2007 STAR Seminars

SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) is a passive remote sensing spectrometer observing backscattered radiation from the atmosphere and Earth's surface, in the wavelength range between 240 and 2380 nm. The instrument flies on board ENVISAT which was launched on 1 March 2002. The spatial resolution is 30x60km2 and the swath width is 960km.

The primary scientific objective of SCIAMACHY is the global measurement of various trace gases in the troposphere and stratosphere, which are retrieved from the reflected light spectra. In addition, hyperspectral measurements of SCIAMACHY can be used to improve aerosol and cloud satellite retrievals.

In this work we analyze four years of SCIAMACHY measurements (2003-2006) with respect to the determination of various cloud parameters such as cloud top height, cloud fraction, cloud thermodynamic state (water/ice) and cloud optical thickness. The cloud top height is retrieved using oxygen A-band spectrometry. The cloud fraction retrieval is based on the analysis of measurements performed by the polarization measurement devices. In depth discussion of the underlying physical principles behind retrievals is given.

Advances in fast radiative transfer modeling and in the quality of ancillary data-sets have led to great progress in our ability to remotely sense clouds from satellite VIS/NIR/IR imagers. In addition, more complex use of the spatial information provided by imagers has also added to our cloud remote sensing abilities.

This talk will describe the use of this information within STAR/ASPB for developing algorithms for present day and future satellite imagers. In addition, results will be shown to illustrate how these improvements improve our cloud climatologies from the three decades of AVHRR data.

NASA's Global Precipitation Measurement (GPM) Mission consists of a core satellite, containing an advanced microwave imager (GMI) and an advanced dual-polarization radar (DPR), and a "constellation" of polar orbiting microwave radiometers (from partner agencies worldwide). GPM's goal is to produce global precipitation estimates every 3 hours or less. These data will be vital to NOAA's "Climate" and "Weather & Water" mission goals.

The GPM core satellite is scheduled for launch in 2014. At present, muck work is taking place to develop advanced retrieval algorithms, conduct "physical process" validation programs, satellite data merging schemes, etc. NOAA and NASA are engaged in various aspects of GPM, partnering early in order for NOAA to be poised to use the GPM products soon after the core satellite launch. This talk will give an overview of GPM and discuss current and future plans for use of this data at NOAA.

WiSAR is a methodology that enables to retrieve high resolution ocean surface wind vectors from satellite borne synthetic aperture radar (SAR) data on a fully operational basis. The algorithm is suited for ocean SAR data, which were acquired at C-band of either vertical (VV) or horizontal (HH) polarization in transmission and reception.

In this presentation WiSAR is introduced in its manual and fully automated mode and validated by comparison to results of numerical models. Furthermore the possibility of estimating hurricane winds using SAR data is demonstrated utilizing C-band SAR data acquired at both VV and HH-polarization. It will be shown that WiSAR enables measure wind directions as well as wind speeds at wind speeds greater than 50 ms-1. Furthermore, the limitations of Cmod5 will be discussed, in particular with respect to hurricane conditions.

Clear air turbulence, also known as CAT, can cause damage to an aircraft's structure and in severe cases, harm passengers. Though CAT has been a thoroughly studied topic since the mid 1960s, a product hasn't been created to forecast CAT accurately. The product tested is known as the Deformation-Vertical Shear Index (DVSI), created by Knox, Ellrod and Williams. The DVSI product is currently used by several commercial carriers and military aircraft. The users of this product were satisfied with its performance; however, results indicate that the product tends to overestimate CAT intensity.

SERVIR is a regional visualization and monitoring system for Mesoamerica that integrates satellite and other geospatial data for improved scientific knowledge and decision making by managers, researchers, students, and the general public. SERVIR addresses GEOSS societal benefit areas, and for example, is being used to monitor and forecast ecological changes and severe events such as forest fires, red tides, and air quality. SERVIR headquarters are located at the Water Center for the Humid Tropics of Latin America and the Caribbean (CATHALAC) in the Republic of Panama, with national nodes in each Central American country. A test bed and rapid prototyping SERVIR facility is managed by the NASA Marshall Space Flight Center at the National Space Science and Technology Center (NSSTC) in Huntsville, Alabama.

