CN116561487A - Inversion method for aerosol instantaneous short wave direct radiation effect - Google Patents

Inversion method for aerosol instantaneous short wave direct radiation effect Download PDF

Info

Publication number
CN116561487A
CN116561487A CN202310836603.4A CN202310836603A CN116561487A CN 116561487 A CN116561487 A CN 116561487A CN 202310836603 A CN202310836603 A CN 202310836603A CN 116561487 A CN116561487 A CN 116561487A
Authority
CN
China
Prior art keywords
adre
aerosol
short wave
features
lookup table
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
CN202310836603.4A
Other languages
Chinese (zh)
Other versions
CN116561487B (en
Inventor
史振伟
龙辉
李建忠
许宁
周浩然
金婷婷
黄博学
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.)
Aerospace Information Research Institute of CAS
Original Assignee
Aerospace Information Research Institute of CAS
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 Aerospace Information Research Institute of CAS filed Critical Aerospace Information Research Institute of CAS
Priority to CN202310836603.4A priority Critical patent/CN116561487B/en
Publication of CN116561487A publication Critical patent/CN116561487A/en
Application granted granted Critical
Publication of CN116561487B publication Critical patent/CN116561487B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • 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
    • G01N15/06—Investigating concentration of particle suspensions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • 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
    • G01N15/06—Investigating concentration of particle suspensions
    • G01N15/075—Investigating concentration of particle suspensions by optical means
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Environmental & Geological Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Algebra (AREA)
  • Dispersion Chemistry (AREA)
  • Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computing Systems (AREA)
  • Atmospheric Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Ecology (AREA)
  • Environmental Sciences (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The invention provides an aerosol instantaneous short wave direct radiation effect inversion method, which comprises the following steps: acquiring numerical distribution conditions of a plurality of initial features required for calculating ADRE based on aerosol data of an AERONET ground station and a CALIOP satellite; according to the numerical distribution condition, the influence degree of each initial feature on ADRE is obtained; determining different feature combinations of a plurality of initial features according to the numerical distribution condition and the influence degree, and calculating instantaneous short wave atmosphere top ADRE and atmosphere bottom ADRE under the different feature combinations based on the SBDART radiation transmission mode to form a sample data set; constructing a non-uniform multidimensional lookup table for calculating the instantaneous ADRE according to the sample data set; correcting the non-uniform multidimensional lookup table according to aerosol data; and inputting remote sensing data to be inverted, calling a corrected non-uniform multidimensional lookup table, and calculating a corresponding aerosol instantaneous short wave direct radiation effect by adopting cubic spline interpolation. The invention has the advantages of high calculation efficiency, high calculation precision and wide application range.

Description

Inversion method for aerosol instantaneous short wave direct radiation effect
Technical Field
The invention relates to the technical fields of atmospheric environment, atmospheric remote sensing and aerosol remote sensing, in particular to an aerosol instantaneous short wave direct radiation effect inversion method.
Background
Aerosols are an important component of the atmosphere and have a significant impact on climate systems. Aerosols can change climate by absorbing and scattering incident solar radiation, but due to their wide variety, short life cycle, rather complex horizontal and vertical distribution, have great uncertainty in regional and global climate and environmental changes. The direct aerosol radiation effect (Aerosol Direct Radiative Effect, ADRE) is one of the most well known effects of aerosols, and its quantification helps to understand the effect of aerosols on earth energy balance. ADRE can be further described as a transient change in net radiant flux with and without aerosols by the following formula:
where ADRE denotes the aerosol direct radiation effect,is the net radiation effect F net Arrows indicate the direction of the radiant flux, F a And F 0 Indicating total radiant flux with and without aerosol, respectively. The downward radiant flux is defined as positive and the upward radiant flux is defined as negative.
The change in ADRE is determined by a combination of atmospheric composition, aerosol-cloud and surface properties and time-space change. However, the different sources and shorter lifetimes make research of ADRE challenging due to the spatiotemporal heterogeneity of aerosol characteristics. The aerosol mainly absorbs and scatters short-wave solar radiation with the wavelength range of 0.25-4 mu m, namely, the aerosol comprises a visible light part and also comprises a part of ultraviolet radiation wave bands and infrared radiation wave bands.
With the development of satellite remote sensing technology, based on the large-scale timely and accurate aerosol monitoring information of satellite observation, surface reflection characteristic data and the like, data support is provided for quantitatively estimating the influence of aerosol on global climate. Common methods for ADRE estimation are global climate mode, direct observation mode, parametric modeling mode and radiation transmission mode.
