In a broad sense, 'Data Assimilation' refers to a technique, whereby the realistic observational datasets are injected to a
model simulation for bringing accurate forecasts. There are several schemes available for insertion of observational
datasets in the model. In this piece of research, we present one of the simplest, yet powerful data assimilation techniques
- known as nudging through optimal interpolation in the ARPS (Advanced Regional Prediction System) model.
Through this technique, we firstly identify the assimilation window in space and time over which the observational
datasets need to be inserted and the model products require to be adjusted. Appropriate model variables are then
adjusted for the realistic observational datasets with a proper weightage being given to the observations. Incorporation
of such a subroutine in the model that takes care of the assimilation in the model provides a powerful tool for improving
the forecast parameters. Such a technique can be very useful in cases, where observational datasets are available at
regular intervals. In this article, we demonstrate the effectiveness of this technique for simulation of profiles of
Atmospheric Boundary Layer parameters for a tiny island of Kaashidhoo in the Republic of Maldives, where regular
GPS Loran Atmospheric Soundings were carried out during the Intensive Field Phase of Indian Ocean Experiment
(INDOEX, IFP-99).
A case study of sea breeze circulation at a coastal region Thumba (8.5°N, 76.9°E) was carried out using Doppler Sodar,
surface wind, temperature, humidity measurements and radiosonde ascents. The analysis of surface meteorological data
showed that the onset of sea breeze on 12th April 2006 was at 0945 hrs. GPS sonde observation over sea at 1425 hrs and
Radiosonde observation over land at 1730 showed a well developed sea breeze circulation over Thumba coast by
afternoon hours. The vertical extent of sea breeze circulation was ~1000m over sea as well as on land. The Thermal
Internal Boundary Layer (TIBL) depth associated with sea breeze circulation was about 400m at 8 km away from coast.
The marine mixed layer height was ~500m about 12 km away from the coast. Numerical simulation of sea breeze was
made using HRM (High Resolution Model) and compared the results with the observations.
In this article, we describe the variation of air-sea exchange coefficients and air-sea interface fluxes over the East Asian marginal seas surrounding the Korean peninsula and compare them with the similar estimates reported for the tropical Indian Ocean. Surface layer meteorological observations for a period of about five years obtained from five oceanic buoys in the adjoining seas of Korean peninsula form the database for this study. Depending on the stability of the atmosphere, buoy data is classified into three categories - unstable, neutral and stable data. For unstable conditions, sensible and latent heat flux show good correlation with the wind speed, whereas it is not so for the neutral and stable condition. Irrespective of the stability of the atmosphere, momentum flux always shows a steady dependence on the varying wind speed. Sensible and latent heat fluxes also show good correlation with the difference between sea surface temperature and air temperature. Unlike the linear regression between the exchange coefficients and wind speeds reported for the Indian Ocean, we suggest second order and exponential fits for these exchange coefficients, which give better representation of their wind speed dependence. The results presented in this article form very useful input to the coupled ocean atmospheric models and the oceanic wave models, hence significant.
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