As we head into the NPOESS era, the community should build as much as possible on the lessons learned from past experience. This talk will present work on validation of global land products and the analysis of time series data; with an emphasis on how these activities can help the community make the most of future data from the NPOESS era. Jeff has been coordinating the MODIS land discipline team validation activities since 1998. In 2000 he helped initiate the Committee on Earth Observing Satellite (CEOS) Land Product Validation subgroup. The talk will present the MODIS land team's validation framework and CEOS intercomparison strategy and discuss how these could evolve to help address NPOESS validation needs. Jeff is also conducted research on the use of time-series data from MODIS within NASA's Applied Sciences program. The talk will demonstrate the importance of phenological data in habitat modeling and show how temporally and spatially gap-filled data are being utilized by carbon modelers. Since NPOESS will extend the time series of AVHRR and MODIS into the next decade, it is important to consider how best to process and utilize this extended time series. The experience with MODIS data can provide some insight.

Globally distributed and uniformly detailed ocean bathymetry is basic infrastructure for safe navigation, maritime law, resource management and research in ecology, geology, oceanography and climate. Bathymetric survey lines cover the remote ocean basins about as sparsely as the Interstate Highway System covers the United States. Therefore the most complete global bathymetric models combine conventional acoustic soundings with more uniformly distributed satellite remote sensing data. Satellites cannot "see" the bottom of the deep sea but densely spaced satellite altimeter profiles of sea surface slope can furnish marine gravity field information that may be correlated with seabed topography to produce "bathymetry from space". This correlation is limited to roughly the 10 km to 200 km band of wavelengths, and optimizing signal to noise in this band is a critical part of our research. In January of 2005, the USS San Francisco nuclear attack submarine struck an "uncharted" seamount that was implied in our product. A Navy study subsequently determined that our product was the best available in more than 90% of the ocean. NOAA, Navy, and the National Geospatial-intelligence Agency (NGA) are now engaged in a broad effort to improve this product, with new infusions of effort, in situ data, and computer time. A still greater leap forward will await the launch of a next-generation altimeter, which could cut noise levels by almost a factor of 5, revealing perhaps 50,000 more seamounts more than 1 km tall which are presently invisible.

Understanding mechanisms that control land surface response and feedbacks in a changing climate is crucial to evaluate the future state of climate and our global environment. This presentation will discuss such response and feedbacks in two case studies through a synthetic analysis of remote sensing data, land surface observations, and climate modeling. The first case study will be focused on the use of nearly 20 years of satellite measured greenness to detect the response of plant growth to climate change. It presents evidence for a warming-enhanced plan growth in the north since 1980s. The second case study will be focused on quantifying feedbacks of changing land surface on climate in a climate model. It develops a new hypothesis that vegetation removal and soil aridation due to drought and land use change in the Sahel can initiate a positive vegetation-soil feedback on warming of surface temperature and decreasing the diurnal temperature range. To quantify land-climate interactions, we need to better characterize the role of the land surface in climate models. So the last part of my presentation will be focused on application of remote sensing data in improving land surface modeling. It demonstrates how I used high quality remote sensing data to identify major model deficiencies and to best improve climate model land surface processes.

A number of operational tropical cyclone analysis and forecast products have been developed by the Regional and Mesoscale Meteorology Branch (RAMMB) over the past several years. These include a satellite-based wind retrieval product, hurricane intensity forecast models, a tropical cyclone formation parameter, and tropical cyclone wind probabilities. Recent improvements to these products for the 2007 hurricane and typhoon seasons will be described. On-going research to further improve these products will also be presented. In addition, RAMMB provides experimental satellite data and products to the OAR Hurricane Research Division (HRD) for support of their annual field program with the NOAA WP- 3D and Gulf Stream Jet aircraft. RAMMB interactions with HRD will also be described.

