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Vinodkumar, A. Chandrasekar, K. Alapaty, and Dev Niyogi

Abstract

This study investigates the impact of the Flux-Adjusting Surface Data Assimilation System (FASDAS) and the four-dimensional data assimilation (FDDA) using analysis nudging on the simulation of a monsoon depression that formed over India during the 1999 Bay of Bengal Monsoon Experiment (BOBMEX) field campaign. FASDAS allows for the indirect assimilation/adjustment of soil moisture and soil temperature together with continuous direct surface data assimilation of surface temperature and surface humidity. Two additional numerical experiments [control (CTRL) and FDDA] were conducted to assess the relative improvements to the simulation by FASDAS. To improve the initial analysis for the FDDA and the surface data assimilation (SDA) runs, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) simulation utilized the humidity and temperature profiles from the NOAA Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS), surface winds from the Quick Scatterometer (QuikSCAT), and the conventional meteorological upper-air (radiosonde/rawinsonde, pilot balloon) and surface data. The results from the three simulations are compared with each other as well as with NCEP–NCAR reanalysis, the Tropical Rainfall Measuring Mission (TRMM), and the special buoy, ship, and radiosonde observations available during BOBMEX. As compared with the CTRL, the FASDAS and the FDDA runs resulted in (i) a relatively better-developed cyclonic circulation and (ii) a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall. The FASDAS run showed a consistently improved model simulation performance in terms of reduced rms errors of surface humidity and surface temperature as compared with the CTRL and the FDDA runs.

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D. T. Mihailovic, K. Alapaty, B. Lalic, I. Arsenic, B. Rajkovic, and S. Malinovic

Abstract

A method for estimating profiles of turbulent transfer coefficients inside a vegetation canopy and their use in calculating the air temperature inside tall grass canopies in land surface schemes for environmental modeling is presented. The proposed method, based on K theory, is assessed using data measured in a maize canopy. The air temperature inside the canopy is determined diagnostically by a method based on detailed consideration of 1) calculations of turbulent fluxes, 2) the shape of the wind and turbulent transfer coefficient profiles, and 3) calculation of the aerodynamic resistances inside tall grass canopies. An expression for calculating the turbulent transfer coefficient inside sparse tall grass canopies is also suggested, including modification of the corresponding equation for the wind profile inside the canopy. The proposed calculations of K-theory parameters are tested using the Land–Air Parameterization Scheme (LAPS). Model outputs of air temperature inside the canopy for 8–17 July 2002 are compared with micrometeorological measurements inside a sunflower field at the Rimski Sancevi experimental site (Serbia). To demonstrate how changes in the specification of canopy density affect the simulation of air temperature inside tall grass canopies and, thus, alter the growth of PBL height, numerical experiments are performed with LAPS coupled with a one-dimensional PBL model over a sunflower field. To examine how the turbulent transfer coefficient inside tall grass canopies over a large domain represents the influence of the underlying surface on the air layer above, sensitivity tests are performed using a coupled system consisting of the NCEP Nonhydrostatic Mesoscale Model and LAPS.

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