2011 STAR Seminars

The main part of the ocean mixing is induced by the sea waves (the surface and internal ones), which can be divided into the wave- generated turbulence mixing and the wave stirring. The ocean turbulence in sub-small scale is mainly generated by the sea waves and its mixing can be described by a closed second order moment model with shear instability generation term in the characteristic variation equations and breaking and collapse generation terms in the boundary conditions. The sea wave stirring described by the Reynolds averaged transport fluxes can be calculated by the unified linear theory of wave-like perturbation in second order accuracy. The results for the surface wave induced mixing have be derived analytically and compared consistently with field measurements and laboratory experiments in quality and quantity.

Microwave (MW) satellite data are widely used as input in the Numerical Weather Prediction (NWP) models and also in climate monitoring and assessments. Like other satellite data, MW data are prone to problems, including geolocation errors. MW data do not have fine spatial resolution like visible and even infrared data, therefore the geolocation accuracy of MW data cannot be easily determined using the normal methods such as superimposing coastlines on the satellite images. Currently, no geolocation correction is performed on the MW instruments onboard the satellites in the NOAA Polar Operational Environmental Satellite (POES) series. Long term monitoring of the geolocation errors is vital to develop Climate Data Records (CDRs) from instruments like AMSU and MHS.

In this study, we investigate and correct the geolocation errors of the observations from AMSU-A, -B and Microwave Humidity Sounder (MHS) onboard NOAA-15 to 19. We use the differences between ascending and descending observations along the coastlines to quantify the geolocation error in terms of the satellite attitude angles, i.e. pitch, roll, and yaw. Once the attitude angles are determined, we apply the corrections to level-1b data and calculate new geographical coordinates and scan/local zenith angles.

High-resolution sea-surface height (SSH) fields constructed from altimeter data have revealed the ubiquity of nonlinear, coherent eddies with mesoscale radii of ~100 km throughout the World Ocean. We investigate the influence of these eddies on oceanic biology from 10 years of upper-ocean chlorophyll (CHL) estimates collocated to the eddies inferred from the SSH fields. The observed westward co- propagation of CHL and SSH previously attributed to linear Rossby waves is shown to be due to nonlinear eddies that were not resolvable in the SSH fields analyzed in past studies. At temporal scales of weeks to months and spatial scales greater than 100 km, the dominant mechanism is shown to be eddy-induced horizontal stirring of the ambient CHL field.

While the horizontal advection of CHL by the rotational velocities of eddies dominates the statistics of CHL variability globally, trapping of CHL within the cores of highly nonlinear eddies is found to be important in anticyclonic eddies in specific regions. From collocation of scatterometer wind fields to the eddies, it is shown that the interaction between the anticyclonic eddy surface currents and the background wind field results in a sustained cyclonic wind stress curl at the cores of anticyclonic eddies. This eddy-induced Ekman pumping injects nutrients into the euphotic zone and thus plays a critically important role in sustaining the ecosystems trapped within the nonlinear cores of anticyclonic eddies.

Vegetation physical and biological properties and vegetation- atmosphere interactions, especially vegetation water content and evapotranspiration (ET), are important aspect of land surface hydrology. Measurements of these properties and interactions in large spatial and long temporal scales are generally not available at present. We have developed a novel technique that links vegetation properties and ET fluxes with a microwave emissivity difference vegetation index? (EDVI), defined as the microwave land surface emissivity differences between two wavelengths. The EDVI values can be derived from a combination of satellite microwave measurements with visible and infrared observations. This technique is applicable both day- and night-times under all-weather conditions, which provides a great potential for monitoring vegetation biomass and ecosystem exchange processes, particularly under cloudy conditions where classic optical indexes are unavailable. The EDVI values represent physical properties of crown vegetation such as vegetation water content of crown canopies, and are statistically sensitive to evapotranspiration fractions (EF) under all-sky conditions. For clear skies, EDVI estimates exhibit a stronger relationship with EF than normalized difference vegetation index (NDVI). Applying this technique to Amazon Basin using satellite measurements shows that the microwave based EDVI can provide the vegetation information over 99% of the land surface while only small fraction (15%) of land surface information can be extracted by the methods with classic vegetation indexes.

GOES hourly SST, RADS along-track altimetry, and surface currents from land-based high-frequency (HF) radars have been routinely assimilated in a pilot real-time coastal ocean model off Oregon, developed in support of a regional integrated ocean observing system (IOOS). Assimilation proceeds in a series of 3-day windows. The variational representer-based method is used to improve initial conditions in each window. The 4DVAR system effectively filters noise, and fills gaps in the data, providing dynamically based interpolation of these data in space and time. The forecast model takes improved initial conditions from the data assimilation system and atmospheric forcing from the NOAA NAM model and provides daily updates of 3-day forecasts of ocean conditions. A series of Observing System Evaluation (OSE) experiments have been performed to understand the impact of each of these data types on the accuracy of the forecasts. We find that assimilation of each along-track altimetry and HF radar surface currents helps to improve geometry of the SST upwelling front. Assimilation of GOES SST improves ocean surface topography and helps to understand connectivity of the interior and coastal ocean areas.

