CN111047083A - Processing method of stepping type vorticity covariance observation data - Google Patents

Processing method of stepping type vorticity covariance observation data Download PDF

Info

Publication number
CN111047083A
CN111047083A CN201911232647.6A CN201911232647A CN111047083A CN 111047083 A CN111047083 A CN 111047083A CN 201911232647 A CN201911232647 A CN 201911232647A CN 111047083 A CN111047083 A CN 111047083A
Authority
CN
China
Prior art keywords
flux
data
sub
period
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911232647.6A
Other languages
Chinese (zh)
Other versions
CN111047083B (en
Inventor
余雪洋
叶思源
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qingdao Institute of Marine Geology
Original Assignee
Qingdao Institute of Marine Geology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qingdao Institute of Marine Geology filed Critical Qingdao Institute of Marine Geology
Priority to CN201911232647.6A priority Critical patent/CN111047083B/en
Publication of CN111047083A publication Critical patent/CN111047083A/en
Application granted granted Critical
Publication of CN111047083B publication Critical patent/CN111047083B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Educational Administration (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The invention discloses a processing method of step-type vorticity covariance observation data, which is characterized in that on the basis of obtained original observation data, a processing mode of sectional moving average is adopted, the whole average time interval is divided into a plurality of sub-time intervals which are mutually overlapped on a time scale by step time with equal length, the number of calculation time intervals is obviously increased, and simultaneously, the original observation data as much as possible are contained, so that more calculation flux values are obtained; in addition, when the gas exchange quantities in each sub-period are accumulated, the exchange quantities in one stepping time in the middle of each sub-period are used as the exchange fluxes of the sub-period to be superposed, so that the superposition effect caused by the accumulation of the whole period is effectively avoided. The scheme not only obviously reduces the utilization rate of original observation data and increases the data volume of the acquired gas exchange flux, but also can improve the space-time resolution of the vorticity covariance flux by utilizing the vorticity covariance data processed by the method, and provides more detailed and accurate basis for community-scale biogeochemical process research and carbon balance evaluation.

