Abstract: Meteorological data are an essential basis for describing regional climate characteristics. Long-term meteorological observation has a profound effect on improving meteorological forecast, the capability of preventing and mitigating weather-related disasters, and the awareness of response to climate change. The meteorological factor is one of the important elements for the field observation at all stations of the Chinese Ecosystem Research Network (CERN). Dinghushan Forest Ecosystem Research Station (hereafter Dinghushan Station) is located in the north margin of the south subtropical zone of Guangdong Province, a typical subtropical monsoon climate with abundant water and heat resources. Based on the meteorological observation field and in line with the atmospheric environment observation standard of CERN, we conducted a long-term observation and data quality control of key meteorological elements at the station. This dataset offers a detailed and systematic report on the monitoring data of the VISILA automatic observation system in the meteorological observation field of Dinghushan Station from 2005 to 2018. It provides data support for the research and production of relevant industries of the regional national economy, such as agriculture, forestry, water conservancy, and other fields.
Keywords: subtropical zone; forest ecosystem; meteorological data; long-term dynamics
|Title||A meteorological dataset observed by Dinghushan Forest Ecosystem Research Station (2005–2018)|
|Data authors||Liu Peiling, Zhang Qianmei, Liu Xiaodong, Meng Ze, Li Yuelin, Liu Shizhong, Chu Guowei, Zhang Deqiang, Liu Juxiu, Zhou Guoyi|
|Data corresponding author||Zhang Qianmei (email@example.com); Liu Xiaodong (firstname.lastname@example.org)|
|Geographical scope||Dinghushan Forest Ecosystem Research Station of the Chinese Ecosystem Research Network (23°09′21" N–23°11′30" N, 112°30′39", E–112°33′41" E) located in Dinghushan National Nature Reserve, Zhaoqing City, Guangdong Province|
|Data volume||227 KB (2856 entries)|
|Data service system||<http://www.sciencedb.cn/dataSet/handle/966>|
|Sources of funding||Dinghushan Forest Ecosystem Research Station of the Chinese Ecosystem Research Network (CERN), National Scientific Observation and Research Field Station of Dinghushan Forest Ecosystem in Guangdong of National Ecosystem Research Network, Ministry of Science and Technology of the People’s Republic of China (CRERN), the Archives of Chinese Academy of Sciences (No. Y821341001), and the Forestry Science and Technology Innovation Platform in Guangdong Province (No. 2019KJCX021).|
|Dataset composition||The dataset contains one data file consisting of 2,856 entries with 17 variable tables: temperature, dew point temperature, relative humidity, air pressure, water vapor pressure, sea-level pressure, precipitation, 10 min monthly average wind speed, solar radiation, monthly average soil surface temperature, and monthly average soil temperature (5, 10, 15, 20, 40, 60, and 100 cm).|
As an essential basis for describing regional climatic characteristics, meteorological data are key elements for conducting weather forecasts, climate prediction, and scientific research[1-2]. To conduct long-term meteorological observation in specific areas is of far-reaching significance on the improvement of the meteorological forecast, the capability of preventing and mitigating weather-related disasters, and the awareness of and response to climate change.
As an important member of the Chinese Ecosystem Research Network (CERN) and National Ecosystem Research Network of China (CNERN), Dinghushan Forest Ecosystem Research Station (Dinghushan Station) is also the 17th station of the Man and the Biosphere Program in the United Nations Educational, Scientific, and Cultural Organization, which is responsible for basic and demonstrative observation tasks, such as regional atmospheric background, terrestrial ecosystem fluxes, and atmospheric nitrogen deposition[3-5]. Dinghushan Station is a well-known comprehensive research base of ecosystem ecology at home and abroad. It plays a significant role in ecological research fields, such as forest ecosystems, global change, ecosystem processes, and forest hydrology. It is located in the northern margin of the south subtropical region of China. This region has a humid monsoon climate, and it is abundant in water and heat resources. The dry season (from October to March) and wet season (from April to September) are distinct. Precipitation in the wet season accounts for about 80% of the total annual precipitation. The typical forest vegetation types in the station, such as monsoon evergreen broad-leaved forest, mixed coniferous and broad-leaved forest, Pinus massoniana coniferous forest, ravine rainforest, and montane evergreen broad-leaved forest, have been protected for a long time. Since its establishment in 1978, the scientific research of Dinghushan Station has gone through such stages as background investigation, community structure and dynamics, biomass and productivity, the structure and functions of the ecosystem, the response and adaptability of ecosystem key processes, and their coupling under the background of global climate change[3,5]. It has made significant contributions to the development of ecology, biodiversity protection, and the sustainable development of society and the environment in China.
