Abstract
AbstractConventional observations of atmospheric rivers (ARs) over the northeastern Pacific Ocean are sparse. Satellite radiances are affected by the presence of clouds and heavy precipitation, which impact their distribution in the lower atmosphere and in precipitating areas. The goal of this study is to document a data gap in existing observations of ARs in the northeastern Pacific, and to investigate how a targeted field campaign called AR Reconnaissance (AR Recon) can effectively fill this gap. When reconnaissance data are excluded, there is a gap in AR regions from near the surface to the middle troposphere (below 450 hPa), where most water vapor and its transport are concentrated. All-sky microwave radiances provide data within the AR object, but their quality is degraded near the AR core and its leading edge, due to the existence of thick clouds and precipitation. AR Recon samples ARs and surrounding areas to improve downstream precipitation forecasts over the western United States. This study demonstrates that despite the apparently extensive swaths of modern satellite radiances, which are critical to estimate large-scale flow, the data collected during 15 AR Recon cases in 2016, 2018, and 2019 supply about 99% of humidity, 78% of temperature, and 45% of wind observations in the critical maximum water vapor transport layer from the ocean surface to 700 hPa in ARs. The high-vertical-resolution dropsonde observations in the lower atmosphere over the northeastern Pacific Ocean can significantly improve the sampling of low-level jets transporting water vapor to high-impact precipitation events in the western United States.
Highlights
Conventional observations of atmospheric rivers (ARs) over the northeastern Pacific Ocean are sparse
Atmospheric rivers (ARs) are elongated corridors that transport water vapor from the subtropics and/or tropics to the extratropics (Zhu and Newell 1998; Ralph et al 2004, 2005; Neiman et al 2008; Ralph et al 2018, 2019). They are typically associated with a pre-cold-frontal low-level jet (LLJ; Ralph et al 2004, 2005) in the warm sector of extratropical cyclones accompanied by an upper-level jet
Observations over continents typically sample meteorological conditions using a variety of conventional observation systems (Table 1), including land surface synoptic observations (SYNOP), radiosondes, pilot balloon observations (PIBAL), meteorological terminal aviation routine (METAR) reports, rain gauges, and aircraft
Summary
Difficulty in height assignment, only sample a few levels (i.e., geostationary images). Within ARs for AIRS, AVHRR, IASI, SSMIS, and Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR), and by 25%–75% for CRIS, Microwave Humidity Sounding (MHS) and SNDRD These results provide a quantitative measure of the gap in the quantity of assimilated clear-sky radiances in the lower to middle troposphere within an AR. One important conclusion of this work is that there may be much to be gained in the existing data assimilation methods for dropsonde data in ARs, by increasing the number of vertical levels for the aircraft data transmission and assimilation, to take full advantage of the unique vertical resolution, especially wind and humidity data To summarize their sampling characteristics, AR Recon data can largely fill the observation gap from near the surface to the middle troposphere, where they contribute 76.8% of the direct temperature, 99.9% of the humidity, and 48.0% of the wind observations in an AR object (Table 4, Fig. ES1). Future efforts could focus on data denial experiments to thoroughly assess the usefulness and effectiveness of different observational datasets in improving initial conditions and predictions generated with numerical models
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