Resumen

XIANG, Tiantian; VIVONI, Enrique R.  y  GOCHIS, David J.. Influence of initial soil moisture and vegetation conditions on monsoon precipitation events in northwest México. Atmósfera [online]. 2018, vol.31, n.1, pp.25-45. ISSN 0187-6236.  https://doi.org/10.20937/atm.2018.31.01.03.

Land surface conditions including soil moisture and vegetation states are expected to play important roles in the development of the daytime boundary layer and the formation of convective precipitation. For areas with an in-phase seasonality of radiation and precipitation, such as the North American Monsoon (NAM) region, diagnosing the direct contributions of each effect is difficult given the co-occurrence of high soil moisture and vegetation greening during the warm season. In this study, we use the WRF-Hydro modeling system to simulate the interactions between the land surface and atmosphere within a large watershed in northwest México subject to the influence of the NAM. After testing the coupled simulations against a bias-corrected reanalysis product for two summer periods in 2004 and 2013, we conduct a series of storm-scale modeling experiments that separately vary the initial soil moisture and vegetation conditions. Results reveal that both soil moisture and vegetation anomalies can positively affect convective precipitation, although their influence on boundary layer development is different. We then diagnose the specific land-atmosphere mechanisms by which the land surface states positively influence convective precipitation. Under high land surface anomalies, such as initial soil moisture equal to field capacity or the maximum vegetation greening state, storm-scale (48 h) precipitation accumulations can be increased up to 26 mm. As a result, improvements in how land surface conditions are initialized either through remote sensing or sensor networks are critical for enhancing precipitation prediction systems in the NAM region.

Palabras llave : Land-atmosphere interactions; hydrometeorology; North American Monsoon (NAM); surface energy balance; Weather Research and Forecasting (WRF) model.

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