CN110837698A - Method and system for simulating growth process of ice cloud - Google Patents
Method and system for simulating growth process of ice cloud Download PDFInfo
- Publication number
- CN110837698A CN110837698A CN201911042282.0A CN201911042282A CN110837698A CN 110837698 A CN110837698 A CN 110837698A CN 201911042282 A CN201911042282 A CN 201911042282A CN 110837698 A CN110837698 A CN 110837698A
- Authority
- CN
- China
- Prior art keywords
- ice cloud
- ice
- particle
- cloud
- scattering
- 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
Links
- 239000008276 ice cloud Substances 0.000 title claims abstract description 293
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000008569 process Effects 0.000 title claims abstract description 28
- 239000002245 particle Substances 0.000 claims abstract description 181
- 238000001228 spectrum Methods 0.000 claims abstract description 39
- 230000003287 optical effect Effects 0.000 claims abstract description 31
- 230000008033 biological extinction Effects 0.000 claims abstract description 20
- 230000005855 radiation Effects 0.000 claims abstract description 18
- 230000005540 biological transmission Effects 0.000 claims abstract description 16
- 239000005457 ice water Substances 0.000 claims abstract description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 238000002329 infrared spectrum Methods 0.000 claims description 11
- 230000003595 spectral effect Effects 0.000 claims description 7
- 238000010521 absorption reaction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 8
- 230000008859 change Effects 0.000 description 5
- 238000004590 computer program Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003746 surface roughness Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
- G01N15/0205—Investigating particle size or size distribution by optical means
- G01N15/0211—Investigating a scatter or diffraction pattern
Landscapes
- Chemical & Material Sciences (AREA)
- Dispersion Chemistry (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a method and a system for simulating an ice cloud growth process, which comprise the following steps: establishing an ice cloud particle spectrum distribution database and a non-spherical ice cloud particle scattering database, wherein the ice cloud particle spectrum distribution database comprises ice cloud particle observation data, and the non-spherical ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters; calculating a fitting relation between optical characteristic parameters of the ice cloud and macroscopic physical quantities of the ice cloud according to the ice cloud particle spectrum distribution database and the non-spherical ice cloud particle scattering database, and determining a fitting coefficient, wherein the optical characteristic parameters of the ice cloud comprise ice cloud extinction efficiency, an ice cloud asymmetry factor and an ice cloud single scattering albedo, and the macroscopic physical quantities of the ice cloud comprise ice water content of the ice cloud and average effective radius of the ice cloud; and applying the fitting coefficient to a radiation transmission model to simulate the ice cloud growth process. According to the method and the device, the accuracy of the optical characteristic expression of the ice cloud in the existing climate mode is improved, and the micro physical parameters of the ice cloud with the global scale can be effectively inverted.
Description
Technical Field
The application relates to the field of climate modes, in particular to a method and a system for simulating an ice cloud growth process.
Background
The ice cloud plays an important role in earth surface radiation balance and earth energy balance as an important medium for heat and water vapor transmission. The research on the growth process of the ice clouds has important significance for global climate change simulation, meteorological disaster early warning and the like. Currently, in climate mode, the optical and radiation properties of ice clouds are often described using a parameterization scheme of single-shape particles or a parameterization scheme of multi-shape particles. Parameterization schemes for single-shape ice cloud particles generally utilize regularly-shaped ice cloud particles, and are difficult to represent most of the optical characteristics of ice clouds; the idea of the polymorphic particle parameterization scheme is to use various regularly-shaped ice cloud particles (such as ellipsoids, flat plates, columns, bullet petals and the like) and make a scattering database according to the given proportion of each shape of ice cloud. Numerous studies have shown that the micro-physical parameters of the shape, surface roughness, size and the like of the ice cloud particles have non-negligible influence on the optical properties and radiation properties of the ice cloud. Meanwhile, a large amount of aircraft observation data show that irregular-shaped ice cloud particles in the ice cloud account for an important proportion. Therefore, the limitation of the existing model is difficult to satisfy the requirement of the optical property description of the ice cloud.
