Huang's scientific career has been centered on radiation transfer/particle transport in complex medium. This interest began while he was a college student at Beijing Normal University where he took a course about Monte Carlo simulations of ion implantation. This was continued at Boston University and he developed a stochastic radiative transfer theory for three-dimensional (3D) vegetation canopies with Profs. Yuri Knyazikhin and Ranga Myneni. The resultant theory becomes the basis of the forward model of the operational MODIS Leaf Area Index and Fraction of Photosynthetically Active Radiation retrievals.
Since joining BNL, Huang has been working on remote sensing of clouds and representing small-scale cloud variability in climate models. Transport theory is still the basis of this research. Several novel techniques have been developed, including cloud tomography and multi-frequency cloud radar, to improve measurements of 3D clouds. The cloud tomography approach is conceptually similar to a medical CT scanner except that the patient here is cloud. While ill-posed inverse problems have drawn quite a bit of Huang's attention, his interest in radiative transfer has not faded: he has developed a stochastic radiative transfer theory that uses statistical characteristics of 3D clouds to accurately calculate the mean radiation fields in cloudy atmosphere. He is trying to apply similar concepts to the well-known cloud problem in large-scale models. One of his current projects is to improve the representations of cloud processes and their consistency through the development of a new statistical-physics-like framework that treats unresolved clouds as a distribution function instead of a mean cloud and cloud fraction. Huang is also involved in a cloud now-casting project in support of the operation of the largest PV solar farm in the Eastern US.
Huang, D., Zhao, C., Dunn, M., Dong, X., Mace, G. G., Jensen, M. P., Xie, S., and Liu, Y. An intercomparison of radar-based liquid cloud microphysics retrievals and implications for model evaluation studies. Atmos. Meas. Tech. 5, 1409-1424, doi:10.5194/amt-5-1409-2012 (2012).
Zhao, C., Xie, S., Klein, S. A., Protat, A., Shupe, M. D., McFarlane, S. A., Comstock, J. M., Delanoë, J., Deng, M., Dunn, M., Hogan, R. J., Huang, D., Jensen, M. P., Mace, G. G., McCoy, R., O’Connor, E. J., Turner, D. D., and Wang, Z. Toward understanding of differences in current cloud retrievals of ARM ground-based measurements. J. Geophys. Res. 117, D10206, doi:10.1029/2011JD016792 (2012).
Huang, D., Gasiewski, A., and Wiscombe, W. Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 1: Field trial results from the Wakasa Bay experiment. Atmos. Chem. Phys. 10, 6685-6697, doi:10.5194/acp-10-6685-2010 (2010).
Huang, D., Gasiewski, A., and Wiscombe, W. Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 2: Observation system simulation experiments. Atmos. Chem. Phys. 10, 6699-6709, doi:10.5194/acp-10-6699-2010 (2010).
Huang, D., Liu, Y., and Wiscombe, W. Replacing pixel representations by point-function schemes for reducing discretization error in ill-posed remote sensing problems, with examples from cloud tomography. IEEE Geosci. Remote Sensing Lettrs 1, 95-102 (2010).
Huang, D., Johnson, K., Liu, Y., and Wiscombe, W. High resolution retrieval of cloud liquid water vertical distributions using collocated Ka-band and W-band cloud radars. Geophys. Res. Lett. 36, L24807, doi:10.1029/2009GL041364 (2009).
Huang, D., Knyazikhin, Y., Wang, W., Deering, D. W., Stenberg, P., Shabanov, N., Tan, B., and Myneni, R. B. Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements. Remote Sensing of Environ. 112, 35-50 (2008).
Huang, D., Liu, Y., and Wiscombe, W. Determination of cloud liquid water distribution using 3D cloud tomography. J. Geophys. Res. 113, D13201, doi:10.1029/2007JD009133 (2008).
Huang, D., Liu, Y., and Wiscombe, W. Cloud tomography: Role of constraints and a new algorithm. J. Geophys. Res. 113, D23203, doi:10.1029/2008JD009952 (2008).