Coupled Water-Cycle Retrieval: Linking Precipitation and Soil Moisture Across NASA Microwave Missions
All applications must be submitted in Zintellect
Please visit the NASA Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org)
A complete application to the NASA Postdoctoral Program includes:
- Research proposal
- Three letters of recommendation
- Official doctoral transcript documents
About the NASA Postdoctoral Program
The NASA Postdoctoral Program (NPP) offers unique research opportunities to highly-talented scientists to engage in ongoing NASA research projects at a NASA Center, NASA Headquarters, or at a NASA-affiliated research institute. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology.
Description:
Soil moisture and precipitation are two views of the same land–atmosphere exchange: rainfall drives soil moisture dynamics, and the land surface retains a measurable memory of precipitation. This research opportunity invites a participant to investigate that coupling in both directions in support of NASA's microwave remote sensing missions.
In the forward direction, physically based inversion of soil-water-balance processes turns satellite soil moisture into an independent constraint on precipitation — a "natural rain gauge" complementary to atmospheric retrievals such as GPM, particularly over regions where ground radar and gauge networks are sparse. In the reverse direction, high-resolution precipitation fields (e.g., MRMS) provide spatially distributed validation for fine-scale soil moisture products where consistent in situ networks do not exist — a critical need as NISAR-era products reach resolutions that conventional validation cannot address.
The participant may research and analyze one or more of the following topics: physically based retrieval of precipitation from spaceborne soil moisture observations (SMAP and downscaled active–passive products); characterization of soil-system memory and its spatial variability; use of precipitation coherence as a validation and uncertainty-quantification pathway for high-resolution soil moisture from NISAR and future L-band missions (e.g., ROSE-L); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time variability of soil-water-balance parameters — while retrievals remain anchored in governing physics.
The participant will collect and analyze data from multiple NASA missions, collaborate with scientists engaged in SMAP algorithm heritage and NISAR-era high-resolution soil moisture development at JPL, and participate in multi-mission integration research spanning SMAP, NISAR, and ROSE-L. This opportunity offers hands-on experience with operational satellite retrieval algorithms, physics-based forward modeling, and mission-scale validation practice, complementing the participant's background and advancing knowledge directly relevant to NASA's Earth science objectives.
Relevant missions and datasets: SMAP, NISAR, GPM, MRMS, CYGNSS, ROSE-L.
Field of Science: Earth Science
Advisors:
Xiaolan Xu
xiaolan.xu@jpl.nasa.gov
(626) 704-0102
Applications with citizens from Designated Countries will not be accepted at this time, unless they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control.
Eligibility is currently open to:
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U.S. Citizens;
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U.S. Lawful Permanent Residents (LPR);
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Foreign Nationals eligible for an Exchange Visitor J-1 visa status; and,
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Applicants for LPR, asylees, or refugees in the U.S. at the time of application with 1) a valid EAD card and 2) I-485 or I-589 forms in pending status
Questions about this opportunity? Please email npp@orau.org
Candidates should hold a Ph.D. (or complete one before the start date) in hydrology, civil or environmental engineering, remote sensing, geophysics, electrical engineering, atmospheric science, or a related field. Training that bridges land-surface hydrology and microwave remote sensing is the strongest preparation.
Favorable skills and experience include: spaceborne microwave observations of the land surface (e.g., SMAP, GPM, CYGNSS, NISAR) and their retrieval products; soil-water-balance physics and land-surface hydrologic processes; inverse methods for retrieving geophysical variables from satellite measurements; uncertainty quantification and validation of satellite products; machine learning applied within physically constrained frameworks rather than as black-box prediction; scientific computing (e.g., Python); and peer-reviewed publications.
Experience in all areas is not expected; a strong foundation in the underlying physics matters most.
- Degree: Doctoral Degree.
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