Soil moisture often limits the exchanges of water and energy between the atmosphere and land surface, controls the partitioning of rainfall among evaporation, infiltration and runoff, and impacts vegetation photosynthetic rate and soil microbiologic respiratory activities. Accurate measurement of this variable across the global land surface is thus required for global water, energy and carbon cycle sciences and many civil and military applications. Remote sensing technology has the potential to provide global information on spatial and temporal soil moisture variation; something that is not possible using sparsely distributed in situ point measurements. NASA's first global remote sensing soil moisture data product has been generated from the Advanced Microwave Scanning Radiometer (AMSR-E) onboard the Aqua satellite since June 2002. Another global soil moisture data product could be generated from the microwave observations of Naval Research Laboratory's WindSat launched in January 2003. Various assessments on the quality of the AMSR-E soil moisture data product suggest that the soil moisture retrieval algorithm need to be calibrated or improved. This talk will describe the background of soil moisture remote sensing, and the currently available soil moisture data products from satellite sensors will be demonstrated. Some preliminary research results on assimilating soil moisture data products into land surface models for improving numerical weather predictions will also be presented.

The Eumetsat Satellite Application Facility for Numerical Weather Prediction (the NWPSAF) forms part of the Eumetsat Distributed Ground Segment. The mission of the NWPSAF is to improve and support the interface between satellite data and products and European activities in global and regional NWP. The NWPSAF partnership involves the Met Office (coordinators), ECMWF, KNMI and Meteo France. An important focus of the NWPSAF is the development of software modules for use in NWP Data Assimilation (DA) systems. Deliverables to date, since the development phase of the project started in 1998, have included AAPP, RTTOV, a range of 1DVar schemes, the Quickscat Data Processor and the SSMIS preprocessor. The NWPSAF also has an active visiting scientist programme.

For the past year, the Environmental Visualization Program (EVP) has been working closely with STAR scientists to promote NOAA research by creating enhanced visualizations for media and outreach activities. These products have been distributed to television network news and documentaries stations, museums, and used by thousands of educators.

This talk will describe the current activities of the Environmental Visualization Program, the process of collaborating with scientists to generate enhanced imagery datasets, the areas of need based on current NOAA outreach priorities such as The International Polar Year and The Year of the Reef, and how STAR scientists can help promote environmental literacy, outreach, and education.

The QuikSCAT satellite was launched on June 19, 1999 into a sun-synchronous, circular, 803 km orbit with a local equator crossing time at the ascending node of 6:30am. QuikSCAT carries a conically-scanning, dual pencil beam Ku-band scatterometer that acquires global backscatter measurements at 47 degrees (H-pol) and 55 degrees (V-pol) incidence angles. These measurements yield high quality 25 km and 12.5 km spatial resolution surface wind vector retrievals over 90% of the world's oceans in a single day.

NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) in cooperation with NASA/JPL has been providing near real-time QuikSCAT ocean surface wind vector products at 25 km and 12.5 km resolutions to the operational community since shortly after launch. Significant improvements in operational weather forecasting and warnings have been realized through utilization of these near real-time products. This real-world experience has also revealed some of the limitations of QuikSCAT, which is a research mission, with respect to the operational forecasting and warning environment.

To address some of these limitations the scatterometer project at JPL implemented several changes in the QuikSCAT processing algorithm, and since May 2006 these improvements have been implemented in a parallel test mode at NOAA / NESDIS / STAR. The NRT QuikSCAT processing improvements were validated by examining 6 months of vector wind data from 2003 processed with both the old and the new algorithms. Validation was conducted by the Ocean Surface Winds Team in STAR with evaluation from the operational forecaster perspective being conducted by colleagues at the Ocean Prediction Center (OPC) and the Tropical Prediction Center (TPC). Results of these analyses are presented here, and show that the retrievals from the new processing performs better than those from the old processing, especially at the swath edges. Also, the rain impact flag, which results in less data being flagged as potentially contaminated by rain, does not result in a degradation of the overall wind vector retrieval. Project website and data links here.

AVHRR (Advanced Very High Resolution Radiometer) instruments have flown on NOAA's polar-orbiting satellites for nearly 30 years, covering the globe twice each day at a resolution of 3km x 5km. Each AVHRR has 6 "window" channels that measure both reflected solar energy and thermal infrared energy emitted from the Earth. Data from these channels is used to generate products such as sea surface temperature (SST), cloud cover, ice cover, snow cover, vegetation, etc. AVHRR data is actually a count with an integer value from 0 to 1,023, so comparison to a known energy source is needed to relate count values to radiance or temperature. This is calibration.