The global climate pattern may output the distribution of aerosols and their radiation effects. However, since its output resolution is typically several hundred kilometers, it is difficult to obtain a fine spatial distribution of ADRE, and the results in specific areas are sometimes problematic, such as in the south africa, when a smoke aerosol is in a cloud scene, ADRE is inconsistent with TOA (Top of Atmosphere, atmospheric roof) ADRE results calculated from radiation transmission modes based on measured aerosol and cloud characteristics.
Direct observation typically uses satellite remote sensing technology in combination with satellite observation of aerosol optical thickness data to obtain TOA ADRE, but no estimation of TOA (Bottom of Atmosphere, atmospheric floor) ADRE can be made. Although TOA ADRE and BOA ADRE can be obtained by the foundation radiation observation mode, the observation mode is limited by the observation range, and continuous ADRE in a large area can not be obtained.
The regression analysis modeling inversion ADRE requires a large amount of data support, has high quality requirements on data, and can cause model inaccuracy if abnormal values, missing values and the like exist in the data. ADRE estimation involves a number of parameters, and thus regression analysis modeling requires the selection of appropriate model types and arguments, which if improperly selected may result in an inaccurate or unexplained model. Models in regression analysis modeling may be affected by sample bias and overfitting, requiring model evaluation and improvement. The existing regression model method can only establish the regression relation of AOT (Aerosol Optical Thickness ), SSA (Single Scattering Albedo, single scattering albedo) and ADRE under the condition of specific solar zenith angle or specific surface characteristics, and can not accurately invert the ADRE under the condition of different aerosol optical characteristics, surface characteristics and solar zenith angles.
The radiation transmission mode is a method with higher calculation accuracy, and the influence of a plurality of influence factors such as optical and physical characteristics of aerosol-cloud in the atmosphere, surface properties and the like on ADRE is considered through solving a radiation transmission equation. The method of radiation transmission mode can calculate TOA and BOA ADRE at the same time, but a large number of features are required to be input to calculate the radiation transmission equation, so that the calculation efficiency is low, and the ADRE for a large-scale and long-time sequence is difficult to calculate.
Disclosure of Invention
In view of the problems, the invention provides an aerosol instantaneous short wave direct radiation effect inversion method which has the advantages of high calculation efficiency, high calculation precision and wide application range.
The invention provides an aerosol instantaneous short wave direct radiation effect inversion method, which comprises the following steps: step S1, acquiring numerical distribution conditions of a plurality of initial features required by ADRE calculation based on aerosol data of an AERONET ground station and a CALIOP satellite, wherein the plurality of initial features comprise aerosol optical characteristics and vertical distribution features; s2, according to the numerical distribution condition, obtaining the influence degree of each initial feature on ADRE; step S3, different feature combinations of a plurality of initial features are determined according to the numerical distribution condition and the influence degree, and based on the SBDART radiation transmission mode, the instantaneous short wave atmosphere layer top ADRE and the atmosphere layer bottom ADRE under the different feature combinations are calculated to form a sample data set; s4, constructing a non-uniform multidimensional lookup table for calculating the instantaneous ADRE according to the sample data set; step S5, correcting the non-uniform multidimensional lookup table according to aerosol data; and S6, inputting remote sensing data to be inverted, calling a corrected non-uniform multidimensional lookup table, and calculating a corresponding aerosol instantaneous short wave direct radiation effect by adopting cubic spline interpolation.
Further, in step S1, the aerosol optical characteristics include: aerosol optical thickness, single scattering albedo, asymmetry factor, angstrom wavelength index, solar zenith angle, earth surface albedo; the vertical distribution features include: aerosol layer bottom height and aerosol layer thickness.
Further, the numerical distribution condition comprises a value range and a reference value; the step S1 specifically comprises the following steps: step S11, setting respective value ranges and reference values of aerosol optical thickness, single scattering albedo, asymmetry factor and Angstrom wavelength index; step S12, setting respective value ranges and reference values of solar zenith angles and earth surface albedo; step S13, setting the respective value ranges and reference values of the bottom height and the thickness of the aerosol layer.