Disclosure of Invention
The method and the system are based on a new ice cloud model, the limitation of the existing ice cloud parameterization scheme based on a highly simplified ice cloud model in the aspect of describing the optical characteristics of the ice cloud is overcome, and technical support is provided for the description of the optical characteristics of the ice cloud in a climate mode and the simulation of the ice cloud growth process.
The embodiment of the application provides a method for simulating an ice cloud growth process, which is used in any system embodiment of the application and comprises the following steps:
establishing an ice cloud particle spectrum distribution database and a non-spherical ice cloud particle scattering database, wherein the ice cloud particle spectrum distribution database comprises ice cloud particle observation data, and the non-spherical ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters;
calculating a fitting coefficient of an ice cloud optical characteristic parameter and an ice cloud macroscopic physical quantity according to the ice cloud particle spectrum distribution database and the non-spherical ice cloud particle scattering database, wherein the ice cloud optical characteristic parameter comprises ice cloud extinction efficiency, an ice cloud asymmetry factor and an ice cloud single scattering albedo, and the ice cloud macroscopic physical quantity comprises ice cloud and ice water content and an ice cloud average effective radius;
and applying the fitting coefficient to a radiation transmission model to simulate the ice cloud growth process.
Preferably, the ice cloud particle observation data is observation data of a region from a low latitude to a medium latitude. Further preferably, the ice cloud particle scattering characteristic parameters are scattering characteristic parameters of ice cloud particles with different sizes in the spectrum range from ultraviolet to far infrared bands.
The embodiment of the present application further provides a system for simulating an ice cloud growth process, including:
the system comprises a first module, a second module and a third module, wherein the first module is used for constructing an ice cloud particle spectrum distribution database and an aspheric ice cloud particle scattering database, the ice cloud particle spectrum distribution database comprises ice cloud particle observation data, and the aspheric ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters;
the second module is used for calculating the fitting coefficient of an ice cloud optical characteristic parameter and an ice cloud macroscopic physical quantity according to the ice cloud particle spectrum distribution database and the non-spherical ice cloud particle scattering database, wherein the ice cloud optical characteristic parameter comprises ice cloud extinction efficiency, an ice cloud asymmetry factor and an ice cloud single scattering albedo, and the ice cloud macroscopic physical quantity comprises ice cloud water content and ice cloud average effective radius;
and the third module is used for applying the fitting coefficient to a radiation transmission model and simulating the ice cloud growth process.
Preferably, the ice cloud particle observation data is observation data of a region from a low latitude to a medium latitude. Further preferably, the ice cloud particle scattering characteristic parameters are scattering characteristic parameters of ice cloud particles with different sizes in the spectrum range from ultraviolet to far infrared bands.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: the accuracy of the optical characteristic expression of the ice cloud in the existing climate mode is improved, and the micro physical parameters of the ice cloud in the global scale can be effectively inverted.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic diagram illustrating steps of a method for simulating an ice cloud growth process according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a system for simulating an ice cloud growth process according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic step diagram of a method for simulating an ice cloud growth process according to an embodiment of the present invention, where the method includes the following steps:
step 101: and constructing an ice cloud particle spectrum distribution database and a non-spherical ice cloud particle scattering database, wherein the ice cloud particle spectrum distribution database comprises ice cloud particle observation data, and the non-spherical ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters.
In step 101, the ice cloud particle spectrum distribution database is constructed, the ice cloud particle observation data can be obtained through aircraft observation, and an ice cloud particle spectrum distribution function is determined by using a gamma function. The ice cloud particle observation data is observation data of a region from a low latitude to a middle latitude. Preferably, the ice cloud particle observations are selected to be at a temperature of less than-40 ℃.
In step 101, the non-spherical ice cloud particle scattering database is constructed, and comprises ice cloud particle scattering characteristic parameters, wherein the ice cloud particle scattering characteristic parameters are ice cloud particle scattering characteristic parameters with different particle sizes in the spectrum range from ultraviolet to far infrared band.
In step 101, constructing the non-spherical ice cloud particle scattering database, wherein the non-spherical ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters at intervals of N bands in an ultraviolet to far-infrared spectrum range, the particle size range is from A to B, and the ice cloud particle scattering characteristic parameters described by the non-spherical ice cloud particle scattering database are compared with the scattering characteristics of spherical and other non-spherical ice cloud particles, wherein N is an integer greater than 0, and A and B are arbitrary values greater than 0.
