Motivated and hardworking individuals, who are seeking graduate studies in HydSens Laboratory, are encouraged to contact Ardeshir Ebtehaj (e-mail:

NEW OPPORTUNITY (Fall 2020 or Spring 2021)

Snowfall is the main input to the accumulation processes that control the mass balance of snowpack, glaciers, sea ice and ice sheets. However, space-time variability of high-latitude snowfall is one the least understood components of hydrologic water and energy cycle—largely due to the lack of long-term and sufficiently dense ground-based observations. Satellite data aims to close this knowledge gap. However, research suggests that the discrepancies between satellite and reanalysis snowfall data grow markedly over high-latitude snow-covered surfaces. This uncertainty is largely because the passive microwave scattering signals of snowfall and snowpack interfere and it is not yet well understood that how theses signatures can be separated under different land and atmospheric boundary conditions.

To advance our understanding of hydrologic water and energy cycle over polar climate regimes, we invite applications for a PhD/Postdoc position at the Saint Anthony Falls Laboratory, University of Minnesota. The main goal is to reduce the uncertainty of passive microwave retrievals of snowfall and snowpack over sea and land ice. The main objectives of the project are (i) to investigate radiometric interactions of snow-covered ice and snowing atmosphere over frequencies 10-200 GHz, using coupled land-atmosphere radiative transfer models; (ii) to quantify uncertainty of radiative transfer modeling using passive/active satellite data; and (iii) to develop a new class of microwave Bayesian retrieval algorithms for simultaneous retrieval of snowfall and snow-cover physical properties.

To initially apply, please send your CV and a two-page statement of purpose (only PhD applicants) to

Current Projects:

  1. Improving Passive Microwave Retrieval of Snowfall and Snowpack on Ice-covered Surfaces, National Aeronautics and Space Administration (NASA), 2020-2023 (PI).

  2. Physically Constrained Inversion of the First-order Radiative Transfer Equations for High-resolution Retrievals of Soil Moisture and Vegetation Water Content using SMAP Data, National Aeronautics and Space Administration (NASA), 2019-2022 (PI).

  3. Reducing Uncertainties in GPM Snowfall Retrievals: Applications for Improved Prediction of Snowstorms, National Aeronautics and Space Administration (NASA), 2018-2021(PI).

  4. Robust Variational Data Assimilation under Incomplete and Inaccurate Data: Extremes, Biases, and Observability in Joint Assimilation of Satellite Precipitation and Soil Moisture, National Aeronautics and Space Administration (NASA), 2018-2021(PI).

  5. Soil Moisture Super-resolution and Regularized Data Assimilation: Algorithms and Hydro-agronomic Application in SMAP Era, National Aeronautics and Space Administration (NASA), 2016-2019 (Co-PI). 

  6. Advanced Inversion Algorithms for GPM Passive Microwave Retrievals and Multi-sensor Merging, National Aeronautics and Space Administration (NASA), 2016-2019 (Co-PI).

© 2015 by Hydrologic Sciences and Remote Sensing Laboratory

The true spirit of delight, the exaltation, the sense of being more than Man, which is the touchstone of the highest excellence, is to be found in mathematics as surely as poetry.  - Bertrand Russell