Further, step S2 specifically includes: s21, analyzing the influence degree of the aerosol layer bottom height change on ADRE; s22, analyzing local sensitivity of ADRE to each initial characteristic; step S23, analyzing global sensitivity of ADRE to each initial feature; and step S24, obtaining the influence degree of each initial feature on the ADRE according to the local sensitivity and the global sensitivity.
Further, the global sensitivity of ADRE to each initial feature was analyzed as follows: hypothesis modelHas the following advantages ofdInput features, sobol sample based generationN×2dIs used for the sampling matrix of the (c),Nthe number of samples for sampling; front of sample matrixdThe columns are arranged as a matrix A, the backdThe columns are arranged as a matrix B, and the matrix A and the matrix B are respectivelyN×dIs a new matrix of (a); the first of the matrix AiColumn matrix B ofiColumn replacement, construction of newN×dMatrix AB i ，i=1,2,3,…,dForming intodNew matrices; matrices A, B and AB i Is summed up withN×(d+2) samples, using a modelYCalculating a sample correspondence valuef(A)、f(B) Andf(AB i ) The method comprises the steps of carrying out a first treatment on the surface of the The total effect index is calculated according to the following formula:
in the method, in the process of the invention,；i=1,2,…,d；j=i+1,i+2,…,d；X i represent the firstiA plurality of input features;E Xi represent the firstiConditional expectation of the individual input features;Var Xi represent the firstiConditional variance of the individual input features;S Ti representation modelYFor the firstiA total effect index of the individual input features;Var(Y) Representation modelYIs a desired value of (2); based on the total effect index, the global sensitivity of the ADRE to each initial feature is determined.
Further, the step S3 specifically includes: step S31, determining the value range and the step length of each initial feature according to the numerical distribution condition and the influence degree; step S32, dividing each initial feature into a plurality of sub-features based on a value range and a step length, and forming a plurality of feature combinations by the plurality of sub-features of the plurality of initial features; step S33, calculating an instantaneous short wave atmospheric layer top ADRE and an atmospheric layer bottom ADRE under each feature combination based on the SBDART radiation transmission pattern, to form a sample dataset.
Further, the step S5 specifically includes: step S51, performing space-time matching on aerosol data of the AERONET ground station and the CALIOP satellite to obtain the bottom height and the thickness of an aerosol layer of the AERONET ground station; step S52, inputting aerosol optical characteristics and albedo data of the AERONET ground station into a constructed non-uniform multi-dimensional lookup table, and calculating a corresponding ADRE inversion result; and step S53, comparing the ADRE inversion result with the actual value of the instantaneous ADRE, and determining parameters of the non-uniform multi-dimensional lookup table to obtain a corrected non-uniform multi-dimensional lookup table.
Further, step S51 specifically includes: spatial matching: taking an AERONET ground station as a center, taking a radius 30 km as a circular area, and extracting the bottom height and the thickness of an aerosol layer in the circular area when a satellite passes; time matching: and in the circular area, extracting the corresponding bottom height and thickness of the aerosol layer and AERONET ground station data within +/-3 hours of the satellite transit time by taking the satellite transit time as a reference.
Further, step S53 includes: measuring the coincidence degree of the ADRE inversion result and the actual value of the instantaneous ADRE by adopting a decision coefficient, a root mean square error and an average absolute error; and adjusting parameters of the non-uniform multidimensional lookup table according to the anastomosis degree.
Compared with the prior art, the inversion method of the aerosol instantaneous short wave direct radiation effect has at least the following beneficial effects:
1. the calculation efficiency is high. The method of the invention utilizes the non-uniform multidimensional lookup table, can process and analyze a large amount of data in a short time, and realizes the quick estimation of ADRE. Compared with the traditional radiation transmission mode, the method has higher calculation efficiency.
2. The calculation accuracy is high. The method of the invention adopts multi-characteristic data, establishes a comprehensive and accurate ADRE inversion model, and can more accurately estimate the ADRE. Meanwhile, inversion capability and stability of the model can be improved by considering the multi-feature value range.
3. The application range is wide. The method of the invention can process aerosol data with large range and high space-time resolution, and is applicable to global, regional and local ADRE research. Meanwhile, the method can be applied to aerosol scenes with different types and complexity, and has wider application prospect.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates a flow chart of an aerosol short wave direct radiation effect inversion method according to an embodiment of the invention;
FIG. 2 schematically illustrates a schematic diagram of an aerosol transient short wave direct radiation effect inversion method according to an embodiment of the invention;
FIG. 3 schematically shows a graph of the results of statistics of numerical distribution of a plurality of initial features based on aerosol data, wherein (a) - (f) are respectively the statistics of the numerical distribution of aerosol optical thickness, aerosol single scattering albedo, aerosol asymmetry factor, angstrom wavelength index, earth albedo, and solar zenith angle;
fig. 4 schematically shows a flow chart of step S1 according to an embodiment of the invention.
