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Cloud Layers & Snow Cover Discriminator - Basic Information

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Fig. 1. Comparison between True Color (left), the binary Cloud /Snow Discriminator product (middle), and the Cloud Layers & Snow Cover Discriminator product (right) imagery over the north-central United States as collected by NASA’s MODerate-resolution Imaging Spectroradiometer (MODIS) sensor. In the Cloud Layers & Snow Cover product, white areas denote snow cover, yellow areas denote low cloud cover (liquid), orange/magenta areas denote high cloud cover (mixed/ice), and green areas denote clear sky surface.

1) Product Information:

Who is developing and distributing this product?

The Cooperative Institute for Research in the Atmosphere (CIRA) in Fort Collins, Colorado, in cooperation with the Naval Research Laboratory (NRL) in Monterey, California are developing and distributing the Cloud Layers & Snow Cover Discriminator product. The product is based on visible and shortwave infrared channels on the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor.

Who is receiving this product, and how?

The Cloud Layers & Snow Cover Discriminator products are being made available to the National Weather Service (NWS) Regional Headquarters from which they are distributed to Weather Forecast Offices (WFOs) for display on their local AWIPS systems. Imagery updates are available approximately two times per day from the MODIS sensors on board Terra (~10:30 AM local time) and Aqua (~1:30 PM local time).

What is the product size?

The size of Cloud Layers & Snow Discriminator images is determined by the span and resolution of the AWIPS domain itself. Since current AWIPS system displays accommodate 1-byte per pixel, a good rule of thumb is that the size of the imagery (in bytes) corresponds roughly to the total number of pixels in a given AWIPS domain. For example, an AWIPS domain having dimensions of 1000 x 1000 pixels will require approximately 1 Megabyte (~106 bytes).

2) Product Description:

Fig. 2. Clockwise from upper-left: Visible (VIS), shortwave infrared (SIR), thermal infrared (TIR), and 1.38 micrometer cirrus-band (CIR) for the same date/time/domain as shown in Fig. 1. Each of these channels contains unique information that can be exploited in complement to discriminate between cloud and snow cover.

Purpose of this product:

Knowledge of snow cover is important for numerous applications, including search and rescue (e.g., assisting pilots over mountainous terrain), water supply resource monitoring and management, short-term forecasting (e.g., of radiation fog or monitoring the progress of a major winter storm), and recreation. Satellites offer a distinct perspective on snow cover, particularly in areas where there are few ground observing stations. The purpose of this product is to assist satellite imagery analysts in distinguishing snow vs. cloud cover during the daytime hours, and is particularly relevant during the winter months when snowfall occurs across many parts of the United States. This product takes an additional step beyond the Cloud / Snow Discriminator product (which depicts all clouds as yellow) by differentiating between low and high cloud cover.

Why is this a GOES-R Proving Ground Product?

The Cloud Layers & Snow Cover Discriminator product demonstrates the kind of imagery that will be possible in the GOES-R era at significantly higher temporal resolution. The GOES-R series of satellites will feature the Advanced Baseline Imager (ABI) sensor, which includes channels that are currently unavailable on GOES satellites. The new channels will enable new and/or improved capabilities which can be demonstrated now only via proxy or simulated datasets.

How is this product created now?

Discriminating between cloud and snow cover over complex terrain using conventional visible and infrared satellite imagery can be extremely challenging, even to experts in field of satellite imagery interpretation. At visible-light wavelengths, snow cover and clouds are both highly reflective and therefore difficult to distinguish. At thermal-infrared wavelengths, the temperature of snow cover can be similar to clouds as well. Stronger absorption by snow/ice at shortwave infrared wavelengths helps to distinguish these features from liquid-phase clouds, but some ambiguity remains between snow on the ground and ice-phase clouds. Even when adding the shortwave infrared channels (e.g., 1.6 and 3.9 micron, which provide improved discrimination of snow from clouds due to preferential absorption by snow cover), analyses are not entirely free of cloud/snow ambiguity due to the spectral behavior of certain varieties of cirrus. Additional channels available to the Moderate-resolution Imaging Spectroradiometer (MODIS), and particularly the 1.38 micron "cirrus detection" channel, improve the handling of this problem. Fig. 2 demonstrates how some of these discriminators appear in single-band imagery for a complex scene over Colorado.

3) Product Examples and Interpretation:

As mentioned above, the Cloud Layers & Snow Cover Discriminator product is actually an extension to the daytime Cloud / Snow Discriminator GOES-R Proving Ground product. It provides additional information on the cloud levels of the scene-in terms of roughly where the clouds reside in the atmospheric column and their liquid/ice/mixed composition. The basic guidelines for interpretation of the colors in this enhancement are as follows: green = land (clear sky, devoid of snow cover), white/bluish-white = snow cover, yellow = low-level liquid-phase clouds, and orange/magenta = mid/high level ice phase clouds.

Fig. 4. Examples of True ColorImagery (left) and the Cloud Layers & Snow Cover Discriminator (right) product for a scene over north/central United States containing snow and multi-layered clouds. Clear-sky surfaces are depicted in dark green.

The left image of Fig. 4 shows a true color image spanning portions of Montana, Wyoming, North and South Dakota. It is difficult to tell in this image what is cloud and what is snow cover based on the visible imagery alone, as both features appear white. The Cloud Layers & Snow Cover Discriminator image on the right removes much of the ambiguity, and even provides some information on where thin cloud layers overlap snow fields.

4) Advantages and Limitations:

A key advantage of the Cloud Layers & Snow Discriminator imagery is the ability to rapidly discriminate between these features without the need to consult several co-located single-channel images. A general advantage of imagery-based snow-cover identification in contrast to digital masks of snow cover is that the latter typically are provided at much coarser spatial resolution and remove the meteorological/terrain context in which the snow cover resides.

  1. Some clouds may appear white (false snow)...most often occurring in clouds transitioning between low and high cloud levels (e.g., parts of thunderstorms as they develop vertically).
  2. Some snow areas may appear magenta (false high cloud), particularly over very high mountain peaks, due to unintentional contributions from the 1.38 micron cirrus channel at high altitudes. (This effect is minimized by including a height-dependent threshold in the algorithm, but sometimes bright snow-covered mountain tops can still give rise to problems).
  3. Sun glint regions over water and certain desert scenes may appear yellow (false low cloud). It is recommended that users cross reference with true color products to identify glint zones.
  4. Some low and warm cloud layers may have green tint (false land)
  5. The product loses its discrimination capability at high latitudes where low sun angles, although this generally is not an issue for the NexSat domain.
  6. The product cannot see below thick clouds to report snow cover, but can often detect snow under thin cirrus and depict both pieces of information simultaneously.

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