Article
Meteorology & Atmospheric Sciences
Joonlee Lee, Myong-In Lee, Joong-Bae Ahn
Summary: This study investigates the impact of imbalanced oceanic initial conditions on seasonal prediction skills using a coupled forecasting system. The results show that balanced initial conditions significantly improve prediction skills, particularly in the winter forecasts for ocean, sea ice, and atmospheric variables. The study also highlights the negative impact of initialization shock caused by spatial discontinuity and dynamic imbalance on prediction skills.
Article
Meteorology & Atmospheric Sciences
Tianying Liu, Zhengyu Liu, Yuchu Zhao, Shaoqing Zhang
Summary: Using a climate model and data assimilation method, this study found that the bias of reversed zonal sea surface temperature gradient in the equatorial Atlantic is mainly caused by the extratropical atmosphere, particularly the northern extratropics. The northern extratropics play a dominant role in the spring equatorial westerly bias and the zonal SST gradient bias.
JOURNAL OF CLIMATE
(2022)
Article
Geosciences, Multidisciplinary
Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Eric J. Anderson, Ayumi Fujisaki-Manome, John G. W. Kelley, Greg E. Mann, Andrew D. Gronewold, Philip Chu, Sean G. T. Kelley
Summary: The accurate initialization of lake temperatures is crucial for the application of lake models in earth-system prediction models. Traditional methods have limitations in capturing the temporal characteristics of lake temperatures, and an alternative lake-initialization method using a two-way coupled cycling approach has been developed. This method has been found to decrease errors in lake surface temperature and improve accuracy compared to other estimates.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Meteorology & Atmospheric Sciences
Ho-Hsuan Wei, Aneesh C. Subramanian, Kristopher B. Karnauskas, Danni Du, Magdalena A. Balmaseda, Beena B. Sarojini, Frederic Vitart, Charlotte A. DeMott, Matthew R. Mazloff
Summary: This study examines how assimilating in situ ocean observations influences the initial ocean state and subseasonal forecasts over the tropical Pacific. The results show that assimilating ocean data leads to smaller sea-surface temperature biases, but does not improve the mixed-layer depth in the forecasts.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2023)
Article
Meteorology & Atmospheric Sciences
Jamese Sims, Tsengdar Lee, Dorothy Koch, Brian Gross, Ivanka Stajner, David Considine, Steven Pawson, Daryl Kleist, Ron Gelaro, Stylianos Flampouris, Youngsun Jung, Marc Gasbarro
Summary: Scientists gathered online to discuss potential ideas and specific subjects on data assimilation and Earth system modeling, organized by center leads and program managers from NOAA and NASA.
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
(2022)
Article
Engineering, Ocean
Minjie Xu, Yuzhe Wang, Jicai Zhang, Dezhou Yang, Xunqiang Yin, Yanqiu Gao, Guansuo Wang, Xianqing Lv
Summary: This study utilizes a regional high-resolution ocean model and assimilates satellite observations and Argo data to improve modeling results during the 2020 cold spell event. The assimilation of data effectively reduces model biases and captures the ocean's response to the cold spell event.
APPLIED OCEAN RESEARCH
(2022)
Article
Meteorology & Atmospheric Sciences
Xu Lu, Xuguang Wang
Summary: The study focuses on improving intensity predictions for strong storms through a four-dimensional incremental analysis update (4DIAU) method, which was found to be more effective than traditional methods.
MONTHLY WEATHER REVIEW
(2021)
Article
Engineering, Marine
Georgy Shapiro, Jose M. Gonzalez-Ondina
Summary: This paper presents a simple and computationally efficient method for creating a high-resolution regional model nested within a coarse-resolution, data-assimilating parent model. The method, called Nesting with Downscaling and Data Assimilation (NDA), reduces bias and root mean square errors (RMSE) of the child model and ensures it stays close to reality. By using a complete 3D set of output data from the parent model, the child model is able to assimilate observations and reduce errors without going through a complex assimilation process.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Geography, Physical
Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, Edward Blanchard-Wrigglesworth
Summary: Uncertain or inaccurate parameters in sea ice models can impact seasonal predictions and climate change projections. Applying an ensemble Kalman filter to estimate parameters in the model can improve performance at local scales, especially during the forecast period when no observations are available for assimilation.