Fig. 5 schematically shows a flow chart of step S2 according to an embodiment of the invention.
Fig. 6 schematically shows a flow chart of step S3 according to an embodiment of the invention.
Fig. 7 schematically shows a flow chart of step S5 according to an embodiment of the invention.
FIG. 8 schematically illustrates a comparison of the results of inverting BOA ADRE with AERONET ADRE for a non-uniform multi-dimensional look-up table before (a) and after (b) correction according to an embodiment of the invention;
FIG. 9 schematically illustrates a comparison of results of non-uniform multi-dimensional look-up table inversion TOA ADRE with AERONET ADRE before correction (a) and after correction (b) according to an embodiment of the invention.
Detailed Description
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Fig. 1 schematically shows a flow chart of an aerosol short wave direct radiation effect inversion method according to an embodiment of the invention. Fig. 2 schematically shows a schematic diagram of an aerosol transient short wave direct radiation effect inversion method according to an embodiment of the invention.
As shown in fig. 1 and 2, the inversion method of the aerosol transient short wave direct radiation effect according to this embodiment may include steps S1 to S6.
Step S1, obtaining a numerical distribution of a plurality of initial features required for calculating ADRE based on aerosol data of AERONET (Aerosol Robotic Network, aerosol monitoring network) ground stations and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization, on-board laser radar) satellites, wherein the plurality of initial features include aerosol optical characteristics and vertical distribution features.
In this embodiment, the aerosol optical characteristics include: aerosol optical thickness (Aerosol Optical Thickness, AOT), single scattering Albedo (Single Scattering Albedo, SSA), asymmetry factor (Asymmetry Parameter, ASY), angstrom wavelength index (Angstrom Exponent, AE), solar zenith angle (Solar Zenith Angle, SZA), earth Albedo (Albedo, ALB). The aerosol vertical distribution characteristics include: aerosol layer bottom height (Aerosol Layer Base Height, ALBH) and aerosol layer thickness (Aerosol Layer Thickness, ALT).
Fig. 3 schematically shows a graph of the result of statistics of numerical distribution of a plurality of initial features based on aerosol data according to an embodiment of the present invention, where (a) - (f) are the statistics of numerical distribution of aerosol optical thickness, aerosol single scattering albedo, aerosol asymmetry factor, angstrom wavelength index, earth albedo, and solar zenith angle, respectively.
And counting the global AERONET ground station Level 2 flat-bed loop data to obtain the distribution situation of AOT, SSA, ASY, AE, SZA and ALB characteristic parameters. Fig. 3 shows the range, mean and maximum minimum of variation of the different features AOT, SSA, ASY, AE, ALB and SZA. The AOT value is concentrated in the range of 0-1, and the average value is 0.24. The theoretical value range of SSA is 0-1, and from the data of the global AERONET ground station, SSA is mainly concentrated in the range of 0.75-0.99, and the average value is 0.92.ASY distribution is concentrated, the average value is 0.71, and the ASY distribution is mainly concentrated in the range of 0.6-0.85 from the statistical data. AE is concentrated in the range of 0-2.5, and the global measurement average value is 1.18. The ALB data are centrally distributed over a range of 0.05 to 0.5, with low albedo being predominantly on dark ground surfaces, such as ocean surfaces. The albedo value of bright ground surfaces is high, typically in desert, polar or glacier coverage areas.
Fig. 4 schematically shows a flow chart of step S1 according to an embodiment of the invention.
As shown in fig. 4, in the present embodiment, the numerical distribution condition includes a value range and a reference value; the step S1 specifically includes steps S11 to S13.
Step S11, setting respective value ranges and reference values of aerosol optical thickness, single scattering albedo, asymmetry factor and Angstrom wavelength index.
For example, the values of AOT, SSA, ASY and AE are set to 0.01 to 1, 0.75 to 0.99, 0.6 to 0.85, 0 to 2.5, respectively, and the reference values are 0.24, 0.92, 0.71, 1.18, respectively.
Step S12, setting respective value ranges and reference values of solar zenith angles and earth surface albedo.
