Clouds are of course a big part of the simulations and 3-D fields quantifying the density and size distributions of the constituent hydrometeors (cloud liquid, cloud ice, etc.) can be obtained from a Numerical Weather Prediction (NWP) model. In NWP, data assimilation procedures are used to combine weather satellite imagery, measured quasi-vertical profiles of temperature and humidity, surface and other observations and prior forecasts into 3-D “analyses” to describe the state of the atmosphere including clouds at any given time. In this case I'm using a cloud analysis from the Local Analysis and Prediction System (LAPS) that matches the time of the DSCOVR image. This system was selected because its cloud analysis is designed to closely match the locations of the actual clouds, particularly in the daylight hemisphere. The NWP analyses are also used to initialize NWP forecast models that project the initial state (including the clouds) into the future.

One aspect of evaluating the synthetic vs actual image match is whether the clouds lie in the correct locations. Another is whether they have the correct brightness, a sensitive indicator of the model's hydrometeors and the associated radiative transfer. All else being equal, optically thicker clouds are generally brighter as seen from above with more light scattered back toward space. This is the opposite of what we see when we're underneath a cloud. The cloud brightness or reflectance changes noticeably over a wide range of cloud thickness, allowing optical depth (tau) values fromSimulated Weather Imagery (SWIM) page including a link to a powerpoint presentation based on a recent seminar.

Aerosols are here defined as particles other than clouds such as haze, dust, or smoke particles. The ways to define aerosols are talked about in this interesting blog post. They are an important consideration and scatter light mostly in a forward direction. They can be characterized by optical thickness (a measure of opacity), and scale height (indicating vertical distribution). The sizes of the aerosols are typically in a bimodal distribution, where the larger dust particles scatter more light more tightly forward and smaller ones with a broader angular distribution. Both aerosols and ozone help to soften the appearance of the Earth near the limb in the DSCOVR images, providing a sensitive test of the simulation. At present some general average values are used for aerosols and ozone. This can be improved by using a more detailed geographic and seasonal climatology. There are also plans to utilize real-time aerosols and ozone (along with clouds) from NOAA's FIM model.

The ray-traced light intensities can be converted into the physical units of spectral radiance by factoring in the solar spectrum. To make a digital RGB color image I took these steps to calculate what the image count values should be:

1) Convolve the spectral radiance with the so-called tristimulus color functions to take into account color perception. A good example illustrating the benefits of this is in how the blue sky looks. The violet component of the light is beyond what the blue phosphor can show, so a little bit of red light is mixed in. This is analogous to what our eye-brain combination will do (for those with typical color vision). We thus perceive spectrally pure violet light in a manner similar to purple (a mix of blue with a little red).

2) Apply the 3x3 transfer matrix that puts the XYZ image into the RGB color space of the computer monitor. This is needed in part because the colors of the monitor aren't spectrally pure. I'm making the assumption that the sun is a pure white color (as it is very nearly when seen from space). Correspondingly I like to set my computer monitor color temperature to 5800K, a value nearly equal to the sun's surface temperature.

3) Include a gamma correction to match the non-linear monitor brightness scaling. This is important if we want the displayed image brightness to be directly proportional to the actual brightness of the scene. Background information about these three steps can be found here with an example for Rayleigh scattering.

While this is all a work in progress, I believe this should give a realistic color and brightness match if you're sitting (floating) out in space holding your computer monitor side-by-side with the Earth. This has somewhat more subtle colors and contrasts compared with many Earth images that we see. The intent here is to make the displayed image have a proportional brightness to the actual scene without any exaggeration in the color saturation (that is common even in everyday photography). The presence of the atmosphere, more fully considered in the rendering helps soften the appearance of the underlying landscape.

I took the liberty of doing some tweaking of the DSCOVR color balance to help get a better match to the synthetic imagery, mainly based on the blue ocean/sky color. The DSCOVR image contrast was reduced since there is apparently some saturation at the bright and dark ends. This is conveniently noticeable during the March solar eclipse sequence where we have a handy gradiation of light intensities to examine. The moon could provide another calibration target during transits. The raw DSCOVR data could be used to help with producing more visually realistic images, borrowing from some of the same techniques described above. Meanwhile, maybe an astronaut can try these comparisons out sometime? Going forward there's much here to learn about all the aspects of Earth modeling and raytracing improvements to really demonstrate that we can model our planet in a wholistic and accurate manner.

Other Phases

The DSCOVR satellite is positioned to always view the fully lit side of the Earth. The simulation package can also show us what DSCOVR would theoretically see if it could magically observe from other phase angles.