Coupled Water-Cycle Retrieval: Linking Precipitation and Soil Moisture NASA Microwave Missions

Organization National Aeronautics and Space Administration (NASA)

Reference Code 0348-NPP-NOV
26-JPL-Earth Sci

Final date to receive applications 11/1/2026 6:00:59 PM Eastern Time Zone

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. 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-pasive 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.

Earth Science

Xiaolan Xu xiaolan.xua.gov

Eligibility is currently open to:

• U.S. Citizens;
• U.S. Lawful Permanent Residents (LPR);
• Foreign Nationals eligible for an Exchange Visitor J-1 visa status; and,
• 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

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://(Use the "Apply for this Job" box below)..

Please email npp

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…