Search Results

You are looking at 1 - 10 of 12 items for :

  • Author or Editor: Aneesh Subramanian x
  • Bulletin of the American Meteorological Society x
  • Refine by Access: All Content x
Clear All Modify Search
Aneesh Subramanian
,
Stephan Juricke
,
Peter Dueben
, and
Tim Palmer

Abstract

Numerical weather prediction and climate models comprise a) a dynamical core describing resolved parts of the climate system and b) parameterizations describing unresolved components. Development of new subgrid-scale parameterizations is particularly uncertain compared to representing resolved scales in the dynamical core. This uncertainty is currently represented by stochastic approaches in several operational weather models, which will inevitably percolate into the dynamical core. Hence, implementing dynamical cores with excessive numerical accuracy will not bring forecast gains, may even hinder them since valuable computer resources will be tied up doing insignificant computation, and therefore cannot be deployed for more useful gains, such as increasing model resolution or ensemble sizes. Here we describe a low-cost stochastic scheme that can be implemented in any existing deterministic dynamical core as an additive noise term. This scheme could be used to adjust accuracy in future dynamical core development work. We propose that such an additive stochastic noise test case should become a part of the routine testing and development of dynamical cores in a stochastic framework. The overall key point of the study is that we should not develop dynamical cores that are more precise than the level of uncertainty provided by our stochastic scheme. In this way, we present a new paradigm for dynamical core development work, ensuring that weather and climate models become more computationally efficient. We show some results based on tests done with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) dynamical core.

Full access
Charlotte A. DeMott
,
Aneesh Subramanian
,
Shuyi Chen
,
Kyla Drushka
,
Yosuke Fujii
,
Adrienne Sutton
,
Janet Sprintall
, and
Dongxiao Zhang
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

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
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
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
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
Emily Shroyer
,
Amit Tandon
,
Debasis Sengupta
,
Harindra J. S. Fernando
,
Andrew J. Lucas
,
J. Thomas Farrar
,
Rajib Chattopadhyay
,
Simon de Szoeke
,
Maria Flatau
,
Adam Rydbeck
,
Hemantha Wijesekera
,
Michael McPhaden
,
Hyodae Seo
,
Aneesh Subramanian
,
R Venkatesan
,
Jossia Joseph
,
S. Ramsundaram
,
Arnold L. Gordon
,
Shannon M. Bohman
,
Jaynise Pérez
,
Iury T. Simoes-Sousa
,
Steven R. Jayne
,
Robert E. Todd
,
G. S. Bhat
,
Matthias Lankhorst
,
Tamara Schlosser
,
Katherine Adams
,
S. U. P Jinadasa
,
Manikandan Mathur
,
M. Mohapatra
,
E. Pattabhi Rama Rao
,
A. K. Sahai
,
Rashmi Sharma
,
Craig Lee
,
Luc Rainville
,
Deepak Cherian
,
Kerstin Cullen
,
Luca R. Centurioni
,
Verena Hormann
,
Jennifer MacKinnon
,
Uwe Send
,
Arachaporn Anutaliya
,
Amy Waterhouse
,
Garrett S. Black
,
Jeremy A. Dehart
,
Kaitlyn M. Woods
,
Edward Creegan
,
Gad Levy
,
Lakshmi H. Kantha
, and
Bulusu Subrahmanyam

Abstract

In the Bay of Bengal, the warm, dry boreal spring concludes with the onset of the summer monsoon and accompanying southwesterly winds, heavy rains, and variable air–sea fluxes. Here, we summarize the 2018 monsoon onset using observations collected through the multinational Monsoon Intraseasonal Oscillations in the Bay of Bengal (MISO-BoB) program between the United States, India, and Sri Lanka. MISO-BoB aims to improve understanding of monsoon intraseasonal variability, and the 2018 field effort captured the coupled air–sea response during a transition from active-to-break conditions in the central BoB. The active phase of the ∼20-day research cruise was characterized by warm sea surface temperature (SST > 30°C), cold atmospheric outflows with intermittent heavy rainfall, and increasing winds (from 2 to 15 m s−1). Accumulated rainfall exceeded 200 mm with 90% of precipitation occurring during the first week. The following break period was both dry and clear, with persistent 10–12 m s−1 wind and evaporation of 0.2 mm h−1. The evolving environmental state included a deepening ocean mixed layer (from ∼20 to 50 m), cooling SST (by ∼1°C), and warming/drying of the lower to midtroposphere. Local atmospheric development was consistent with phasing of the large-scale intraseasonal oscillation. The upper ocean stores significant heat in the BoB, enough to maintain SST above 29°C despite cooling by surface fluxes and ocean mixing. Comparison with reanalysis indicates biases in air–sea fluxes, which may be related to overly cool prescribed SST. Resolution of such biases offers a path toward improved forecasting of transition periods in the monsoon.

Full access