Meteorological data are the basic elements of daily observation of water, soil, climate, and biology in the positioning station. The standardized and complete meteorological dataset plays a significant role in the development and innovation of ecology, geography, hydrology, and other related disciplines in the new era. Fourteen years (2005–2018) data of key meteorological elements monitored using the VISILA automatic observation system in the meteorological observation field of Dinghushan Station were collated systematically in this dataset. Then, it was reported in the form of data papers to provide basic support for scientific research, ecological security, and the development of related industries of the national economy.
2.1 Description of the sampling plots
The meteorological observation field of Dinghushan Station was established in 1992 at Mitaling, the buffer zone of Dinghushan National Nature Reserve. The altitude is 100 m, and the longitude and latitude of the center point are 112°32'57.51"E and 23°9'50.84"N, respectively. Figure 1 shows the specific facility layout of the sample site. The standard meteorological observation field equipped with an AMRS-1 meteorological radiation automatic observation system has been established since 1997. In November 2004, the VISILA automatic observation system (MILOS520, Vaisala company, Finland) was used instead, and in 2014, the system’s model was updated to MAWS301.
2.2 Data sources
The monthly data of meteorological factors of Dinghushan Station from 2005 to 2018 were published in this dataset, obtained from daily meteorological monitoring data collected using VISILA automatic observation system. The staff of the ecological station first used the “Ecological meteorological workstation” software to download the original observation data. They conducted a report-processing program of the software to generate the meteorological data and radiation data tables (M report for short). Then, the daily observation data in the monthly data file can be manually confirmed or corrected using the relevant functions in the M report. For example, when abnormal data occurred, the data could be retained or deleted according to the system prompts. After completing the daily data statistics and audit of each month, the M report was converted into the standard meteorological data report (A report for short), and the statistical processing for ten days and a month were conducted[2,6-7]. The relevant data of manual observation were entered into the meteorological report, and the program performed statistical processing for ten days and a month. Finally, the A report meeting the requirements of the observation specification was submitted to the atmospheric subcenter. The atmospheric subcenter checked the A report again and then submitted it to the comprehensive center database to complete the final processing and audit [6,8]. This dataset was generated using the statistics of the A report returned after the audit by the atmospheric subcenter.
2.3 Observation instrument facilities and data acquisition accuracy
Table 1 presents the collection and processing methods of the original data of various meteorological indicators.
