Skip navigation

RAMMB: Regional and Mesoscale Meteorology Branch logo CIRA: Cooperative Institute for Research in the Atmosphere logo NESDIS: NOAA Environmental Satellite, Data, and Information Service logo

Cloud / Snow Discriminator - Detailed Information

Show basic information...

Fig. 1. Example of True Color (left) and Cloud / Snow Discriminator product (right) imagery over Colorado and surrounding regions as collected by NASA’s MODerate-resolution Imaging Spectroradiometer (MODIS) sensor. White areas denote snow cover, yellow areas are low or high cloud cover, and green areas denote clear sky surface. The GOES-R Advanced Baseline Imager (ABI) will be able to produce imagery of similar quality but at dramatically higher temporal refresh (minutes, vs. the 1-2 times per day currently available from the MODIS Terra + Aqua tandem).

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 / Snow 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 / Snow 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 / 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, water supply monitoring, short-term forecasting (e.g., of radiation fog), 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.

Why is this a GOES-R Proving Ground Product?

The Cloud / Snow 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?

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. Fig. 2 demonstrates how some of these discriminators appear in single-band imagery for a complex scene over Colorado. Combining information from several channels allows us to isolate the snow cover, but doing so via toggling back and forth between the individual channels can be a tedious and inaccurate process. The Cloud / Snow Discriminator product (see Fig. 1) attempts to eliminate this legwork by combining the complementary information from multiple channels into a single visual aide.

3) Product Examples and Interpretation:

In the Cloud / Snow Discriminator product, clouds are color-coded as yellow/light-green, snow cover appears white, and clear-sky surfaces appear darker green. The left image of Fig. 3 below shows a visible image centered on Italy. 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 / Snow Discriminator image on the right removes much of the ambiguity. Whereas experienced analysts may have identified the dendritic features as snow-covered mountain ridges, snow fields may not always coincide with characteristic spatial patterns. This example is shown as a form of validation for the correct classification of snow cover.

Fig. 3. Examples of Visible Imagery (left) and the Cloud /Snow Discriminator (right) product for a scene over Europe containing snow over the Alps (dendritic features) and low cloud/fog in yellow. Clear-sky surfaces are depicted in dark green.

4) Advantages and Limitations:

The main advantage of the Cloud / Snow Discriminator imagery is the ability to rapidly discriminate between these features without the need to consult several co-located single-channel images.

In areas of broken snow cover the Cloud / Snow Discriminator may miss-classify snow as cloud. This occurs most often at the boundaries of snow fields and is usually easy to identify. In some cases, small regions of thick ice cloud may be misclassified as snow cover.

Show basic information...