Accurate and timely remote measurements of ocean temperature and salinity are essential for numerical prediction models¹ to forecast the ocean and atmosphere dynamics. Obtaining precise global sea surface temperature (SST) estimates has been the focus of the study of many groups for the last few decades. In a classical study, Brown et al.² analyzed calibration methods of the U.S. National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), by deriving accurate SST fields from satellite infrared (IR) observations, based on vacuum test datasets. Similarly, Kumar et al.³ explicitly examined the global Pathfinder algorithm’s performance in regional conditions, by comparing satellite data to buoy data. They concluded that a variation of $\sim 5°\mathrm{C}$ existed between these two sources. Recently, a saildrone instrument was used by Vazquez-Cuervo et al.⁴ to study SST retrievals. Among the six different Group for High Resolution Sea Surface Temperature (GHRSST) Level-4 SST and Level-2 SSS products, they found good agreement with satellite-derived SST and less correlation to the SSS datasets.

In-situ measurement of physical characteristics of seawater (temperature, salinity, and density) collected with a single fire module CTD (Conductivity, Temperature, Depth) in the northern Bay of Bengal (NBoB), as a part of the Indian Ocean, are given in this article. Due to the scanty of data in the NBoB, such in-situ measurements will certainly play a crucial role in understanding regional oceanic processes. The presented data were collected onboard during two cruises covering 12 stations with high resolution (0.2 m depth) up to 50-meter contour depth from 20.5778 N, 92.3578 E to 21.287 N, 89.696 E. We present raw CTD data as ASCII (.dat) and Comma Separated Value (.csv), and processed CTD data as netCDF (.nc) format in this article. Additionally, the calculated Mixed Layer Depth that was derived from the netCDF (.nc) file was included in the tabular format. This processed in-situ data would be most useful as a baseline for further studies in coastal and offshore physicochemical properties in the NBoB.

Chlorophyll-a (Chl-a) and its correlation with different parameters are one of the major indicators to understand marine ecosystems. This study was conducted to explore the seasonal and spatial variability of Chl-a at three different stations (onshore, midshore, and offshore) across the maritime boundary of Bangladesh in the northern BoB with its response to the surface current speed and recent ENSO (El Niño/La Niña-Southern Oscillation) events by using satellite data. Moderate resolution imaging spectroradiometer (MODIS) aqua satellite level-3 data of Chl-a was used in this study. Ocean currents datasets were obtained from the Asia-Pacific Data Research Center (APDRC) live access service, LAS8.6.13 of NOAA (National Oceanic and Atmospheric Administration), whereas the SST anomalies dataset was collected from NOAA Climate Prediction Center. This study revealed that the onshore region showed the highest (1.121 mgm^-3) abundance of Chl-a, whereas the offshore region showed the lowest (0.136 mgm^-3). The offshore and midshore regions showed a homogenous distribution of Chl-a, whereas the observed trend of seasonal fluctuation was southwest monsoon > postmonsoon > northeast monsoon > premonsoon. There is a seasonal variation in the relationship between Chl-a and surface current speed, with moderate correlations during northeast (Dec-Feb) and premonsoon (Mar-May). The effect of ENSO on Chl-a was observed as insignificant ( ) in the northern BoB. However, Chl-a variability in response to ENSO events across the northern region of BoB requires more investigation.