|Observation index||Observation equipment||Observation level||Data unit||Decimal places||Accuracy of raw data acquisition|
dew point temperature
|1.5 m from the ground inside the radiation shield||°C||1||One measurement every 10 s, six measurements per minute in total, removed the maximum and minimum values, then took the average value and stored it as the observation value per minute. After taking the punctuality, the 00-min observation value was stored as the punctuality data.|
|1.5 m from the ground inside the radiation shield||%||0|
|Air pressure, water vapor pressure, sea-level pressure||DPA501|
|Less than 1 m from the ground||hPa||1|
ground temperature sensor
|0 cm above the ground and 5, 10, 15, 20, 40, 60, 100 cm below the ground||°C||1|
|70 cm from the ground||mm||1||The accumulated precipitation of 12 h from 8:00 a.m. to 8:00 p.m. every day was observed manually.|
|10 min average wind speed||WAA151 or WAC151 wind speed sensor||10 m wind pole||m/s||1||The wind speed data was collected once per second, and the 3-s moving average value was calculated in the 1 s step. The 1-min moving average wind speed was calculated in the 3 s step, and then the 10-min moving average wind speed was calculated in the 1 min step. The 10-min average wind speed of 00 min was stored at the time of punctuality.|
|Solar radiation||MAWS110 system||1.5 m above the ground||MJ/m2 or mol/ m2||3||The irradiance was measured once every 10 s and six times per minute (instantaneous value), and the average value was taken after removing one maximum value and one minimum value. The irradiance was collected at punctuality 00 min (local mean solar time), and the accumulated irradiance was calculated simultaneously.|
Specific data processing methods: When the data of the observation indexes about air temperature, dew point temperature, relative humidity, air pressure, 10-min average wind speed, and soil temperature were missing at a certain time, the former and latter two-timing data were used for interpolation. If two or more consecutive timing data were missing, interpolation could not be performed, and processing was still performed as missing[2,6-7,9]. If there were a lack of 24 regular observation records in a day, the daily average value was calculated according to four regular records of 02:00, 08:00, 14:00, and 20:00. If there were one or more missing times of four-time records, but there were five or fewer missing times of total time records on that day, the daily statistics should be made according to the actual records. If there were six or more missing time records in a day, the daily average value should not be calculated. The monthly average value was obtained by dividing the total value of the daily average value after quality control by corresponding days. The monthly average extreme value was obtained by dividing the total value of daily extreme value by corresponding days. The monthly maximum and minimum values were derived from the daily timing data statistics of the monthly report[2,6-7].
When the solar radiation exposure was not measured for several hours, but not all day, the daily total is calculated according to the actual records. When there was no measurement in the entire day, the daily total value was not calculated[2,6]. When the amount of total daily in a month was missing for nine days or fewer, the total monthly value was equal to the sum of the actual total daily records. In the case of missing tests for ten days or more, monthly statistics would not be made in that month, and it would be treated as a missing test. The total monthly precipitation was equal to the sum of daily accumulated precipitation. The monthly maximum was the maximum daily total of the month.
3.1 Database structure
This dataset consists of 17 data tables, given as follows:
Table of air temperature and dew point temperature: year, month, daily average monthly average, daily maximum monthly average, daily minimum monthly average, monthly maximum, date of monthly maximum, monthly minimum, and date of monthly minimum.
Table of relative humidity: year, month, daily average monthly average, daily minimum monthly average, monthly minimum, and date of monthly minimum.
Table of air pressure, water vapor pressure, sea-level pressure: year, month, daily average monthly average, daily maximum monthly average, daily minimum monthly average, monthly maximum, date of monthly maximum, monthly minimum, and date of monthly minimum.
Table of precipitation: year, month, monthly total value (manual observation), daily precipitation monthly maximum, and date of monthly maximum.
Table of 10-min monthly average wind speed: year, month, monthly average wind speed, monthly dominant wind direction, maximum wind speed, direction of maximum wind, occurrence date of maximum wind, and occurrence moment of maximum wind.
Table of solar radiation: year, month, monthly total radiation, reflected radiation, ultraviolet radiation, net radiation, photosynthetic effective radiation, sunshine hours, and sunshine minutes.
Table of the monthly average soil temperature at the surface and in layers (5, 10, 15, 20, 40, 60, and 100 cm): year, month, daily average monthly average, daily maximum monthly average, daily minimum monthly average, monthly maximum, date of monthly maximum, monthly minimum, and date of monthly minimum.