Article
Meteorology & Atmospheric Sciences
Ankur Srivastava, Suryachandra A. Rao, Maheswar Pradhan, Prasanth A. Pillai, V. S. Prasad
Summary: Reasonable seasonal prediction skill for the Indian summer monsoon rainfall has been achieved using the burst ensemble approach to enhance lead time. The model forecasts, although slightly under-dispersive, satisfactorily represent the spread-error relationship for major tropical oceanic climate modes. Monsoon teleconnections are found to be sensitive to the initialization strategy, with the burst initialization method providing a gain of 1-month lead time without compromising prediction skill.
THEORETICAL AND APPLIED CLIMATOLOGY
(2021)
Article
Geosciences, Multidisciplinary
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjoerg Kutterer, Harald Kunstmann
Summary: Tropospheric water vapor is crucial for cloud and precipitation formation, but its variable nature makes high-resolution mapping challenging. GNSS meteorology and InSAR satellite remote sensing offer potential solutions with their complementary temporal and spatial resolutions. By assimilating these datasets with atmospheric models, high-resolution tropospheric water vapor fields can be determined. This study presents a collection of datasets describing water vapor states and variations in the GNSS Upper Rhine Graben Network region, obtained through GNSS and InSAR methods. The assimilation of these datasets improves the accuracy of water vapor measurements, particularly in summer.
EARTH SYSTEM SCIENCE DATA
(2022)
Article
Multidisciplinary Sciences
Sarah M. Kang, Yechul Shin, Hanjun Kim, Shang-Ping Xie, Shineng Hu
Summary: Most state-of-art models predict a weakening of the Walker circulation and a reduced east-west temperature gradient in the equatorial Pacific under global warming, but the causes of this projection are still unknown. Through a series of model experiments, this study decomposes the global warming response into the contributions from direct CO2 forcing, sea ice changes, and regional ocean heat uptake. The results show that the CO2 forcing dominates the slowdown of the Walker circulation, while Antarctic sea ice changes and local ocean heat release are the main drivers for the reduced temperature gradient in the equatorial Pacific.
Article
Meteorology & Atmospheric Sciences
D. Koshin, M. Kohma, K. Sato
Summary: This study analyzed the climatological features of the intraseasonal oscillation (ISO) in the equatorial mesosphere and lower thermosphere. The results showed the global structure of ISO anomalies and the driving mechanisms of ISO.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Article
Meteorology & Atmospheric Sciences
Yu Zhang, Shiyun Yu, Dillon J. Amaya, Yu Kosaka, Sarah M. Larson, Xudong Wang, Jun-Chao Yang, Malte F. Stuecker, Shang-Ping Xie, Arthur J. Miller, Xiaopei Lin
Summary: Investigating the North PMM and South PMM through a mechanically decoupled climate model simulation revealed new insights into their associated atmospheric forcing and response processes.
JOURNAL OF CLIMATE
(2021)
Article
Meteorology & Atmospheric Sciences
Francois Counillon, Noel Keenlyside, Thomas Toniazzo, Shunya Koseki, Teferi Demissie, Ingo Bethke, Yiguo Wang
Summary: Anomaly coupling improves the accuracy and reliability of reanalysis in the tropical Atlantic and enhances seasonal prediction skill in the equatorial Atlantic. However, it may slightly dampen the amplitude of Atlantic Nino and Nina events, and there is a predictability barrier in June forecasting.
Article
Meteorology & Atmospheric Sciences
Robert R. King, James While, Matthew J. Martin, Daniel J. Lea, Benedicte Lemieux-Dudon, Jennifer Waters, Enda O'Dea
Article
Meteorology & Atmospheric Sciences
Matthew J. Martin, Robert R. King, James While, Ana Barbosa Aguiar
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2019)
Article
Meteorology & Atmospheric Sciences
E. K. Fiedler, C. Mao, S. A. Good, J. Waters, M. J. Martin
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2019)
Article
Meteorology & Atmospheric Sciences
Robert R. King, Daniel J. Lea, Matthew J. Martin, Isabelle Mirouze, Julian Heming
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2020)
Article
Environmental Sciences
Simon Good, Emma Fiedler, Chongyuan Mao, Matthew J. Martin, Adam Maycock, Rebecca Reid, Jonah Roberts-Jones, Toby Searle, Jennifer Waters, James While, Mark Worsfold
Article
Environmental Sciences
Rebecca Reid, Simon Good, Matthew J. Martin
Article
Meteorology & Atmospheric Sciences
M. J. Martin, E. Remy, B. Tranchant, R. R. King, E. Greiner, C. Donlon
Summary: Observing system experiments have shown that assimilating satellite salinity data can significantly reduce salinity errors in the tropical Pacific and tropical Atlantic, as well as temperature and sea level anomaly errors in the central tropical Pacific and Amazon outflow regions.
