Editorial Type:
Article
Article Type:
Research Article

Enhanced Automated Quality Control Applied to High-Density Satellite-Derived Winds

Kenneth Holmlund EUMETSAT, Darmstadt, Germany

Search for other papers by Kenneth Holmlund in
Current site
Google Scholar
PubMed Close
,
Christopher S. Velden Cooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin

Search for other papers by Christopher S. Velden in
Current site
Google Scholar
PubMed Close
, and
Michael Rohn European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Search for other papers by Michael Rohn in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Mar 2001
DOI:
https://doi.org/10.1175/1520-0493(2001)129<0517:EAQCAT>2.0.CO;2
Page(s):
517–529
Restricted access

Abstract

The coverage and quality of atmospheric motion vectors (AMVs) derived from geostationary satellite imagery have improved considerably over the past few years. This is due not only to the deployment of the new generation of satellites, but is also a result of improved data processing and automated quality control (AQC) schemes. The postprocessing of the Geostationary Operational Environmental Satellite (GOES) derived displacement vectors at the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has been fully automated since early 1996. At the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) AQC was used as support to the manual quality control (MQC) for Meteosat vector fields until September 1998 when the MQC was discontinued and fully replaced with the automated procedure. The AQC schemes at the two organizations are quite diverse. Based on a method developed at the University of Wisconsin–Cooperative Institute for Meteorological Satellite Studies, the NOAA/NESDIS AQC involves an objective three-dimensional recursive filter analysis of the derived wind fields. The fit of each vector to that analysis yields a recursive filter flag (RFF). The AQC scheme employed at EUMETSAT derives a quality indicator (QI) for each individual vector based on the properties of the vector itself and its consistency with other AMVs in close proximity. Mainly relying on satellite data, this QI-based scheme has been proven to provide a good estimate of the reliability of the derived displacements, but it fails to identify fleets of winds that are consistently assigned to a wrong height. The RFF-based scheme is capable of readjusting the heights attributed to the wind vectors, which yields a better fit to the analysis and ancillary data. These quality estimates can be employed by the user community to select the part of the vector field that best suits their application, as well as in data assimilation schemes for optimizing the data selection procedures. Even though both schemes have already been successfully implemented into operational environments, the possibility exists to exploit the advantages of both approaches to create a superior combined methodology.

In order to substantiate the differences of the two methodologies, the two schemes were applied to the high-density AMV fields derived during the 1998 North Pacific Experiment from GOES and the Geostationary Meteorological Satellite (controlled by the Japan Meteorological Agency) multispectral imagery data. The performance of the two schemes was evaluated by examining departures from the analysis fields of the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation scheme, and a new combined methodology was further evaluated by looking at the impact on medium-range forecasts verified against the operational forecasts. It will be shown that the new combined AQC approach yields superior ECMWF forecast results.

Corresponding author address: Kenneth Holmlund, EUMETSAT, Am Kavalleriesand 31, 64295 Darmstadt, Germany.

Abstract

The coverage and quality of atmospheric motion vectors (AMVs) derived from geostationary satellite imagery have improved considerably over the past few years. This is due not only to the deployment of the new generation of satellites, but is also a result of improved data processing and automated quality control (AQC) schemes. The postprocessing of the Geostationary Operational Environmental Satellite (GOES) derived displacement vectors at the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has been fully automated since early 1996. At the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) AQC was used as support to the manual quality control (MQC) for Meteosat vector fields until September 1998 when the MQC was discontinued and fully replaced with the automated procedure. The AQC schemes at the two organizations are quite diverse. Based on a method developed at the University of Wisconsin–Cooperative Institute for Meteorological Satellite Studies, the NOAA/NESDIS AQC involves an objective three-dimensional recursive filter analysis of the derived wind fields. The fit of each vector to that analysis yields a recursive filter flag (RFF). The AQC scheme employed at EUMETSAT derives a quality indicator (QI) for each individual vector based on the properties of the vector itself and its consistency with other AMVs in close proximity. Mainly relying on satellite data, this QI-based scheme has been proven to provide a good estimate of the reliability of the derived displacements, but it fails to identify fleets of winds that are consistently assigned to a wrong height. The RFF-based scheme is capable of readjusting the heights attributed to the wind vectors, which yields a better fit to the analysis and ancillary data. These quality estimates can be employed by the user community to select the part of the vector field that best suits their application, as well as in data assimilation schemes for optimizing the data selection procedures. Even though both schemes have already been successfully implemented into operational environments, the possibility exists to exploit the advantages of both approaches to create a superior combined methodology.

In order to substantiate the differences of the two methodologies, the two schemes were applied to the high-density AMV fields derived during the 1998 North Pacific Experiment from GOES and the Geostationary Meteorological Satellite (controlled by the Japan Meteorological Agency) multispectral imagery data. The performance of the two schemes was evaluated by examining departures from the analysis fields of the European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation scheme, and a new combined methodology was further evaluated by looking at the impact on medium-range forecasts verified against the operational forecasts. It will be shown that the new combined AQC approach yields superior ECMWF forecast results.

Corresponding author address: Kenneth Holmlund, EUMETSAT, Am Kavalleriesand 31, 64295 Darmstadt, Germany.

