Contrastive Analysis of Ecological Meteorological Observation Experiment in Urban Area and Woodland

Ecological Meteorological Observation (EMO) underlies ecological meteorological service, acquisition of parameters in weather and climate prediction models and most other researches related to ecological meteorology, observing and measuring the main features of water, atmosphere and soil in various ecological systems, which reflects the energy flow and matter cycle between the earth and atmosphere in diverse ecological systems. But ecological meteorological requirements about operations and services are both unable to be met on the basis of present thinly scattered ecological meteorological observation networks, even almost no corresponding product applications. In order to analyze the brief features of ecological meteorological observation data in different ecological systems, the basic functions of the first EWOS-1 observation instrument with the most complete ecological meteorological elements independently developed in China were introduced, then locations with basic observation environment characteristics of Guangzhou Urban Ecological Meteorological Observatory and Yunji Mountain Woodsy Ecological Meteorological Observatory were briefly described, by which the main sources of data samples were defined. The experiment exhibited a range of contrastive analyses of the observation figures between them, which covers visibility, atmospheric oxygen anion, oxygen content, PM2.5, ambient noise, the PH of soil, biological comfortable index, high temperature in simulative architectures, etc. The value-changing rules and inherent features of the two distinct ecological environments were revealed, with similarities and dissimilarities of reflections in various ecological elements of the two being verified. The experiment also provided a reference for normalized and standardized construction of Ecological Meteorological Observatory Networks. Besides, it would be a pattern in element-selecting in diverse ecological services, such as woodland, grassland, farmland, urban area and ocean, offering some relevant data support.