Upcoming STAR Seminars

A suite of products has been developed and evaluated to assess hazards presented by convective downbursts to aircraft in flight derived from the current generation of Geostationary Operational Environmental Satellite (GOES) (11-P). The existing suite of GOES microburst products employs the GOES sounder to calculate risk based on conceptual models of favorable environmental profiles for convective downburst generation. Recent testing and validation have found that the GOES microburst products are effective in the assessment and short-term forecasting of downburst potential and associated wind gust magnitude. Two products, the Microburst Windspeed Potential Index (MWPI) and a multispectral GOES imager product, have demonstrated capability in downburst potential assessment. Both the GOES sounder MWPI and imager microburst risk products are predictive linear models that consist of a set of predictor variables that generates output of expected microburst risk. This presentation compares and contrasts the sounder and imager microburst products and outlines the advantages of each product in the nowcasting process. An updated assessment of the sounder MWPI and imager microburst products, case studies demonstrating effective operational use of the microburst products, and validation results is presented.

Climate monitoring and research require development of thematic long-term satellite data products as well as comprehensive reanalysis data products. However, calibration and data consistency have been a major issue in producing reliable satellite and reanalysis climate products. Many long-term satellite climate products suffer from spurious climate jumps induced by satellite transition and calibration-related instrument changes. To reconcile the problem, intercalibration and reprocessing are required for intersatellite biases removal before satellite data are used for climate analysis and reanalysis data assimilation. For these purposes, NOAA/NESDIS is recalibrating MSU/AMSU/SSU observations from NOAA, NASA, and MetOp orbiting satellite series. Climate quality atmospheric temperature climate data records (CDRs) are generated from these recalibration/reprocessing effort.

This talk will review the current status on the reprocessing of 30-year MSU/AMSU/SSU data using simultaneous nadir overpass methods. We introduce a well- intercalibrated MSU/AMSU atmospheric temperature CDR for climate change monitoring. We discuss and present our proposed solutions for bias correction issues in the MSU/AMSU/SSU CDR development that includes elimination of the warm target contamination, limb adjustment, diurnal drift adjustment, short overlap problem, channel frequency differences, residual bias removal, and CO₂ leaking problem in the SSU cell pressure, etc. Updated 30-year atmospheric temperature trends derived from the MSU/AMSU CDRs will be presented. We also propose some methods/principles for testing reliability of the MSU/AMSU-derived climate trends. Finally, a science team on the MSU/AMSU/SSU CDRs is established under NOAA Scientific Data Stewardship Program and we discuss plans for the team work.

Accurate representation of the regional characteristics of anisotropic light scattering by land surfaces under a wide range of sky conditions is required (1) for modeling atmospheric shortwave radiative fluxes; (2) for modeling the energy exchange between the earth and atmosphere; and (3) for determining the lower boundary conditions for atmospheric radiative transfer models. However, uncertainties arise when satellite retrievals of surface bidirectional reflectance distribution function (BRDF) are directly compared against in-situ observations. In particular, the spatial variability of ground-based estimates of the BRDF introduces errors within the footprint of satellite sensor retrievals that are very difficult to quantify and are oftentimes ignored. Empirical quality of BRDF data is rarely certain and knowledge of their uncertainties is essential to understand its effect on higher-level surface biophysical products (e.g. vegetation indexes, surface albedo, LAI/FPAR, burned area, land cover, and land cover change). This would enable robust accuracy assessments that include evaluations of measurement, scaling, and analytical (or model-driven) errors. Linking airborne angular reflectance measurements for a given surface location yields the underlying reflectance anisotropy (or BRDF shape) of that location.

This talk will outline an algorithm suitable for such a task using airborne angular reflectance measurements available from NASA's Cloud Absorption Radiometer (CAR); a 14-channel airborne scanning radiometer with a spectral range from 0.331– 2.345µm. This information was used to quantify the differences in the directional reflectance data, and related measures of vegetation structure, at multiple spatial scales. A new set of gridding functions were also created to exploit the geometric efficiency of CAR observations. The routines allocate the airborne angular reflectance measurements acquired by the CAR into the most frequently sampled spatial intervals obtained for a given flight path. Under well-planned flight scenarios, this technique can be used to derive a combination of one-of-a-kind maps of the underlying reflectance anisotropy that are optimized to a specific spatial scale. This enables "datamatchups" with ancillary data sources (e.g., land use/cover maps), thereby improving the utility of the CAR retrievals in regional mapping and characterization of terrestrial ecosystems.