Description

Processing method of stepping type vorticity covariance observation data
Technical Field
The invention belongs to the field of environmental science and carbon cycle research, and particularly relates to a processing method of step-type vorticity covariance observation data.
Background
The vorticity covariance method is an international universal method for exchanging greenhouse gases between an in-situ observation ecosystem and the atmosphere, the quality of observation data is limited by meteorological conditions (precipitation, strong wind, turbulence sufficiency and stability) to a great extent, and although a plurality of data post-processing methods can screen and eliminate exchange fluxes under the non-ideal conditions, the finally obtained flux data set still has data loss.
The traditional vorticity covariance method needs to calculate average exchange flux in a period of time by utilizing sectional average, divides the whole observation period into time periods with the same length and linked end to end, calculates the average greenhouse gas exchange flux in the time period in each time period, and simply accumulates to obtain the total gaseous carbon balance. In addition, the abandonment rate of original data is relatively high in the traditional method for processing the vorticity covariance data, waste of the original data is caused, and the time and the spatial resolution of the gas exchange flux data volume obtained through processing are relatively low.
Under actual conditions, the environmental factors of the ecosystem change rapidly along with time, and the traditional data processing method obtains gas exchange flux meeting high time resolution, so that the rapid response of greenhouse gas exchange rate to the environmental factors cannot be accurately evaluated, further application of a vorticity covariance observation data set in the research of the biogeochemical process is limited, and accurate calculation and evaluation of carbon exchange of the ecosystem are also limited.
Disclosure of Invention
The invention provides a stepping type vorticity covariance observation data processing method aiming at the defects of relatively high discarding rate of original data, relatively less obtained gas exchange flux data and the like of the traditional vorticity covariance method.
The invention is realized by adopting the following technical scheme: a processing method of step-type vorticity covariance observation data comprises the following steps:
step 1, dividing original observation data into a plurality of sub-periods which are mutually overlapped on a time scale by a stepping time average with equal length in a sectional moving average mode, and respectively obtaining flux data in each sub-period;
and 2, based on the flux data in each sub-period obtained in the step 1, selecting the middle stepping time interval of each sub-period as an actual exchange amount calculation interval, calculating the gas exchange amount in the stepping time interval, and accumulating the actual exchange amount in the middle stepping interval of each sub-period to obtain the total gas exchange amount in the observation time.
Further, when the flux data in each sub-period is obtained in step 1, the method specifically includes the following steps:
step 1-1, screening and processing original observation data to obtain corrected gas flux:
step 1-2, performing quality evaluation on the gas flux corrected in the step 1-1 to select the gas flux with higher quality and remove the gas flux with poorer quality;
and step 1-3, taking the gas flux data obtained after the quality evaluation in the step 1-2 as a data set for researching the correlation between the flux and the environmental factor.
Further, the step 1-1 comprises the following steps:
(1) in the calculation interval of each sub-period, checking the original observation data to eliminate noise, amplitude abnormality, concentration abnormality and data mutation formed in the original observation data;
(2) coordinate transformation is carried out on the three-dimensional wind speed data based on a quadratic coordinate rotation method, and after two times of coordinate axis rotation, the average wind speed in the vertical direction and the lateral wind speed in the horizontal direction are both zero;
(3) performing covariance analysis on the gas concentration and the corrected vertical wind speed based on Reynolds decomposition to obtain the gas vertical flux of a certain point in space;
(4) and obtaining the original gas exchange flux, and obtaining the corrected gas flux through correction processing.
Furthermore, the subsection time of the sub-period is 30min-60min, and the stepping time is 5min-10 min.
Further, in the step 1-2, the obtained flux data is screened by adopting turbulence sufficiency, turbulence stability and contribution area evaluation when quality evaluation is carried out.
Compared with the prior art, the invention has the advantages and positive effects that:
the scheme of the invention adopts a processing method of sectional moving average, divides the whole average time period into a plurality of sub-time periods which are mutually overlapped on the time scale by stepping time with equal length, can obviously increase the number of calculation time periods and contain as much original observation data as possible, thereby obtaining more calculation flux values; and each data processing time interval is mutually superposed on a time scale, and when the net exchange quantity is calculated, the exchange quantity in one stepping time in the middle of each time interval is used as the exchange flux of the time interval for superposition, so that the superposition effect caused by the accumulation of the whole time interval can be effectively avoided. The vorticity covariance data processed by the method can improve the space-time resolution of vorticity covariance flux, provide more detailed and accurate basis for community-scale biogeochemical process research and carbon balance evaluation, and have higher practical application and popularization values.
Drawings
FIG. 1 is a schematic diagram illustrating a processing mode of vorticity covariance data segmentation moving average according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing the comparison of the flux of carbon dioxide and methane obtained by processing the vorticity covariance data using the conventional method and the method of the present invention in the example of the present invention;
FIG. 3 is a schematic diagram showing the synergistic effect of carbon dioxide exchange flux on the change of illumination intensity and air temperature in accordance with the embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a relationship between a methane flux and an environmental factor obtained by processing vorticity covariance data according to the method of the present invention in an embodiment of the present invention.
Detailed Description
In order that the above objects and advantages of the present invention may be more clearly understood, a detailed description of the embodiments of the present invention will be made below with reference to the accompanying drawings: it should be noted that: the original data of vorticity covariance observation are based on high acquisition frequency (10 times per second) to observe environmental factors such as three-dimensional wind speed and concentration of emerging gas in the environment, and the original data in a period of time is calculated and converted into average gas exchange flux in the period of time by utilizing a mathematical method in the field of micrometeorology to represent the greenhouse gas exchange quantity between the surface of an ecosystem in unit time and unit area and the atmosphere.
Conventionally, the vorticity covariance observation data processing process improvement is mainly performed by a microclimatist, so that the improvement is mainly focused on a flux calculation process, namely a mathematical conversion process of calculating average flux within a period of time from original data, and a few data analysis angles are used for performing overlapping calculation on time intervals of flux calculation; the technical scheme creatively provides that the step-by-step data processing method adopts the statistical idea of moving average, and all the segmented fluxes cannot be simply accumulated due to the repeated utilization of the original fluxes, which is one of the technical difficulties and the ingenious points applied by the step-by-step processing method; the scheme of the invention carries out long-time flux calculation by extracting the time period of the middle part of each average time period and the time period of the stepping duration, thereby solving the technical problem that the flux cannot be simply accumulated due to the repeated utilization of the original data.
Specifically, this embodiment provides a method for processing stepped vorticity covariance observation data, including the following steps:
step 1, averagely dividing original observation data (generally, meteorological data with a sampling frequency of 10 Hz and greenhouse gas content data in the atmosphere) into a plurality of sub-periods which are mutually superposed on a time scale in a segmented moving average mode by step time with equal length, and respectively calculating greenhouse gas exchange flux (flux data) in each sub-period; the method specifically comprises the following steps:
