COCONet and TLALOCNet: Providing the Intra-Americas Seas Region with Enhanced Atmospheric Observational Capacity

COCONet (Continuously Operating Caribbean Observational Network) and TLALOCNet (Trans‐boundary Land and Atmosphere Long‐term Observational and Collaborative Network) are companion infrastructure projects that provide significant observational capacity to the Intra-Americas Seas (IAS) region. More than one hundred continuously operating GNSS stations have been installed in the IAS region as part of these two projects. Stations are distributed across the Caribbean and Central America on small cays, small to large islands, and on continental landmasses. Each station has a geodetic quality GNSS instrument and a surface meteorology package. Together, they are capable of providing continuous estimates of total column integrated water vapor, also called precipitable water (PW), surface pressure, surface temperature, relative humidity, rainfall and horizontal winds. More than a third of these stations have a data record of more than three years, with some stations having a data record that is now approaching a decade. Data from these sites are useful for understanding moist atmospheric processes that range in temporal scale from sub-daily to seasonal. Previous research indicates that current atmospheric analyses and reanalyses for the Caribbean have biases in PW that are dependent on the total water vapor in the atmosphere. Analyses appear too moist in relatively low PW conditions, and too dry when there is relatively large PW. Stations with data records spanning multiple seasonal cycles can now be used to evaluate some of the annual signals that are contained within the data record. This presentation will provide an overview of the two projects, show some examples of the data products that have been generated from these sites, and highlight results related to ocean-atmospheric coupling and seasonal and diurnal moisture variability across the region. A primary objective of this presentation will be to introduce the IAS community to these relatively new observational networks.