RAIN (Regional Analysis of Indian OceaN) is a data assimilation system developed at INCOIS, where the ROMS (Regional Ocean Modeling System) serves as the forecast model for the Indian Ocean. ROMS, an ocean general circulation model tailored for regional basins, is integrated with the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme. LETKF is an advanced and efficient variant of the traditional ensemble Kalman Filter.
RAIN consists of 80 ensemble members, which are 80 similar versions of the model that evolve over time from slightly different initial conditions. The physical parameters, such as tracer diffusion coefficients, viscosity coefficients, and mixing parameterization schemes, also vary slightly among the ensemble members. This approach leverages the benefits of diverse mixing parameterizations and helps maintain a healthy spread among the ensemble members, which is crucial for preventing filter divergence.
The ensemble members are forced every six hours by 80 ensembles of fluxes from the atmospheric model GFS, operated by the National Centre for Medium Range Weather Forecasting (NCMRWF). Identical boundary conditions, derived from INCOIS-GODAS, are provided to all the ensemble members. This system generates ocean state vectors for the Indian Ocean basin (30°N - 30°S; 30°E - 120°E) on a regular grid with a horizontal resolution of approximately 9 km, and the ocean is vertically divided into 40 levels.
The model ensemble runs for 5 days, with assimilation performed every fifth day to generate an ocean analysis. The system assimilates satellite swath data of sea surface temperature (SST) and temperature and salinity profiles from Argo floats, moored buoys, and ship tracks. An improved representation error has been developed for the observations, varying across space and time. Since the ocean model setup does not include river discharge fluxes, sea surface salinity (SSS) is relaxed to the World Ocean Atlas (WOA) monthly climatology over a 30-day relaxation timescale.
To enhance ocean state forecasting, the RAIN system has been upgraded to assimilate sea-level anomalies (SLA). The steric effect associated with thermal expansion of the water column, observed by altimeters and included in the SLA satellite observation, is neglected in ROMS. Therefore, a steric correction is applied to observations before they are provided to the RAIN system. The RAIN system is adjusted to assimilate these modified SLA observations along with in-situ temperature and salinity profiles and satellite-observed SST. A sequential assimilation strategy is employed to incorporate SLA observations and manage steric height.
RAIN provides an improved initial condition (ensemble mean of analyses) for the operational ocean forecast model ROMS and offers an enhanced analysis of the Indian Ocean basin. This analysis has been available since August 2016.
