Romanov s method binary options. ERRIC Machine Code Simulator
Introduction Snow has far-reaching effects on climate and ecosystems.
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In weather forecasting and climate research, the effects of snow have been widely considered, but a recent study by Niittynen et al. Snow is also an important factor in hydrology, as discussed by Thirel et al.
Pullen et al. Current remote sensing satellites used for snow detection are either in polar or geostationary orbits, which have their advantages and disadvantages. Most of the seasonal snow is in high latitudes, which are poorly covered by geostationary satellites.
Whereas instruments aboard geostationary satellites provide excellent temporal resolution, polar satellite instruments have a better spatial resolution and a better polar coverage, making them often a better option in snow detection.
However, due to their orbital characteristics, only a few observations per day may be available, making them more susceptible to, for example, cloudiness preventing surface observation. Other challenges, such as topography, surface properties, weather, and snow-cover evolution are present in the satellite snow product development for both orbit types.
The AVHRR on board polar-orbiting satellites is a well-known imager instrument with a long history in remote sensing. The visual and IR ranges of the electromagnetic spectrum are covered by six channels five in simultaneous use.
The product presented in this paper resembles binary products, with an additional class for a partial snow cover. There are, of course, many previous snow extent or coverage algorithms and products based on those algorithms.
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Notarnicola et al. In the paper by Hori et al. Even though the AVHRR and MODIS instruments are perhaps the most well-known and most Romanov s method binary options polar-orbiting instruments for meteorological and hydrological applications, other instruments can be utilized.
Key et al. Riggs et al. A similar snow product as the one presented here will be developed for the METimage instrument. In the geostationary orbit, there are several satellites [such as GOES, Meteorological Satellite MeteosatFY-2, and Himawari] and instruments that can be used to provide snow products for different regions. GOES data are used for snow fraction detection Romanov et al. Rather than being fully automatic, the production employs human analysts who merge data from different sources.
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Validation results for IMS are presented, for example, by Chen et al. The pros and cons of different snow products have been studied by many authors. Frei et al. While optical instruments employing visual and IR bands provide high-quality high-resolution data, they have weaknesses.
The most important weakness of using optical channels for snow detection is the requirement of cloud-free conditions. Also, during nights the optical instruments have only limited applicability.
The high temporal resolution of the instruments on the geostationary orbit helps to mitigate this in midlatitudes, as it is more probable that there are cloud-free moments during the day, but for polar orbiters, night and cloud cover are a serious hindrance.
There are regions that can be cloud covered for several days and vast areas may be too dark for snow detection for several weeks during the polar night. However, on favorable conditions, instruments on polar satellites provide excellent spatial resolution. They show that more progressive merging decreases cloud covered area, but with reduced accuracy of the snow detection.
Active and passive microwave instruments radars and radiometers have advantages in cloud-covered and night conditions. Unfortunately, these methods have also associated restrictions, such as lower resolution microwave radiometers or very narrow swath widths radars. Many SWE products need ancillary data such as snow-depth observations in the product generation. Such dependence on ancillary data is a limiting factor, for example, in NWP, where independent data are required or preferred.
Even the best satellite-based snow products are useless if the users do not have any indication of the exmo me exchange and accuracy of the product.
However, such measurements are not available on operational basis. Regional or local measurement campaigns do not allow continuous global validation.
Fortunately, synoptic weather stations provide in situ snow depths and the state-of-the-ground observations that can be used for satellite snow product validation. While the weather station network provides global coverage in general, there are regions where the network is sparse. When using weather station data for satellite product validation, the representativeness of the observations should be considered.
At the moment, weather station observations seem to be the best in situ option for large-scale operational validation of snow products. There are still limitations in the way the weather stations report snow-cover measurements.
Many stations report the snow observations only when snow is present, others do not provide snow measurements at all. Therefore, a missing snow observation cannot be interpreted as lack of snow at the station.
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Automatic weather stations can measure snow depth, but many commonly used snow-depth instruments do not provide reliable snow-depth observations of thin less than 2. This lack of snow coverage observations has stimulated creativity and new innovative methods for snow product validation have been described by many authors. For example, Salvatori et al. Piazzi et al.
They also evaluate the consistency of Sentinel-2 observations based on in situ observations and webcam photographs. Even though webcams and high-resolution imagery can be used for validation, both methods are better suited for regional validation or case studies. Both products aim specifically to fill the needs of NWP and hydrological modeling as discussed later in the paper. Single product example files can be retrieved from product description pages.
Even though the algorithm was developed for operational use, it can be used to process archived data to produce snow extent datasets covering longer time spans that are needed in reanalysis and similar applications. While snow cover itself may vary considerably inside one satellite pixel, there are also other surface features that must be taken in account.
Vegetation type and density what are the most effective indicators in binary options a significant impact on snow detection. The vegetation can vary from sparse and small e. There may be small-scale topography and water bodies of different sizes and shapes.
Another source of variability is the snow on the canopy, which can vary from thin sprinkled snow to thick crown-covering snow causing damage to the trees.
Finally, the snow cover itself can be thin and patchy melting season, new snow or thick enough to cover small surface features. While the properties of snow, vegetation, and surface features cause a significant part of the variability, one must account for the viewing angle, which can have large effect. In nadir, trees may cover the surface below, Romanov s method binary options at the edge of the satellite Romanov s method binary options the large viewing angle means that the obscuring effect of the canopy is considerably larger.
In dense forests, there may be several trees between the surface and the satellite.