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A multifunctional climate data acquisition method

A data acquisition and multi-functional technology, applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of few climate variables, affecting local interpolation effect and data accuracy, and occupying large storage space.

Active Publication Date: 2017-03-01
NANJING FORESTRY UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] To sum up, how to effectively obtain high-precision climate data has become an important research issue, but the methods provided by the above studies still have certain shortcomings. High-resolution climate variable raster data will take up a lot of storage space, and its data can only be applied to a fixed scale
Secondly, the above methods all use weather station observations for interpolation, so a large number of high-precision observations are needed to ensure the accuracy of the results generated. However, in many cases, the lack of observation data of some weather stations will directly affect the local interpolation. Performance and Data Accuracy
Thirdly, the climatic variables generated by the above methods are relatively few, and often some climatic variables that have a greater impact on tree growth cannot be obtained, so it is difficult to meet the actual needs of forestry-related research
Finally, the climate data obtained by the above methods are often only for a certain time range or a few specific periods, and cannot provide comprehensive data support on the time scale

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0067] Obtain the downloadable public meteorological station observation data in the man-earth system subject database (http: / / www.data.ac.cn / index.asp), including the annual average monthly temperature, the annual average monthly minimum temperature, The average monthly maximum temperature and annual precipitation over the years; form the observation value of the weather station. Substitute the latitude and longitude values ​​of each weather station into the bilinear distance weighted interpolation algorithm (such as figure 1 ), on the cumulative baseline climate grid surface and baseline digital elevation grid surface from 1961 to 1990 (the baseline air temperature grid surface includes monthly average temperature, monthly average minimum temperature, and monthly average maximum temperature, with a spatial resolution of 0.5°, from CRU TS3.21 at http: / / www.cru.uea.ac.uk / cru / data / hrg / , the baseline precipitation grid surface spatial resolution is 1km, from the Human-Earth Syst...

Embodiment 2

[0083] By collecting the annual average daily temperature observation data of 32 cities across the country, the monthly average temperature, monthly average maximum temperature and monthly average minimum temperature are obtained through monthly statistics, and the monthly average values ​​of these three temperature variables are respectively substituted into the following formula to calculate daily Fitting value of air temperature data,

[0084]

[0085] In the formula, t is the time; T is the month semicolon (1 to 12); D is the number of days in each month; m is the day of the month; Y T Monthly averages of relevant climate variables;

[0086]

[0087]

[0088]

[0089] b 6 = 0,

[0090] t=(T-0.5)+(m-0.5) / D.

[0091] The month semicolon T=1 to 12 in the definition formula, the number of days (D) per month from January to December is 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31 days, Climate variable Y T is the monthly average temperature, the monthly ave...

Embodiment 3

[0096] A set of daily maximum temperature, daily minimum temperature and photosynthetically active radiation data from southeastern Australia in 2006 was used, and the latitude of the sample plot was -42.9°. Firstly, the monthly average minimum temperature and monthly average maximum temperature are obtained by monthly statistics; the monthly average values ​​of these two temperature variables are respectively substituted into the following formula to calculate the daily daily minimum temperature and daily maximum temperature;

[0097]

[0098] In the formula, t is the time; T is the month semicolon; D is the number of days in each month; m is the day of the month; Y T Monthly averages of relevant climate variables;

[0099]

[0100]

[0101]

[0102] b 6 = 0,

[0103] t=(T-0.5)+(m-0.5) / D.

[0104] The month semicolon T=1 to 12 in the definition formula, the number of days (D) per month from January to December is 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31...

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Abstract

The invention discloses a multifunctional climate data model and an application thereof. The multifunctional climate data model takes methods, such as an air temperature elevation regulation equation, a bilinear distance weighting interpolation, harmonic fitting and the like, as a basis to realize the generation of climate variables at any scale. According to data, by combining a species distribution model, tree species suitability distribution under a future climate condition can be predicted, and the basis is provided for tree species selection during the afforestation or reafforestation of a forestry department so as to improve forestry productivity. The climate data generated by the multifunctional climate data model also can provide long-term climate data support for a forest ecosystem growth model so as to improve the prediction precision of the growth model. According to a downscaling method provided by the invention, a plurality of atmospheric general circulation model prediction results of the Intergovernmental Panel on Climate Change can be converted at any scale so as to provide the data support for relevant researches under multiple models and multiple scenes.

Description

technical field [0001] The invention belongs to the technical field of climate data models, and in particular relates to a method for constructing a multifunctional climate data model and its application. Background technique [0002] From 1906 to 2005, the global average temperature increased by 0.56-0.92°C; according to different climate scenario simulations, it is estimated that in the next 100 years, the global average temperature will continue to rise by 1.4°C-5.8°C, and the frequency and intensity of extreme climate events will also continue Increase. Such large-scale climate change will affect the type and distribution of vegetation in a large area, increase the frequency and intensity of forest disasters (fires, pests and diseases, etc.), and have many impacts on the development of forestry in my country. [0003] Predicting the impact of global climate change on forest ecosystems and evaluating the response and adaptation of forest ecosystems to global climate chan...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 曹福亮代劲松曹林汪贵斌
Owner NANJING FORESTRY UNIV