The theoretical range of ALB is 0-1, the maximum and minimum values are usually not available, the value range of ALB is 0.05-0.95, and the reference value is 0.19. The value range of SZA is set to 0-90 DEG, and the reference value is set to 60 deg.
Step S13, setting the respective value ranges and reference values of the bottom height and the thickness of the aerosol layer.
Based on global aerosol extinction coefficient profile (Aerosol Extinction Coefficient Profile, AECP) observation data of 2018 CALIOP satellite, ALBH value range is set to be 0-8 km, ALT value range is set to be 0-3 km. The ALBH reference value was set to 1.24 km, and the alt reference value was set to 0.92 km. Meanwhile, four AECP variation profiles of linear, gaussian, exponential and logistic profiles were obtained based on CALIOP satellite observations.
To compare the effect of the four profile height variations on ADRE, linear, gaussian, exponential and logistic distributions, the profile heights were normalized to 0.5-3 km, i.e. 0.5 km at the bottom and 3 km at the top. The height of the aerosol layer is changed from 0.5 to km to 10.5 to km at intervals of 1 km.
And S2, obtaining the influence degree of each initial feature on the ADRE according to the numerical distribution condition.
Fig. 5 schematically shows a flow chart of step S2 according to an embodiment of the invention.
In this embodiment, as shown in fig. 5, step S2 specifically includes steps S21 to S24.
Step S21, analyzing the influence degree of the aerosol layer bottom height change on ADRE.
For example, ALT employs the annual average thickness in step S13. SSA is 0.85 and 0.97, respectively, for reacting absorptive and non-absorptive aerosols, ALB is 0.19 and 0.3, respectively, other initial characteristics are as the mean/reference value in step S1 described previously.
Step S22, analyzing the local sensitivity of ADRE to each initial feature.
And respectively changing the input initial characteristics to obtain the influence of each initial characteristic on the ADRE result. Taking the mean value/reference value in the step S1 as the input of the SBDART radiation transmission mode, calculating BOA ADRE and TOA ADRE to be-15.75 Wm respectively -2 And-51.54 Wm -2 As a reference value for ADRE. Specifically, the AOT, SSA, ASY, AE, ALB feature is reduced by-6%, -4%, -2%, increased by 2%, 4%, 6%, and the ADRE is recalculated. The baseline AECP was a gaussian profile with initial aerosol layer bottom heights and thicknesses of 2 km and 1 km, respectively, for aerosol layer bottom heights reduced by-1.5, -1, -0.5 km, respectively, by 0.5, 1, and 1.5 km, respectively, and then the ADRE was calculated and compared to the baseline ADRE results.
Step S23, analyzing global sensitivity of ADRE to each initial feature.
The percentage of influence of each feature on the direct radiation effect of the aerosol is obtained by global sensitivity analysis of different initial features affecting the ADRE. That is, the output of the model is attributed to the percentages of the input variables, which can be interpreted as a measure of the sensitivity of the model dependent variables to the input features. And (3) setting a value range of the features according to the statistical result of each feature in the step S1, and then analyzing the influence percentage of each feature on the ADRE. The characteristics are sampled within the value range, and the Monte Carlo sampling method is used for sampling.
In this embodiment, the global sensitivity of the ADRE to each initial feature is analyzed as follows:
(1) Hypothesis modelHas the following advantages ofdInput features, sobol sample based generationN×2dIs used for the sampling matrix of the (c),Nthe number of samples for sampling;
(2) Front of sample matrixdThe columns are arranged as a matrix A, the backdThe columns are arranged as a matrix B, and the matrix A and the matrix B are respectivelyN×dIs a new matrix of (a);
(3) The first of the matrix AiColumn matrix B ofiColumn replacement, construction of newN×dMatrix AB i ，i=1,2,3,…,dForming intodNew matrices;
(4) Matrices A, B and AB i Is summed up withN×(d+2) samples, using a modelYCalculating a sample correspondence valuef(A)、f(B) Andf(AB i )；
(5) The total effect index is calculated according to the following formula:
in the method, in the process of the invention,；i=1,2,…,d；j=i+1,i+2,…,d；X i represent the firstiA plurality of input features;E Xi represent the firstiConditional expectation of the individual input features;Var Xi represent the firstiConditional variance of the individual input features;S Ti representation modelYFor the firstiA total effect index of the individual input features;Var(Y) Representation modelYIs a desired value of (2);
(6) Based on the total effect index, the global sensitivity of the ADRE to each initial feature is determined.
