Measuring coastal upwelling using IMOS Himawari-8 and Multi-Sensor SST Helen Beggs, Christopher Griffin, Gary Brassington and Pallavi Govekar Bureau of Meteorology, Melbourne, Victoria, Australia Contact: helen.beggs@bom.gov.au www.imos.org.au IMOS is a national collaborative research infrastructure, supported by Australian Government. It is led by University of Tasmania in partnership with the Australian marine and climate science community. 8. Acknowledgements We acknowledge the provision of raw AVHRR data from ground-stations operated by the Bureau of Meteorology, Australian Institute of Marine Science, Western Australian Satellite Technology Applications Consortium, Geosciences Australia and Commonwealth Scientific and Industrial Research Organisation (CSIRO).We also acknowledge the provision of NPP VIIRS SSTskin retrievals from the National Oceanic and Atmospheric Administration (NOAA) and the provision of the Himawari-8 AHI L1b data from the Japanese Meteorological Agency (JMA) and Japanese Space Agency (JAXA). 1. Introduction The Australian Bureau of Meteorology (BoM) have since 24th March 2016 produced operational, real-time, sea surface temperatures (SST) from the Himawari-8 geostationary satellite every 10 minutes at ~2 km spatial resolution (at nadir) on the native satellite ("GEO") projection. For ease of use, and to reduce spatial data gaps due to cloud, these native resolution SST data have been composited to hourly and daily night-only mean SST files, projected onto the rectangular Integrated Marine Observing System (IMOS) grid at 0.02o x 0.02o. The compositing of the Himawari-8 data on the IMOS grid presents opportunities for use alongside operational IMOS "Multi-Sensor" SST products incorporating data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Very High-Resolution Radiometer (AVHRR) satellite sensors, to identify coastal upwelling events. The aim of this study is to determine if these new IMOS satellite products can be used to verify the efficacy of the Bureau's operational version 3.1 Ocean Modelling, Analysis and Prediction System (OceanMAPS) 10 km resolution model (BoM, 2017) in resolving and predicting coastal upwelling around the Australian coast. To assist the study, we also compare with an experimental Ensemble Optimal Interpolation (EnOI) 10 km resolution SST analysis ("GSAS") of the same satellite SST data assimilated into OceanMAPS, using the same Sakov (2014) assimilation system. 4. Coastal upwelling case studies • Himawari-8 Level 2 Pre-processed (L2P) SSTskin is produced by training Himawari- 8 brightness temperatures to NOAA's ACSPO Suomi-NPP VIIRS L2P bias corrected SSTsubskin on a single day in 2015, followed by subtracting 0.17oC • Quality level (QL) is derived for each SST value based on proximity to cloud, identified using GEOCAT method (http://cimss.ssec.wisc.edu/csppgeo/geocat.html), and size of estimated error, estimated on "local brightness temperature variability” • Possible quality levels are 0 to 5, with 5 identifying highest quality • 10 minute Himawari-8 L2P SSTs are composited to hourly Level 3 Collated (L3C) SST by selecting best quality, spatially and temporally consistent value • Hourly SSTs are composited to daily, night-time L3C SST by selecting best quality value, closest in time to local sunrise • Hourly or daily L3C data on GEO projection are mapped to IMOS 0.02o x 0.02o grid using sub-pixel area-weighted averaging of any overlapping pixels (Fig 1a-b) • Night-time Himawari-8 L3C QL ≥ 4 SST from 1 Nov 2017 to 30 Apr 2019 over IMOS domain biased -0.4oC with 0.9oC standard deviation compared with collocated drifting and tropical moored buoys (BoM fv01 SST Validation Web Page) • Himawari-8 L3C netCDF files from 1st October 2017 to present available on request 5. Conclusions We compared night-time 2 km composites of Himawari-8 and Multi-Sensor (AVHRR and VIIRS) SST data at ~0.2 m depth with the BoM operational OceanMAPS v3.1 SST analyses and forecast