
SPC forecasters routinely use a product originally developed by the NWS Salt Lake City and the Eastern Great Basin Predictive Service Office called the Predictive Service Area Dryness (psadryness) product (see above) to help make their day 1-8 fire weather outlooks. This product provides the forecasters with an idea of the dryness of burnable fuels near the surface. In addition, the forecasters use a high-resolution 'land-use' product that attempts to simulate the NDVI product, but is rarely, if ever, updated. One of the goals we wanted to accomplish from this experiment is to see how the higher resolution datasets of observed NDVI and NDVI change (below), as well as the GOES surface dryness and dryness anomaly products (also below), compare to products currently in operations, such as the psadryness product (above).


During our first day, 5 SPC fire weather forecasters participated and examined these products to make an experimental "update" for their day-1 fire weather outlook, or out to 12 UTC the next day. In particular, forecasters were asked to make a forecast graphic depicting the areas of high threat for burnable fuels. When comparing the psadryness product and the satellite-based products, we noticed that there was a noticeable discrepancy over some areas, specifically over central ID (see above). While the psadryness product said that the area was extremely dry, the NDVI and NDVI change depicted areas of increasing 'greenness'. In addition, the GOES surface dryness and dryness anomaly products indicated no significant drying over the area.


So what gives? Well we explained to the forecasters that the satellite-based products are limited to sensing the canopy of the location they are observing. this means that if there is any forest in the area, we cannot see the undergrowth, which could be dry and only measurable from surface instruments or observers. Unfortunately, this is a limitation we have to deal with, particularly from geostationary satellite-based instruments. However, the forecasters were impressed by the spatial resolution and relative rapid updates of the products, which is not provided to them from the psadryness or land-use products. It may be useful to combine these datasets to get a more detailed picture of what may actually be going on at the surface when it comes to burnable fuels.
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