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Synthetic NSSL WRF-ARW Imagery - 7.34 µm

Figure 1. Example of a synthetic 7.34 µm image from 12 April 2011 at 14 UTC. The image is based on a 14-hour forecast from NSSL's 4-km WRF-ARW.

1) Product Information:

- Who is developing and distributing this product?

This product is a combined effort between the National Severe Storms Laboratory (NSSL) in Norman, Oklahoma, and The Cooperative Institute for Research in the Atmosphere (CIRA) in Fort Collins, Colorado, together with the NOAA/NESDIS RAMM Branch.

- Who is receiving this product, and how?

Daily output from NSSL's 4-km WRF-ARW is provided to CIRA, who then generate synthetic satellite imagery, which is sent to the Storm Prediction Center (SPC) via a McIDAS ADDE server. The output is also converted to AWIPS-compatible NETCDF format and provided to the National Weather Service (NWS) Central Region, where a number of NWS offices are displaying it in real-time via AWIPS.

- What is the product size?

Each image is just under 1 MB, and every day 25 images are provided.

2) Product Description:

- Purpose of this product:

The 7.34 µm band is a water vapor band whose central wavelength is larger than the current GOES-13 6.5 µm band. Its weighting function peaks lower in the troposphere, usually near 600 mb, resulting in significantly warmer clear sky brightness temperatures. This product can be used to track mid-tropospheric features in the model, such as vorticity maxima or mid-level jet streaks. It is also nice for easily locating mid-level dry air in the model.

- Why is this a GOES-R Proving Ground Product?

The synthetic imagery is a Proving Ground Product because it replicates how actual features will appear in GOES-R ABI bands.

- How is this product created now?

Every day at 00 UTC, NSSL runs their 4-km WRF-ARW. As soon as the 12-hour forecast is completed, several variables are extracted and scp'ed to CIRA. These variables include temperature, water vapor, and other physical and microphysical parameters which are needed for the next step. When all variables have been receieved at CIRA, an observational operator is run to generate the synthetic imagery for 5 GOES-R ABI bands (6.95, 7.34, 8.5, 10.35, and 12.0 µm). The simulated imagery is then converted to McIDAS AREA format and made avaiable for the SPC, who then makes the output viewable on their NAWIPS system. It is also converted to netcdf format and made available to the NWS to view in AWIPS. Hourly output between 12-12 UTC is processed daily. The resolution of the output is 4-km to match the input resolution of the cloud model; the real GOES-R ABI bands will have 2-km resolution.

3) Product Examples and Interpretation

The synthetic 7.34 µm loop is available in real-time around by around 13 UTC every day. In the example below, the 7.34 µm band is compared to the observed GOES-13 Sounder data at a similar central wavelength. Note how the simulated data is at a better resolution that the sounder data (4 km v/s 10 km; GOES-R ABI will be 2 km), but the largest advantage is that the synthetic loop was available for viewing in the morning, thus providing an excellent resource for forecasters.

Figure 2. Synthetic 7.34 µm imagery from 24 April 2010 at between 12 and 00 UTC, and the observed imagery from the GOES-13 Sounder at a similar wavelength. Click for a larger view. In this case, the model does a fairly good job with the timing of the convection forming in the southeast U.S., but appears to overdo the coverage. The brightness temperatures in the clear areas match well, but the model has a cold bias with the thin cirrus clouds in the Great Lakes region.

4) Advantages and Limitations

Advantages of the synthetic ABI imagery include: 1) Satellite imagery can be viewed before the simulated time actually occurs, so forecasters know what to expect, 2) multiple water vapor bands allow one to view different atmospheric levels since the weighting functions peak at different levels, and 3) forecasters can use this imagery to prepare themselves for what actual GOES-R ABI imagery will look like. The biggest limitation is that the forecast is only as good as the cloud model forecast; if the model does not initiate convection, for example, then the convection will also be absent from the synthetic imagery.