step 1-1, screening and processing data to obtain corrected greenhouse gas exchange flux:
(1) in the calculation interval of each sub-period, checking the original observation data to eliminate the conditions of noise, abnormal amplitude, abnormal concentration, abrupt change of data and the like formed in the original observation data; determining the working condition of the gas analyzer based on the signal intensity value of the gas analyzer, and rejecting data with the signal value less than 20% and the carbon dioxide signal value less than 50% in the methane data;
(2) and (3) carrying out coordinate transformation on the three-dimensional wind speed data based on a quadratic coordinate rotation method:
the first rotation of the axis acting in the main direction
Figure BDA0002303987250000031
In the direction such that the lateral wind speed is zero, the angle of rotation is αDRDetermined by equation (1):
Figure BDA0002303987250000032
wherein the content of the first and second substances,
Figure BDA0002303987250000033
and
Figure BDA0002303987250000034
respectively representing the average components of the wind speed in the directions of an x axis and a y axis on a horizontal plane;
the second axis rotation acts in the vertical direction, rotation angle βDRDetermined by equation (2):
Figure BDA0002303987250000035
wherein the content of the first and second substances,
Figure BDA0002303987250000036
and
Figure BDA0002303987250000037
respectively representing the average wind speed in the vertical direction and the average wind speed in the x-axis direction after one rotation of the coordinate axis;
after two coordinate axis rotations, the average wind speed in the vertical direction and the lateral (y-axis) wind speed in the horizontal direction are both zero.
(3) And (3) performing covariance calculation on the gas concentration and the corrected vertical wind speed based on Reynolds decomposition, and obtaining the gas vertical conveying flux of a certain point in space according to a continuity equation:
Figure BDA0002303987250000041
wherein w is the instantaneous vertical wind speed component, c is the concentration of the gas to be measured in the air, and F is the vertical transport flux of the gas to be measured.
Applying Reynolds equation (4) transforms equation (3) into the form of the sum of the average and pulsatile quantities:
Figure BDA0002303987250000042
wherein
Figure BDA0002303987250000043
Is the average vertical wind speed over a certain period of time,
Figure BDA0002303987250000044
is the average concentration of the trace gas in a certain time period, w 'and c' respectively represent the deviation (i.e. pulsation) of the instantaneous value and the average value of the vertical wind speed and the trace gas concentration;
on a uniformly flat ground surface, assume
Figure BDA0002303987250000045
Thus in equation (4)
Figure BDA0002303987250000046
Negligible, flux is the covariance of the vertical wind speed w and the gas concentration c
Figure BDA0002303987250000047
To indicate.
(4) The original gas exchange flux can be obtained through the steps, and the corrected gas flux is obtained after a series of correction methods such as time lag correction, spectrum analysis and spectrum correction, hydrothermal correction, gas storage correction and the like.
Step 1-2, performing quality evaluation on the original gas flux corrected in the step 1-1, screening obtained flux data by using turbulence sufficiency, turbulence stability and contribution area evaluation, selecting calculated flux with higher quality, and removing calculated flux with poorer quality;
and step 1-3, taking the flux data obtained after the quality evaluation in the step 1-2 as a data set for researching the correlation between the flux and the environmental factor.
And 2, based on the flux data in each sub-period obtained in the step 1, aiming at the missing values in the data set, completing the missing values by using a data interpolation method, taking the middle stepping time interval of each sub-period as an actual exchange quantity calculation interval, calculating the gas exchange quantity in the stepping time, and accumulating the actual exchange quantity in the middle stepping interval of each sub-period to obtain the total gas exchange quantity in the observation time.
In this embodiment, the segment time t of each sub-period is preferably 30min, and the step time t1 is preferably 5min, and in specific implementation, other times may be selected according to actual situations, which is not limited herein.
As shown in fig. 1, assuming that the meteorological observation conditions are occasionally not suitable for vorticity covariance observation within a period of time (4 hours), if a conventional data processing method is adopted (the original observation data is divided into a plurality of time intervals connected end to end in sequence, and the original data in each time interval is taken in sequence for calculation to obtain the average greenhouse gas exchange rate within each time interval), there will be 2 segmental flux losses (F2 and F5) within the time interval, the flux quality in 4 time intervals is general (F1, F4, F6 and F7), and the flux quality in only two time intervals is good (F3 and F8).
By adopting the processing method of the segmented moving average in the embodiment, the whole average time period is divided into a plurality of time periods which are mutually overlapped on the time scale by the stepping time with the same length, the number of the calculation time periods can be obviously increased, the original observation data as much as possible are contained, so that more calculation flux values are obtained, each data processing time period is mutually overlapped on the time scale, when the net exchange quantity is calculated, the exchange quantity in the middle of one stepping time of each sub-time period is used as the time exchange flux to be overlapped, and the overlapping effect caused by the accumulation of the whole time period can be effectively avoided.
With continued reference to fig. 1, the present embodiment divides the whole flux calculation interval into 36 sub-intervals that are more refined according to the step value (5 minutes), and calculates each sub-interval to obtain 36 flux calculation intervals, and according to the estimation, 12 good-quality gas exchange fluxes, 14 general-quality gas exchange fluxes, and 10 missing value intervals will be obtained. This can not only increase the amount of flux data obtained, but also its temporal resolution; and a time period with the same length as the stepping value (5 minutes) is intercepted in the middle of each average time period by adopting a moving sectional averaging method to represent the average exchange flux of the time period (5 minutes), so that the influence of the mutual superposition of the moving average time periods on the final calculated value of the gas exchange quantity is effectively avoided, and the actual greenhouse gas exchange quantity can be calculated more accurately.
Example verification:
to further illustrate the effectiveness of this solution, verification is performed in combination with actual observation data. And (3) processing data of 6 whole days in total from 7 month and 13 days to 7 month and 18 days in 2019 by using the vorticity covariance observation system of the reed wetland in Jiangsu salt city.
As shown in fig. 2, the whole observation period is divided into 288 segments and 1728 segments by applying the conventional data processing method and the moving average segmentation method, the calculated values of carbon dioxide and methane flux are obtained by applying the conventional data processing method respectively as 252 and 212, while the calculated values of carbon dioxide and methane flux are obtained by applying the moving average segmentation method of the present embodiment as 1496 and 1273, respectively, the rejection rate of the original data of carbon dioxide flux is reduced from 12.8% to 4.9%, and the rejection rate of the original data of methane flux is reduced from 26.6% to 6.6%.
In addition, the research and analysis of carbon dioxide and methane exchange characteristics and influence factors thereof on a high time and spatial resolution scale can be supported by using the data volume obtained by moving average segmentation. As shown in fig. 3, it is possible to explain the synergistic effect of the carbon dioxide exchange rate with the change in air temperature and the change in illumination intensity within one day; as shown in fig. 4, it is also possible to explain the temporal, spatial variability of methane release over the observation period and its response to changes in environmental factors. The data volume of the flux data obtained by the traditional data processing method is not enough to support the analysis, so that the method can be used for processing the vorticity covariance original data by using a moving sectional average mode, can more completely reserve the response characteristic of the greenhouse gas exchange flux changing along with the environmental factor, and is better applied to wetland carbon cycle evaluation and research.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.

