scholarly journals The Major Stratospheric Sudden Warming of January 2013: Analyses and Forecasts in the GEOS-5 Data Assimilation System

2015 ◽  
Vol 143 (2) ◽  
pp. 491-510 ◽  
Author(s):  
Lawrence Coy ◽  
Steven Pawson
Keyword(s):  
Data Assimilation ◽  
Polar Vortex ◽  
Wave Activity ◽  
Time Data ◽  
Weather System ◽  
Stratospheric Sudden Warming ◽  
The Pacific ◽  
Data Assimilation System ◽  
Wave Activity Flux ◽  
Assimilation System

Abstract The major stratospheric sudden warming (SSW) of 6 January 2013 is examined using output from the NASA Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System version 5 (GEOS-5) near-real-time data assimilation system (DAS). GEOS-5 analyses showed that the SSW of January 2013 was a major warming by 1200 UTC 6 January, with a wave-2 vortex-splitting pattern. Upward wave activity flux from the upper troposphere (~23 December 2012) displaced the ~10-hPa polar vortex off the pole in a wave-1 pattern, enabling the poleward advection of subtropical values of Ertel potential vorticity (EPV) into a developing anticyclonic circulation region. While the polar vortex subsequently split (wave-2 pattern) the wave-2 forcing [upward Eliassen–Palm (EP) flux] was smaller than what was found in recent wave-2, SSW events, with most of the forcing located in the Pacific hemisphere. Investigation of a rapidly developing tropospheric weather system over the North Atlantic on 28–29 December 2012 showed strong transient upward wave activity flux from the storm with influences up to 10 hPa; however, the Pacific hemisphere wave forcing remained dominate at this time. Results from the GEOS-5 five-day forecasts showed that the forecasts accurately predicted the major SSW of January 2013. The overall success of the 5-day forecasts provides motivation to produce regular 10-day forecasts with GEOS-5, to better support studies of stratosphere–troposphere interaction.

scholarly journals “Big Data Assimilation” Revolutionizing Severe Weather Prediction

2016 ◽  
Vol 97 (8) ◽  
pp. 1347-1354 ◽  
Author(s):  
Takemasa Miyoshi ◽  
Masaru Kunii ◽  
Juan Ruiz ◽  
Guo-Yuan Lien ◽  
Shinsuke Satoh ◽  
...  
Keyword(s):  
Big Data ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Severe Weather ◽  
Weather System ◽  
3 Dimensional ◽  
Parallel Simulations ◽  
Convective Weather ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Sudden local severe weather is a threat, and we explore what the highest-end supercomputing and sensing technologies can do to address this challenge. Here we show that using the Japanese flagship “K” supercomputer, we can synergistically integrate “big simulations” of 100 parallel simulations of a convective weather system at 100-m grid spacing and “big data” from the next-generation phased array weather radar that produces a high-resolution 3-dimensional rain distribution every 30 s—two orders of magnitude more data than the currently used parabolic-antenna radar. This “big data assimilation” system refreshes 30-min forecasts every 30 s, 120 times more rapidly than the typical hourly updated systems operated at the world’s weather prediction centers. A real high-impact weather case study shows encouraging results of the 30-s-update big data assimilation system.

scholarly journals The Reanalysis for the Global Ensemble Forecast System, version 12.

2021 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Anna Shlyaeva ◽  
Gary Bates ◽  
Sherrie Fredrick ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Reanalysis Data ◽  
Ensemble Forecast ◽  
Time Data ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Data Set ◽  
Control Member ◽  
Data Assimilation System ◽  
Assimilation System

AbstractNOAA has created a global reanalysis data set, intended primarily for initialization of reforecasts for its Global Ensemble Forecast System, version 12 (GEFSv12), which provides ensemble forecasts out to +35 days lead time. The reanalysis covers the period 2000-2019. It assimilates most of the observations that were assimilated into the operational data assimilation system used for initializing global predictions. These include a variety of conventional data, infrared and microwave radiances, Global Positioning System radio occultations, and more. The reanalysis quality is generally superior to that from NOAA’s previous-generation Climate Forecast System Reanalysis (CFSR), demonstrated in the fit of short-term forecasts to the observations and in the skill of 5-day deterministic forecasts initialized from CFSR vs. GEFSv12. Skills of reforecasts initialized from the new reanalyses are similar but slightly lower than skills initialized from a pre-operational version of the real-time data assimilation system conducted at the higher, operational resolution. Control member reanalysis data on vertical pressure levels are made publicly available.

scholarly journals Flow Analysis in the Pacific Ocean Using Data Assimilation System

1997 ◽  
Vol 41 ◽  
pp. 521-528
Author(s):  
Akira Wada ◽  
Seiichiro Nagoya ◽  
Tairyu Takano ◽  
Masataka Hishida ◽  
Motoji Kawanabe
Keyword(s):  
Pacific Ocean ◽  
Data Assimilation ◽  
Flow Analysis ◽  
The Pacific ◽  
The Pacific Ocean ◽  
Using Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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