Save
Cover Monthly Weather Review
Monthly Weather Review

  • Bhatia, R. C., P. N. Khanna, and S. Prasad, 1996: Improvements in Automated Cloud Motion Vectors (CMWs) derivation scheme using INSAT VHRR data. Proc. Third Int. Winds Workshop, Ascona, Switzerland, EUMETSAT, 37–43.

  • Courtier, P., E. Andersson, W. Heckley, J. Pailleux, D. Vasiljevic, M. Hamrud, A. Hollingsworth, F. Rabier, and M. Fisher, 1998: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). Part 1: Formulation. Quart. J. Roy. Meteor. Soc.,124, 1783–1807.

  • Hayden, C. M., and R. J. Purser, 1995: Recursive filter objective analysis of meteorological fields: Applications to NESDIS operational processing. J. Appl. Meteor.,34, 3–15.

  • Holmlund, K., 1998: The utilization of statistical properties of satellite-derived atmospheric motion vectors to derive quality indicators. Wea. Forecasting,13, 1093–1104.

  • Järvinen, H., and P. UndĂ©n, 1997: Observation screening and background quality control in the ECMWF 4D-Var data assimilation system. ECMWF Research Dept. Tech. Memo. 236, 33 pp. [Available from ECMWF Librarian, Reading, Berkshire RG2 9AX, United Kingdom.].

  • Kelly, G. A., 1993: Numerical experiments using cloud motions winds at ECMWF. Proc. Second Int. Winds Workshop, Tokyo, Japan EUMETSAT, 227–244.

  • Klinker, E., F. Rabier, G. Kelly, and J.-F. Mahfouf, 1999:The ECMWF operational implementation of four-dimensional variational assimilation. Part III: Experimental results and diagnostics with operational configuration. ECMWF Research Dept. Tech. Memo 273, 27 pp. [Available from ECMWF Librarian, Reading, Berkshire RG2 9AX, United Kingdom.].

  • Langland, R. H., and Coauthors, 1999: The North Pacific Experiments (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc.,80, 1363–1384.

  • Le Marshall, J., N. Pescod, B. Seaman, G. Mills, and P. Stewart, 1994: An operational system for generating cloud drift winds in the Australian region and their impact on numerical weather prediction. Wea. Forecasting,9, 361–370.

  • Nieman, S. J., W. P. Menzel, C. M. Hayden, D. Gray, S. T. Wanzong, C. S. Velden, and J. Daniels, 1997: Fully automated cloud-drift winds in NESDIS operations. Bull. Amer. Meteor. Soc.,78, 1121–1133.

  • Rabier, F., A. McNally, E. Andersson, P. Courtier, P. UndĂ©n, J. Eyre, A. Hollingsworth, and F. Bouttier, 1998: The ECMWF implementation of three dimensional variational assimilation (3D-Var). Part II: Structure functions. Quart. J. Roy. Meteor. Soc.,124, 1809–1830.

  • Rohn, M., G. Kelly, and R. W. Saunders, 1998a: Experiments with atmospheric motion vectors at ECMWF. Proc. Fourth Int. Wind Workshop, Saanenmöser, Switzerland, EUMETSAT, 139–146.

  • ——, ——, and ——, 1998b: Use and impact of atmospheric motion vectors at ECMWF. Proc. 9th Conf. on Satellite Meteorology and Oceanography, Paris, France, Amer. Meteor. Soc., SociĂ©tĂ© MĂ©tĂ©or. de France, and EUMETSAT, 360–363.

  • Schmetz, J., K. Holmlund, J. Hoffman, B. Strauss, B. Mason, V. Gärtner, A. Koch, and L. van de Berg, 1993: Operational cloud-motion winds from Meteosat infrared images. J. Appl. Meteor.,32, 1206–1225.

  • Szunyogh, I., Z. Toth, S. Majumdar, R. Morss, C. Bishop, and S. Lord, 1999: Ensemble-based targeted observations during NORPEX. Preprints, Third Symp. on Integrated Observing Systems, Dallas, TX, Amer. Meteor. Soc., 74–77.

  • Tokuno, M., 1996: Operational system for extracting cloud motion and water vapour motion winds from GMS-5 image data. Proc. Third Int. Winds Workshop, Ascona, Switzerland, EUMETSAT, 61–68.

  • Tomassini, C., 1981: Objective analysis of cloud fields. Proc. Satellite Meteorology of the Mediterranean, Erice, Italy, ESA, SP-159, 73–78.

  • Velden, C. S., 1996: Winds derived from geostationary satellite moisture channel observations: Applications and impact on numerical weather prediction. Meteor. Atmos. Phys.60, 37–46.

  • ——, C. M. Hayden, M. S. J. Nieman, W. P. Menzel, S. Wanzong, and J. S. Goers, 1997: Upper-Tropospheric winds derived from geostationary satellite water vapor observations. Bull. Amer. Meteor. Soc.,78, 173–195.

  • ——, T. L. Olander, and S. Wanzong, 1998: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset methodology, description, and case analysis. Mon. Wea. Rev.,126, 1202–1218.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 894 606 30
PDF Downloads 266 85 2