3.2 Data missing situation
Sensors, collectors, transmission channel failures, and other reasons caused the problem of data missing. It was unsuitable for calculating the corresponding monthly statistical value when the data missing reached a certain amount. In this situation, it has been indicated by “–” in the data table. The missing data appeared in the dew point temperature table (32 records), water vapor pressure table (18 records), sea-level pressure table (5 records), solar radiation table (18 records), and table of monthly average soil temperature for 5–100 cm (all appeared in August 2007, seven records). In total, the cumulative number of missing records of monthly statistical indicators was 80, accounting for 2.80% of the total number of records in the dataset, and the data integrity was high.
The meteorological data management of Dinghushan Station consists of two parts: meteorological monitoring and database management. The meteorological monitoring management examines and maintains the sensors and lines, such as sensor sensitivity inspection, wiping and cleaning, and cable maintenance. The database management was used to save, backup, collate, and make statistics of the original observation data. The original data measured by MILOS520 was easy to be stored in the wrong date and time, garbled and lost due to interference of the acquisition program and quality problems of the power supply system. The M report plays a significant role in data processing. The report-processing program examines the quality of the observation data. The staff entered the corresponding inspection parameters according to historical data and theoretical calculation values of the observation elements in different seasons in the local area. In the process of generating the M report, the program will mark the data exceeding the threshold red according to the inspection parameters and generates the log report file of the inspection result simultaneously. Then, the staff can correct the errors of the original observation data in time according to the log report. If the time difference between the adjacent data was too large, it may indicate a signal of sensor damage. The staff can discover the instrument fault and repair it as soon as possible. When a factor was calibrated by the sensor and the instrument coefficient changed greatly, the twice processing method for the data was added to the CERN meteorological data software (2010), which can correct the monthly observation data and effectively avoid a large number of data errors. Table 2 shows the specific quality control and evaluation methods of different meteorological observation indexes.
Table 2 Specific quality control and evaluation methods of different meteorological observation indexes[2,6,8]
|Observation index||Data quality control and evaluation methods|
|Air temperature, dew point temperature||(1) Air temperature was greater than or equal to the dew point temperature. (2) Divided the data beyond the range of climatological limit value (−80℃ to 60℃) as wrong data. (3) The maximum variation for 1 min was 3℃, and the minimum variation within 1 h was 0.1℃. (4) The variation range of air temperature in 24 h was less than 50℃. (5) The temperature data of one or more adjacent stations, similar to the underlying surface and surrounding environment of Dinghushan Station, were used to calculate the temperature value of Dinghushan Station, then compared with the observation and calculated values. If their differences exceed the threshold, the observation value is judged as abnormal data.|
|Relative humidity||(1) The relative humidity was between 0% and 100%. (2) The timing relative humidity was greater than or equal to the minimum daily relative humidity. (3) The dry bulb temperature was greater than or equal to the wet-bulb temperature (except during glaciation).|
|Air pressure||(1) Divided the data beyond the range of climatological limit value (300−1100 hPa) as wrong data. (2) The observed air pressure was not less than the daily minimum pressure and not more than the daily maximum pressure. The station’s altitude was higher than 0 m, the air pressure of Dinghushan Station should be less than the sea-level pressure. (3) The absolute value of air pressure changed for 24 h was less than 50 hPa.|
|Precipitation||(1) The rainfall intensity should not exceed the range of 0−400 mm/min. (2) When the precipitation was more than 0.0 mm or trace amount, there should be a weather phenomenon of precipitation. (3) Due to the difference between the automatic monitoring and manual measurement data of Dinghushan Station, and the manual measurement data was close to that of the Gaoyao station, the manual statistical data was used uniformly.|
|10-min average wind speed||(1) Divided the data beyond the range of climatological limit value (0−75 m/s) as wrong data.|
(2) The average wind speed in 10 min was less than the maximum wind speed.
