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Abstract This study investigates the influence of land surface processes on short-spell monsoonal heavy rainfall events under varying soil wetness conditions in India, using the Weather Research and Forecasting Model coupled with two land surface schemes: Noah and SLAB. To represent contrasting soil conditions, four rainfall events are chosen, two in onset (June) and two in active (August) months, during the Indian summer monsoon season. The results indicate that rainfall sensitivity differs notably between onset and active cases. Specifically, in onset, the SLAB overpredicts rainfall to the north of the storm compared to the Noah. The northward displacement of rainfall is attributed to the sensitivity of evapotranspiration to the preferential soil moisture regime in onset. Furthermore, the higher surface air saturation deficit in onset favors plant transpiration, resulting in increased boundary layer moisture. This contributes to enhanced moist static energy, thereby affecting potential vorticity and precipitation. In contrast, evapotranspiration sensitivity is modest during active months, under wet soil and lower surface air saturation deficit conditions. The study reveals the distinct soil moisture feedback mechanisms during the onset and active phases, through variations in evapotranspiration sensitivity. Variations in soil moisture and surface air saturation deficit in these phases play a crucial role in modulating evapotranspiration, which in turn affects precipitation. These findings underscore the importance of land surface initialization and land data assimilation in land–atmosphere interaction studies.
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Improved Simulation of Monsoon Depressions and Heavy Rains From Direct and Indirect Initialization of Soil Moisture Over India
Abstract This study investigates the impact of direct versus indirect initialization of soil moisture (SM) and soil temperature (ST) on monsoon depressions (MDs) and heavy rainfall simulations over India. SM/ST products obtained from high‐resolution, land data assimilation system (LDAS) are used in the direct initialization of land surface conditions in the ARW modeling system. In the indirect method, the initial SM is sequentially adjusted through the flux‐adjusting surface data assimilation system (FASDAS). These two approaches are compared with a control experiment (CNTL) involving climatological SM/ST conditions for eight MDs at 4‐km horizontal resolution. The surface fields simulated by the LDAS run showed the highest agreement, followed by FASDAS for relatively dry June cases, but the error is high (~15–30%) for the relatively wet August cases. The moisture budget indicates that moisture convergence and local influence contributed more to rainfall. The surface‐rainfall feedback analysis reveals that surface conditions and evaporation have a dominant impact on the rainfall simulation and these couplings are notable in LDAS runs. The contiguous rain area (CRA) method indicates better performance of LDAS for very heavy rainfall distribution, and the location (ETS > 0.2), compared to FASDAS and CNTL. The pattern error contributes the maximum to the total rainfall error, and the displacement error is more in August cases' rainfall than that in June cases. Overall analyses indicated that the role of land conditions is significantly high in the drier month (June) than a wet month (August), and direct initialization of SM/ST fields yielded improved MD and heavy rain simulations.
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- PAR ID:
- 10448428
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 125
- Issue:
- 14
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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