Abstract: When it is clear or there are just clouds without rain, wide spread non-precipitation meteorological echoes are often observed near the radars, which have notable effect on rainfall estimation and radar data assimilation. To discriminate these echoes efficiently, a Non-Precipitation Meteorological echo Detection Algorithm (NPMDA) based on fuzzy logical and SCIT is developed. Using surface and star data, a standard to identify non-precipitation meteorological echoes is established. With data observed by the SA radar in Beijing, after analyzing the statistical characteristics of non-precipitation meteorological echo, membership functions are obtained. Echoes are assembled to pieces using SCIT. If an echo piece meets one of some special conditions, the whole PPI would be recognized as precipitation echoes. If no piece meets any of the special conditions, the threshold is set as 0.5, for the echoes which can't be assembled to pieces. If the area of an echo piece is less than 2000 km², the echoes in the piece are also recognized with a threshold of 0.5. If the area of an echo piece reaches 2000 km², the attribute value of this echo piece would be calculated with an algorithm based on fuzzy logical. The threshold of echoes in the piece would be calculated with the attribute value of this echo piece. If the attribute value of one piece is greater than or equal to 0.5, the echoes in the piece would be recognized with the threshold of 0.5. If the attribute value of one piece is less than 0.5, the echoes in the piece would be recognized with a threshold obtained by subtracting the attribute value of the piece from 1. The echoes would be recognized as non-precipitation meteorological echoes if their attribute values are greater than the threshold or equal to it. After using dynamic thresholds, the thresholds of most precipitation echoes would be greater than 0.5. The method can efficiently avoid this situation that the precipitation echoes are recognized as non-precipitation echoes. It should also be noticed that the thresholds of some non-precipitation echoes would also be greater than 0.5, which would decrease the identifiable accuracy for non-precipitation echoes to some extent. The algorithm does well with most of non-precipitation meteorological echoes and precipitation echoes, but it does not handle some weak precipitation echoes well. Compared with the ICADA used by NCAR, the identifiable accuracy for non-precipitation echoes can be improved remarkably with NPMDA, and the erroneous recognition for precipitation is also decreased obviously.