Search

Under the Atmospheric River Reconnaissance (AR Recon) Program, ocean drifting buoys (drifters) that provide surface pressure observations were deployed in the northeastern Pacific Ocean to improve forecasts of U.S. West Coast high-impact weather. We examine the impacts of both AR Recon and non-AR Recon drifter observations in the U.S. Navy’s global atmospheric data assimilation (DA) and forecast system using data-denial experiments and forecast sensitivity observation impact (FSOI) analysis, which estimates the impact of each observation on the 24-h global forecast error total energy. Considering all drifters in the eastern North Pacific for the 2020 AR Recon season, FSOI indicates that most of the beneficial impacts come from observations in the lowest quartile of observed surface pressure values, particularly those taken late in the DA window. Observations in the upper quartile have near-neutral impacts on average and are slightly nonbeneficial when taken late in the DA window. This may occur because the DA configuration used here does not account for model biases, and innovation statistics show that the forecast model has a low pressure bias at high pressures. Case studies and other analyses indicate large beneficial impacts coming from observations in regions with large surface pressure gradients and integrated vapor transport, such as fronts and ARs. Data-denial experiments indicate that the assimilation of AR Recon drifter observations results in a better-constrained analysis at nearby non-AR Recon drifter locations and counteracts the NAVGEM pressure bias. Assimilating the AR Recon drifter observations improves 72- and 96-h Northern Hemisphere forecasts of winds in the lower and middle troposphere, and geopotential height in the lower, middle, and upper troposphere. Significance Statement The purpose of this study is to understand how observations of atmospheric pressure at the ocean surface provided by drifting buoys impact weather forecasts. Some of these drifting buoys were deployed under a program to study atmospheric rivers (ARs) to improve forecasts of high-impact weather on the West Coast. We find that these observations are most effective at reducing forecast errors when taken in regions near fronts and cyclones. The additional drifting buoys deployed under the AR Reconnaissance project reduce forecast errors at 72 and 96 h over North America and the Northern Hemisphere. These results are important because they illustrate the potential for improving forecasts by increasing the number of drifting buoy surface pressure observations over the world oceans.

In this review, data assimilation (DA) techniques used for tropical cyclones (TCs) are briefly overviewed. The strength and weakness of variational methods, ensemble methods, hybrid methods, and particle filter methods are also discussed. Several global numerical weather prediction models and their corresponding DA systems frequently used for TC forecasting and verification are described first. The DA research and development efforts in the operational regional model from the National Centers for Environmental Prediction's Hurricane Weather Research and Forecasting are then discussed in greater detail. Focused remarks on TC observations from reconnaissance, ground-based radar, enhanced satellite-derived atmospheric motion vectors and all-sky satellite radiances and their impacts on TC analyses and forecasts are addressed. Recent TC DA advancements and challenges on better use of observations and more advanced DA methods for TC application are also briefly reviewed.