For example 7 initial features were selected for global sensitivity analysis, wherein the aerosol optical properties and albedo variation interval settings were as described in step S1. The variation range of the bottom height of the aerosol layer is 0.2-4 km, and the variation range of the thickness is 0.1-2 km.
And step S24, obtaining the influence degree of each initial feature on the ADRE according to the local sensitivity and the global sensitivity.
Thus, the local and global sensitivity of the ADRE to the initial features AOT, SSA, ASY, AE, SZA, ALB, ALBH and ALT were analyzed to obtain the extent of impact of these features on ADRE.
And S3, determining different feature combinations of a plurality of initial features according to the numerical distribution condition and the influence degree, and calculating the instantaneous short-wave atmosphere top ADRE and the atmosphere bottom ADRE under the different feature combinations based on the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) to form a sample data set.
For example, according to step S1, the range and step of values for the different initial features AOT, SSA, ASY, AE, SZA, ALB, ALBH and ALT are determined, and then the different combinations are input into the SBDART radiation transmission mode to calculate the corresponding BOA ADRE and TOA ADRE, forming a sample dataset.
Fig. 6 schematically shows a flow chart of step S3 according to an embodiment of the invention.
In this embodiment, as shown in fig. 6, the step S3 specifically includes steps S31 to S33.
Step S31, according to the numerical distribution condition and the influence degree, determining the value range and the step length of each initial feature.
For example, the range and step size of the values of features AOT, SSA, ASY, AE, SZA, ALB, ALBH and ALT are set:
AOT = [0.001 0.005 0.01 0.025 0.05:0.05:1 1.1:0.1:3];
SSA = 0.75:0.01:0.99;
ASY = [0.6 0.72 0.85];
AE = [1.18];
SZA = 0:1:90;
ALB = [0.04:0.01:0.9];
ALBH = [0.2 0.5 1 2 4];
ALT = [0.92];
wherein the symbol ":" means increasing in specified steps. The AOT takes the value of 44, the SSA takes the value of 0.75-0.99, the step length of 0.01, the value of 25, and the ASY, ALB and SZA take the values of 3, 87 and 91 respectively. ADRE is relatively weak to ASY and ASY does not affect ADRE symbols, so three values are taken. The ALB marine surface albedo is about 0.05, so the value range is set to 0.04-0.9, and the TOA ADRE symbol is determined by combining with SSA, so the step size is set to 0.01. These features are sequentially cycled through the SBDART calculation ADRE, together forming 44×25×3×87×91×5= 130630500 sets of sample data.
Step S32, based on the value range and the step length, dividing each initial feature into a plurality of sub-features, and forming a plurality of feature combinations by the plurality of sub-features of the plurality of initial features.
Step S33, calculating an instantaneous short wave atmospheric layer top ADRE and an atmospheric layer bottom ADRE under each feature combination based on the SBDART radiation transmission pattern, to form a sample dataset.
Then, based on the SBDART radiation transmission pattern, instantaneous short wave BOA ADRE and TOA ADRE at different feature combinations are calculated, and the continuous variation of BOA ADRE and TOA ADRE at different feature combinations are listed below, respectively.
(1) Continuous variation of BOA ADRE under different feature combinations
First, the continuous variation of BOA ADRE along with different features AOT, SSA, ASY, AE, ALB and SZA is calculated, and the feature reference value required by ADRE is estimated as described in step S1. From the continuous variation of ADRE with different features, it can be inferred that the profile of ADRE varies approximately with the feature, which is used to guide the setting of the lookup table input feature step size. Larger step sizes can be selected for the features that are approximately linear in distribution or that have little effect and do not affect the ADRE symbols, thus ensuring both higher query efficiency and inversion accuracy. The BOA ADRE sign is always negative, and approximately shows exponential decay profile as the AOT changes. In view of this, the step size of the AOT can be set finer when constructing the look-up table to reduce inversion errors due to interpolation. BOA ADRE changes with SSA when the AOT is 0.001 and 0.1-0.9, the absolute value of the BOA ADRE is reduced due to the increase of SSA, the BOA ADRE and the SSA are approximately linearly distributed when the AOT is smaller than 0.5, and the BOA ADRE and the SSA obey the curtain distribution when the AOT exceeds 0.5. Under different AOT values, the BOA ADRE is approximately linearly distributed with ASY, AE and ALB. The feature steps may be considered larger in constructing the look-up table, but the TOA ADRE's response to the features is considered. The change in BOA ADRE with AE exhibits a linear distribution with a slope of approximately 0, indicating that under this condition the change in AE has a weak effect on BOA ADRE. ADRE reaches an extremely low value under certain SZA condition and presents approximate bias distribution characteristics.