Sea oil fields are of paramount economical interest in our fossil fuels economy. Large exploiting activities are currently undergoing in various part of the globe and new sea oil fields are under approval by several national governments. Oil growing demand and the spiking of market price, propelled oil extracting industries to explore and drill in deep waters and therefore new areas are now considered over the globe. Operational services can be driven by international remote sensing constellation of constellations.

With these respects the key remote sensing sensor is the active microwave Synthetic Aperture Radar (SAR). In fact, it provides high spatial resolution, limited sensitivity to cloud cover, other atmospheric phenomena and day-and-night coverage. Sea oil field monitoring must effective to observe oil at sea and oil rigs. The seminar first deals the two issues separately than it shows how it is possible to effectively provide a service that combines the two needs. The seminar shows as SAR polarimetry and physical based approaches, instead of image based approaches, can provide successful operational results.

Satellite Application Facilities (SAF) are long-term European projects funded by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The SAF network was created to make full use of the additional capabilities provided by EUMETSAT's latest generation of meteorological satellites, i.e. Meteosat Second Generation (MSG) and the European Polar System (EPS). The SAFs are centres of excellence for the processing of satellite data and form an integral part of EUMETSAT's distributed Applications Ground Segment. Products derived by SAFs address specific areas of application and give researchers an opportunity to exploit data in new and creative ways. Currently, there are eight SAFs: Support to Nowcasting and Very Short Range Forecasting (NWC), Ocean and Sea Ice (OSI), Climate Monitoring (CM), Numerical Weather Prediction (NWP), Land Surface Analysis (LSA), Ozone and Atmospheric Chemistry, Monitoring (O3M), Global Navigation Satellite System Receiver for Atmospheric Sounding Meteorology (GRAS), Support to Operational Hydrology and Water Management (H).

LSA SAF is a consortium of 7 Institutions in 6 countries dedicated to the effective use of MSG and METOP data for applications related to the land, land-atmosphere interactions and the biosphere. Consequently, LSA SAF focuses on algorithm development, validation and operational production of land surface related products. Once a new algorithm has been developed and validated by one of the partners, it is handed over to the hosting institution (Portuguese Weather Service) where it is integrated into the operational chain. LSA SAF performs real time operations (24/7), i.e. some products are available every 15 min about one hour after observation. The products are disseminated in near-real time via Eumetcast and off-line via ftp. Furthermore, LSA SAF provides active user support and is reviewed (~annually) by technical and scientific review panels. As a contribution to the SAF, Karlsruhe Institute of Technology (KIT) operates four permanent stations dedicated to the validation of land surface temperature (LST) in Africa and Europe: Evora (Portugal, cork oak tree forest), Dahra (Senegal, tiger bush), Gobabeb (Namibia, gravel plains), and RMZ-Farm (Namibia, Kalahari- bush). These are the only dedicated LST validation stations within the field of view of the MSG satellites. The presentation will give an overview of LSA SAF and then show results from the validation sites.

While the demand for spaceborne Earth observation (EO) data has continued to increase in terms of both data quantity and quality, the automatic or semi-automatic transformation of huge amounts of multi-source multi-resolution remote sensing (RS) imagery into information still remains far more problematic than might be reasonably expected. For example, supervised data learning algorithms (say, Support Vector Machines, SVMs), considered successful at local/regional scale, turn out to be impracticable/unable to classify remote sensing (RS) image mosaics at national/continental/ global scale. This means that, in RS common practice, the cost, timeliness, quality and availability of adequate reference (ground truth) data sets are becoming the most limiting factors on RS data product generation and validation. To increase the operational quality indicators of RS image understanding systems (RS-IUSs), namely, degree of automation, efficiency, effectiveness, robustness to changes in the input data set, robustness to changes in input parameters, scalability, timeliness and economy, two actions are undertaken. (1) A novel two-stage stratified hierarchical hybrid RS-IUS architecture is proposed. (2) An operational, near real-time, multi-sensor, multi-resolution prior knowledge-based decision-tree classifier, called Satellite Image Automatic Mapper™ (SIAM™), is implemented as the preliminary classification first stage of a two-stage stratified hierarchical hybrid RS-IUS.

In this seminar SIAM™ is proposed to the multi-disciplinary NOAA community of experts by means of few technical details together with a variety of application examples employing as input RS imagery acquired by nearly all existing spaceborne EO optical imaging sensors (e.g., AVHRR, MODIS, MSG, Landsat, SPOT, AATSR, AVNIR-2, RapidEye, IKONOS, QuickBird, WorldView-2), namely: automatic Land Cover (LC) classification from regional to continental scale, automatic LC change (LCC) detection from regional to continental scale, flaming and smoldering fire detection, burned area detection, cloud detection, semantic querying of large-scale RS image databases.