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Abstract Irrigation representation in land surface models has been advanced over the past decade, but the soil moisture (SM) data from SMAP satellite have not yet been utilized in large‐scale irrigation modeling. Here we investigate the potential of improving irrigation representation in the Community Land Model version‐4.5 (CLM4.5) by assimilating SMAP data. Simulations are conducted over the heavily irrigated central U.S. region. We find that constraining the target SM in CLM4.5 using SMAP data assimilation with 1‐D Kalman filter reduces the root‐mean‐square error of simulated irrigation water requirement by 50% on average (for Nebraska, Kansas, and Texas) and significantly improves irrigation simulations by reducing the bias in irrigation water requirement by up to 60%. An a priori bias correction of SMAP data further improves these results in some regions but incrementally. Data assimilation also enhances SM simulations in CLM4.5. These results could provide a basis for improved modeling of irrigation and land‐atmosphere interactions.
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This content will become publicly available on August 28, 2026
Impact of Assimilating GPS Precipitable Water Vapor on Simulations of Two North American Monsoon Convective Events Using Observing System Simulation Experiments
Abstract This study evaluates the impact of assimilating precipitable water vapor (PWV) within an observing system simulation experiment (OSSE) framework to improve forecasts of monsoonal mesoscale convective systems (MCSs) in Arizona. Two contrasting case studies differing in convective forcing, longevity, intensity, and coverage are analyzed using a 40‐member ensemble of 1.8‐km resolution Weather Research and Forecasting (WRF) convective‐permitting model (CPM) simulations including the Data Assimilation Research Testbed (DART) system. Synthetic PWV data are derived from a nature run (NR) and bias corrected using real GPS‐derived PWV observations from a campaign during the North American monsoon (NAM) season 2021. These synthetic PWV are assimilated in an inferior model simulation called the control run (CR) to avoid the identical twin problem. Horizontal GPS station spacing experiments (e.g., superobbed, 50 km, 100 km, and 200 km) are conducted to identify configurations that maximize forecast skills. Assimilating the synthetic PWV reduces mean errors (∼2 mm) and dry bias during the first 4–6 hr of the predictions using analyses improved with PWV data assimilation. The 100‐km GPS network optimally captures convective precipitation patterns, outperforming coarser (200‐km) and finer (50‐km) grids due to an improved representation of moisture and winds afforded by PWV data assimilation at the appropriate scales. Topography strongly influences moisture distribution, with elevation‐dependent biases, overestimation in low elevations (0–500 m), underestimation in midelevations (500–2,000 m), and systematic high‐elevation (>2,000 m) biases due to vertically integrated PWV constraints. This study provides actionable insights for optimizing GPS network design and improving convective‐scale modeling in arid/semiarid regions.
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- Award ID(s):
- 2308409
- PAR ID:
- 10658111
- Publisher / Repository:
- JGR Atmospheres
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 130
- Issue:
- 16
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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