(2) Continuous variation of TOA ADRE under different feature combinations
The continuous variation of TOA ADRE with different features AOT, SSA, ASY, AE, ALB and SZA is then calculated and the reference values for the features required for ADRE are estimated as described in step S1. TOA ADRE exhibits an exponential distribution with AOT, which may be positive or negative in value, depending on the combined effect of aerosol single scattering albedo and surface albedo. An increase in SSA will change the sign of TOA ADRE. As SSA increases while TOA ADRE is being made, the heating effect of the aerosol at TOA decreases; at negative TOA ADRE, the cooling effect of the aerosol at the TOA increases as SSA increases. Under different AOT values, both TOA ADRE and ASY and AE exhibit approximately linear distributions. The cooling effect of the aerosol on the TOA gradually decreases as the ASY increases. TOA ADRE, in turn, exhibits a linear distribution with AE having a slope of approximately 0. The change in TOA ADRE is approximately linear with albedo, and the slope and intercept of this linear relationship is related to the change in AOT. The sign of TOA ADRE is influenced by both SSA and ALB, and the increase in ALB results in a gradual decrease in the cooling effect and a gradual increase in the heating effect of the original TOA. In the case of certain SSA, the size of ALB is more determinative of the sign of TOA ADRE. It follows that the surface albedo is critical in studying the effect of aerosols on solar radiation. Finer step size settings may be considered for the construction of the inverted ADRE look-up table. From the variation of TOA ADRE with SZA cosine, it can be found that TOA ADRE will reach a minimum under certain SZA conditions and exhibit similar bias distribution characteristics.
Step S4, constructing a non-uniform multidimensional lookup table for calculating the instantaneous ADRE according to the sample data set.
The non-uniform multidimensional lookup table constructed in the step comprehensively considers the optical characteristics and vertical characteristics of aerosol, the surface characteristics and the solar zenith angle influence.
It should be noted that the non-uniform multidimensional lookup table is inverted by instantaneous ADRE, and daily ADRE is more significant than instantaneous ADRE in climatology. Therefore, the local daily ADRE can be obtained by calculating the instantaneous ADRE at any time in any geographic position of the world through the change of the input characteristics, especially the SZA.
And S5, correcting the non-uniform multidimensional lookup table according to aerosol data.
Specifically, the non-uniform multi-dimensional lookup table is corrected based on high accuracy aerosol and ADRE data of the AERONET ground station.
Fig. 7 schematically shows a flow chart of step S5 according to an embodiment of the invention.
As shown in fig. 7, in this embodiment, step S5 specifically includes steps S51 to S53.
And step S51, performing space-time matching on aerosol data of the AERONET ground station and the CALIOP satellite to obtain the bottom height and the aerosol layer thickness of the AERONET ground station.
Spatio-temporal matching includes spatial matching and temporal matching. Wherein, spatial matching refers to: ALBH and ALT data in a circular area when a satellite passes through are extracted by taking an AERONET ground station as a center and taking a radius 30 km as a circular area.
The ALBH and ALT data after spatial matching also need to be matched in time. Time matching refers to: and in the circular area, extracting the corresponding bottom height and thickness of the aerosol layer and AERONET ground station data within +/-3 hours of the satellite transit time by taking the satellite transit time as a reference.
And S52, inputting the aerosol optical characteristics and the albedo data of the AERONET ground station into the constructed non-uniform multi-dimensional lookup table, and calculating a corresponding ADRE inversion result.
Once the non-uniform multi-dimensional look-up table is constructed, the corresponding instantaneous ADRE can be calculated by AOT, SSA, ASY, AE, SZA, ALB, ALBH and ALT.
To verify the performance of the constructed look-up table, ADRE was calculated by AERONET ground station aerosol optical properties and albedo data as look-up table inputs and then compared to AERONET ADRE. Because AERONET does not have aerosol vertical detection capability, considering that most aerosol layers are low in height, the aerosol layer bottom height in the verification and correction stage is 0.2 km.