Because NOAA polar-orbiters have been flying for ~30 years, the time series of AVHRR products can be useful for monitoring global change. However, to detect a surface or cloud property change, we first make sure that changes among the 14 different AVHRR instruments are smaller than changes in the climate signal we are looking at. In other words, we need to inter-calibrate all 14 instruments. This talk will describe some methods we now use to calibrate and inter-calibrate the series of AVHRRs. It is broken into 5 parts:

1. AVHRR thermal channel calibration,
2. AVHRR visible channel calibration,
3. SNOs (Simultaneous Nadir Observations),
4. SNOs applied to MSU (Microwave Sounding Unit),
5. Navigation

Robust, operational methodologies for mapping daily evapotranspiration (ET), soil moisture, and moisture stress over large areas using satellite remote sensing will have widespread utility in applications such as drought detection, crop yield forecasting, irrigation scheduling, water resource management, and weather and climate forecast initialization. Using thermal- infrared imagery from the Geostationary Operational Environmental Satellites (GOES), a fully automated inverse model of Atmosphere- Land Exchange (ALEXI) has been used to model daily ET and surface moisture stress over a 10-km resolution grid covering the continental United States. Examining monthly clear-sky composites of evaporative stress index for April-October 2002-2004, the ALEXI moisture stress index shows good spatial and temporal correlation with the Palmer Drought Index. The ALEXI index has the advantage of having higher spatial resolution and better comparability across seasons and climatic zones than do the Palmer Indices. The ALEXI moisture index also compares well to anomalies in monthly precipitation fields, proving that surface moisture has an identifiable thermal signature. The surface flux modeling techniques described here have demonstrated skill in identifying areas subject to soil moisture stress based on the thermal land- surface signature, without requiring information regarding antecedent rainfall. ALEXI therefore may have potential for operational drought monitoring in countries lacking well-established precipitation measurement networks.

The National Ice Center (NIC) is a U.S. Government agency that brings together the Navy, NOAA, and the U.S. Coast Guard (USCG) to support coastal and marine sea ice operations and research. The NIC provides specialized strategic and tactical ice products to support operational needs of the U.S. government. NIC is the only national operational ice service in the world that monitors sea ice in both the Arctic, Antarctic as well as in other ice-infested waters.

The Center utilizes multiple sources of satellite and in- situ observations as well as NWP and ocean-sea ice model output to produce sea ice analyses. Parameters of interest include sea and lake ice extent, concentration, thickness along with calving icebergs and ice shelves monitoring. The NIC maintains a Science and Applied Technology Department responsible for the evaluation of new developments in remote sensing, digital image processing, and automated sea ice analysis and forecasting methods as well as the transition of mature scientific research and applications to operations. An overview of some of these developmental and transition activities will be presented.

The accuracy of analyses produced by modern data assimilation systems depends strongly on the precision of forecast error covariances specified as input. Typically, these errors are synoptically dependent, anisotropic, and and inhomogeneous. This talk will begin with a review of techniques used to date to represent flow-dependent errors in variational data assimilation systems. Current NCAR efforts in this direction are based on the WRF model, and are two-fold. Firstly, the application of 4D-Var implicitly introduces flow-dependent covariances via the use of a linearised forecast model (and its adjoint). Secondly, the use of ensemble-based forecast error covariances in 3/4D-Var via additional control variables in a hybrid approach is seen as a way to practically combine the best of both variational and ensemble approaches to data assimilation for operational NWP. Preliminary results from WRF applications for both 4D-Var and the hybrid will be presented.

Convective downbursts are known to produce potentially hazardous weather conditions. Currently, severity indices are used to estimate the strength of a potential downburst, but this information does not readily translate to the variables affected by downburst events. The effects of downbursts are often associated with aviation because of rapid changes in wind direction and speed, but can also be observed in marine conditions. Three recently observed downburst events have been selected as case studies to evaluate the effects of the downbursts on the marine environment. The information gathered on these events includes wind speed, gusts and direction at the surface, air temperature and pressure, water level, and Geostationary Operational Environmental Satellite (GOES) Wet Microburst Severity Index (WMSI) values. The events selected occurred at Tolchester Beach, Maryland, on 28 September 2006, and at North Jetty, near Galveston, TX, on 13 October 2006 and 27 October 2006. Correlation between the WMSI values, the maximum wind gust, and the change in water level is suggested.

If given a consecutive set of images of the same region of the Earth obtained from space, an untrained human will easily solve many of the remote sensing problems, such as selecting clear-skies areas, identifying clouds, distinguishing between clear and hazy conditions, and so on. What, in contrast to our brain's work, is missing in our algorithms, armed with the modern science, that solving these problems becomes difficult and fraught with uncertainties?

One answer is that the contemporary concepts of aerosol and land surface remote sensing from whiskbroom (MODIS-like) sensors are pixel-based and disregard history of previous measurements. In this case, some important invariants of the atmosphere-surface system are overlooked. One example is a spatial structure (or texture) of the land surface at the landscape level, which generally changes at a very slow rate as compared to the frequency of spaceborne observations.

In this seminar, I will describe a new MODIS land algorithm, which uses multi-temporal observations and an image-based rather than pixel-based processing. The new algorithm retrieves aerosol optical thickness simultaneously with surface bidirectional reflectance and albedo. It is generic and works over the dark vegetated and bright non-vegetated world regions with the current exception of snow-covered areas. The algorithm has internal water vapor retrievals and a new cloud mask algorithm. An initial validation shows an excellent agreement of our retrievals with AERONET water vapor and aerosol measurements.

Total precipitable water (TPW), the amount of water vapor in a column from the surface of the earth to space, is used by forecasters to predict heavy precipitation. At CIRA a method was developed for blending TPW values retrieved from two satellite sources: the Advanced Microwave Sounding Unit (AMSU) instruments on three NOAA satellites and the Special Sensor Microwave/ Imager (SSM/I) instruments on three Defense Meteorological Satellite Program (DMSP) satellites. The program runs hourly, and the data are made available to forecasters in real time. In this seminar the history of the method will be revealed, its workings will be explained, and its future will be guessed at.

The long-term satellite observations are unique source of information about the earth's surface, ocean and atmosphere. For country as big and sparsely populated as Canada, the satellite observations are frequently the only source of information about terrestrial and atmospheric processes.

This talk describes the efforts carried out at the Canada Centre for Remote Sensing (CCRS) on developing satellite climate data records of Canada's landmass. To produce comprehensive temporal and spatial records, the multitude of sensors and spatial scales needs to be considered. The optical data from medium and coarse resolution sensors, such as AVHRR and MODIS plays a central role. They deliver time series of surface products of the longest duration and the national scale spatial coverage.

The archive of 25 years (since 1981) of observation from AVHRR / NOAA, followed by MODIS / Terra&Aqua has been generated at CCRS. To derive this data set, all available historical and current AVHRR High Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) 1-km AVHRR observations collected in Canada and the NOAA Comprehensive Large Array-data Stewardship System (CLASS) have been collected. The newly developed processing system was employed to generate baseline 1-day and 10-day clear-sky composites for a 5700 x 4800 sq. km area of North America year round. This region is centered over Canada, but also includes the northern USA, Alaska, Greenland and surrounding oceanic regions. The AVHRR 1-km archive overlaps with time series of MODIS (Terra&Aqua) since 2000, which are also assembled at the CCRS and used for intercomparison, validation purposes and generation of advanced high-level surface data products. New data processing system was designed to ensure consistent sensor calibration, high-precision image georeferencing, accurate cloud and cloud shadow identification, consistent surface product retrievals. Details of these components will be presented and discussed.

This work has been supported by the Canadian Space Agency under the Government Related Initiatives Program (GRIP) and the Earth Sciences Sector of the Department of Natural Resources Canada under the Program on "Enhancing Resilience in a Changing Climate".