1991•Journal of Geophysical Research AtmospheresRequires access

A study of the use of scatterometer data in the European Centre for Medium‐Range Weather Forecasts operational analysis‐forecast model: 1. Quality assurance and validation

David L. T. Anderson, Anthony Hollingsworth, S. Uppala, Peter M. Woiceshyn

Open publisher page 17 citations

Abstract

This paper is Part 1 of a study concerned with the feasibility of using wind scatterometer data in a real‐time data assimilation system. The potential that such a system offers for quality assurance and validation of scatterometer data is illustrated using Seasat A satellite scatterometer (SASS) data. The results of passive assimilations of scatterometer data (where the SASS data are passed to the assimilation system but not used), and of active assimilations of scatterometer data (where the data are used), are presented. It is shown that assimilation allows validation and quality assurance of the scatterometer data, through comparisons with collocated ship data and through comparison with the wind fields generated by the assimilation. Intercomparison can be carried out quickly and efficiently at an operational weather centre where large amounts of data are collected, so allowing comprehensive quality assurance and validation of wind scatterometer data to be carried out in near‐real‐time. A number of comparisons are made which illustrate a speed‐dependent bias between SASS and ship observations and between SASS and forecast model, as well as directional irregularities in the SASS data.

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What this paper is about

This paper is Part 1 of a study concerned with the feasibility of using wind scatterometer data in a real‐time data assimilation system. The potential that such a system offers for quality assurance and validation of scatterometer data is illustrated using Seasat A satellite scatterometer (SASS) data. The results of passive assimilations of scatterometer data (where the SASS data are passed to the assimilation system but not used), and of active assimilations of scatterometer data (where the data are used), are presented. It is shown that assimilation allows validation and quality assurance of the scatterometer data, through comparisons with collocated ship data and through comparison with the wind fields generated by the assimilation. Intercomparison can be carried out quickly and efficiently at an operational weather centre where large amounts of data are collected, so allowing comprehensive quality assurance and validation of wind scatterometer data to be carried out in near‐real‐time. A number of comparisons are made which illustrate a speed‐dependent bias between SASS and ship observations and between SASS and forecast model, as well as directional irregularities in the SASS data.

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Available abstract

This paper is Part 1 of a study concerned with the feasibility of using wind scatterometer data in a real‐time data assimilation system. The potential that such a system offers for quality assurance and validation of scatterometer data is illustrated using Seasat A satellite scatterometer (SASS) data. The results of passive assimilations of scatterometer data (where the SASS data are passed to the assimilation system but not used), and of active assimilations of scatterometer data (where the data are used), are presented. It is shown that assimilation allows validation and quality assurance of the scatterometer data, through comparisons with collocated ship data and through comparison with the wind fields generated by the assimilation. Intercomparison can be carried out quickly and efficiently at an operational weather centre where large amounts of data are collected, so allowing comprehensive quality assurance and validation of wind scatterometer data to be carried out in near‐real‐time. A number of comparisons are made which illustrate a speed‐dependent bias between SASS and ship observations and between SASS and forecast model, as well as directional irregularities in the SASS data.

Key concepts: Scatterometer, Sass, Data assimilation, Meteorology, Environmental science, Quality assurance, Remote sensing, Data quality

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