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Rebecca E. Stone
,
Carolyn A. Reynolds
,
James D. Doyle
,
Rolf H. Langland
,
Nancy L. Baker
,
David A. Lavers
, and
F. Martin Ralph

Abstract

Atmospheric rivers, often associated with impactful weather along the west coast of North America, can be a challenge to forecast even on short time scales. This is attributed, at least in part, to the scarcity of eastern Pacific in situ observations. We examine the impact of assimilating dropsonde observations collected during the Atmospheric River (AR) Reconnaissance 2018 field program on the Navy Global Environmental Model (NAVGEM) analyses and forecasts. We compare NAVGEM’s representation of the ARs to the observations, and examine whether the observation–background difference statistics are similar to the observation error variance specified in the data assimilation system. Forecast sensitivity observation impact is determined for each dropsonde variable, and compared to the impacts of the North American radiosonde network. We find that the reconnaissance soundings have significant beneficial impact, with per observation impact more than double that of the North American radiosonde network. Temperature and wind observations have larger total and per observation impact than moisture observations. In our experiment, the 24-h global forecast error reduction from the reconnaissance soundings can be comparable to the reduction from the North American radiosonde network for the field program dates that include at least two flights.

Free access
David A. Lavers
,
N. Bruce Ingleby
,
Aneesh C. Subramanian
,
David S. Richardson
,
F. Martin Ralph
,
James D. Doyle
,
Carolyn A. Reynolds
,
Ryan D. Torn
,
Mark J. Rodwell
,
Vijay Tallapragada
, and
Florian Pappenberger

Abstract

A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts.

Open access
Carolyn A. Reynolds
,
Rebecca E. Stone
,
James D. Doyle
,
Nancy L. Baker
,
Anna M. Wilson
,
F. Martin Ralph
,
David A. Lavers
,
Aneesh C. Subramanian
, and
Luca Centurioni

Abstract

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.

Free access
David A. Lavers
,
Anna M. Wilson
,
F. Martin Ralph
,
Vijay Tallapragada
,
Florian Pappenberger
,
Carolyn Reynolds
,
James D. Doyle
,
Luca Delle Monache
,
Chris Davis
,
Aneesh Subramanian
,
Ryan D. Torn
,
Jason M. Cordeira
,
Luca Centurioni
, and
Jennifer S. Haase
Open access
A. B. White
,
M. L. Anderson
,
M. D. Dettinger
,
F. M. Ralph
,
A. Hinojosa
,
D. R. Cayan
,
R. K. Hartman
,
D. W. Reynolds
,
L. E. Johnson
,
T. L. Schneider
,
R. Cifelli
,
Z. Toth
,
S. I. Gutman
,
C. W. King
,
F. Gehrke
,
P. E. Johnston
,
C. Walls
,
D. Mann
,
D. J. Gottas
, and
T. Coleman

Abstract

During Northern Hemisphere winters, the West Coast of North America is battered by extratropical storms. The impact of these storms is of paramount concern to California, where aging water supply and flood protection infrastructures are challenged by increased standards for urban flood protection, an unusually variable weather regime, and projections of climate change. Additionally, there are inherent conflicts between releasing water to provide flood protection and storing water to meet requirements for the water supply, water quality, hydropower generation, water temperature and flow for at-risk species, and recreation. To improve reservoir management and meet the increasing demands on water, improved forecasts of precipitation, especially during extreme events, are required. Here, the authors describe how California is addressing their most important and costliest environmental issue—water management—in part, by installing a state-of-the-art observing system to better track the area’s most severe wintertime storms.

Full access
F. Martin Ralph
,
Forest Cannon
,
Vijay Tallapragada
,
Christopher A. Davis
,
James D. Doyle
,
Florian Pappenberger
,
Aneesh Subramanian
,
Anna M. Wilson
,
David A. Lavers
,
Carolyn A. Reynolds
,
Jennifer S. Haase
,
Luca Centurioni
,
Bruce Ingleby
,
Jonathan J. Rutz
,
Jason M. Cordeira
,
Minghua Zheng
,
Chad Hecht
,
Brian Kawzenuk
, and
Luca Delle Monache

Abstract

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.

Free access
Alison Cobb
,
F. Martin Ralph
,
Vijay Tallapragada
,
Anna M. Wilson
,
Christopher A. Davis
,
Luca Delle Monache
,
James D. Doyle
,
Florian Pappenberger
,
Carolyn A. Reynolds
,
Aneesh Subramanian
,
Peter G. Black
,
Forest Cannon
,
Chris Castellano
,
Jason M. Cordeira
,
Jennifer S. Haase
,
Chad Hecht
,
Brian Kawzenuk
,
David A. Lavers
,
Michael J. Murphy Jr.
,
Jack Parrish
,
Ryan Rickert
,
Jonathan J. Rutz
,
Ryan Torn
,
Xingren Wu
, and
Minghua Zheng

Abstract

Atmospheric River Reconnaissance (AR Recon) is a targeted campaign that complements other sources of observational data, forming part of a diverse observing system. AR Recon 2021 operated for ten weeks from January 13 to March 22, with 29.5 Intensive Observation Periods (IOPs), 45 flights and 1142 successful dropsondes deployed in the northeast Pacific. With the availability of two WC-130J aircraft operated by the 53rd Weather Reconnaissance Squadron (53 WRS), Air Force Reserve Command (AFRC) and one National Oceanic and Atmospheric Administration (NOAA) Aircraft Operations Center (AOC) G-IVSP aircraft, six sequences were accomplished, in which the same synoptic system was sampled over several days.

The principal aim was to gather observations to improve forecasts of landfalling atmospheric rivers on the U.S. West Coast. Sampling of other meteorological phenomena forecast to have downstream impacts over the U.S. was also considered. Alongside forecast improvement, observations were also gathered to address important scientific research questions, as part of a Research and Operations Partnership.

Targeted dropsonde observations were focused on essential atmospheric structures, primarily atmospheric rivers. Adjoint and ensemble sensitivities, mainly focusing on predictions of U.S. West Coast precipitation, provided complementary information on locations where additional observations may help to reduce the forecast uncertainty. Additionally, Airborne Radio Occultation (ARO) and tail radar were active during some flights, 30 drifting buoys were distributed, and 111 radiosondes were launched from four locations in California. Dropsonde, radiosonde and buoy data were available for assimilation in real-time into operational forecast models. Future work is planned to examine the impact of AR Recon 2021 data on model forecasts.

Full access
F. Martin Ralph
,
Forest Cannon
,
Vijay Tallapragada
,
Christopher A. Davis
,
James D. Doyle
,
Florian Pappenberger
,
Aneesh Subramanian
,
Anna M. Wilson
,
David A. Lavers
,
Carolyn A. Reynolds
,
Jennifer S. Haase
,
Luca Centurioni
,
Bruce Ingleby
,
Jonathan J. Rutz
,
Jason M. Cordeira
,
Minghua Zheng
,
Chad Hecht
,
Brian Kawzenuk
, and
Luca Delle Monache
Full access