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Aneesh C. Subramanian
,
Markus Jochum
,
Arthur J. Miller
,
Raghu Murtugudde
,
Richard B. Neale
, and
Duane E. Waliser

Abstract

This study assesses the ability of the Community Climate System Model, version 4 (CCSM4) to represent the Madden–Julian oscillation (MJO), the dominant mode of intraseasonal variability in the tropical atmosphere. The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group’s prescribed diagnostic tests are used to evaluate the model’s mean state, variance, and wavenumber–frequency characteristics in a 20-yr simulation of the intraseasonal variability in zonal winds at 850 hPa (U850) and 200 hPa (U200), and outgoing longwave radiation (OLR). Unlike its predecessor, CCSM4 reproduces a number of aspects of MJO behavior more realistically.

The CCSM4 produces coherent, broadbanded, and energetic patterns in eastward-propagating intraseasonal zonal winds and OLR in the tropical Indian and Pacific Oceans that are generally consistent with MJO characteristics. Strong peaks occur in power spectra and coherence spectra with periods between 20 and 100 days and zonal wavenumbers between 1 and 3. Model MJOs, however, tend to be more broadbanded in frequency than in observations. Broad-scale patterns, as revealed in combined EOFs of U850, U200, and OLR, are remarkably consistent with observations and indicate that large-scale convergence–convection coupling occurs in the simulated MJO.

Relations between MJO in the model and its concurrence with other climate states are also explored. MJO activity (defined as the percentage of time the MJO index exceeds 1.5) is enhanced during El Niño events compared to La Niña events, both in the model and observations. MJO activity is increased during periods of anomalously strong negative meridional wind shear in the Asian monsoon region and also during strong negative Indian Ocean zonal mode states, in both the model and observations.

Full access
Charlotte A. DeMott
,
Aneesh Subramanian
,
Shuyi Chen
,
Kyla Drushka
,
Yosuke Fujii
,
Adrienne Sutton
,
Janet Sprintall
, and
Dongxiao Zhang
Full access
William E. Chapman
,
Luca Delle Monache
,
Stefano Alessandrini
,
Aneesh C. Subramanian
,
F. Martin Ralph
,
Shang-Ping Xie
,
Sebastian Lerch
, and
Negin Hayatbini

Abstract

Deep-learning (DL) postprocessing methods are examined to obtain reliable and accurate probabilistic forecasts from single-member numerical weather predictions of integrated vapor transport (IVT). Using a 34-yr reforecast, based on the Center for Western Weather and Water Extremes West-WRF mesoscale model of North American West Coast IVT, the dynamically/statistically derived 0–120-h probabilistic forecasts for IVT under atmospheric river (AR) conditions are tested. These predictions are compared with the Global Ensemble Forecast System (GEFS) dynamic model and the GEFS calibrated with a neural network. In addition, the DL methods are tested against an established, but more rigid, statistical–dynamical ensemble method (the analog ensemble). The findings show, using continuous ranked probability skill score and Brier skill score as verification metrics, that the DL methods compete with or outperform the calibrated GEFS system at lead times from 0 to 48 h and again from 72 to 120 h for AR vapor transport events. In addition, the DL methods generate reliable and skillful probabilistic forecasts. The implications of varying the length of the training dataset are examined, and the results show that the DL methods learn relatively quickly and ∼10 years of hindcast data are required to compete with the GEFS ensemble.

Full 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
Minghua Zheng
,
Luca Delle Monache
,
Xingren Wu
,
F. Martin Ralph
,
Bruce Cornuelle
,
Vijay Tallapragada
,
Jennifer S. Haase
,
Anna M. Wilson
,
Matthew Mazloff
,
Aneesh Subramanian
, and
Forest Cannon

Abstract

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.

Full access
Minghua Zheng
,
Luca Delle Monache
,
Xingren Wu
,
F. Martin Ralph
,
Bruce Cornuelle
,
Vijay Tallapragada
,
Jennifer S. Haase
,
Anna M. Wilson
,
Matthew Mazloff
,
Aneesh Subramanian
, and
Forest Cannon
Full 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
Anna M. Wilson
,
Alison Cobb
,
F. Martin Ralph
,
Vijay Tallapragada
,
Chris Davis
,
James Doyle
,
Luca Delle Monache
,
Florian Pappenberger
,
Carolyn Reynolds
,
Aneesh Subramanian
,
Forest Cannon
,
Jason Cordeira
,
Jennifer Haase
,
Chad Hecht
,
David Lavers
,
Jonathan J. Rutz
, and
Minghua Zheng
Full access
Yolande L. Serra
,
Jennifer S. Haase
,
David K. Adams
,
Qiang Fu
,
Thomas P. Ackerman
,
M. Joan Alexander
,
Avelino Arellano
,
Larissa Back
,
Shu-Hua Chen
,
Kerry Emanuel
,
Zeljka Fuchs
,
Zhiming Kuang
,
Benjamin R Lintner
,
Brian Mapes
,
David Neelin
,
David Raymond
,
Adam H. Sobel
,
Paul W. Staten
,
Aneesh Subramanian
,
David W. J. Thompson
,
Gabriel Vecchi
,
Robert Wood
, and
Paquita Zuidema
Full access