Claims (5)

1. A processing method of step-by-step vorticity covariance observation data is characterized by comprising the following steps:
step 1, dividing original observation data into a plurality of sub-periods which are mutually overlapped on a time scale by a stepping time average with equal length in a sectional moving average mode, and respectively obtaining flux data in each sub-period;
and 2, based on the flux data in each sub-period obtained in the step 1, selecting the middle stepping time interval of each sub-period as an actual exchange amount calculation interval, calculating the gas exchange amount in the stepping time interval, and accumulating the actual exchange amount in the middle stepping interval of each sub-period to obtain the total gas exchange amount in the observation time.
2. The method of processing stepped vorticity covariance observation data according to claim 1, wherein: when the flux data in each sub-period is obtained in the step 1, the method specifically includes the following steps:
step 1-1, screening and processing original observation data to obtain corrected gas flux:
step 1-2, performing quality evaluation on the gas flux corrected in the step 1-1 to select the gas flux with higher quality and remove the gas flux with poorer quality;
and step 1-3, taking the gas flux data obtained after the quality evaluation in the step 1-2 as a data set for researching the correlation between the flux and the environmental factor.
3. The method of processing stepped vorticity covariance observation data according to claim 2, wherein: the step 1-1 comprises the following steps:
(1) in the calculation interval of each sub-period, checking original observation data to eliminate noise, amplitude abnormality, concentration abnormality and data mutation formed in the original observation data;
(2) coordinate transformation is carried out on the three-dimensional wind speed data based on a quadratic coordinate rotation method, and after two times of coordinate axis rotation, the average wind speed in the vertical direction and the lateral wind speed in the horizontal direction are both zero;
(3) performing covariance analysis on the gas concentration and the corrected vertical wind speed based on Reynolds decomposition to obtain the gas vertical flux of a certain point in space;
(4) and obtaining the original gas exchange flux, and obtaining the corrected gas flux through correction processing.
4. The method of processing stepped vorticity covariance observation data according to claim 1, wherein: the subsection time of the sub-period is 30min-60min, and the stepping time is 5min-10 min.
5. The method of processing stepped vorticity covariance observation data according to claim 2, wherein: in the step 1-2, the obtained flux data is screened by adopting turbulence sufficiency, turbulence stability and contribution area evaluation during quality evaluation.
CN201911232647.6A 2019-12-05 2019-12-05 Processing method of stepping type vorticity covariance observation data Active CN111047083B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911232647.6A CN111047083B (en) 2019-12-05 2019-12-05 Processing method of stepping type vorticity covariance observation data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911232647.6A CN111047083B (en) 2019-12-05 2019-12-05 Processing method of stepping type vorticity covariance observation data