Preferably, the non-spherical ice cloud particle scattering database comprises the ice cloud particle scattering characteristic parameters at 101 wavelength intervals in the ultraviolet to far-infrared spectrum range. Further preferably, the ice cloud particle size parameter ranges from 0.25 to 21428.
For example, when the non-spherical ice cloud particle scattering database is constructed, a total of 101 wave bands with a spectral range of 0.21-13.5 um and a total of 26 particle size parameters with a range of 0.25-21428 can be selected, and the ice cloud particle scattering characteristic parameters are calculated in the range.
The ice cloud particle scattering characteristic parameters comprise ice cloud particle extinction efficiency, ice cloud particle scattering efficiency, ice cloud particle absorption efficiency, ice cloud particle asymmetry factors and ice cloud particle phase matrix elements.
Step 102: and calculating a fitting coefficient of an ice cloud optical characteristic parameter and an ice cloud macroscopic physical quantity according to the ice cloud particle spectrum distribution database and the non-spherical ice cloud particle scattering database, wherein the ice cloud optical characteristic parameter comprises ice cloud extinction efficiency, an ice cloud asymmetry factor and an ice cloud single scattering albedo, and the ice cloud macroscopic physical quantity comprises ice cloud and ice water content and an ice cloud average effective radius.
In step 102, the optical characteristic parameter of the ice cloud and the macro-average physical quantity are calculated, the fitting relation between the optical characteristic parameter of the ice cloud and the macro-average physical quantity is calculated, and the fitting coefficient covering the ultraviolet to far infrared spectrum range is determined.
The step of calculating the optical characteristic parameters of the ice cloud comprises calculating the extinction efficiency of the ice cloud, calculating an asymmetry factor of the ice cloud and calculating the single scattering albedo of the ice cloud. The ice cloud extinction efficiency calculation steps are as follows: the extinction efficiency of the ice cloud particles is multiplied by the particle spectrum distribution function, and the product is integrated within the range of the maximum diameter of the particles; the ice cloud single scattering albedo calculation step comprises the following steps: the ice cloud particle scattering efficiency is multiplied by the particle spectrum distribution function and integrated within the range of the maximum diameter of the particles; the ice cloud asymmetry factor calculation steps are as follows: the ice cloud particle asymmetry factor is multiplied by the scattering cross section multiplied by the particle spectral distribution function and integrated over the particle maximum diameter range.
Calculating the macroscopic average physical quantity comprises calculating the ice water content of the ice cloud and calculating the average effective radius of the ice cloud, wherein the ice water content of the ice cloud is calculated by the following steps: multiplying the volume size of the ice cloud particles by the density of the ice cloud particles by the particle spectrum distribution function, and integrating in the range of the maximum diameter of the particles; the calculation steps of the average effective radius of the ice cloud are as follows: and dividing the product of the volume size of the ice cloud particles and the particle spectrum distribution function by the product of the geometric cross section of the ice cloud particles and the particle spectrum distribution function, and integrating in the range of the maximum diameter of the particles.
In step 102, a fitting relationship between the optical characteristic parameters of the ice cloud and the macroscopic average physical quantity is obtained, and a fitting coefficient covering the range from ultraviolet to far infrared spectrum is determined.
Calculating the ratio of the ice cloud extinction efficiency to the ice cloud water content in the ultraviolet-far infrared spectrum range, and then calculating a fitting coefficient of the reciprocal of the average effective radius of the ice cloud and the ratio of the ice cloud extinction efficiency to the ice cloud water content; calculating a fitting coefficient of the average effective radius of the ice cloud and the single scattering albedo of the ice cloud; and calculating a fitting coefficient of the average effective radius of the ice cloud and the asymmetry factor of the ice cloud.
Step 103: and applying the fitting coefficient to a radiation transmission model to simulate the ice cloud growth process.
In step 103, the fitting coefficients are applied to a radiation transmission model to simulate the ice cloud growth process. The radiation transmission model is a module RRTMG radiation transmission model in a CIESM climate mode.
And applying the fitting coefficient to a CIESM climate mode, simulating the position and height change of the ice cloud by changing the setting of the temperature and humidity parameter in the mode, and monitoring the development process of the ice cloud and the quantitative and qualitative influence of the ice cloud on the climate change.
Fig. 2 is a schematic diagram of a system for simulating an ice cloud growth process according to an embodiment of the present invention, the system includes a first module 201, a second module 202, and a third module 203, wherein,
the first module 201 is configured to construct an ice cloud particle spectrum distribution database and an aspheric ice cloud particle scattering database, where the ice cloud particle spectrum distribution database includes ice cloud particle observation data, and the aspheric ice cloud particle scattering database includes ice cloud particle scattering characteristic parameters.
And constructing the ice cloud particle spectrum distribution database, acquiring ice cloud particle observation data through aircraft observation, and determining an ice cloud particle spectrum distribution function by using a gamma function. The ice cloud particle observation data is observation data of a region from a low latitude to a middle latitude. Preferably, the ice cloud particle observations are selected to be at a temperature of less than-40 ℃.
And constructing the non-spherical ice cloud particle scattering database, wherein the non-spherical ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters, and the ice cloud particle scattering characteristic parameters are ice cloud particle scattering characteristic parameters with different particle sizes in the spectrum range from ultraviolet to far infrared band.
And constructing the non-spherical ice cloud particle scattering database, wherein the non-spherical ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters at intervals of N wave bands in an ultraviolet to far-infrared spectrum range, the particle size range is from A to B, and the ice cloud particle scattering characteristic parameters described by the non-spherical ice cloud particle scattering database are compared with the scattering characteristics of spherical and other non-spherical ice cloud particles, wherein N is an integer larger than 0, and A and B are arbitrary values larger than 0.
Preferably, the non-spherical ice cloud particle scattering database comprises the ice cloud particle scattering characteristic parameters at 101 wavelength intervals in the ultraviolet to far-infrared spectrum range. Further preferably, the ice cloud particle size parameter ranges from 0.25 to 21428.
For example, when the non-spherical ice cloud particle scattering database is constructed, a total of 101 wave bands with a spectral range of 0.21-13.5 um and a total of 26 particle size parameters with a range of 0.25-21428 can be selected, and the ice cloud particle scattering characteristic parameters are calculated in the range.
The ice cloud particle scattering characteristic parameters comprise ice cloud particle extinction efficiency, ice cloud particle scattering efficiency, ice cloud particle absorption efficiency, ice cloud particle asymmetry factors and ice cloud particle phase matrix elements.
The second module 202 is configured to calculate the fitting coefficient of an optical characteristic parameter of ice clouds and a macroscopic physical quantity of ice clouds according to the ice cloud particle spectrum distribution database and the non-spherical ice cloud particle scattering database, where the optical characteristic parameter of ice clouds includes an ice cloud extinction efficiency, an ice cloud asymmetry factor, and an ice cloud single scattering albedo, and the macroscopic physical quantity of ice clouds includes an ice cloud and ice water content and an average effective radius of ice clouds.
Calculating the optical characteristic parameters of the ice cloud and the macroscopic average physical quantity, calculating the fitting relation between the optical characteristic parameters of the ice cloud and the macroscopic average physical quantity, and determining the fitting coefficient covering the range from ultraviolet to far infrared spectrum.
The step of calculating the optical characteristic parameters of the ice cloud comprises calculating the extinction efficiency of the ice cloud, calculating an asymmetry factor of the ice cloud and calculating the single scattering albedo of the ice cloud. The ice cloud extinction efficiency calculation steps are as follows: the extinction efficiency of the ice cloud particles is multiplied by the particle spectrum distribution function, and the product is integrated within the range of the maximum diameter of the particles; the ice cloud single scattering albedo calculation step comprises the following steps: the ice cloud particle scattering efficiency is multiplied by the particle spectrum distribution function and integrated within the range of the maximum diameter of the particles; the ice cloud asymmetry factor calculation steps are as follows: the ice cloud particle asymmetry factor is multiplied by the scattering cross section multiplied by the particle spectral distribution function and integrated over the particle maximum diameter range.
