Introduction and Methodology
Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMBLouie Grasso, Cooperative Institute for Research in the Atmosphere
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*Corresponding author address: 1375 Campus Delivery, CIRA/CSU, Fort Collins, CO, 80523. [email protected]
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Example from MSG/SEVIRI
The model output above (left) shows the simulated 10.35-12.3 µm product (K) based on output from a RAMS simulation over southeast Wyoming. The model surface wind vectors are overlaid. Note the orange to red colors (+4 to +5 K) in regions of converging surface wind vectors. A model cross section along the black line is also shown above (right). Water vapor mixing ratio (contours, in g/kg) and vertical velocity (colors, in m/s) are plotted. Note that the region of largest 10.35-12.3 µm values corresponds to rising motion and an upward bulge of water vapor. The pooling water vapor would be detected by GOES-R observations. Thunderstorms formed in that location later in the model simulation.
MSG 10.8 – 12.0 µm (top) and HRV (bottom) from 21 Dec. 2009 at 0830 UTC (left) and 1245 UTC (right)
Note the local maximum in the 10.8 – 12.0 µm image, and the lack of clouds at this time
By 1245 UTC, cumulus clouds have formed in this same region
Simulated GOES-R ABI 10.35 – 12.3 µm (left, in K) and 0-3 km AGL precipitable water (mm) from the RAMS cloud model output. Purple colors in the PW image indicate cloud material exists within the column.
In the Figure above, notice how the simulated 10.35 – 12.3 µm maxima are correlated with low level PW maxima. In many of those maxima, clouds eventually form (indicated by the purple colors on the right panel).
A GOES-R Risk Reduction Project is currently underway that is investigating how this information might be used to improve Convective Initiation (CI) 1-6 hours in advance. See the Mecikalski et al. poster in this same session for details on that effort.
Acknowledgements
The views, opinions, and findings in this report are those of the authors, and should not be construed as an official NOAA and or U.S. Government position, policy, or decision.
Predicting Where Convective Clouds Will Form with the GOES-R ABI
We’ve recently begun investigating an old idea which will become much more practical with the launch of GOES-R. As far back as the 1970’s, researchers have attempted to retrieve information about low-level water vapor using the so-called split-window difference, or the brightness temperature difference (BTD) between a window IR band and a band near 12 µm. The idea is that radiation emitted from the surface is preferentially absorbed by water vapor in the boundary layer and re-emitted at a colder temperature at the longer wavelength band, resulting in a positive BTD. In order for this idea to be operationally practical, one needs to have good temporal resolution (not available with polar-orbiting instruments such as AVHRR), good spatial resolution (10-km with the current GOES sounder is not good enough), and a minimum of striping/noise. Incidentally, the SEVIRI instrument aboard MSG has these characteristics, and the ABI should be even better.
Our initial approach is to monitor the daytime trends in the 10.35-12.3 µm BTD and look for linear regions in which the BTD increases more rapidly than surrounding areas. This nearly always represents a region with converging low-level water vapor, and if the moisture convergence is great enough, convective clouds and sometimes (depending on the strength of the cap) thunderstorms will form. This technique allows for a several hour lead time on convective cloud formation. The biggest limitation of his method is that it requires clear skies.
The magnitude of the BTD is a function of both the moisture profile and the temperature profile, so work is currently underway to use a priori information about the low-level temperature profile to correct for (or normalize) the observed BTD values. If this normalization is done properly, then the diurnal increase in surface temperature will have no effect on the normalized BTD measurement, meaning the BTD is providing information only about changes in moisture.
In order to approximate the imagery observed by the GOES-R ABI, we make use of a radiative transfer model (RTM) system developed at CIRA (Grasso and Greenwald 2004). Output from a high resolution (<= 4 km grid spacing) cloud model (such as RAMS or WRF) is used as input to the RTM. Input variables include temperature, water vapor, pressure, heights, and whatever microphysical variables are available. This microphysical information is particularly important because it provides the location of both liquid water and ice clouds.
NSSL-WRF ARW Synthetic ABI imagery from 21 May 2011. Note that the regions in which clouds (blue) form show maxima in the 10.35-12.3 µm imagery at least 1 hour before cloud formation.
Until the launch of GOES-R, the SEVIRI instrument aboard the Meteosat Second Generation (MSG) satellite (centered at 0º longitude) can be used as a proxy for some of the ABI bands. It has a band centered at 10.8 µm and one at 12.0 µm. In the example below over South Africa, note that a local maxima in the 10.8-12.0 µm field can be seen several hours before any convective clouds form in that region.