JOURNAL OF OPERATIONAL OCEANOGRAPHY
(2022)
Article
Geochemistry & Geophysics
Lokesh Kumar Pandey, Suneet Dwivedi, Matthew Martin
Summary: The study utilizes the NEM version 3.6 model to conduct high-resolution ocean simulations in the Indian Ocean region, successfully capturing the spatiotemporal variations of ocean parameters such as temperature, salinity, and currents. The FTLE analysis reveals significant seasonal changes in predictability in the Bay of Bengal region.
Article
Meteorology & Atmospheric Sciences
Davi Mignac, Matthew Martin, Emma Fiedler, Ed Blockley, Nicolas Fournier
Summary: This study demonstrates the improvements in sea ice forecasting by assimilating sea ice thickness (SIT) observations from CryoSat-2 and SMOS into the global ocean-sea ice forecasting system FOAM. The assimilation of SIT observations from both satellites enhances the accuracy of sea ice thickness forecasts and improves the short-term predictive skill of sea ice concentration.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Meteorology & Atmospheric Sciences
Anitha Gera, Ankur Gupta, Ashis K. Mitra, Nagarjuna D. Rao, Imranali M. Momin, Madhavan N. Rajeeavan, Sean F. Milton, Gill M. Martin, Matthew J. Martin, Jennifer Waters, Daniel Lea
Summary: The study assesses the skill of the extended range prediction system (NERP) based on the Unified global coupled modelling system over India during the summer monsoon period. The model shows good prediction skill for rainfall anomaly forecasts in the short term, with moderate to good skill in some regions even in weeks 3-4. The model also accurately represents the variability of the monsoon seasonal cycle and the spatial distribution of summer monsoon rainfall peaks.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Meteorology & Atmospheric Sciences
Bo Dong, Keith Haines, Matthew Martin
Summary: A simple smoother is introduced for data adjustments in sequentially generated reanalysis products, utilizing knowledge of future assimilation increments. The decay time parameter is applied to account for memory decay timescales in the ocean. Results show significant improvements in temperature, salinity, sea surface height, ocean currents, and other variables after applying the smoother method.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2021)
Article
Meteorology & Atmospheric Sciences
Tsz Yan Leung, Amos S. Lawless, Nancy K. Nichols, Daniel J. Lea, Matthew J. Martin
Summary: This study investigates the hybrid approach of combining traditional covariance modeling strategy with flow-dependent estimates of the ocean's error covariance structures in oceanic data assimilation. The experiments show that the hybrid approach produces time-varying, more anisotropic, and vertically less uniform analysis increments. Incorporating the hybrid oceanic covariances into a weakly coupled data assimilation system alters the sea-surface temperature along the cyclone's path and leads to further SST differences due to the different representations of the cyclone's cold wake.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Meteorology & Atmospheric Sciences
Daniel J. Lea, James While, Matthew J. Martin, Anthony Weaver, Andrea Storto, Marcin Chrust
Summary: In this study, a global ocean and sea-ice ensemble forecasting system was developed based on the FOAM system. The system utilized the NEMO and CICE models for data assimilation and showed promising results in forecasting Sea-Level Anomaly, temperature, and salinity. The hybrid ensemble/variational assimilation technique demonstrated improved reliability and accuracy compared to the deterministic system.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Geography, Physical
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, Rachel Tilling
Summary: This study successfully demonstrates the feasibility of assimilating CryoSat-2 sea ice thickness (SIT) observations to improve the analysis and forecast of sea ice thickness. The assimilation leads to significant improvements in the SIT analysis and forecast fields, especially in the Canadian Arctic. The findings are validated by independent in situ observations, confirming the effectiveness of the assimilation.
Article
Meteorology & Atmospheric Sciences
Robert R. King, Matthew J. Martin
Summary: The assimilation of SWOT observations can reduce errors in sea surface height and surface current speeds, but the presence of correlated errors can degrade data quality and reduce prediction accuracy.