Calculating the macroscopic average physical quantity comprises calculating the ice water content of the ice cloud and calculating the average effective radius of the ice cloud, wherein the ice water content of the ice cloud is calculated by the following steps: multiplying the volume size of the ice cloud particles by the density of the ice cloud particles by the particle spectrum distribution function, and integrating in the range of the maximum diameter of the particles; the calculation steps of the average effective radius of the ice cloud are as follows: and dividing the product of the volume size of the ice cloud particles and the particle spectrum distribution function by the product of the geometric cross section of the ice cloud particles and the particle spectrum distribution function, and integrating in the range of the maximum diameter of the particles.
And acquiring a fitting relation between the optical characteristic parameters of the ice cloud and the macroscopic average physical quantity, and determining a fitting coefficient covering the range from ultraviolet to far infrared spectrums.
Calculating the ratio of the ice cloud extinction efficiency to the ice cloud water content in the ultraviolet-far infrared spectrum range, and then calculating a fitting coefficient of the reciprocal of the average effective radius of the ice cloud and the ratio of the ice cloud extinction efficiency to the ice cloud water content; calculating a fitting coefficient of the average effective radius of the ice cloud and the single scattering albedo of the ice cloud; and calculating a fitting coefficient of the average effective radius of the ice cloud and the asymmetry factor of the ice cloud.
The third module 203 is configured to apply the fitting coefficient to a radiation transmission model to simulate the ice cloud growth process.
And applying the fitting coefficient to a radiation transmission model to simulate the ice cloud growth process. The radiation transmission model is a module RRTMG radiation transmission model in a CIESM climate mode.
And applying the fitting coefficient to a CIESM climate mode, simulating the position and height change of the ice cloud by changing the setting of the temperature and humidity parameter in the mode, and monitoring the development process of the ice cloud and the quantitative and qualitative influence of the ice cloud on the climate change.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for simulating an ice cloud growth process, comprising the steps of:
establishing an ice cloud particle spectrum distribution database and a non-spherical ice cloud particle scattering database, wherein the ice cloud particle spectrum distribution database comprises ice cloud particle observation data, and the non-spherical ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters;
calculating a fitting coefficient of an ice cloud optical characteristic parameter and an ice cloud macroscopic physical quantity according to the ice cloud particle spectrum distribution database and the non-spherical ice cloud particle scattering database, wherein the ice cloud optical characteristic parameter comprises ice cloud extinction efficiency, an ice cloud asymmetry factor and an ice cloud single scattering albedo, and the ice cloud macroscopic physical quantity comprises ice cloud and ice water content and an ice cloud average effective radius;
and applying the fitting coefficient to a radiation transmission model to simulate the ice cloud growth process.
2. The method of claim 1, wherein the ice cloud particle observations are observations from low to mid latitude areas.
3. The method of claim 1, wherein the ice cloud particle scattering characteristic parameters are ice cloud particle scattering characteristic parameters of different particle size in the ultraviolet to far infrared band spectral range.
4. The method according to any one of claims 1 or 3, wherein the ice cloud particle scattering characteristic parameters comprise ice cloud particle extinction efficiency, ice cloud particle scattering efficiency, ice cloud particle absorption efficiency, ice cloud particle asymmetry factor, and ice cloud particle phase matrix elements.
5. The method of claim 1, wherein the radiation transmission model is a modular RRTMG radiation transmission model in a CIESM climate mode.
6. The method of claim 2, wherein the ice cloud particle observations are selected from observations at a temperature of less than-40 ℃.
7. The method of claim 3, wherein the ice cloud particle scattering property parameters comprise scattering property parameters spaced apart by N bands in the ultraviolet to far-infrared spectral range, and the particle size range is from A to B, wherein N is an integer greater than 0, and A and B are any values greater than 0.
8. The method of claim 7, wherein the ice cloud particle scattering property parameters comprise scattering property parameters in the ultraviolet to far-infrared spectral range at 101 band intervals, and the particle size ranges from 0.25 to 21428.
9. The method according to claim 3, wherein the ice cloud particle scattering property parameter comprises an ultraviolet to far-infrared spectrum ranging from 0.21 to 13.5 um.