Publications (2)

Publication Number Publication Date
CN111047083A true CN111047083A (en) 2020-04-21
CN111047083B CN111047083B (en) 2022-10-18

Family

ID=70234946

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911232647.6A Active CN111047083B (en) 2019-12-05 2019-12-05 Processing method of stepping type vorticity covariance observation data

Country Status (1)

Country Link
CN (1) CN111047083B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112035089A (en) * 2020-08-20 2020-12-04 中国林业科学研究院林业研究所 Data processing method for vorticity-related observation flux data
CN115629164A (en) * 2022-12-21 2023-01-20 天津飞眼无人机科技有限公司 Unmanned aerial vehicle carbon flux monitoring data acquisition equipment and processing method

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107292446A (en) * 2017-07-03 2017-10-24 西南交通大学 A kind of mixing wind speed forecasting method based on consideration component relevance wavelet decomposition
CN109522655A (en) * 2018-11-19 2019-03-26 武汉大学 Based on the local ground watering increment calculation method for becoming saturation moisture kinematic system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107292446A (en) * 2017-07-03 2017-10-24 西南交通大学 A kind of mixing wind speed forecasting method based on consideration component relevance wavelet decomposition
CN109522655A (en) * 2018-11-19 2019-03-26 武汉大学 Based on the local ground watering increment calculation method for becoming saturation moisture kinematic system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112035089A (en) * 2020-08-20 2020-12-04 中国林业科学研究院林业研究所 Data processing method for vorticity-related observation flux data
CN112035089B (en) * 2020-08-20 2024-03-29 中国林业科学研究院林业研究所 Data processing method for vorticity related observation flux data
CN115629164A (en) * 2022-12-21 2023-01-20 天津飞眼无人机科技有限公司 Unmanned aerial vehicle carbon flux monitoring data acquisition equipment and processing method

Also Published As

Publication number Publication date
CN111047083B (en) 2022-10-18

Similar Documents

Publication Publication Date Title
CN111047083B (en) Processing method of stepping type vorticity covariance observation data
Mauder et al. A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements
CN111027175B (en) Method for evaluating social and economic influences of flood based on coupling model integrated simulation
CN105760978B (en) One kind being based on the agricultural drought disaster grade monitoring method of temperature vegetation drought index (TVDI)
CN110909483A (en) Point source atmospheric pollutant emission list verification method based on gridding data
Galván et al. Drought-induced weakening of growth–temperature associations in high-elevation Iberian pines
CN109544038B (en) Biomass carbon emission estimation system and method based on life cycle analysis
CN104102845B (en) The interpolation method of dimension self-adaption and the interplotation system of dimension self-adaption
CN103971199B (en) A kind of remote sensing ranking method of a wide range of Grain Growth Situation
Wang et al. MAX-DOAS retrieval of aerosol extinction properties in Madrid, Spain
Järvi et al. Uncertainty of eddy covariance flux measurements over an urban area based on two towers
CN110390343B (en) Correction method and system for space meteorological data
CN108564484A (en) Wind power plant surveys wind and represents year sliding choosing method
CN108983324A (en) A kind of temperature forecast method and system based on Kalman filtering
Constantino et al. Plant height measurement and tiller segmentation of rice crops using image processing
CN110782112B (en) Method and system for estimating greenhouse gas emission reduction potential in crop production
US20180143943A1 (en) Method and system for analyzing process factors affecting trend of continuous process
CN117078033A (en) Typhoon disaster influence assessment method, typhoon disaster influence assessment device and computer
Guillemin et al. Real-time three dimensional wind field reconstruction from nacelle LiDAR measurements
CN107316017B (en) Non-point source pollution source intensity estimation method based on crop information remote sensing accurate inversion
Watterson et al. Extreme monthly rainfall over Australia in a changing climate
CN115115137A (en) Photovoltaic power prediction method and device based on matching statistical wavelets
CN113282883B (en) Comprehensive interpolation method for day-by-day rainfall data
CN112365562A (en) Description method of ecosystem attribute component composition structure
Knox et al. Least-squares fitting smooth curves to decadal radiocarbon calibration data from AD 1145 to AD 1945

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant