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Quality quantifier of indirect measurements
Simone Ceccherini1, Bruno Carli, Piera Raspollini
1Istituto di Fisica Applicata Nello Carrara del Consiglio Nazionale delle Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy. S.Ceccherini@ifac.cnr.it
Optics Express
|March 16, 2012
Summary
A new measurement quality quantifier (MQQ) is introduced for indirect measurements. This MQQ is additive for combined independent measurements and applicable to ill-posed problems, aiding atmospheric data assessment.
Area of Science:
- Geosciences and Remote Sensing
- Data Science and Measurement Theory
Background:
- Indirect measurements present challenges in quality assessment.
- Standardized quantifiers are needed for complex data fusion and ill-posed problems.
Purpose of the Study:
- To introduce a novel Measurement Quality Quantifier (MQQ) for indirect measurements.
- To establish a robust method for assessing data quality in complex scenarios, including atmospheric sensing.
Main Methods:
- Developed an MQQ based on covariance and Jacobian matrices of observations.
- Demonstrated MQQ calculation using covariance and averaging kernel matrices of retrieved quantities.
- Derived an information distribution quantifier from MQQ for continuous data.
Main Results:
- The proposed MQQ is additive for independent measurements.
- MQQ can be determined in absolute terms, even for ill-posed problems.
- Successfully applied MQQ for quality assessment of atmospheric ozone data from IASI and MIPAS instruments.
Conclusions:
- The MQQ provides a reliable and versatile metric for indirect measurement quality.
- The MQQ framework enhances the assessment of atmospheric data, improving scientific interpretation.
- The derived information distribution quantifier offers deeper insights into measurement data.
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