Since the early 2000s, the Geonor and Pluvio automated weighing gauges have been part of the standard configuration of climate monitoring Reference Climate Stations (RCS) and the Meteorological Service of Canada Surface Weather and Climate Network (MSC SWCN). To more closely reflect true precipitation amounts, 15-minute observations from 312 Geonor and 34 Pluvio gauges were used to recover small amounts of 0.2 mm or less that were inadvertently filtered from 2002 until December 2013 by the hourly data-archiving CODECON algorithm. The precipitation data were quality controlled to remove spurious outliers and new derived hourly amounts were adjusted for wind undercatch according to the hourly 10-m wind speed, and dry bulb temperature. The recovered small amounts averaged for each month at individual stations were up to 16 mm in January, 10 mm in April, 8 mm in July, and 11 mm in October. The medians of average monthly adjustments for the wind undercatch were 74%, 38%, less than 1%, and 7% of the unadjusted amounts for January, April, July and October, respectively. Preliminary comparison of the monthly total precipitation amounts from parallel-adjusted automated and manual observations at 59 sites showed monthly amounts from the automated gauges generally higher during cold months in the Prairies and the North. Occasional large outliers in adjusted amounts at coastal stations in Newfoundland and Labrador and Quebec, and in the Prairies in winds greater than 9 m s⁻¹, suggest that the measured amounts were possibly already augmented by blowing snow. Further research is needed to determine how to adjust precipitation during very windy snowstorms. The adjusted hourly data sets were aggregated into the daily and monthly amounts for the climatological day ending at 06 GMT and 07 LST for compatibility with the two major observing programs at the principal aviation and cooperative networks.

自2000年代初以来，Geonor和Pluvio自动称重计已成为气候监测参考气候站（RCS）和加拿大气象局地面天气和气候网络（MSC SWCN）的标准配置的一部分。为了更准确地反映真实的降水量，使用了312个Geonor和34个Pluvio仪器进行的15分钟观测，回收了从2002年到2013年12月通过每小时数据归档CODECON算法无意过滤的少量0.2 mm或更小。对降水数据进行质量控制，以消除虚假的异常值，并根据每小时10米的风速和干球温度对新的每小时导出量进行风向不足调整。1月份每个站点每个月平均回收的少量水量高达16毫米，4月为10毫米，7月为8毫米，10月为11毫米。风力不足的平均每月调整中位数分别为1月，4月，7月和10月未调整金额的74％，38％，不到1％和7％。初步比较了在59个站点进行的并行调整的自动和手动观测的月总降水量，结果表明，在大草原和北部寒冷月份，来自自动测量仪的月降水量通常较高。在纽芬兰，拉布拉多和魁北克的沿海站点以及大草原中风量大于9毫秒的偶然较大异常值 初步比较了在59个站点进行的并行调整的自动和手动观测的月总降水量，结果表明，在大草原和北部寒冷月份，来自自动测量仪的月降水量通常较高。在纽芬兰，拉布拉多和魁北克的沿海站点以及大草原中风量大于9毫秒的偶然较大异常值 初步比较了在59个站点进行的并行调整的自动和手动观测的月总降水量，结果表明，在大草原和北部寒冷月份，来自自动测量仪的月降水量通常较高。在纽芬兰，拉布拉多和魁北克的沿海站点以及大草原中风量大于9毫秒的偶然较大异常值^-1，表明吹雪可能已经增加了测量量。需要进行进一步的研究以确定在大风暴风雪期间如何调整降水。调整后的每小时数据集被汇总为06 GMT和07 LST结束的气候日的每日和每月金额，以与主要航空和合作网络上的两个主要观测计划兼容。