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Global mapping of cloudiness using MODIS imagery

This web page shows average cloudiness derived using observations from the MODIS instrument onboard the NASA Terra and Aqua satellites. We have used the MODIS imagery that is available from the MODIS Rapid Response Website (http://rapidfire.sci.gsfc.nasa.gov/subsets/). The latter site provides navigated imagery for selected sectors around the globe with a focus on fire detection. However, the website provides a convenient source for quickly accessing MODIS imagery for cloud mapping. Using this imagery, generally available since late 2003 or early 2004, we have generated averages of the true color images and also we have extracted the cloudiness from each image through a simple process that examines the average pixel brightness and counts values above a certain threshold as cloud. This leads to some problems in areas of very high reflectivity, such as salt flats and ice fields, but for most tropical regions the cloudiness is easily extracted from the generally darker forest or ocean background.

Our current work, not formally funded, involves activities that are an outgrowth of rainfall studies of the eastern Andean slopes and altiplano regions of Bolivia and Peru. These rainfall studies were carried out under the umbrella of the South American Low-Level Jet EXperiment (SALLJEX), which is a study focused on the variations of the strong low-level winds found over central South America east of the Andes. Our MODIS-related studies partners with research that has been carried out for years by members of the Museo Noel Kempff in Santa Cruz, Bolivia and by members of Conservation International, whose focus has been on describing and quantifying the biological diversity of the eastern Andean slope region.

Mapping the distribution of vegetation types can be a complicated task, especially in remote locations such as the eastern Andes. One example of this is determining the distribution of so-called tropical "cloud forests". These regions, as their name implies, are forests that are immersed within clouds for a large fraction of the time. Because these environments have relatively high relative humidity, are usually very rainy, and are generally well above freezing, there is luxuriant plant growth. These regions also show very high diversity of plants and animals, and so they are important to map accurately to aid in conservation planning.

It is very difficult to map the distribution of cloud forest from the ground. Most cloud forests occur on the sides of steep slopes, a consequence of the interaction of the topography, clouds, and wind. Steep, wet slopes make for difficult road construction, which can sometimes minimize human impact but also makes access difficult for botanists, zoologists and others seeking to describe this environment. Although cloud forests and wet forests at lower elevations (well below cloud base) may look superficially similar from satellite imagery they can have very different flora and fauna. Thus, there is a real need to map these regions. Simple techniques compare satellite-based estimates of forest cover with topographic data and where the two coincide—we can claim cloud forest. Our approach is meteorological—we determine the frequency of cloudiness from the satellite archive and then relate it to topography. One manageable disadvantage of our approach is that data volumes are significant—a single MODIS image can be 5 MB, and with about 1000 images from each satellite available for each geographical sector, the data storage and processing is substantial. Work done by an REU (Research Experiences for Undergraduates) student on this subject is summarized here.

Other uses of interest

We have begun to evaluate the MODIS imagery as an aid in explaining observed vegetation gradients that have been described throughout the tropics, but where existing explanation based on conventional climatological information seems inadequate. Imagery at relatively coarse resolution (for example 30 km spatial averages available from the International Satellite Cloud Climatology Project (ISCCP) are not sufficient to describe these topographically-induced gradients in cloudiness. Conventional climatological data measured from surface stations is even more limited.

Although the averages shown on this web site are made using only about 3 years of data, they appear to reflect longer-term average conditions, in that the known vegetation patterns reflect the cloudiness distribution to a high degree.

Concluding remarks

While our procedures are in some ways simplistic and have limitations, such as the inability to determine the cloud base height, the images themselves should stimulate interest in the atmospheric processes that produce such cloud distributions. Our composites clearly show the very strong role of topography in modulating the cloudiness, and we hope they will serve to stimulate our intended audience (botanists, zoologists, ecologists, biogeographers, etc.) to use these sources of data for their work.

Atmospheric scientists could use high spatial resolution cloud climatologies to aid in detecting systematic errors in mesoscale models. A very strict test of the accuracy of any high-resolution mesoscale model might be its fidelity in reproducing the observed cloud field, as observed by MODIS. That may ultimately be a valuable application of such mean cloudiness fields.