Deliverables Retrieval algorithm of sea ice concentration from passive (SSM/I) and active (QuikSCAT) satellite microwave sensors Intercomparison of 7 passive microwave ice concentration algorithms against each other and against reference data derived from ship observations and Synthetic Aperture Radar. The result quantifies the performance of these algorithms at high ice concentrations and makes it possible to assess the uncertainty due to variations in ice/snow surface radiative properties. This finding has implications in climate monitoring, when passive microwave ice concentration retrievals are to be reconciled with e.g. ice chart data and in operational applications when passive microwave ice concentration retrievals are to be assimilated in meteorological and oceanographic models. Real time assimilation system of humidity and temperature information from satellite microwaves radiometers into the HIRLAM NWP model If brought to operational application (which is beyond the scope of this project) the results should improve operational weather forecasts, helping to improve the living conditions in Northern Europe and especially human off shore activities in the Arctic region, such as navigation, fisheries, tourism, and exploitation of marine mineral resources. Increase prosperity and strengthen security in Northern Europe. Greater confidence in short and medium term weather predictions which will benefit the entire population of this region, the environment through improved risk management possibilities and better disaster control. Reliable forecasts are the first step in risk management and disaster control whether in the marine environment, the atmosphere or on land, and all economic activities and developments. It is important in making decisions on capital expenditure regarding investments in industry and infrastructure. AMSU-A Improved weather forecasts for Northern Europe, in particular over areas weak coverage of conventional meteorological observations, which are over or close to Arctic sea ice. AMSU-B Improved weather forecasts for Northern Europe - especially for precipitation. Enhance value of data of meteorological European satellites because sensors similar to AMSU-B are planned on future METOP satellites. Pave the way for optimum use of future METOP. Surface flux Improved weather forecasts for Northern Europe and especially of clouds in the Arctic. Efforts to make results useful, e.g., implement in HIRLAM, NWP-SAF and validation reports. Dissemination Forecasts, reports on implementation and validation, SAF visiting scientists. Retrieval algorithm for total water vapour over the arctic from data of microwave sounder AMSU-B on the NOAA satellites This algorithm takes the radiances measured by the satellite-bourne microwave radiometer AMSU-B (Advanced Microwave Sounding Unit B) at five different frequencies and calculates the total water vapour (also: integrated water vapour) of the atmosphere. It is possible to retrieve total water vapour (TWV) up to about 7 kg/m2 independent of the potentially unknown surface emissivity. Such TWV values are typical over most of the Arctic in late autumn, winter and spring. With the additional input of a rough estimate of sea ice emissivities, TWV over sea ice can be determined up to at least 12 kg/m2, but with a lower accuracy. Maps of the polar total water vapour derived by our method show details that are missed by, e.g., model or reanalysis data because of the sparsity of observations. There is currently no other way to retrieve water vapour data from satellite over the Arctic all year round, which makes this method particularly interesting for climate and weather modelling. The possibility to assimilate total water vapour derived in such a way into numerical weather prediction models has been explored within this project. The total water vapour data might also be used together with regional models for water cycle investigations. Another possible application is to merge the water vapour produced by this algorithm with data from other algorithms (that work, e.g., over non-polar regions in order to generate a global water vapour climatology. Interactive web-based user interface for data An Internet based distribution system for ice, weather and ocean information has been set up. The system provides near real time access to a large variety of data about the polar environment in a standard user environment. The system is freely available at: http://www.seaice.dk The amount of information available on the sea-ice, ocean and weather conditions for a particular region such as the Greenland Sea today is very large. However, the information is found in many different places, typically in incompatible formats that makes the task of optimally combining data into the desired set of information for a particular application very difficult. The applications vary from near real time usage for navigation, to off-line browsing of ice information for climate studies. The task of this work thus was to process the information into common formats and build a browser that would allow user-defined views of the data. The Internet was the obvious data distribution medium and Java the programming language of choice because it allowed the generation of a tool that could be centrally maintained, and globally applied. Users will always get the latest version of the code. The JAVA run-time environment (Virtual Machine (VM)) is freely available for most platforms from Sun Microsystems (http://java.sun.com). Specific target groups for the browser are companies involved in planning and/or carrying out navigation in ice frequented waters, ice services in the Northern and Southern hemisphere, scientific partners in various EC projects etc. Retrieval algorithm for surface emissivity at AMSU-A window channel frequencies from AMSU-A data This algorithm retrieves the surface emissivity at window channels of the