10. A system for simulating an ice cloud growth process for use in the method of any one of claims 1-9, comprising:
the system comprises a first module, a second module and a third module, wherein the first module is used for constructing an ice cloud particle spectrum distribution database and an aspheric ice cloud particle scattering database, the ice cloud particle spectrum distribution database comprises ice cloud particle observation data, and the aspheric ice cloud particle scattering database comprises ice cloud particle scattering characteristic parameters;
the second module is used for calculating the fitting coefficient of an ice cloud optical characteristic parameter and an ice cloud macroscopic physical quantity according to the ice cloud particle spectrum distribution database and the non-spherical ice cloud particle scattering database, wherein the ice cloud optical characteristic parameter comprises ice cloud extinction efficiency, an ice cloud asymmetry factor and an ice cloud single scattering albedo, and the ice cloud macroscopic physical quantity comprises ice cloud water content and ice cloud average effective radius;
and the third module is used for applying the fitting coefficient to a radiation transmission model and simulating the ice cloud growth process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911042282.0A CN110837698B (en) | 2019-10-30 | 2019-10-30 | Method and system for simulating growth process of ice cloud |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911042282.0A CN110837698B (en) | 2019-10-30 | 2019-10-30 | Method and system for simulating growth process of ice cloud |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110837698A true CN110837698A (en) | 2020-02-25 |
| CN110837698B CN110837698B (en) | 2021-06-04 |
Family
ID=69576046
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911042282.0A Active CN110837698B (en) | 2019-10-30 | 2019-10-30 | Method and system for simulating growth process of ice cloud |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110837698B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112985347A (en) * | 2021-05-11 | 2021-06-18 | 中国空气动力研究与发展中心低速空气动力研究所 | Method for calculating roughness of icing surface of airplane |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090208911A1 (en) * | 2008-02-14 | 2009-08-20 | Doug Macalister | Flight simulator yoke |
| CN103217367A (en) * | 2013-04-22 | 2013-07-24 | 中国科学院东北地理与农业生态研究所 | Snow grain size spectral distribution characteristic parameter optimization method based on three-dimensional random sphere simulation |
| CN103616078A (en) * | 2013-11-19 | 2014-03-05 | 中国科学院地理科学与资源研究所 | Portable thermal infrared load field calibration and water surface temperature verification system device |
| US20140324350A1 (en) * | 2013-04-30 | 2014-10-30 | International Business Machines Corporation | Machine Learning Approach for Analysis and Prediction of Cloud Particle Size and Shape Distribution |
| CN106019314A (en) * | 2016-08-05 | 2016-10-12 | 成都信息工程大学 | Method for short wave infrared satellite remote sensing of carbon dioxide in the presence of cirrus cloud |
| CN106778516A (en) * | 2016-11-24 | 2017-05-31 | 中国科学院地理科学与资源研究所 | A kind of method and device that surface temperature is determined using Chinese No. three satellite remote sensing dates of wind and cloud |
| CN107943174A (en) * | 2017-12-13 | 2018-04-20 | 中国飞机强度研究所 | A kind of ground icing cloud and mist analog machine control system |
| CN108802853A (en) * | 2018-06-13 | 2018-11-13 | 中国科学院遥感与数字地球研究所 | A kind of cloud phase recognition methods |
| CN110455689A (en) * | 2019-08-01 | 2019-11-15 | 中国科学院遥感与数字地球研究所 | A method of the light scattering characteristic of simulation ice crystals |
-
2019
- 2019-10-30 CN CN201911042282.0A patent/CN110837698B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090208911A1 (en) * | 2008-02-14 | 2009-08-20 | Doug Macalister | Flight simulator yoke |
| CN103217367A (en) * | 2013-04-22 | 2013-07-24 | 中国科学院东北地理与农业生态研究所 | Snow grain size spectral distribution characteristic parameter optimization method based on three-dimensional random sphere simulation |
| US20140324350A1 (en) * | 2013-04-30 | 2014-10-30 | International Business Machines Corporation | Machine Learning Approach for Analysis and Prediction of Cloud Particle Size and Shape Distribution |
