CUNY-Snow Analysis and Field Experiment


Research Objectives:

Enhance comprehension of microwave radiation interaction with snow, focusing on absorption and scattering processes, and investigate time series analysis of microwave brightness temperature for snow-covered terrain throughout the winter season.

Develop new algorithms and validate existing ones (CRTM and HUT) for snow cover and snow water equivalent estimation, while creating techniques for precise snow depth and moisture assessment using microwave radiometry.

Examine the impact of snow moisture on microwave emissions, considering potential differences between wet and dry snow, and analyze microwave data for three distinct snowpack stages to comprehend seasonal property changes: early and mid-winter, spring (melt-freeze period), and melting period.

Simulate snowpack physical properties using snow physics models, such as SNTHERM, and investigate the viability of employing microwave radiometry for remote snow property sensing, like depth and wetness, in areas where on-site measurements are impractical.

Create innovative satellite-based snow property retrieval methods and develop advanced satellite-based snow retrieval techniques.

Offer hands-on field work training for undergraduate and graduate students, and evaluate newly developed instruments in the field of snow research.

 





   

Publications:

Sthapit, E., Lakhankar, T., Hughes, M., Khanbilvardi, R., Cifelli, R., Mahoney, K., Currier, W.R., Viterbo, F. and Rafieeinasab, A., 2022. Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine. Water, 14(14), p.2145.

Chiu, J., Paredes-Mesa, S., Lakhankar, T., Romanov, P., Krakauer, N., Khanbilvardi, R., & Ferraro, R. (2020). Intercomparison and Validation of MIRS, MSPPS, and IMS Snow Cover Products Advances in Meteorology, 2020.

Pérez-Díaz C.L., Lakhankar T., Romanov R., Muñoz J., Khanbilvardi J. & Yunyue Yu (2017) Evaluation of MODIS land surface temperature with in situ snow surface temperature from CREST-SAFE, International Journal of Remote Sensing, Vol. 38 , Iss. 16,2017.

Pérez Díaz, C.L.; Muñoz, J.; Lakhankar, T.; Khanbilvardi, R.; P. Romanov (2017). Proof of Concept: Development of Snow Liquid Water Content Profiler Using CS650 Reflectometers at Caribou, ME, USA. Sensors 2017, 17, 647.

Pérez Díaz, C.L.; Lakhankar, T.; Romanov, P.; Khanbilvardi, R.; Y. Yu (2015) Evaluation of VIIRS Land Surface Temperature Using CREST-SAFE Air, Snow Surface, and Soil Temperature Data. Geosciences, 2015, 5, 334-360.

Corona, J.A.I.; Muñoz, J.; Lakhankar, T.; Romanov, P.; R. Khanbilvardi (2015) Evaluation of the Snow Thermal Model (SNTHERM) through Continuous in situ Observations of Snow’s Physical Properties at the CREST-SAFE Field Experiment. Geosciences, 5, 310-333.

Corona, J.A.I.; Lakhankar, T.; Pradhanang, S.; R. Khanbilvardi (2014). Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation Hydrology,  1, 89-111.

Munoz J., J.A.I. Corona, T. Lakhankar, R. Khanbilvardi, P. Romanov, N. Krakauer, A. Powell (2013) Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation British Journal of Environment and Climate Change., 3(4): 612-627. DOI : 10.9734/BJECC/2013/7699.

Lakhankar, T., Muñoz, J., Romanov, P., Powell, A. M., Krakauer, N. Y., Rossow, W. B., and R. M. Khanbilvardi (2013) CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data, Hydrology Earth System Science, 17, 783-793, doi:10.5194/hess-17-783-2013.

Chen C.,T. Lakhankar, P. Romanov, S. Helfrich, A. Powell, R. Khanbilvardi (2012) Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States. Remote Sensing Vol. 4(5), pages 1134-1145.

Lakhankar T., A.E. Azar, N. Shahroudi, A. Powell, and R. Khanbilvardi (2012), Analysis of the Effects of Snowpack Properties on Satellite Microwave Brightness Temperature and Emissivity Data, Journal of Geophysics & Remote Sensing, !:101, doi: 10.4172/jgrs.1000101