Advanced Microwave Sounding Unit (AMSU) radiometers in polar regions. The instruments are on the new generation satellites of National Oceanic and Atmospheric administration (NOAA-15, 16, 17). The method assumes hypothetical surfaces with emissivities 0 and 1 and simulates brightness temperatures at the top of the atmosphere using atmospheric profiles, e.g., from the European Centre for Medium Range Weather Forecasts (ECMWF), as input for a radiative transfer model. Surface emissivity can then be calculated from simulated brightness temperatures and satellite-measured brightness temperatures. Improved turbulence scheme and flux parameterisation Practically oriented flux-calculation techniques based on correction functions to the neutral drag and heat/mass transfer coefficients have been further developed. In the traditional formulation, the correction functions depend only on the bulk Richardson number. However, data from measurements of turbulent fluxes and mean profiles in stable stratification over different sites exhibit too strong variability in this type of dependencies. Indirect evidence from climate and weather prediction modelling also suggests that the traditional flux calculation technique is not sufficiently advanced. It is conceivable that other mechanisms besides the surface-layer stratification and, therefore, other arguments besides the bulk Richardson number must be considered. The proposed technique accounts for a generally essential difference between the roughness lengths for momentum and scalars and includes a new effect of the static stability in the free atmosphere on the surface layer scaling. Recommended correction functions depend, besides bulk Richardson number, on one more stability parameter, involving the Brunt-Väisälä frequency in the free atmosphere, and on the roughness lengths. The project has resulted in an improved surface flux treatment and a new surface scheme using OSI-SAF ice concentrations implemented in HIRLAM. However, the flux treatment is not yet implemented over sea ice and it also need to be adapted to the new snow and ice parameterisation in HIRLAM. Emissivity and backscatter model of sea ice in the microwave range Objectives: to establish a set of baseline emissivity values of various sea ice types in order to improve ice concentration retrievals as well as in order to improve atmospheric retrievals over ice. The task was solved in two parts: - Empirical emissivities were derived from areas of known ice composition using microwave radiometer data from the AMSR-E and AMSU instruments. - A sea ice and snow emissivity model was developed and utilized in the evaluation and understanding of the empirically derived emissivities from 1) The Microwave Emission Model for Layered Snow-packs (MEMLS) is a model suitable for simulations of all kinds of physical effects and it has been tested and validated for snow-cover on land with satisfactory results. Here MEMLS is extended to include emission from sea ice. The sea ice model and modifications are described in the next section. Extension of MEMLS to sea ice emission: MEMLS, developed by Wiesmann and Mätzler uses the physical snow quantities and structure as input i.e. sequence of layers (j), density (D), exponential correlation length (pec), thermometric temperature (T) and moisture (W). In order to apply this model to compute the emission of both snow and sea ice it is necessary to include modules that compute the dielectric properties, and scattering of sea ice. Small liquid brine inclusions also called brine pockets dominate scattering in nilas and first-year ice. In multiyear ice, the voids and air bubbles in the upper ice are the primary scatters. The permittivity of liquid brine is an order of magnitude larger than the permittivity of solid ice and the permittivity of sea ice is therefore primarily a function of brine volume. The permittivity of sea ice is computed using Polder - Van Santen mixing formulas. It is a function of pure ice permittivity, inclusion shape and orientation, volume and the brine pockets permittivity (spheres are used because sea ice is assumed isotropic here). These mixing formulas do not account for scattering and therefore the accuracy of the permittivity estimates decreases as a function of frequency. Radiative processes at high frequency are usually confined to the snow cover and it is therefore not expected to be a significant source of error. MEMLS is valid for snow cover in the range 5-100 GHz. The primary limitation is the estimation of the scattering coefficient using empirical relations, which fit scattering in natural snow cover. For use of MEMLS outside of this frequency range, and for sea ice, it is necessary to compute the scattering coefficient using theoretical relations. The scattering in sea ice is therefore computed using the improved Born approximation The scattering [using the improved Born approximation] increases by a power law of the microwave frequency times the correlation length with a power of approximately 2.5. Above a certain frequency or above a certain correlation length, the increase will saturate in a similar way as Mie scattering does for spheres. It is further noted by Mätzler and Wiesmann that the improved Born approximation fits observations for snow grains which are large compared to the wavelength. It is therefore assumed, in this study, that the improved Born approximation is valid also at high frequency (157 and 183 GHz). Scatters are exclusively air bubbles and voids in multiyear ice and brine pockets in first-year ice. The scattering coefficient is in general a function of the permittivity of pure ice, the permittivity of brine or air, the permittivity of the sea ice mixture, volume of brine or air, microwave frequency and the correlation length of scatterers. Searching for OpenAIRE data... There was an error trying to search data from OpenAIRE No results available
