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Lev S. Gandin

Abstract

A survey of the so-called complex quality control (CQC) of meteorological information is presented. The principles of the CQC approach are formulated. The CQC of rawinsonde height and temperature data at mandatory isobaric surfaces is described in detail. This CQC has been implemented into routine practice at the Hydro-meteorological Center in Moscow. It has been also applied at the World Data Center in Obninsk for the quality control of the FGGE Level IIb data. Some results of the CQC operations are presented. Possibilities and ways to apply the CQC approach to other kinds of meteorological information are also discussed.

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William G. Collins and Lev S. Gandin

Abstract

The Comprehensive Hydrostatic Quality Control (CHQC) of rawinsonde data on height and temperature at mandatory isobaric surfaces designed and implemented at the National Meteorological Center in Washington is described in detail. Main principles of the quality control design are discussed, followed by a brief description of the CHQC design and implementation at NMC. The CHQC algorithm is presented with particular emphasis on the Decision Making Algorithm. Numerous examples taken from the operational CHQC outputs illustrate the CHQC performance in general as well as its reaction to errors of various types and to their combinations.

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Lev S. Gandin and Allan H. Murphy

Abstract

Many skill scores used to evaluate categorical forecasts of discrete variables are inequitable, in the sense that constant forecasts of some events lead to better scores than constant forecasts of other events. Inequitable skill scores may encourage forecasters to favor some events at the expense of other events, thereby producing forecasts that exhibit systematic biases or other undesirable characteristics.

This Paper describes a method of formulating equitable skill scores for categorical forecasts of nominal and ordinal variables. Equitable skill scores are based on scoring matrices, which assign scores to the various combinations of forecast and observed events. The basic tenets of equitability require that (i) all constant forecasts—and random forecosts—receive the same expected score, and (ii) the elements of scoring matrices do not depend on the elements of performance matrices. Scoring matrices are assumed here to be symmetric and to possess other reasonable properties related to the nature of the underlying variable. To scale the elements of scoring matrices, the expected scores for constant and random forecasts are set equal to zero and the expected score for perfect forecasts is set equal to one. Taken together, these conditions are necessary but generally not sufficient to determine uniquely the elements of a scoring matrix. To obtain a unique scoring matrix, additional conditions must be imposed or some scores must be specified a priori.

Equitable skill scores are illustrated here by considering specific situations as well as numerical examples. These skill scores possess several desirable properties: (i) The score assigned to a correct forecast of an event increases as the climatological probability of the event decreases and (ii) scoring, matrices in n+1–event and n-event situations may be made consistent, in the sense that the former approaches the latter as the climatological probability of one of the events approaches zero. Several possible extensions and applications of this method are discussed.

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Lev S. Gandin, Lauren L. Morone, and William G. Collins

Abstract

A comprehensive hydrostatic quality control (CHQC) procedure for rawinsonde heights and temperatures was implemented into operational use at the National Meteorological Center (NMC) in December 1988. The CHQC uses a sophisticated decision-making algorithm to detect so-called rough errors in rawinsonde observations and to confidently correct many of them. Statistics gathered over a two-year period are presented to provide information on the frequency, geographical distribution, and origin of these errors. During this period, approximately 7% of the rawinsonde reports received at the NMC contained a hydrostatically detectable error. The number of errors has stayed relatively constant over the two-year period. The geographic distribution of the errors is uneven, with most of them originating in countries where many of the steps involved in computing and coding the reports are performed manually. Other characteristics as well indicate that almost all problems that are detected by the CHQC are caused by human error. This article proposes several measures as a means of reducing these errors. An analysis of the performance of the CHQC, which reveals that fully 50% of the errors that are detected by the CHQC are corrected automatically by it as well, is also presented. Information about the remaining errors along with suggested corrections is made available to specialists in NMC's Meteorological Operations Division where a final decision is made. This type of information has been discovered to also be quite useful in monitoring the quality of data in near-real time. Its use has led to a quick resolution of many problems associated with data transmission and decoding procedures. Several examples are discussed.

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