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09. 1. FY12-13 GIMPAP Project Proposal Title Page date: 7 August 2012. Title : Enhanced use of GOES for estimating land surface wetness with application to wildfire forecasting at the NOAA Storm Prediction Center Status : Progress Report Project Leads:
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1. FY12-13 GIMPAP Project Proposal Title Pagedate: 7 August 2012
Title: Enhanced use of GOES for estimating land surface wetness with application to wildfire forecasting at the NOAA Storm Prediction Center
Status: Progress Report
Robert M RabinNOAA/[email protected]
Phillip Bothwell NOAA/NWS/[email protected]
Marouane Temimi NOAA/CREST/CCNY
Jan Stepinski CREST/CCNY student
AMSR-E, SMOS, and in-situ data
fuel loads for forecasting wildfires (SPC) and model initialization
Validate GOES-based Dryness Index with temporal and spatial patterns of:
University of Amsterdam/NASA multi-sensor products of surface soil moisture, vegetation water content, and land surface temperature (Owe et al. 2008, Holmes et al., 2009)
SMOS L-Band estimates of soil moisture (European Space Agency)
In situ soil moisture: such as are available CREST network, OK meso network
Surface Bowen ratio and evapotranspiration from the GOES-based ALEXI model (previously funded GIMPAP project).
New tool to provide fire weather forecasters daily changes in surface wetness and fuel loads.
It is expected that the products will be enhanced with the availability of GOES-R data; especially with the enhanced resolution and capability of directly estimating NDVI from the ABI
Index will be more readily accepted with further validation
Shrub, Grassland: negative early in growing season
Forest, Crop: negative later in growing season
Starting comparisons with in-situ soil moisture
(Milbrook, NY NOAA/CREST station)
Heating Rate: 25 June-09 July 2011
Yellow/Red: Dry Green: Wet
Heating Rate Anomaly: 25 June-09 July 2011
Surface type: scrub
Date of Scatter plot
AMSR-E problem sensing moisture through dense vegetation?
corr = -0.38
corr = -0.29
corr = -0.10
corr = 0.12
corr = 0.48
corr = 0.37
Unexpected positive Correlation: Lack of data?
Next steps: Segregate results by NDVI
USGS land use
Already available in NAWIPS at SPC
Further Refinement as needed
Implement at NESDIS SAB
Make product available to AWIPS2
1. Acquire and process data for validation
2. Analyze and compare AMSR-E, SMOS (if available) and in situ data
1. Publish results
2. Implement and train for use at SAB and elsewhere
Travel support (NSSL): $1.5 K
$ 1.5K Travel (R. Rabin) Q2 and Q3
2 trips Norman-NESDIS/SAB (Maryland)