Recognizing the need for a long-term database to address the problem of global climate change, the National Climatic Data Center has embarked on a project called the Comprehensive Aerological Reference Data Set to create an upper-air database consisting of radiosondes, pibals, surface reports, and station histories for the Northern and Southern Hemispheres. Unfortunately, these data contain systematic errors caused by changes in instruments, data acquisition procedures, etc. It is essential that systematic errors be identified and/or removed before these data can be used confidently in the context of greenhouse-gas-induced climate modification. The purpose of this paper is to illustrate the use of an adaptive moving average filter in detecting systematic biases and to compare its performance with the Schwarz criterion, a parametric method. The advantage of the adaptive filter over traditional parametric methods is that it is less affected by seasonal patterns and trends. The filter has been appli... Abstract Recognizing the need for a long-term database to address the problem of global climate change, the National Climatic Data Center has embarked on a project called the Comprehensive Aerological Reference Data Set to create an upper-air database consisting of radiosondes, pibals, surface reports, and station histories for the Northern and Southern Hemispheres. Unfortunately, these data contain systematic errors caused by changes in instruments, data acquisition procedures, etc. It is essential that systematic errors be identified and/or removed before these data can be used confidently in the context of greenhouse-gas-induced climate modification. The purpose of this paper is to illustrate the use of an adaptive moving average filter in detecting systematic biases and to compare its performance with the Schwarz criterion, a parametric method. The advantage of the adaptive filter over traditional parametric methods is that it is less affected by seasonal patterns and trends. The filter has been appli...