| CN103616078A (en) * | 2013-11-19 | 2014-03-05 | 中国科学院地理科学与资源研究所 | Portable thermal infrared load field calibration and water surface temperature verification system device |
| CN106019314A (en) * | 2016-08-05 | 2016-10-12 | 成都信息工程大学 | Method for short wave infrared satellite remote sensing of carbon dioxide in the presence of cirrus cloud |
| CN106778516A (en) * | 2016-11-24 | 2017-05-31 | 中国科学院地理科学与资源研究所 | A kind of method and device that surface temperature is determined using Chinese No. three satellite remote sensing dates of wind and cloud |
| CN107943174A (en) * | 2017-12-13 | 2018-04-20 | 中国飞机强度研究所 | A kind of ground icing cloud and mist analog machine control system |
| CN108802853A (en) * | 2018-06-13 | 2018-11-13 | 中国科学院遥感与数字地球研究所 | A kind of cloud phase recognition methods |
| CN110455689A (en) * | 2019-08-01 | 2019-11-15 | 中国科学院遥感与数字地球研究所 | A method of the light scattering characteristic of simulation ice crystals |
Non-Patent Citations (2)
| Title |
|---|
| LETU ET AL: "Investigation of effective ice particle habits for ice cloud remote sensing", 《ATMOSPHERIC CHEMISTRY AND PHYSICS (DISCUSSION)》 * |
| 于记华: "水云和冰云的光散射与辐射传输特性的研究", 《万方数据》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112985347A (en) * | 2021-05-11 | 2021-06-18 | 中国空气动力研究与发展中心低速空气动力研究所 | Method for calculating roughness of icing surface of airplane |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110837698B (en) | 2021-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ahn et al. | MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis | |
| Saito et al. | Turbulence and cloud droplets in cumulus clouds | |
| Lac et al. | Overview of the Meso-NH model version 5.4 and its applications | |
| Hernández-Díaz et al. | Climate simulation over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5) | |
| Pandey et al. | Climate change impact assessment on blue and green water by coupling of representative CMIP5 climate models with physical based hydrological model | |
| Beaucage et al. | Evaluation of four numerical wind flow models for wind resource mapping | |
| Sui et al. | Radiative–Convective Processes in Simulated Diurnal Variations ofTropical Oceanic Convection | |
| Giorgetta et al. | The atmospheric general circulation model ECHAM6-model description | |
| Mesinger et al. | An upgraded version of the Eta model | |
| Ohunakin et al. | The effect of climate change on solar radiation in Nigeria | |
| Žagar et al. | Normal-mode function representation of global 3-D data sets: Open-access software for the atmospheric research community | |
| Cronin et al. | Low clouds suppress Arctic air formation and amplify high-latitude continental winter warming | |
| Pattanayak et al. | Impact of parameterization of physical processes on simulation of track and intensity of tropical cyclone Nargis (2008) with WRF‐NMM model | |
| Kent et al. | Dynamical core model intercomparison project: Tracer transport test cases | |
| Coelho et al. | Evaluation of climate simulations produced with the Brazilian global atmospheric model version 1.2 | |
| CN110837698B (en) | Method and system for simulating growth process of ice cloud | |
| Lau et al. | Structural changes and variability of the ITCZ induced by radiation–cloud–convection–circulation interactions: inferences from the Goddard Multi-scale Modeling Framework (GMMF) experiments | |
| Fersch et al. | Atmospheric and terrestrial water budgets: sensitivity and performance of configurations and global driving data for long term continental scale WRF simulations | |
| Federico et al. | Impact of radar reflectivity and lightning data assimilation on the rainfall forecast and predictability of a summer convective thunderstorm in Southern Italy | |
| Klich et al. | The role of horizontal model resolution in assessing the transport of CO in a middle latitude cyclone using WRF-Chem | |
| Abdelwares et al. | Customized framework of the WRF model for regional climate simulation over the Eastern NILE basin | |
| González-Aparicio et al. | Dynamically based future daily and seasonal temperature scenarios analysis for the northern Iberian Peninsula. | |
| Lacagnina et al. | Evaluation of clouds and radiative fluxes in the EC-Earth general circulation model | |
| Morrison et al. | Advection of coupled hydrometeor quantities in bulk cloud microphysics schemes | |
| Giorgetta et al. | The atmospheric general circulation model ECHAM6 |
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 |