Atmospheric Rivers (AR’s) are long narrow bands of moisture that transport energy in the form of latent heat poleward from the Equatorial Warm Pool. AR’s are usually confined to the warm sector of extratropical cyclones which form along the leading edge of cold fronts, and are sometimes referred to as the “Warm Conveyor Belt,” a term coined by Browning (1990) and Carlson (1991). A well-known example of a strong AR event that occasionally hits the west coast of the U.S. is the Pineapple Express. These storms are so-named because of their apparent origin in the tropics near Hawaii. Observational studies of atmospheric rivers prior to landfall made using in situ aircraft and remote sensing satellite observations indicate that AR’s are usually characterized by warm air temperatures, large water vapor content, and strong winds at low altitudes (Ralph et al., 2004, 2005). The importance of AR’s and AR-like features is underscored by the fact that they are responsible for about 90% of the total meridional water vapor transport at mid latitudes on the planet. The impact of AR’s are felt on landfall. Not all ARs cause damage – most are weak, and simply provide beneficial rain or snow that is crucial to local and regional water supplies. Those that contain the largest amounts of water vapor, the strongest winds, and stall over watersheds vulnerable to flooding can create extreme rainfall and floods. These events commonly disrupt travel, induce mud slides, and cause catastrophic damage to life and property. The challenges to operational meteorologists are to accurately identify those events that are most likely to cause catastrophic damage with as much lead-time as possible, and provide decision makers and the public with accurate and timely information as these storms evolve. There are several aspects to this, including observations, data assimilation, analysis, prediction and verification. GNSS observations provide critical and heretofore unavailable information about the moisture content of atmospheric rivers at all stages of their evolution. GNSS observations offshore are used to calibrate satellite sensors and validate data products. Onshore, GNSS observations provide continuous monitoring of the upslope component of the moisture flux that is highly correlated with heavy precipitation. Assimilated into numerical weather prediction models in conjunction with Doppler radar wind profiler data, GNSS observations are responsible for significant improvements in objective short-term relative humidity and heavy precipitation forecasts (Neiman et al. 2009). Nonetheless, the greatest remaining challenge is to predicting rainfall totals in these events as models struggle with the details of the location, duration and timing of AR's as they make landfall.
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