Precipitation event detection based on air temperature over the Equatorial Indian Ocean

Air temperature (AT) and precipitation observations obtained from RAMA (Research Moored Array for African-Asian-Australian Monsoon Analysis and prediction) buoy at 0° N, 90° E from July 2009 to June 2017 are used to identify rainfall events. Based on the Random forest method, which consists of classification and regression based on decision trees, an algorithm is developed to identify the rainfall events from the change in AT data with high accuracy. During the study period, a total of 22461 abrupt drops in air-temperature events were identified by the algorithm. Around 75 % of these events were used to train and develop the clustering algorithm, and the rest of the events were used for validation with the precipitation data available from the buoy. The algorithm can identify more than 94 % of rain events accurately when the classification is binary. When the rain events are classified similar to the India Meteorological Department's classification, the algorithm is still able to identify the rain events; however, the performance degrades to ~ 84 % accuracy

INCOIS-Real time Automatic Weather Station(IRAWS) dataset - Quality control and significance of height correction

The INCOIS-Real time Automatic Weather Station(IRAWS) program was started in the year 2009 and was first installed onboard ORV Sagar Nidhi. Currently, there are 36 ships carrying IRAWS setup. Apart from storing one minute observations in the log onboard the ship, hourly averaged observations are reported through INSAT satellite communication. This report briefs about the hourly dataset of IRAWS and its quality control. In this report, QC results of SST and all meteorological parameters except radiation parameters is discussed. Specific quality check was applied to wind speed (WS) and sea surface temperature (SST) observations. The WS observations measured onboard few ships had a dimensional correction and SST was observed only on few ships. As SST observations are required to compute meteorological variables like DBT, RH, WS to standard height of 10 m, level-3 dataset of AVHRR SST was utilized in place of IRAWS SST wherever the data is found to be faulty. On similar terms bias correction could not be applied to IRAWS SST with the help of AVHRR SST as the error in SST observations are due to the failure of sensor. However all those IRAWS SST observations that passed the QC check were observed to be of high quality and have a correlation coefficient of 0.5 with AVHRR SST and is significant at 95% significant level. Apart from SST and radiation observations, all other parameters observations are found out to be of good quality with 70 to 90 QC pass percentage . Apart from the details of QC check, significance of representing climate variable at a homogeneous standard height is also shown in this repor

COASTAL OCEAN OBSERVING NETWORK – OPEN SOURCE ARCHITECTURE FOR DATA MANAGEMENT AND WEB-BASED DATA SERVICES

The observations from the oceans are the backbone for any kind of operational services, viz. potential fishing zone advisory services, ocean state forecast, storm surges, cyclones, monsoon variability, tsunami, etc. Though it is important to monitor open Ocean, it is equally important to acquire sufficient data in the coastal ocean through coastal ocean observing systems for re-analysis, analysis and forecast of coastal ocean by assimilating different ocean variables, especially sub-surface information; validation of remote sensing data, ocean and atmosphere model/analysis and to understand the processes related to air-sea interaction and ocean physics. Accurate information and forecast of the state of the coastal ocean at different time scales is vital for the wellbeing of the coastal population as well as for the socio-economic development of the country through shipping, offshore oil and energy etc. Considering the importance of ocean observations in terms of understanding our ocean environment and utilize them for operational oceanography, a large number of platforms were deployed in the Indian Ocean including coastal observatories, to acquire data on ocean variables in and around Indian Seas. The coastal observation network includes HF Radars, wave rider buoys, sea level gauges, etc. The surface meteorological and oceanographic data generated by these observing networks are being translated into ocean information services through analysis and modelling. Centralized data management system is a critical component in providing timely delivery of Ocean information and advisory services. In this paper, we describe about the development of open-source architecture for real-time data reception from the coastal observation network, processing, quality control, database generation and web-based data services that includes on-line data visualization and data downloads by various means

Precipitation event detection based on air temperature over the Equatorial Indian Ocean

117-125Air temperature (AT) and precipitation observations obtained from RAMA (Research Moored Array for African- Asian-Australian Monsoon Analysis and prediction) buoy at 0° N, 90° E from July 2009 to June 2017 are used to identify rainfall events. Based on the Random forest method, which consists of classification and regression based on decision trees, an algorithm is developed to identify the rainfall events from the change in AT data with high accuracy. During the study period, a total of 22461 abrupt drops in air-temperature events were identified by the algorithm. Around 75 % of these events were used to train and develop the clustering algorithm, and the rest of the events were used for validation with the precipitation data available from the buoy. The algorithm can identify more than 94 % of rain events accurately when the classification is binary. When the rain events are classified similar to the India Meteorological Department's classification, the algorithm is still able to identify the rain events; however, the performance degrades to ~ 84 % accuracy