GOESR3 Periodic Reporting
Project Team:
Reporting Period: September 2012 - February 2013
Team Lead: Jaime Daniels
Team Members: Wayne Bresky, James Jung, Sharon Nebuda, Dave Santek
Project Title : Developing Assimilation Techniques for Atmospheric Motion Vectors Derived via a New
Nested Tracking Algorithm Derived for the GOES-R Advanced Baseline Imager (ABI)
Project Number: 50
A new Atmospheric Motion Vector (AMV) nested tracking algorithm developed for the GOES-R
Advanced Baseline Imager (ABI) has been demonstrated to significantly improve the slow speed bias inherent in the AMVs derived from geostationary satellites (Daniels and Bresky, 2010). The significant reduction in the speed bias of the AMVs derived using this nested tracking algorithm could benefit
Numerical Weather Prediction (NWP) by improving the analyses and the accuracy of NWP forecasts.
NESDIS/STAR and CIMSS propose to investigate the quality of the nested tracking AMVs with respect to the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS).
NESDIS/STAR will compare AMVs derived from the new nested tracking algorithm to AMVs derived from the current operational NESDIS AMV algorithm, as well as to winds obtained from various independent observational platforms (rawinsondes, wind profilers, etc.). NESDIS/STAR and CIMSS will then generate larger spatial and temporal datasets and compare them to the NCEP global analysis.
Quality control procedures used in the NCEP GFS analysis system will be reviewed by CIMSS and improved as necessary. Finally, impact studies using at least one operational geostationary satellite and an operational version of the NCEP GFS will be conducted by CIMSS.
If these new AMVs show improvements to NWP, NESDIS/STAR and CIMSS will aid in transitioning their use to the current GOES satellites and support implementation of the newly developed techniques into NCEP operations.
Reference:
Bresky, W. C., J. M. Daniels, A. A. Bailey and S. T. Wanzong, 2012: New Methods toward Minimizing
the Slow Speed Bias Associated with Atmpspheric Motion Vectors. J. Appl. Meteor. Climatol.
,
51 , 2137-2151.
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
Milestones

Complete generation of Meteosat/SEVIRI AMV datasets (“Test” AMVs) for two selected months
(summer and winter) using new nested tracking approach (“Test AMVs) and current NESDIS operational winds algorithm (“Control” AMVs) -
(November 2012)

Complete Meteosat/SEVIRI AMV/rawinsonde comparisons for “control” and “test” AMVs -
(November 2012)

Complete development of data assimilation quality control procedures for AMVs
–
(September 2012)

Complete a two season retroactive data assimilation experiment using “control” and
“test” AMVs. – (April 2013)

Complete generation of data assimilation and forecast impact statistics – (May 2013)

Completion of AMV BUFR table and encoder -
(May 2013)

Establish ability to routinely generate
Meteosat/SEVIRI AMV datasets (“Test” and
“Control” AMVs”) in real-time - (May 2013)

Develop presentation/report for Annual JCSDA
Workshop

Deliverables

Meteosat/SEVIRI AMV datasets (in any format) for the two selected months (summer and winter). (“Control” and
“Test” AMVs)

Validation report

Final report that outlines and describes new quality control procedures


Data assimilation experiment report

New AMV BUFR table and encoder

Meteosat/SEVIRI AMV datasets (“Control” and
“Test” AMVs)

