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Spatial Interpolation of Satellite- derived Temperature and Salinity in the Chesapeake Bay: an Ecological Forecasting Application Erin Urquhart 1, Rebecca Murphy 1,Matt Hoffman 2, Ben Zaitchik 1, 1 Johns Hopkins University, 2 Rochester Institute of Technology
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Major Inputs Atlantic Ocean Susquehanna River Salinity gradient (0-30psu) Sea surface temperature (-0.5 C to 31 C) 2-Layer gravitational circulation scheme Chesapeake Bay Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Percent Satellite Coverage by Month Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing Percent Satellite Coverage by Month & Station
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Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Geostatistical Spatial Interpolation: Kriging What is Kriging? Determine data dependency Semivariogram – Spatial autocorrelation – Model fitting Predictions Ordinary Kriging – Y(s) = β 0 + ε(s) – Constant mean is unknown Universal Kriging – Y(s) = β 0 +β 1 X 1 (s) + … + β p X p (s) + ε(s) – Trend in the data Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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ChesROMS Numerics are obtained from (ROMS) 100x150x20 curvilinear grid 1-5km spatial; 6hr temporal resolution Model forcing 9 tidal constituents (ADCIRC) Non-tidal water levels (NOAA National Ocean Service Program) River discharges/forcing (USGS) Air-surface boundary (NARR) 3-hr winds, net shortwave/longwave radiation Temperature Relative humidity Pressure 2003 & 2007 Xu et al. (2002) Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Comparison of Interpolated and ChesROMS Parameter Output ParameterMethodMEMAERMSE Salinity UK ROMS UK ROMS Temperature 0.61 -0.52 0.28 -0.76 0.94 0.99 0.98 R2R2 (psu) ( o C) 1.89 1.46 0.63 -1.14 2.47 1.86 0.84 -1.39 0.86 Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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SO WHAT? Methods had similar SSS errors during data heavy months Interpolated SST had a smaller average error than ChesROMS Interpolated observations act as a second-source of Bay-wide observations Extreme event accuracy Data assimilation potential (future work) Observations can be utilized when they are available/reliable Hoffman et al. (2012) Two techniques offer complementary information that can be applied to Vibrio spp. monitoring Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Vibrio in the Chesapeake Bay * V. vulnificus * V. parahaemolyticus Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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z(V.c)= -1.1939 + (0.1233 * Temp) – (0.1997 * Saln) – (0.0324 * (Temp * Saln) Remote Sensing of Vibrio in the Chesapeake Bay Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Remote Sensing of Vibrio in the Chesapeake Bay Urquhart et al. ASPRS 2013 Annual Conference March 26, 2013 Special Session: Maritime Applications of Remote Sensing
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Acknowledgments Johns Hopkins Applied Physics Lab, Carlos del Castillo Johns Hopkins University, Rebecca Murphy Cornell University, Dr. Bruce Monger University of Delaware, Erick Geiger University of Maryland, Bradd Haley, Elisa Taviani NASA Goddard, Molly Brown, Vanessa Escobar Funding Sources Johns Hopkins University
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