Raj Shekhar Singh

Raj Shekhar Singh

San Francisco, California, United States
7K followers 500+ connections

Join to view profile

About

I'm a passionate builder and investor driven by the potential of technology to create…

Activity

Join now to see all activity

Experience

  • Z21 Ventures Graphic

    Z21 Ventures

    San Francisco Bay Area

  • -

    San Francisco Bay Area

  • -

  • -

  • -

  • -

  • -

    San Francisco Bay Area

  • -

    San Francisco Bay Area

  • -

    San Francisco Bay Area

  • -

    San Francisco Bay Area

  • -

    San Francisco Bay Area

  • -

    San Francisco Bay Area

  • -

    Berkeley

  • -

    Singapore

Education

  • University of California, Berkeley Graphic

    UC Berkeley

    -

    Activities and Societies: Graduate student researcher at Lawrence Berkeley National Labs Graduate researcher at NSA JPL Graduate researcher at UC Irvine

    My Ph.D. research was on finding new data assimilation and modeling techniques that helped improve predictive modeling of groundwater at high-resolution and high data fidelity.

  • -

    Activities and Societies: General Secretary of social and Cultural events, Head Procurement Team Spring Fest, Assistant Coordinator Banners Team Kshitij, Secretary Communiqué , DramaticsSecretary , Football Team , Hockey Team

    Completed my BTech with Honors from IIT kharagpur in Biotechnology and Biochemical Engineering. My Honors Project here was on Hydrogen production from waste water using microbial fuel cells.

  • -

    Completed the MOT diploma offered by Haas School of business together with UC Berkeley Engineering School to Graduate students interested in learning about entrepreneurship, business and Marketing

  • -

    Activities and Societies: Elected Vice-Captain , St. Xavier’s School, Bokaro

    Vice Captain of the School

Licenses & Certifications

Volunteer Experience

  • Co-Founder

    YourAdhikar

    - 3 years 6 months

    Civil Rights and Social Action

    YourAdhikar was the largest online RTI filing platform in India. It allowed its users to demand/obtain information from the government and share it with wider community.

  • Nalanda 2.0 Graphic

    Team member

    Nalanda 2.0

    - Present 8 years 3 months

    Education

    Nalanda 2.0 is a nonprofit and a nonpartisan group committed to the public interest . Epitomized by the Nalanda University of 1400+ years ago, Nalanda 2.0 wants to usher excellence into India's higher education system. Now.

Publications

  • Toward hyper‐resolution land‐surface modeling: The effects of fine‐scale topography and soil texture on CLM4. 0 simulations over the Southwestern US

    Water Resources research

    Increasing computational efficiency and the need for improved accuracy are currently driving the development of “hyper-resolution” land-surface models that can be implemented at continental scales with resolutions of 1 km or finer. Here we report research incorporating fine-scale grid resolutions into the NCAR Community Land Model (CLM v4.0) for simulations at 1, 25, and 100 km resolution using 1 km soil and topographic information. Multiyear model runs were performed over the Southwestern…

    Increasing computational efficiency and the need for improved accuracy are currently driving the development of “hyper-resolution” land-surface models that can be implemented at continental scales with resolutions of 1 km or finer. Here we report research incorporating fine-scale grid resolutions into the NCAR Community Land Model (CLM v4.0) for simulations at 1, 25, and 100 km resolution using 1 km soil and topographic information. Multiyear model runs were performed over the Southwestern U.S., including the entire state of California and the Colorado River basin. The results show changes in the total amount of CLM-modeled water storage, and changes in the spatial and temporal distributions of water in snow and soil reservoirs, as well as changes in surface fluxes and the energy balance. To inform future model progress and continued development needs and weaknesses, we compare simulation outputs to station and gridded observations of model fields. Although the higher grid-resolution model is not driven by high-resolution forcing, grid resolution changes alone yield significant improvement (reduction in error) between model outputs and observations, where the RMSE decreases by more than 35%, 36%, 34%, and 12% for soil moisture, terrestrial water storage anomaly, sensible heat, and snow water equivalent, respectively. As an additional exercise, we performed a 100 m resolution simulation over a spatial subdomain. Those results indicate that parameters such as drainage, runoff, and infiltration are significantly impacted when hillslope scales of ∼100 m or finer are considered, and we show the ways in which limitations of the current model physics, including no lateral flow between grid cells, may affect model simulation accuracy.

    Other authors
    See publication

  • Dissertation thesis: Hyper-Resolution Global Land Surface Model at Regional-to-Local Scales with observed Groundwater data assimilation

    ProQuest

    Modeling groundwater is challenging: it is not readily visible and is difficult to measure, with limited sets of observations available. Even though groundwater models can reproduce water table and head variations, considerable drift in modeled land surface states can nonetheless result from partially known geologic structure, errors in the input forcing fields, and imperfect Land Surface Model (LSM) parameterizations. These models frequently have biased results that are very different from…

    Modeling groundwater is challenging: it is not readily visible and is difficult to measure, with limited sets of observations available. Even though groundwater models can reproduce water table and head variations, considerable drift in modeled land surface states can nonetheless result from partially known geologic structure, errors in the input forcing fields, and imperfect Land Surface Model (LSM) parameterizations. These models frequently have biased results that are very different from observations. While many hydrologic groups are grappling with developing better models to resolve these issues, it is also possible to make models more robust through data assimilation of observation groundwater data. The goal of this project is to develop a methodology for high-resolution land surface model runs over large spatial region and improve hydrologic modeling through observation data assimilation, and then to apply this methodology to improve groundwater monitoring and banking.

    The high-resolution LSM modeling in this dissertation shows that model physics performs well at these resolutions and actually leads to better modeling of water/energy budget terms. The overarching goal of assimilation methodology is to resolve the critical issue of how to improve groundwater modeling in LSMs that lack sub-surface parameterizations and also run them on global scales. To achieve this, the research in this dissertation has been divided into three parts. The first goal was to run a commonly used land surface model at hyper resolution (1 km or finer) and show that this improves the modeling results without breaking the model. The second goal was to develop an observation data assimilation methodology to improve the high-resolution model. The third was to show real-world applications of this methodology.

    See publication

Honors & Awards

  • NSF I corps

    National Science Foundation

  • GSVC western region finals

    GSVC - Haas School of business

  • McCone Fellowhsip

    University of California, Berkeley

  • PK Sinha Fellowship

    Prabha Sinha (Founder, ZS Associates)

  • National Science Olympiad

    National Science Olympiad, India

  • National Education Council Scholarship

    National Education Council, India

Languages

  • Hindi

    Native or bilingual proficiency

  • Bengali

    Limited working proficiency

  • Chinese

    Elementary proficiency

  • English

    Native or bilingual proficiency

Organizations

  • National Groundwater Association

    Member

    -

  • American Geophysical Union

    Member

    -

More activity by Raj Shekhar

View Raj Shekhar’s full profile

  • See who you know in common
  • Get introduced
  • Contact Raj Shekhar directly
Join to view full profile

Other similar profiles

Explore more posts

Explore collaborative articles

We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.

Explore More

Others named Raj Shekhar Singh

124 others named Raj Shekhar Singh are on LinkedIn

See others named Raj Shekhar Singh

Add new skills with these courses

See all courses