Assessment of Data Assimilation Approaches for the Simulation of a Monsoon Depression Over the Indian Monsoon Region |
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| Authors: | Vinodkumar A Chandrasekar K Alapaty Dev Niyogi |
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| Institution: | (1) Department of Physics and Meteorology, Indian Institute of Technology, Kharagpur, 721302, India;(2) Division of Atmospheric Sciences, National Science Foundation, Arlington, VA, USA;(3) Present address: Office of Science, Department of Energy, Office of Biological and Environmental Research, Germantown, MD 20874, USA;(4) Department of Agronomy and Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, IN, USA; |
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| Abstract: | A low pressure system that formed on 21 September 2006 over eastern India/Bay of Bengal intensified into a monsoon depression
resulting in copious rainfall over north-eastern and central parts of India. Four numerical experiments are performed to examine
the performance of assimilation schemes in simulating this monsoon depression using the Fifth Generation Mesoscale Model (MM5).
Forecasts from a base simulation (with no data assimilation), a four-dimensional data assimilation (FDDA) system, a simple
surface data assimilation (SDA) system coupled with FDDA, and a flux-adjusting SDA system (FASDAS) coupled with FDDA are compared
with each other and with observations. The model is initialized with Global Forecast System (GFS) forecast fields starting
from 19 September 2006, with assimilation being done for the first 24 hours using conventional observations, sounding and
surface data of temperature and moisture from Advanced TIROS Operational Vertical Sounder satellite and surface wind data
over the ocean from QuikSCAT. Forecasts are then made from these assimilated states. In general, results indicate that the
FASDAS forecast provides more realistic prognostic fields as compared to the other three forecasts. When compared with other
forecasts, results indicate that the FASDAS forecast yielded lower root-mean-square (r.m.s.) errors for the pressure field
and improved simulations of surface/near-surface temperature, moisture, sensible and latent heat fluxes, and potential vorticity.
Heat and moisture budget analyses to assess the simulation of convection revealed that the two forecasts with the surface
data assimilation (SDA and FASDAS) are superior to the base and FDDA forecasts. An important conclusion is that, even though
monsoon depressions are large synoptic systems, mesoscale features including rainfall are affected by surface processes. Enhanced
representation of land-surface processes provides a significant improvement in the model performance even under active monsoon
conditions where the synoptic forcings are expected to be dominant. |
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