Presentation for JCSDA
Workshop
Current Numerical Weather Prediction (NWP) AMV quality control procedures avoid parameters which rely on the departure from the forecast model background state. Traditional AMV quality control parameters of Quality Indicator (QI, Holmlund,1998) and Expected Error (EE, Le Marshall et al., 2004) have proven to be useful in predicting AMV quality. To maintain this preference, the modified QI parameter, which does not contain forecast information, was chosen to begin testing. Likewise, the use of EE was limited to screening slow winds with large error by testing against a threshold of the ratio of
EE to the observation speed. Two nested tracking parameters related to cluster size and cluster correlation were also included in initial quality control tests. The smallest values of these parameters showed an increase in wind vector difference RMSE with respect to the background state. The statistics
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from the two short runs were also examined for quality dependency on observation zenith angle, surface type, and cloud type. No concerns with data quality were found with these parameters.
Evaluation of the GOES-R AMVs assimilation statistics from two short GFS simulations during two different seasons was conducted. These results were examined to identify possible quality control procedures for the AMVs and provided an initial look at their performance in the GFS. The initial results did reveal a fast bias for the ABI Channel 8 Cloud Top Water Vapor (CTWV) AMVs which have since prompted an adjustment to the algorithm. The modified algorithm produces CTWV AMVs with increased height which subsequently reduces their bias compared to the background state as wind speed tends to increase with height in the troposphere. Results from these short GFS simulations have provided guidance for how to proceed with quality control settings for the longer seasonal simulations as well as identified a modification needed for the CTWV AMVs.
The first season results have revealed an observation minus analysis wind vector difference RMSE which is not ideal for the ABI Channel 14 (11um) infrared AMVs. Comparing with in situ data, radiosondes and wind information from the Aircraft Meteorological Data and Reporting (AMDAR)
World Meteorological Organization (WMO) pilot reports illustrates the desired performance of the satellite winds to be on the order of 2.5 m/s RMSE instead of the 4.0 m/s shown in figure 1. The large
RMSE for the GOES-R AMVs indicates either the quality control is too lenient or the specified observation error is too large. Tuning experiments, which varied the observation error in the GSI, showed a positive response by the wind vector difference RMSE. Examining observation minus background state statistics from this first seasonal run demonstrated that the EE would provide stronger screening of AMVs due to its inclusion of a forecast component. Both observation error and quality control settings continue to be investigated to optimize the GOES-R AMV performance in the GFS.
Once ideal settings are selected, the simulations which include the GOES-R AMVs will be completed.
We have completed the design of the new BUFR tables (B, D) for the new GOES-R AMV product. The new BUFR tables capture the new parameters output from the GOES-R AMV algorithm, many of which will be used to effectively assimilate the GOES-R AMVs.
References:
Holmlund, K. 1998. The utilization of statistical properties of satellite-derived atmospheric motion vectors to derive quality indicators. Wea. Forecasting 13 .1093–1105.
Le Marshall, J., Rea, A., Leslie, L., Seecamp, R. and Dunn, M. 2004. Error Characterization of
Atmospheric Motion Vectors. Aust. Met. Mag ., 53 , 123 – 131.
1.
Interaction with operational partners –
We attend and have communicated progress of this project with NCEP at NCEP’s bi-weekly GSI radiance meetings. We have given presentations on both the new GOES-R AMV algorithm and the results of assimilation tests involving AMVs generated from SEVIRI imagery (e.g., ABI proxy) using the new GOES-R AMV algorithm. The intent of this interaction with NCEP is to keep them informed of progress and to facilitate NCEP’s readiness for assimilating AMVs from GOES-R.
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2. Conference/workshop participation –
Nebuda, S., J. Jung, D. Santek, J. Daniels, and W. Bresky 2013: GOES-R AWG Atmospheric Motion Vectors:
First Look at Assimilation in NCEP GFS . Special Symposium on the Joint Center for Satellite Data
Assimilation , 93rd Annual AMS Meeting, Austin TX, 6-10 Janurary 2013.
Nebuda, S., J. Jung, D. Santek, J. Daniels, and W. Bresky 2013: GOES-R Atmospheric Motion Vectors:
Assimilation in NCEP GFS . NOAA Satellite Science Week Virtual Meeting , 18-22 March 2013.
3. Funding concerns – None
4. Outside project publicity –
5. Journal articles –
Bresky, W. C., J. M. Daniels, A. A. Bailey and S. T. Wanzong, 2012: New Methods toward Minimizing the Slow
Speed Bias Associated with Atmpspheric Motion Vectors. J. Appl. Meteor. Climatol.
, 51 , 2137-2151.
Plans for the next Reporting Period:
The final step of this effort will be to do a two season assimilation experiment that mimics operational processing to the extent possible. These experiments will be during different time periods than previously used. The different time periods will improve the chances of encountering issues which may affect the operational implementation. To this end, we will coordinate this demonstration activity with
NCEP. We will use a version of the NCEP GFS that is as close to the operational version (including horizontal resolution) as possible and that uses all available operational data types. This may be a preimplementation version of the Gridpoint Statistical Interpolation (GSI) software.
We will supply NCEP personnel with our new quality control procedures and forecast impact results from our two season assimilation experiments. Assuming the two season experiment shows improvement in the analysis and/or forecast, we will work with NESDIS and NCEP operations to prepare them for the eventual transfer of the new AMV derivation and assimilation techniques into
NESDIS and NCEP operations. From our previous experience, we understand that NCEP may recommend some further modifications to the GSI assimilation code to further optimize its performance.
We will make the necessary code modifications in the GSI as suggested by NCEP in order to complete the various assimilation tests and will make these software modifications available to NCEP. These modifications will be closely coordinated with the data assimilation branch of NCEP and will be made available for follow-on testing and possible implementation into NCEP operations. We will also complete and test any refinements to the AMV BUFR encoder software that may be necessary and document the contents of the updated AMV BUFR template. We will coordinate with NESDIS operations to transition the new AMV algorithm into their operational data stream.
We plan to meet with Jeff Ator (NCEP) to discuss the newly designed winds BUFR tables and make any changes suggested by Jeff.
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Figure 1. Vertical profiles of speed bias and wind vector difference RMSE for radiosondes, AMDAR winds and the GOES-R proxy AMVs with respect to the GFS analysis. Observations are within the region 35-
45N and 5W-45E and for 16 May – 30 June, 2012.
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