Output products #2

MOHID output

Dimensions: grid_y: 896, grid_x: 396, grid_z: 40
OilConcentration_2D [ppm] (time, grid_y, grid_x)
Thickness_2D [microns] (time, grid_y, grid_x)
Dissolution_3D [Kg/m3] (time, grid_z, grid_y, grid_x)
OilConcentration_3D [Kg/m3] (time, grid_z, grid_y, grid_x)
OilWaterColumnOilVol_3D [m3/gridcell] (time, grid_z, grid_y, grid_x)
Latitude [degrees_north] (grid_y, grid_x)
Longitude [degrees_east] (grid_y, grid_x)
Beaching_Time [datetime64[ns]] (grid_y, grid_x)
Beaching_Volume [m3] (grid_y, grid_x)
Oil_Arrival_Time [datetime64[ns]] (grid_y, grid_x)

Desired monte-carlo products

Beaching:

• MeanBeachTime [hours] (grid_y, grid_x): Mean, non-zero Beaching_Time across runs, indicating the average oil arrival time to beaches.
• MinBeachTime [hours] (grid_y, grid_x): Min Beaching_Time across runs where oil arrival time is greater than 10 minutes to reduce cases of shoreline spill incidents. This is an exploratory value that may not yield useful information or that may only yield useful information for specific oil types, like ANS. It's just a hunch at this point that this may prove a useful companion to the mean.
• TotalBeachVolume [m3] (grid_y, grid_x): Integrated sum of beached volume across all runs. #3
• BeachPresence_24h [no units] (grid_y, grid_x): [1] where oil is present on beaches, above our beach volume threshold, within 24 hours of spill incident; [0] elsewhere.
• BeachPresence_72h [no units] (grid_y, grid_x): [1] where oil is present on beaches, above our beach volume threshold, within 72 hours of spill incident; [0] elsewhere.
• BeachPresence_168h [no units] (grid_y, grid_x): [1] where oil is present on beaches, above our beach volume threshold, within 168 hours of spill incident; [0] elsewhere. #4

Surface Oiling:
(in development)

1. Surface analysis netcdf:

• SurfacePresence [no units] (grid_y, grid_x): [1] where maximum oil across time and runs is greater than our surface volume threshold; [0] elsewhere. #5
• SurfaceVolume_SumSum [m3](grid_y, grid_x): Total integrated oil volume in time for each grid cell within individual spill scenario. Integrated oil volume over time, within individual spill scenario and for every grid location, reflects oiling exposure at a given location over the period of spill duration. Adding across spill scenarios sets us up for calculating the mean, and the mean of this value across runs reflects the average oil exposure for the given set of spill scenarios. (I think median is more what we want here, to capture most likely, but I'm not sure how to best calculate this across run sets. One idea I have for addressing this issue is to create a histogram of the integrated volume for each run set would have SurfaceVolume_Sum with dimensions of (volume_bin, grid_y, grid_x) #6
• SurfaceVolume_SumSum_24h [m3](grid_y, grid_x): Same as SurfaceVolume_SumSum but for first 24hrs after individual spill scenario. (Consider replicating for 24-72h, and 72-168h)
• SurfaceVolume_MaxSum[m3](grid_y, grid_x): Sum across spill scenarios of max oil volume in time for each grid cell within an individual spill scenario. The maximum oil volume in time represents a metric for the greatest oil volume at each location for an individual spill scenario. Adding this value across spill scenarios puts us in the position of diving by total number of spills to represent locations where, on average, the instantaneous oil exposure is expected to be greatest. #7
• SurfaceVolume_SumHist [exposure count] (volume_bin, grid_y, grid_x): A proposed idea/concept. See comment in SurfaceVolume_SumMean.

2. Indexing netcdf for evaluating water column presence

• SurfacePresence [no units] (nruns, grid_y, grid_x): [1] where maximum oil across time and runs is greater than our surface volume threshold; [0] elsewhere. Presence map saved for each individual run for use in extracting water column data.

Water column:
(in development)

• Mean depth [m] (grid_y, grid_x)