Erik Crosman and John Horel Department of Atmospheric Sciences, University of Utah, Salt Lake City, UT; erik.crosman@utah.edu Lake SST Algorithms • Generally use a single satellite platform • Historically used split-window algorithms --Tuned to buoy if data available • Recent more sophisticated algorithms using radiative transfer models using ECMWF or NCEP reanalyses to estimate atmospheric profiles over various lakes to estimate the atmospheric variability observed in different lake locations (Hulley et al. 2011; McCallum and Merchant 2012) Motivation for Study •Lake surface temperature critical for ecology, climate change, and numerical weather prediction •The quality and availability of satellite- derived lake SST for cloudy seasons and smaller lakes in many regions is poor •No consensus on appropriate algorithms •Cloud contamination and sampling errors not well-documented •What is the path forward for obtaining improved lake SST? Satellite-derived Lake Surface Temperature: Review of Current State and Future Needs Key Sources of Error •Cloud contamination (insufficent cloud mask) •Sampling and representativeness, processing, limited QC flags •Geolocation and land contamination •Retrieval algorithm •Air-water interactions and diurnal effects . We gratefully acknowledge discussions with all members of the the GHRSST Near Shore Water Working Group (NSWWG), and helpful discussions with Jorge Vazquez, Ed Armstrong, Mike Chin, Simon Hook, Chris Merchant, Robert Grumbine and John D. Lenters and discussions with the Global Lake Temperature Collaboration (http://www.laketemperature.org/). Funding for this work is through NASA grant #NNH13CH09C entitled “Multi-sensor Improved Sea Surface Temperature (MISST) for IOOS.” We also are grateful to Chelle L. Gentemann and Remote Sensing Systems for the opportunity to collaborate with this work Chin, T.M., Milliff, R.F., and Large, W.G., (1998). Basin-scale, high- wavenumber sea surface wind fields from a multiresolution analysis of scatterometer data. Journal of Atmospheric and Oceanic Technology, 15: 741-763 Crosman, E., and J. Horel, 2009: MODIS-derived surface temperature of the Great Salt Lake, Remote Sensing of Environment., 113, 73-81 Fiedler, E., Martin, M. and Roberts-Jones, J. 2014. An operational analysis of lake surface water temperature. Tellus A. 66, 21247. DOI: 10.3402/tellusa.v66.21247 Grim, J.A., J.C. Knievel, and E. Crosman, 2013: Techniques for Using MODIS Data to Remotely Sense Lake Water Surface Temperatures. J. Atmos. Oceanic Technol., 30, 2434–2451 Hulley, G.C., S.J. Hook & P. Schneider, 2011, Optimized split-window coefficients for deriving surface temperatures from inland water bodies, Remote Sensing of Environment, 115, 3758-3769 Layden, A., Merchant, C., and MacCallum, S. 2015. Global climatology of surface water temperatures of large lakes by remote sensing. International Journal of Climatology, 35 (15). pp. 4464-4479. ISSN 0899-8418 MacCallum, S.N., and C.J. Merchant, 2012.:Surface Water Temperature Observations of Large Lakes by Optimal Estimation. MacCallum. Can J Remote Sensing, 38(1), 25 – 45 O’Reilly, C. M., et al. , 2015: Rapid and highly variable warming of lake surface waters around the globe, Geophys. Res. Lett., 42, 10,773– 10,781, doi:10.1002/ 2015GL066235 Riffler et al., 2015: Lake surface water temperatures of European alpine lakes (1989–2013) based on the advanced very high resolution radiometer (AVHRR) 1 km data set Earth System Science Datas, 7 (2015), pp. 1–17 Sharma, S., et al., 2015: A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009, Sci. Data, 2 Liu, G. et al. ,2015: Validating and Mapping Surface Water Temperatures in Lake Taihu: Results From MODIS Land Surface Temperature Products." Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 8.3: 1230-1244 Cloud Contamination and Temporal Averaging Errors • Crosman and Horel (2009) required visual inspection of all