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Synthetic NSSL WRF-ARW Imagery - 10.35 µm

Figure 1. Example of a synthetic 10.35 µm image from 20 June 2010 at 23 UTC. The image is based on a 23-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 10.35 µm band is a window IR band, similar to the GOES 10.7 µm band, except a little "cleaner," meaning less water vapor absorption. This product has two primary purposes: 1) Synthetic imagery from cloud model output can be used to evaluate each model run. For example, one might compare a simulated IR band to observed GOES imagery from 12-18 UTC to see how well the model is handling low clouds, which can have a big impact on convection later in the date. 2) Since the simulated bands are based on the GOES-R ABI, looking at the imagery will prepare forecasters for how the actual GOES-R imagery will look when it becomes operational. For example, certain features may be visible at these wavelengths which are not viewable in the current GOES bands.

- 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 imagery loops are available in real-time starting around 10 UTC every day. In the example below, the 10.35 µm band is compared to the observed GOES-13 10.7 µm band. The best use of the data is to compare the morning imagery with that of the observed to see whether the model has a good handle on things like high and low cloud timing and location. If it does, then it should increase your confidence that the remaining model forecast times are accurate. In this case, the model appears to have a good handle on the low clouds in Kansas and the clearing near the Oklahoma border. Later in the day (not shown), storms formed near the clearing region and were well forecast by the model.

Figure 2. Synthetic 10.35 µm imagery from 12 May 2010 at between 12 and 18 UTC, and the observed imagery from GOES-13 at 10.7 µm. Click for a larger view.

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.