Overview of NAAPS: Navy Aerosol Analysis and Prediction System
A number of naturally occurring and anthropogenic aerosols affect U.S. Navy operations in nearly all parts of the world. Presently the Navy relies on a combination of diagnostic tools for estimating localized aerosol effects on sensors and systems. Previous to NAAPS, no operational global predictive capability existed nor was there any operational analyses of aerosols. But predictive models being developed by the Navy for regional applications will need initial and boundary conditions from a global model in the same way that the mesoscale weather model (e.g. COAMPS) depends upon a global weather model (e.g. the Navy Operational Global Atmospheric Prediction System, NOGAPS.)
Coincidentally, the academic community and other agencies and countries are also interested in the global aerosol distributions of natural and anthropogenic aerosols because of their potential climatic implications. As a result, aerosols are receiving more attention than ever before. This includes the development aerosol retrieval algorithms for existing satellites (Table 1) and the deployment of several satellite sensors designed specifically for aerosol detection (Table 2). Each of the sensors have strengths and weaknesses that depend on the spatial and spectral resolution and on the measurement technology. For example, each AVHRR satellite yields high spatial resolution, but only one daytime overpass per day. SAGE II gives vertical resolution, but only a few point measurements per day. TOMS detects aerosols over land, but at coarse resolution and the technique has not yet been calibrated. However, research groups usually specialize in a single sensor. No groups are processing and assimilating the data from several satellites.
Statement of Work
In response to Navy needs, NRL has developed a global, multi-component aerosol analysis and modeling capability (called NAAPS: Navy Aerosol Analysis and Prediction System) that combines the current and expected satellite data streams with other available data and the global aerosol simulation and prediction . Specifically, we propose to investigate and evaluate the existing and proposed satellite-based aerosol retrievals (Table 3 and Table 4) and implement those that are relevant and practical. We will utilize the unique processing capabilities within NRL's remote sensing section to develop one of the most complete suites of aerosol retrieval products in the world, at relatively small expense since several of the data streams are received at NRL/MRY for other purposes. Satellite retrievals will be evaluated and new retrieval algorithms may need to be considered.
We will also utilize several sources of surface-based aerosol measurements. These include surface synoptic reports of visibility and current and past weather . These data have been used by us in previous studies to follow large dust storms and smoke plumes. Data from the AERONET aerosol monitoring network are also available. The network is comprised of 60 sun-sky scanning spectral radiometers deployed permanently around the world (Figure 1). NRL has recently arranged for the installation of an AERONET instrument in Bahrain and facilitated an installation in South Korea. The data yield optical depth at eight wavelengths every minute and are available in near real-time via satellite link. The aerosol size distribution is then inferred from the wavelength dependence of optical depth.
We will develop and test analysis and data assimilation techniques for processing the satellite retrievals and the surface-based data. Scientific challenges include developing methods for assigning altitude to satellite-retrieved aerosols, developing techniques to convert synoptic reports such as visibility to aerosol amount, developing methods for characterizing source regions and source strength using aerosol retrievals, adapting data assimilation techniques to aerosol data streams, and generalizing the global aerosol model to allow multiple components and sizes for use as background fields for data assimilation.