|Solar radiation||(1) The maximum value of the total radiation was not more than the climatological limit of 2000 W/m2. (2) The difference between the current instantaneous and previous values was less than the maximum amplitude of 800 W/m2. (3) The hourly total radiation was greater than or equal to the hourly net radiation, hourly reflected radiation, and hourly ultraviolet radiation. The maximum of total radiation usually appeared around noon, except for cloudy, rainy, and snowy days. (4) The accumulated hourly total radiation should be less than the total radiation at the top of the atmosphere at the same geographical location. The accumulated value of hourly total radiation could be slightly greater than the accumulated value of the hourly total radiation at the same geographical location under the condition of high atmospheric transmittance and a clear sky. When the cumulative value of the hourly total radiation of nighttime observations was less than 0, data was recorded with 0.|
|Monthly average soil surface temperature, monthly average soil temperature in layers||(1) Divided the data beyond the range of climatological limit value (−90 to 90℃) as wrong data. (2) The maximum allowable variation for 1 min was 5℃, while the minimum variation range for 1 h was 0.1℃. (3) The surface temperature measured regularly was greater than or equal to the minimum daily surface temperature and less than or equal to the maximum daily surface temperature. (4) The 24-h variation range of soil temperature in layers of 0, 5, 10, 15, 20, 40, 60, and 100 cm was less than 60℃, 40℃, 40℃, 40℃, 30℃, 30℃, 20℃, and 20℃, respectively.|
To ensure the integrity, accuracy, and consistency of data, the staff conducted manual observation on air pressure, air temperature, relative humidity, wind direction, wind speed, surface temperature, and precipitation. The database manager can compare manual monitoring data with automatic observation data and correct it. And manual monitoring data can be used to interpolate the missing data in the automatic monitoring database. Furthermore, the temperature and precipitation data of Dinghushan meteorological station were compared with those of Gaoyao meteorological station in Zhaoqing City, Guangdong Province (112°16'9.6″ E, 23°1'12″ N, the observation site is 40 m above sea level, the pressure sensor is 41.9 m high). The published data of various meteorological indicators were analyzed rationally and processed. First, examine whether there were extreme values in the data table and verify the size relationship of each index in the sub table, such as the monthly maximum value should be greater than the daily maximum monthly average. When there were unreasonable data, the original monitoring data within the corresponding time were queried, and the data fluctuation, the number of missing values, and the data statistical processes should be observed. Then, the corresponding correction should be made according to the measured data.
Meteorological data reflects the water and heat conditions of the observation site. Besides, it reflects the processes of air-flow movement, solar radiation balance, and water balance in the vegetation-atmosphere interface. Meteorological data is the basic data for researchers in agriculture, forestry, water conservancy, and other related fields to understand the relationship between local climate, topography, soil, and vegetation characteristics[11-12].
The problem of water and heat has always been the core of ecosystem environment research. Regional climate characteristics are closely related to the growth and geographical distribution of vegetation, litter decomposition, soil respiration, and other biochemical processes. Scientific understanding of the climate response mechanism of terrestrial ecosystems has become one of the hotspots of global climate change research[14-15]. Based on the formation mechanism of the subtropical zone in China, the hydrothermal environment of Dinghushan is extremely sensitive to global climate change. Thus, Dinghushan is a typical area for studying the feedbacks and regulation processes of terrestrial ecosystems to climate change. The detailed and systematic meteorological data report could provide a reference for the development and innovation research of ecology, geography, hydrology, and other related disciplines under the background of climate and land cover change.
With the aggravation of climate warming and the increase in precipitation variability, ecological problems, such as flood disasters and forest fires have become more frequent; it has a significant impact on forest ecological resources and residents’ life. Meteorological data plays a significant role in meteorological forecasts and public meteorological services. The standardized systematic, and long-term local meteorological elements observation and data sharing can effectively improve the accuracy of the regional meteorological forecast, especially extreme weather warning, and provide essential support for public services, disaster prevention, and mitigation. In addition, long-term dynamic analysis of meteorological factors is conducive for stable prediction of ecosystem structure and function. It provides the basis for regional ecological security assessment and ecological civilization construction from a climate perspective .
