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Atmospheric rivers (ARs) are long, narrow corridors of water vapor transport that serve as the primary mechanism to advect moisture into midlatitude continental regions, including the U.S. West Coast. Associated [...]

During the North American Monsoon, low-to-midlevel moisture is transported in surges from the Gulf of California and Eastern Pacific Ocean into Mexico and the American Southwest. As rising levels of [...]

We are deeply concerned with the trend toward the purchase of commercial weather and climate data by the National Oceanic and Atmospheric Administration (NOAA) and other government agencies that may [...]

Conventional 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. [ABSTRACT FROM AUTHOR]

Water management and flood control are major challenges in the western United States. They are heavily influenced by atmospheric river (AR) storms that produce both beneficial water supply and hazards; for example, 84% of all flood damages in the West (up to 99% in key areas) are associated with ARs. However, AR landfall forecast position errors can exceed 200 km at even 1-day lead time and yet many watersheds are <100 km across, which contributes to issues such as the 2017 Oroville Dam spillway incident and regularly to large flood forecast errors. Combined with the rise of wildfires and deadly post-wildfire debris flows, such as Montecito (2018), the need for better AR forecasts is urgent. Atmospheric River Reconnaissance (AR Recon) was developed as a research and operations partnership to address these needs. It combines new observations, modeling, data assimilation, and forecast verification methods to improve the science and predictions of landfalling ARs. ARs over the northeast Pacific are measured using dropsondes from up to three aircraft simultaneously. Additionally, airborne radio occultation is being tested, and drifting buoys with pressure sensors are deployed. AR targeting and data collection methods have been developed, assimilation and forecast impact experiments are ongoing, and better understanding of AR dynamics is emerging. AR Recon is led by the Center for Western Weather and Water Extremes and NWS/NCEP. The effort's core partners include the U.S. Navy, U.S. Air Force, NCAR, ECMWF, and multiple academic institutions. AR Recon is included in the "National Winter Season Operations Plan" to support improved outcomes for emergency preparedness and water management in the West. [ABSTRACT FROM AUTHOR]

This study determined the accuracy and biases associated with retrieved Moderate Resolution Imaging Spectroradiometer (MODIS) total precipitable water (TPW) data, and it investigated the impact of these data on severe weather simulations using the Weather Research and Forecast (WRF) model. Comparisons of MODIS TPW with the global positioning system (GPS) TPW and radiosonde-derived TPW were carried out. The comparison with GPS TPW over the United States showed that the root-mean-square (RMS) differences between these two datasets were about 5.2 and 3.3 mm for infrared (IR) and near-infrared (nIR) TPW, respectively. MODIS IR TPW data were overestimated in a dry atmosphere but underestimated in a moist atmosphere, whereas the nIR values were slightly underestimated in a dry atmosphere but overestimated in a moist atmosphere. Two cases, a severe thunderstorm system (2004) over land and Hurricane Isidore (2002) over ocean, as well as conventional observations and Special Sensor Microwave Imager (SSM/I) retrievals were used to assess the impact of MODIS nIR TPW data on severe weather simulations. The assimilation of MODIS data has a slightly positive impact on the simulated rainfall over Oklahoma for the thunderstorm case, and it was able to enhance Isidore’s intensity when the storm track was reasonably simulated. The use of original and bias-corrected MODIS nIR TPW did not show significant differences from both case studies. In addition, SSM/I data were found to have a positive impact on both severe weather simulations, and the impact was comparable to or slightly better than that of MODIS data. [ABSTRACT FROM AUTHOR]