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GOES-R Cloud Top Temperature

Frequently Asked Questions about the GOES-R Cloud Top Temperature Product

 

1) What is this product?

The GOES-R Cloud Top Temperature product produces cloud top temperature in degrees K (which is converted to degrees C locally in AWIPS) for all cloudy pixels.  The resolution of this product is 2km.  The GOES-R Cloud Top Temperature product is created both day and night.  It is accurate to within 3 degrees C. The product has a range of -93 degrees C to 27 degrees C. The GOES-R Cloud Top Temperature product is considered quantitative out to 65 degrees local zenith angle.  At larger zenith angles, it is considered qualitative.1

This product is useful when trying to assess the height of clouds, both convective and stratiform.  It can be used to assess the rapid cooling of convective clouds.  It can also be useful when trying to determine if cloud is present at low levels.

 

2) How often do I receive this data?

The cadence of the Cloud Top Temperature product is dependent upon which image from the satellite one is looking at. For Full Disk imagery, an image is produced every 15 minutes.  For a Mesoscale scan, an image is produced every 5 minutes.

 

3) How do I display this product in AWIPS-II?

  To display this product in AWIPS-II, go to the "GOES-R" tab of the CAVE menu, then select "Derived Products."  From there, select the region of interest (GOES-E, GOES-W, or GOES Test and Full Disk or Mesoscale).  Then, select the Cloud Top Temperature product.

Alternately, use the AWIPS Product Browser.  Select "Sat", then either "GOES-16" or "GOES-17".  From there, choose "Full Disk" or "Meso", then select "CTT."

 

4) How do I interpret the color maps associated with this product?

Dark colors imply colder clouds whereas light colors imply warmer clouds.  Turn sampling "on" to view the temperature values (in degrees C).

 

5) What other imagery/products might I use in conjunction with this product?

This product can be used in conjunction with visible 0.64um satellite imagery (if available) and also 10.33 um satellite imagery.  Because of the increased cadence of these imagery channels, cloud vertical growth can be assessed using the imagery, then compared to the Cloud Top Temperature product to see how much cooling has occurred..  The GLM Lightning Detection product can also be used, as the amount of lightning will likely increase as the cloud top height increases.  Comparisons can also be made to the GOES-R Cloud Top Height Product, such as how quickly is the height increasing compared to the temperature of the cloud cooling?

 

6) How is this product created?

 

The GOES-R Cloud Top Temperature product is only calculated on pixels that are determined to be either "cloudy" or "probably cloudy" by the GOES-R Cloud Mask algorithm.  It utilizes GOES-R bands 11.2 um, 12.3 um, and 13.3 um.   The GOES-R Cloud Top Temperature product relies on the infrared observations to avoid discontinuities associated with the transition from day to night.  The algorithm performance is sensitive to imagery artifacts or instrument noise.  The ability to perform the algorithm requires an accurate forward model, accurate ancillary data, and well-characterized spectral response functions.1

 

The ancillary data used to calculate the cloud top height include1:

- surface elevation

- surface type

- NWP level associated with the surface

- NWP level associated with the tropopause

- NWP tropopause temperature

- profiles of height, pressure, and temperature from the NWP

- inversion level profile from NWP

- surface temperature and pressure from NWP

- local zenith angle bin

- NWP line and element indices

- clear-sky transmission, and radiance profiles for channels 14, 15, and 16 from the radiative transfer model

- blackbody radiance profiles for channels 14, 15, and 16 from the radiative transfer model

- clear-sky estimates of channels 14, 15, and 16 from the radiative transfer model

 

The derived data used to calculate the cloud top height include1:

- Cloud Mask:  A cloud mask is required to determine which pixels are cloudy and which are not, which in turn determines which pixels are processed. This information is provided by the ABI Cloud Mask (ACM) algorithm.

- Cloud Type/Phase: A cloud type and phase are required to determine which a priori information for the forward model are used. It is assumed that both the cloud type and phase are inputs to the ACHA algorithm. These products are provided by the ABI Cloud Type/Phase Algorithm

- Local Radiative Centers: Given a derived channel 14 top of troposphere emissivity, the local radiative center (LRC) is defined as the pixel location, in the direction of the gradient vector, upon which the gradient reverses or when an emissivity value  greater than or equal to 0.75 is found, whichever occurs first. The gradient filter routine is required as an input to the Cloud Top Height algorithm.

- Derived Channel 14 Top of Troposphere Emissivity: The Cloud Top Height algorithm requires knowledge of the channel 14 emissivity of a cloud assuming that its top coincides with the tropopause. This calculation is done by using the measured channel 14 radiance, clear sky channel 14 radiance from the RTM, space mask, latitude/longitude cell index from the NWP, tropopause index from the NWP, local zenith angle bin index, and channel 14 um blackbody radiance.

- Standard deviation of the channel 14 brightness temperature over a 3x3 pixel array.

- Standard deviation of the channel 14 – channel 15 brightness temperature difference over a 3x3 pixel array.

- Standard deviation of the channel 14 – channel 16 brightness temperature difference over a 3x3 pixel array.

 

The GOES-R Cloud Top Temperature Product uses the infrared observations from the ABI to extract the desired information on cloud height.  Infrared observations are impacted not only by the height of the cloud, but also its emissivity and how the emissivity varies with wavelength (a behavior that is tied to cloud microphysics).  In addition, the emissions from the surface and the atmosphere can also be major contributors to the observed signal.  Lastly, clouds often exhibit complex vertical structures that violate the assumptions of the single layer plane parallel models (leading to erroneous retrievals). 1

 

 

1Heidinger, Andrew. NOAA NESDIS Center for Satellite Applications and Research Algorithm Theoretical Basis Document: ABI Cloud Height v.2.0. 7 June 2011. http://www.goes-r.gov/products/ATBDs/baseline/Cloud_CldHeight_v2.0_no_color.pdf

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