Dr. Lipton has done a broad range of work satellite remote sensing, including applications in the infrared, microwave, and visible spectra. His research topics have included retrieval techniques for properties of the Earth surface and atmosphere, including cloud and surface characteristics. In addition, he has developed methods for assimilation of satellite sounder and imager data in mesoscale numerical weather prediction models, to improve analyses and forecasts of water vapor, clouds, and surface temperatures. He was Deputy Algorithm Lead on the algorithm development effort for NPOESS Conically-scanning Microwave Imager/Sounder. In that capacity, he provided technical direction to members of the development team, oversaw system performance assessment, sensor simulation testbed development, and calibration algorithm development, and was heavily involved in atmospheric algorithm system development. He has also been Deputy Project Manager for AER’s role in the GOES-R Ground System implementation, and is a member of the science team for the NASA Energy and Water Studies program. Dr. Lipton joined AER in 1997, and is currently manager of Microwave Remote Sensing.
Current data assimilation (DA) systems use only a small fraction of the thousands of channels from hyperspectral infrared sounding instruments, including IASI, AIRS, and CrIS. An alternative that retains nearly the full hyperspectral information content at greatly reduced computation cost, while filtering instrument noise, is to assimilate the optimal spectral sampling (OSS) node radiances. T...