Impacts of Northeastern Pacific Buoy Surface Pressure Observations

February 8, 2023

A new study using data from the Atmospheric River Reconnaissance program finds potential for improving weather forecasts, particularly errors in short range prediction of atmospheric rivers (ARs), by increasing the number of drifting buoy surface pressure observations over the world oceans. The paper “Impacts of Northeastern Pacific Buoy Surface Pressure Observations” was recently published in Monthly Weather Review by authors Carolyn Reynolds (U.S. Naval Research Laboratory), Rebecca Stone (Science Applications International Corporation), James Doyle (U.S. Naval Research Laboratory), Nancy Baker (U.S. Naval Research Laboratory), Anna Wilson (CW3E), Marty Ralph (CW3E), David Lavers (European Centre for Medium-Range Weather Forecasts), Aneesh Subramanian (University of Colorado Boulder), and Luca Centurioni (University of California San Diego). This work contributes to the goals of CW3E’s 2019-2024 Strategic Plan to lead AR Research and Applications because the paper illustrates how novel observations of ARs improve forecasts.

Atmospheric River Reconnaissance (AR Recon) is a program led by CW3E and guided by an international, interagency Atmospheric River Modeling and Data Assimilation Steering Committee (Ralph et al. 2020; OFCM 2022). Dropsondes released from Air Force Reserve Command and the NOAA Aircraft Operations Center aircraft are the cornerstone of this effort. The effort also includes a partnership with the NOAA-funded Global Drifter Program led by Dr. Centurioni at the Scripps Lagrangian Drifter Laboratory to add barometers to the regular drifters (Centurioni et al. 2017). Those observations are critical in filling observation gaps in and near ARs over the North Pacific Ocean (Zheng et al. 2021). In this study, the authors investigated the impact of drifter-observed sea level pressure in the northeastern Pacific during the AR Recon 2020 season for their impact on the U.S. Navy’s global atmospheric forecasting system. The authors used Forecast Sensitivity Observation Impact (FSOI) to measure the contribution of individual observations and sets of observations on short-term forecast error reduction, and data-denial experiments to quantify forecast error associated with removing observations.

The results of this study find that observational impacts vary with placement, observation value, and timing. Drifters placed in time-averaged low-pressure regions, such as the Gulf of Alaska, and in isolation from other drifters, have the largest average impacts. Observations in the lowest quartile of sea surface pressures are significantly more beneficial to the model than higher pressure quartiles at the 95% level, with 54.5% beneficial impacts from the lowest quartile, and between 47.6% and 51.3% for the higher quartiles. The greatest impacts for individual drifters occur during periods of tight pressure gradients and strong integrated vapor transport (IVT), which are associated with fronts and ARs, respectively. The time when the observations are taken within the data assimilation (DA) window is also important. The lowest quartile of sea surface pressure observations have smaller beneficial impacts during the first half of the DA window and increasingly larger beneficial impacts during the second half of the DA window, whereas the small beneficial impacts of the upper quartile observations early in the DA window become nonbeneficial as the window progresses. Lastly, data-denial experiments show that AR Recon drifters better constrain the analysis of nearby non AR-Recon drifters, help correct for biases in the model, and contribute statistically significant improvements to tropospheric winds and geopotential height forecasts over the Northern Hemisphere at 72- and 96-hour lead times (Fig. 1).

The results of this study have applications for future AR Recon seasons and the Global Drifter Program to expand the density and targeted spatial coverage in the northeastern Pacific. Motivated in part by this investigation, the 2022 AR Recon deployment included drifters in the Gulf of Alaska and additional buoys off southern Greenland.

This research was funded by the Chief of Naval Research through the Naval Research Laboratory Base Program, by the California Department of Water Resources AR program, the U.S. Army Engineer Research and Development Center, and the National Ocean and Atmospheric Association.

Figure 1: (Fig. 10 from Reynolds et al. 2023): Standard scorecard metrics for North America and Northern Hemisphere NAVGEM forecasts as a function of forecast hour as verified against ECMWF operational analyses for forecast start times of 0000 UTC 22 Jan 2020–0000 UTC 13 Mar 2020. Green colors indicate improvements in the metric with the assimilation of the AR-Recon drifter surface pressure observations that are statistically significant at the 95% level. Pink colors indicate degradations at the 95% level.

Centurioni, L., Horaìnyi, A., Cardinali, C., Charpentier, E., & Lumpkin, R. (2017). A global ocean observing system for measuring sea level atmospheric pressure: Effects and impacts on numerical weather prediction. Bulletin of the American Meteorological Society, 98, 231-238. https://doi.org/10.1175/BAMS-D-15-00080.1

ICAMS, 2022: National Winter Season Operations Plan (NWSOP). Interagency Meteorology Coordination Office, 124 pp., https://www.icams-portal.gov/resources/ofcm/nwsop/2022_nwsop.pdf.

Reynolds, C. A., Stone, R. E., Doyle, J. D., Baker, N. L., Wilson, A. M., Ralph, F. M., Lavers, D. A., Subramanian, A. C., & Centurioni, L. (2023). Impacts of Northeastern Pacific Buoy Surface Pressure Observations. Monthly Weather Review, 151, 211-216. https://doi.org/10.1175/MWR-D-22-0124.1

Ralph, F. M., Cannon, F., Tallapragada, V., Davis, C. A., Doyle, J. D., Pappenberger, F., Subramanian, A., Wilson, A. M., Lavers, D. A., Reynolds, C. A., Haase, J., Rutz, J. J., Cordeira, J. M., Zheng, M., Hecht, C. W., Kawzenuk, B., & Delle Monache, L. (2020). West Coast Forecast Challenges and Development of Atmospheric River Reconnaissance. Bulletin of the American Meteorological Society, 101, E1357-E1377. https://doi.org/10.1175/BAMS-D-19-0183.1

Zheng, M., Delle Monache, L., Wu, X., Ralph, F. M., Cornuelle, B., Tallapragada, V., Haase, J. S., Wilson, A. M., Mazloff, M., Subramanian, A., & Cannon, F. (2021). Data Gaps within Atmospheric Rivers over the Northeastern Pacific. Bulletin of the American Meteorological Society, 102, E492-E524. https://doi.org/10.1175/BAMS-D-19-0287.1