Gentemann C.L.,Helios Remote Sensing Systems, Inc.
Journal of Geophysical Research: Oceans | Year: 2014
The estimation of retrieval uncertainty and stability are essential for the accurate interpretation of data in scientific research, use in analyses, or numerical models. The primary uncertainty sources of satellite SST retrievals are due to errors in spacecraft navigation, sensor calibration, sensor noise, retrieval algorithms, and incomplete identification of corrupted retrievals. In this study, comparisons to in situ data are utilized to investigate retrieval accuracies of microwave (MW) SSTs from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and infrared (IR) SSTs from the Moderate Resolution Imaging Spectroradiometer (MODIS). The highest quality MODIS data were averaged to 25 km for comparison. The in situ SSTs are used to determine dependencies on environmental parameters, evaluate the identification of erroneous retrievals, and examine biases and standard deviations (STD) for each of the satellite SST data sets. Errors were identified in both the MW and IR SST data sets: (1) at low atmospheric water vapor a posthoc correction added to AMSR-E was incorrectly applied and (2) there is significant cloud contamination of nighttime MODIS retrievals at SST <10°C. A correction is suggested for AMSR-E SSTs that will remove the vapor dependency. For MODIS, once the cloud contaminated data were excluded, errors were reduced but not eliminated. Biases were found to be -0.05°C and -0.13°C and standard deviations to be 0.48°C and 0.58°C for AMSR-E and MODIS, respectively. Using a three-way error analysis, individual standard deviations were determined to be 0.20°C (in situ), 0.28°C (AMSR-E), and 0.38°C (MODIS). Key Points A global validation of MODIS and AMSR-E SSTs is completed AMSR-E v7 has biasing at low values of water vapor; a correction is suggested MODIS c5 has cloud contamination in night SST retrievals at surface temperatures <10°C © 2014. American Geophysical Union. All Rights Reserved. Source
Reynolds R.W.,Carolina National |
Gentemann C.L.,Helios Remote Sensing Systems, Inc. |
Corlett G.K.,University of Leicester
Journal of Climate | Year: 2010
The purpose of this paper is to investigate two satellite instruments for SST: the infrared (IR) Advanced Along Track Scanning Radiometer (AATSR) and the microwave (MW) Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). Because of its dual view, AATSR has a potential for lower biases than other IR products such as the Advanced Very High Resolution Radiometer (AVHRR), while the tropical TMI record was available for a longer period of time than the global MW instrument, the Advanced Microwave Scanning Radiometer (AMSR). The results show that the AATSR IR retrievals are good quality with biases lower than or as low as other satellite retrievals between 50°S and 50°N. Furthermore, the dual-view algorithm reduces the influence of aerosol contamination. However, the AATSR coverage is roughly half that of AVHRR. North of 50°N there appear to be biases and high variability in summer daytime retrievals, with smaller but consistent biases observed below 50°S. TMI data can significantly improve coverage offshore in regions where IR retrievals are reduced by cloud cover. However, TMI data have small-scale biases from land contamination that should be removed by modifying the land-sea mask to remove more coastal regions. © 2010 American Meteorological Society. Source
Wentz F.J.,Helios Remote Sensing Systems, Inc.
Journal of Climate | Year: 2015
The Tropical Rainfall Measuring Mission (TRMM) satellite began operating in December 1997 and was shut down on 8 April 2015. Over the oceans, the microwave (MW) sensor aboard TRMM measures sea surface temperature, wind speed, and rain rate as well as atmospheric columnar water vapor and cloud liquid water. Improved calibration methods are applied to the TRMM Microwave Imager (TMI), and a 17-yr climate record of these environmental parameters is produced so as to be consistent with the climate records from 13 other MW sensors. These TMI retrievals are validated relative to in situ observations over its 17-yr mission life. All indications point to TMI being an extremely stable sensor capable of providing satellite climate records of unprecedented length and accuracy. © 2015 American Meteorological Society. Source
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015
ABSTRACT:Helios Remote Sensing Systems, Inc. proposes to research applicable technologies related to Doppler weather radar, processing data from the radar sensor, and delivering the data over the DoD IT infrastructure. The Doppler weather radar will be designed for use by the Air Force Weather Agency and will detect 1 in/hr precipitation from 5-180 nmi, winds up to 50 kt from 5-50 nmi, have a 2-4 degree beamwidth, provide 360 deg azimuth and up to 60 degree elevation coverage, and be two man transportable. As part of Phase I, we will conduct antenna trade studies between mechanical and electronic scan, single versus dual polarization, single versus dual frequency, single versus multiple faces, and multi-functionality to include air surveillance versus single weather detection. During Phase II, we will design, develop, deliver, and demonstrate a physical and electronic brassboard-level prototype of a Doppler radar solution, defined in Phase I. Assembly, disassembly and transport will be demonstrated through production representative article physical mock-up. We will work closely with the Air Force to provide feedback on operational suitability.BENEFIT:The technology from this SBIR effort will become instrumental in the development of a variety of Department of Defense and commercial radar applications. Small, portable, light weight radars suitable for weather monitoring, combined with counter-airborne vehicle detection and track promises to be of significant interest to many agencies including the Air Force, Army, Navy, Marines, and Special Forces. In addition, with the growing use of UASs in the world, this capability promises to have many applications for local UAS operation, both commercially and for additional Federal agencies such as Department of Homeland Security and Department of Energy.
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 500.00K | Year: 2012
The objective of this effort is to develop innovative methods to improve the efficiency and accuracy of Radar Cross Section (RCS) based target classification by exploiting naturally occurring multipath signals. Our goal is to develop a mathematical basis of the multipath exploitation concepts to be incorporated into an analytical model which will be used to prove (or reject) the utility of the approach. We are developing promising approaches incorporating multipath isolation processing and compressive sensing techniques to reconstruct the target"s free-space scattering behavior. In addition, we plan to utilize electromagnetic-based multipath modeling as an integral part of our Phase II research. Ultimately, we will use experimentation, in which live data from a controlled multipath environment is collected and processed off-line to develop the RCS signature of an actual target. The techniques to be developed will provide surveillance and tracking radars, or similar radars, improved RCS based target classification by exploiting naturally occurring multipath. Our focus during this Phase II effort will be on low frequency UHF surveillance radar applications.