports on H SAF snow detection product H34 that is derived from the SEVIRI instrument on-board MSG H34 differs for flat and mountainous regions.
H34 is a snow mask product, which is retrieved from optical imaging radiometer Spinning Enhanced Visible and Infrared Imager (SEVIRI) mounted aboard the geostationary Meteosat Second Generation (MSG) satellite operated by EUMETSAT. MSG/SEVIRI provides continuous imaging of the earth in 12 spectral channels with a repeat cycle of 15 min. The imaging spatial resolution is 3 km at sub-satellite point and degrades to 5 km over Europe. The snow cover mapping is based on a multi-channel retrieval algorithm. It exploits the high reflectivity of snow in the visible spectrum and the low reflectivity at shorter wavelengths. The snow cover retrieval algorithm differs for flat and mountainous regions. Considering the different characteristics of snow for mountainous and flat areas, two different algorithms are used in producing the snow products for flat and mountainous areas, and then the products are merged to have a single snow product.
Niilo Siljamo reports on H SAF snow detection product H31 that is derived from the SEVIRI instrument on-board MSG.
In this session, EUMETSAT HSAF snow products H31 and H34 are explained in detail.
H31 is a full disk snow mask product for flatland areas, which is retrieved from optical imaging radiometer Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellites operated by EUMETSAT. MSG/SEVIRI provides continuous imaging of the earth in 12 spectral channels with a repeat cycle of 15 min. The imaging spatial resolution is 3 km in nadir and degrades to 5 km over Europe. The snow cover retrieval algorithm used in the product is based on empirical approach, which takes into account the highly variable nature of the snow-covered surface in satellite resolution. Validation results based on weather station observations(snow depth and the state of the ground observations) are very good.
Ali Nadir Arslan gave an introduction to microwave remote sensing covering radiometry, characteristics, microwave sensors and applications.
During winter season, snow covers about 40 million km2 in the Northern hemisphere. Snow is a vital water resource in many regions of the world. Climatic changes, Earth’s energy balance, water resources are strongly affected by the presence of snow. Knowledge of the amount of snow water equivalent from year to year is essential to estimate the effects of snow melt run-off. Knowing the snow characteristics helps to improve weather forecasts, to predict water supply for hydropower stations, and to anticipate flooding. Microwave sensors such asradiometers and radars are often used because of their usability under varying conditions, factors like clouds, rain and lack of light do not affect the measurement, the large penetration depth into the surface with increasing wavelength, sensitive to liquid water. Understanding of the relationship between microwave signatures and snow is very important for retrieving desired snowpack parameters such as snow density, snow water equivalent and snow wetness.
In this session, we will present a general introduction to microwave remote sensing covering radiometry, characteristics, microwave sensors and applications. We will also provide information on algorithms used to retrieve HSAF snow products from microwave sensors.
Semih Kuter and Burak Simsek introduce the H SAF snow detection products H32 and H35 derived from polar orbiter MetOp.
H35 is effective snow cover product, which is retrieved from optical imaging radiometer AVHRR mounted aboard NOAA and MetOP satellites. The third generation of AVHRR, i.e., AVHRR/3, is a multi-spectral scanning radiometer with three solar channels in the VIS/NIR region and three thermal infrared channels. It is currently onboard to NOAA-15, -18, -19 and MetOp-A, -B, -C satellites. The operational H35 is confined to the Northern Hemisphere The fractional snow cover (fSCA) value within a pixel is estimated by using the reflectance data obtained from AVHRR spectral bands. The final fSCA product has ~1 km spatial resolution and it incorporates snow cover fraction percentage from 0 to 100% as well as cloud and water classes. The product for flat/forested regions is generated by Finnish Meteorological Institute (FMI) and the product for mountainous areas is generated by Turkish State Meteorological Service (TSMS). Both products, thereafter, are merged at FMI. A full disk H35 product is an image of 8,999 rows by 35,999 columns(i.e., 324 M pixels approximately).
Niilo Siljamo reports on H SAF the daily snow mask product H32 that is derived from the AVHRR instrument on-board MetOp satellites.
H32 is a global daily snow mask product, which is retrieved from the Advanced Very-High Resolution Radiometer (AVHRR) onboard the polar orbiting MetOp satellites operated by EUMETSAT. Metop/AVHRR provides daily global coverage on 5 channels. The spatial resolution is 1.1 km in nadir. The snow cover retrieval algorithm used in the product is based on empirical approach which takes into account the highly variable nature of the snowcovered surface in satellite resolution. Validation results based on weather station observations (snow depth and the state of the ground observations) are very good.
Simone Gabellani addresses the potential of using Sentinel-2 high-resolution imagery to validate moderate-resolution snow products.
In this part we will address the potential of using Sentinel-2 high-resolution imagery to validate moderate-resolution snow products supplied by the Hydrological Satellite Facility (HSAF) Project of EUMETSAT.
The consistency of Sentinel-2 observations has been assessed against both in-situ snow measurements and webcam digital imagery and they can be used as reference data (Piazzi et al 2019). We will show the comparison of HSAF products and Sentinel 2 dataset in different region of the world with different snow seasonality.