LIU PL, ZHANG QM, LIU XD, et al. A meteorological dataset observed by Dinghushan Forest Ecosystem Research Station (2005–2018). Science Data Bank, 2020. (April 06, 2020). DOI: 10.11922/sciencedb.966.
ZHOU GY, WEI XH, CHEN XZ, et al. Global pattern for the effect of climate and land cover on water yield. Nature Communications, 6 (2015): 5918.
LI M, HU B, HAN XZ, et al. A meteorological dataset observed by Hailun Agroecosystem Experimental Station, Chinese Academy of Sciences (2009–2018). China Scientific Data, 5 (2020). (2020-03-14). DOI: 10.11922/csdata.2019.0034.zh.
Dinghushan Forest Ecosystem Research Station, Chinese Academy of Sciences. Bulletin of Chinese Academy of Sciences, 32 (2017): 1047-1049.
ZHANG QM. Datasets from Chinese Ecosystem Positioning Observation and Research (Volume on Forest Ecosystems: Dinghushan Station of Guangdong (1998-2008)). Beijing: China Agricultural Press, 2011.
ZHANG QM, ZHANG DQ, LI YL, et al. Construction of an intelligent field observation station: a case study from Dinghushan Forest Ecosystem Research Station. Ecological Science, 34 (2015): 139-145.
QIAO TH & SONG CC. Meteorological and radiant dataset observed by Sanjiang Plain Experimental Station of Wetland Ecology, Chinese Academy of Sciences during 2009-2018. China Scientific Data, 5 (2020). (2019-11-10). DOI: 10.11922/csdata.2019.0049.zh.
YANG Y, WANG KQ, HU ZY, et al. A radiation dataset observed by Alpine Ecosystem Observation and Experiment Station of Gongga Mountain, Chinese Academic of Sciences, during 1998–2018. China Scientific Data, 2020. (2020-11-10). DOI: 10.11922/csdata.2020.0045.zh.
HU B, LIU GR, WANG YS, et al. Observation indexes and specifications for atmospheric environment in terrestrial ecosystem. Beijing: China Environmental Science Press, 2019.
HAO LP & ZHOU LJ. Some experience in learning the new version of ground meteorological observation specifications. Inner Mongolia Meteorology, 2012: 46-48.
HU B, LIU GR, WANG YS, et al. Quality control and management of meteorological radiation monitoring in ecosystem. Beijing: China Environmental Science Press, 2012.
LIU XD, ZHOU GY, CHEN XZ, et al. Forest microclimate change along with the succession and response to climate change in south subtropical region. Acta Ecologica Sinica, 34 (2014): 2755-2764.
ZHOU GY. Principle and application of water and heat in ecosystem. Beijing: Meteorological Press, 1997.
LIU YL, ZHUANG QL, MIRALLES D, et al. Evapotranspiration in Northern Eurasia: Impact of forcing uncertainties on terrestrial ecosystem model estimates. Journal of Geophysical Research Atmospheres, 120 (2015): 2647-2660.
MIAO Y, WANG GL, PARR D, et al. Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs. Climatic Change, 127 (2014): 257-271.
LI YL, LIU SZ, HUANG JQ, et al. A dataset of monthly recovery amount of litter fall production in a monsoon evergreen broad-leaved forest at Dinghushan (1999-2016). China Scientific Data, 5 (2020). (2020-02-24). DOI: 10.11922/csdata.2019.0073.zh.
WEI SJ, LUO SS, LUO BZ, et al. Occurrence regularity of forest fire under the background of climate change. Forestry and Environmental Science, 36 (2020): 133-143.
LIU PL, ZHANG QM, LIU XD, et al. A meteorological dataset observed by Dinghushan Forest Ecosystem Research Station (2005–2018). China Scientific Data 5 (2020). (December 27, 2020). DOI: 10.11922/csdata.2020.0016.zh.