Researcher to evaluate and improve model-based sub-seasonal soil moisture forecasts

The Earth Science and Interdisciplinary Center at the University of Maryland is looking for a researcher to evaluate and improve model-based sub-seasonal soil moisture forecasts through two various research objectives.

 

Date posted

Dec. 6, 2021 2:30 pm

Application deadline

Feb. 6, 2022 5:00 pm

Organization

Earth Science and Interdisciplinary Center at the University of Maryland

Location

  • United States

Job description

The CPC produces and releases a monthly drought outlook (MDO) on the last day of the month, forecasting drought conditions for the end of the upcoming month. The MDO provides stakeholders drought forecast guidance at one month lead time, thereby facilitating their decision-making to prepare for and mitigate drought-related impacts. This project supports the CPC MDO by evaluating and improving GEFSv12-based sub-seasonal soil moisture forecasts. Specifically, it consists of the following activities.

Objective 1: Evaluate the GEFSv1.12 sub-seasonal reforecasts of U.S. drought with a focus on investigating the link to soil moisture. Identify the skill dependence on aspects including regions, seasons, and initial soil moisture anomalies and assess the skill for individual drought events during the GEFSv1.12 reforecast period. The soil moisture forecasts will be evaluated in comparison with in-situ observations and existing land analysis estimates of soil moisture produced by offline Land Surface Models (LSMs) forced with observed meteorological forcings.

Objective 2:  Identify ways to improve sub-seasonal reforecasts for soil moisture by driving the offline Noah LSM with post-processed GEFSv1.12 meteorological reforecasts. Develop and produce land surface reforecasts by forcing an offline version of the Noah LSM with post-processed GEFSv1.12 meteorological reforecasts.  Assess and quantify the forecast skill improvement for soil moisture as compared against the soil moisture forecast developed in the first objective. The post-processed GEFSv12 reforecasts will be made available from a separate CPC project.[EHB1]

Qualifications:

  • Understanding of drought prediction and predictability on subseasonal-to-seasonal (S2S) timescales, and drought-related climate processes.
  • Experience running models, preferably land surface models.
  • Experience analyzing large climate observation and climate model forecast data sets.
  • Experience handling NetCDF files, including file visualization, generation and manipulation.
  • Strong programming capabilities in Fortran, Python, GrADS, and Unix scripts.
  • Strong oral and written communication skills.
  • Ability to work both independently and as part of a team.
  • MS in Atmospheric Science, Meteorology, or related degree. PhD preferred

U.S. Citizens and Permanent Residents only