Ocean Science Journal, Vol. 43 No. 4, 165-173(2008) http://osj.kr Nudging of Vertical Profiles of Meteorological Parameters in One-Dimensional Atmospheric Model: A Step Towards Improvements in Numerical Simulations D. Bala Subrahamanyam * , S. Indira Rani ,Radhika Ramachandran , and P. K. Kunhikrishnan Space Physics Laboratory, Vikram Sarabhai Space Centre, Thiruvananthapuram – 695 022, Kerala, India ISRO Technical Liaison Unit, Embassy of India, Paris, France Received 6 March 2008; Revised 17 September 2008; Accepted 17 October 2008 Abstract - In this article, we describe a simple yet effective method for insertion of observational datasets in a mesoscale atmospheric model used in one-dimensional configuration through Nudging. To demonstrate the effectiveness of this technique, vertical profiles of meteorological parameters obtained from GLASS Sonde launches from a tiny island of Kaashidhoo in the Republic of Maldives are injected in a mesoscale atmospheric model - Advanced Regional Prediction System (ARPS), and model simulated parameters are compared with the available observational datasets. Analysis of one-time nudging in the model simulations over Kaashidhoo show that incorporation of this technique reasonably improves the model simulations within a time domain of +6 to +12 Hrs, while its impact on +18 Hrs simulations and beyond becomes literally null. Key words - Advanced Regional Prediction System (ARPS), Kaashidhoo Climate Observatory (KCO), nudging, numerical simulation of atmosphere, marine atmospheric boundary layer 1. Introduction The field of micrometeorology, which essentially deals with various small-scale phenomena in the atmosphere, has always relied heavily on field experiments to learn more about the lowest part of atmosphere, often referred to as the Atmospheric Boundary Layer (ABL). Unfortunately, the large variety of scales involved in the atmospheric processes and the tremendous variability in the vertical require a large array of sensors including airborne platforms and remote sensors. The relatively high cost of such instruments has limited the scope of many field experiments aimed towards the characterization of lower atmosphere. Therefore, in the recent past, several scientific projects are heading towards numerical simulation of ocean-atmosphere interaction processes and characterization of the ABL over land as well the oceans. Numerical simulation of the atmospheric processes through models has its own advantages and disadvantages. Due to non-linearity of the governing equations of the atmosphere and constraints involved in obtaining observational datasets, which can be fed to the models, errors in numerical simulations are obvious. Nonetheless, in the past few decades, significant research has gone towards minimizing the errors in the model simulations, and in due course of time, several techniques have evolved for attaining higher accuracy in the model forecasts (Anthes 1983; Pielke 1984; Subrahamanyam 2003, 2005; Subrahamanyam et al. 2006). Incorporation of observational data in the model, also known as ‘Data Assimilation’ happens to be one of the most powerful and widely used tools to bring the model forecasts closer to realistic observations. In other words, 'Data Assimilation' is an analysis technique in which the observed information is accumulated into the model state by taking advantage of consistency constraints with laws of time evolution and physical properties. There are two basic approaches to data assimilation: sequential assimilation, that only considers observation made in the past until the time of analysis, which is the case of real-time assimilation systems, and non-sequential, or retrospective assimilation, where observation from the future can be used, for instance *Corresponding author. E-mail: subrahamanyam@gmail.com Article