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Ciencia e investigación agraria

versión On-line ISSN 0718-1620

Resumen

SILVA, Daniel O; MEZA, Francisco J  y  VARAS, Eduardo. Use of mesoscale model MM5 forecasts as proxies for surface meteorological and agroclimatic variables. Cienc. Inv. Agr. [online]. 2009, vol.36, n.3, pp. 369-380. ISSN 0718-1620.  http://dx.doi.org/10.4067/S0718-16202009000300004.

There is increasing interest in meteorological information and its application to strategic planning at the farm as well as regional level. Although we have recently seen significant improvements to strengthen and enlarge networks of weather observations, their density is still insufficient to cover large extents at the desired spatial and temporal resolution. Climate scientists have developed and used mesoscale models to understand and predict future atmospheric conditions. These models represent a major contribution to objective weather forecasts throughout numerical simulations. They use global circulation outputs as boundary conditions and can be run in a nested manner so as to increase their spatial resolution. Because of this, we can obtain information about weather variables in grid cells spaced 15 km apart covering important areas and providing information in places where analog or automatic stations are not available. The objective of this work is to evaluate the use of raw data from the MM5 mesoscale model as well as MOS-corrected information (a statistical post-processing of MM5 outputs) as a proxy for surface meteorological data. Temperature, wind speed, relative humidity, and daily solar radiation forecasts were evaluated for eleven stations in the Maipo river basin. In all cases, the MOS forecast produced better results than the raw MM5 data. Determination coefficients reached values near 0.9, and the RMSE was usually smaller for MOS-corrected data. The small variability of the MOS parameters allows their use as regional values to estímate meteorological data for the whole region, particularly at a weekly time step.

Palabras clave : Agroclimatological variables; Maipo river basin; MM5 data; MOS.

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