scholarly journals Use and Impact of Automated Aircraft Data in a Global 4DVAR Data Assimilation System

2003 ◽  
Vol 131 (8) ◽  
pp. 1865-1877 ◽  
Author(s):  
Carla Cardinali ◽  
Lars Isaksen ◽  
Erik Andersson
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Positive Impact ◽  
Impact Study ◽  
Forecast Performance ◽  
The North ◽  
Medium Range ◽  
Data Assimilation System ◽  
Aircraft Data ◽  
Assimilation System

Abstract The use of automated aircraft data [Aircraft Meteorological Data Relay (AMDAR) and Aircraft Communication Addressing and Reporting System (ACARS)] has recently been extended in ECMWF's operational 4DVAR data assimilation system. Herein, a modified data selection procedure is reported on that allows the use of more aircraft profiling data during the aircraft's ascending and descending phase, and more of the most frequent reports at cruise level. It is shown that the accuracy of analyzed jet streams is improved through these changes, as verified against independent (non–real time) aircraft data that had not been used in the experiments. The modifications are shown to have a clear positive impact on the short- and medium-range forecast performance. The revised aircraft usage was implemented operationally in January 2002. The impact in 4DVAR of profiles from American and European automated aircraft in ascending and descending phase has been tested in a data denial impact study, for January and July 2001. This particular impact study was run partly on the request of the WMO/Commission for Basic Systems (CBS) Expert Team on data requirements and the redesign of the global observing system. Their interest is in testing whether a modern data assimilation system (such as 4DVAR) obtains substantial benefit from the aircraft profiles, which sample very irregularly in space and time, given that America and Europe are relatively well covered by radiosondes and wind profilers. The results show a substantial positive impact of the profiling aircraft data on analysis and forecast accuracy. The short-range forecast performance is improved over North America, the North Atlantic, and Europe. In the medium range a clear positive impact is found in the North Atlantic, the European, and Arctic areas in the winter period, and beyond day 6 in the summer period. These results are statistically significant and support the ongoing WMO initiative for further expansion of the AMDAR/ACARS coverage. The results also illustrate the effectiveness of 4DVAR with respect to observations that are irregularly distributed in space and time.

scholarly journals TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 633-656 ◽  
Author(s):  
P. Sakov ◽  
F. Counillon ◽  
L. Bertino ◽  
K. A. Lisæter ◽  
P. R. Oke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
North Atlantic ◽  
The Arctic ◽  
Ensemble Spread ◽  
The North ◽  
State Dependent ◽  
The North Atlantic ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.

scholarly journals TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

2012 ◽  
Vol 9 (2) ◽  
pp. 1519-1575 ◽  
Author(s):  
P. Sakov ◽  
F. Counillon ◽  
L. Bertino ◽  
K. A. Lisæter ◽  
P. R. Oke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
North Atlantic ◽  
The Arctic ◽  
Ensemble Spread ◽  
The North ◽  
State Dependent ◽  
The North Atlantic ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation and the sea ice. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level.

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.

Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

2017 ◽  
Vol 32 (4) ◽  
pp. 1603-1611 ◽  
Author(s):  
Brett T. Hoover ◽  
David A. Santek ◽  
Anne-Sophie Daloz ◽  
Yafang Zhong ◽  
Richard Dworak ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

scholarly journals An Impact Study of Five Remotely Sensed and Five In Situ Data Types in the Eta Data Assimilation System

2002 ◽  
Vol 17 (2) ◽  
pp. 263-285 ◽  
Author(s):  
Tom H. Zapotocny ◽  
W. Paul Menzel ◽  
James P. Nelson ◽  
James A. Jung
Keyword(s):  
Data Assimilation ◽  
Remotely Sensed ◽  
Impact Study ◽  
Data Types ◽  
In Situ Data ◽  
Data Assimilation System ◽  
Assimilation System
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