These scripts will take large historical time series stacks of daily climate variables for the US and aggregate to monthly. Steps are unpacking NetCDf, aggregate, mask/clip, and save as GeoTiff Edit Add topics
We should have a data dictionary that corresponds to the variable name in the raster filename associated with its long name. This is a start:
Gridded WUI (1990, 2000, 2010): ??????
Number of residential housing structures from the Zillow database (yearly 1980-2015): ??????
Gridded census data (1990, 2000, some have 2010). Variables includes population, # with bachelors degree, # with high school degree, # low income, # below the 50% and 200% below poverty line: ??????
Gridded Latitude, Longitude: latitude, lonitude (note spelling error for coding)
Land mask: land-mask
Mean monthly climate (AET, DEF, Fosberg Fire Weather Index, Fuel Moisture 100hr, PDSI, Precipitation, Max temp, VPD, Wind speed)
AET: aet-95th, aet-mean
DEF: def-95th, def-mean
Fosberg Fire Weather Index: ffwi-95th, ffwi-mean
Fuel Moisture 100hr: fm100-95th, fm100-mean
PDSI: pdsi-95th, pdsi-mean
Precipitation: pr-95th, pr-mean
Max temp: ??????
VPD: vpd-95th, vpd-mean
Wind speed: ??????
Number of days above the 95th percentile (Fuel Moisture 100hr, Precipitation, Max temp, Wind speed): ??????
Terrain (elevation, aspect, roughness, ruggedness, slope, folded aspect): (can we use one dataset for all of these instead of repeating for every year month? would require re-thought on year-month-tile configuration)
elevation: elevation
aspect: aspect
roughness: roughness
ruggedness: ruggedness
slope: slope
folded aspect: folded-aspect
Distance from power lines, railroads, roads (primary, secondary, tertiary): ??????
Density of power lines, railroads, roads (primary, secondary, tertiary): ??????
We should have a data dictionary that corresponds to the variable name in the raster filename associated with its long name. This is a start:
Gridded WUI (1990, 2000, 2010): ??????
Number of residential housing structures from the Zillow database (yearly 1980-2015): ??????
Gridded census data (1990, 2000, some have 2010). Variables includes population, # with bachelors degree, # with high school degree, # low income, # below the 50% and 200% below poverty line: ??????
Modeled landcover (yearly 1992-2100). Historical baseline (1992-2005), A1, A1B, B1, B2 scenarios (2006-2100): ??????
Gridded ecoregion (L1-3): ecoregions-l1, ecoregions-l2, ecoregions-l3
Gridded States: states
Gridded Latitude, Longitude: latitude, lonitude (note spelling error for coding)
Land mask: land-mask
Mean monthly climate (AET, DEF, Fosberg Fire Weather Index, Fuel Moisture 100hr, PDSI, Precipitation, Max temp, VPD, Wind speed)
Number of days above the 95th percentile (Fuel Moisture 100hr, Precipitation, Max temp, Wind speed): ??????
Terrain (elevation, aspect, roughness, ruggedness, slope, folded aspect): (can we use one dataset for all of these instead of repeating for every year month? would require re-thought on year-month-tile configuration)
Distance from power lines, railroads, roads (primary, secondary, tertiary): ??????
Density of power lines, railroads, roads (primary, secondary, tertiary): ??????