And step S53, comparing the ADRE inversion result with the actual value of the instantaneous ADRE, and determining parameters of the non-uniform multi-dimensional lookup table to obtain a corrected non-uniform multi-dimensional lookup table.
In this embodiment, step S53 includes: by employing a decision coefficient (R 2 ) Root mean square error (Root Mean Square Error, RMSE) and mean absolute error (Mean Absolute Error, MAE), measure how well the ADRE inversion results agree with the actual values of the instantaneous ADRE; and adjusting parameters of the non-uniform multidimensional lookup table according to the anastomosis degree.
Specifically, R 2 RMSE and MAE are defined as follows:
in the method, in the process of the invention,ADRE c for the look-up table to invert the values,ADRE m in the case of the AERONET ADRE,nis the total number of samples.
For example, the input features of the non-uniform multi-dimensional lookup table are AOT, SSA, and ASY, and ALB and SZA at wavelength 532 nm. AOT, SSA and ASY at 532 nm wavelengths can be obtained by interpolation from data of 443, 667, 865 and 1020 nm. From the global multi-year data, 5 tens of thousands of data were randomly selected for verification and correction of the look-up table results. The sample data are divided into three data sets of I, II and III according to a ratio of 7:2:1, wherein the data set I is used for constructing a non-uniform multidimensional lookup table, the data set II is used for correcting the non-uniform multidimensional lookup table, and the data set III is used for checking the non-uniform multidimensional lookup table.
FIG. 8 schematically illustrates a comparison of the results of a non-uniform multi-dimensional look-up table inversion BOA ADRE with AERONET ADRE before correction (a) and after correction (b) according to an embodiment of the invention. FIG. 9 schematically illustrates a comparison of results of non-uniform multi-dimensional look-up table inversion TOA ADRE with AERONET ADRE before correction (a) and after correction (b) according to an embodiment of the invention.
As shown in fig. 8 and 9, the correlation R of the BOA ADRE inverted by a non-uniform multi-dimensional look-up table 2 0.99, RMSE and MAE of 4.90 and 3.31 Wm, respectively -2 . Correlation R of TOA ADRE and AERONET ADRE inverted by non-uniform multi-dimensional lookup table 2 0.97, 2.54 and 1.52. 1.52 Wm for RMSE and MAE, respectively -2 。
Correcting the non-uniform multi-dimensional look-up table results based on the linear relationship of the non-uniform multi-dimensional look-up table inversion results to AERONET ADRE, FIG. 8 shows a comparison of the data set II by the corrected non-uniform multi-dimensional look-up table inversion ADRE to AERONET ADRE results, R 2 0.99, RMSE and MAE of 1.87 and 1.25. 1.25 Wm, respectively -2 . Under the condition of unchanged correlation, the RMSE and MAE of the BOA ADRE and the AERONET ADRE of the non-uniform multidimensional lookup table are improved compared with those before correction, and the inversion precision after the TOA non-uniform multidimensional lookup table is corrected is also improved. Indicating that the accuracy is effectively improved by correcting the non-uniform multidimensional lookup table. After correction, the inversion result of the lookup table and the AERONET ADRE scattered points are uniformly distributed on two sides of a 1:1 line, which proves that the algorithm can realize short wave instantaneous with higher precisionInversion of ADRE.
And S6, inputting remote sensing data to be inverted, calling a corrected non-uniform multidimensional lookup table, and calculating a corresponding aerosol instantaneous short wave direct radiation effect by adopting cubic spline interpolation.
Through the embodiment, the inversion of the short wave ADRE under the clear sky condition can be realized, the ADRE calculation accuracy is ensured, and the method is suitable for different surface features, different aerosol physical and optical characteristics and has high calculation efficiency.
The invention comprehensively considers the vertical distribution of aerosol, the optical characteristics, the albedo and the inversion of the influence of solar zenith angles under the condition of clear sky. Based on multi-feature data of AERONET ground stations and CALIOP satellite data, such as characteristics of earth surface albedo, solar zenith angles, aerosol optical thickness and the like, the difference between the non-uniform multi-dimensional lookup table and SBDART radiation transmission mode results is compared in the global scope, so that TOA and BOA ADRE can be inverted easily and efficiently under the condition of clear sky without introducing obvious errors.
Compared with the prior art, the invention has the main advantages that:
1. the calculation efficiency is high. The method of the invention utilizes the non-uniform multidimensional lookup table, can process and analyze a large amount of data in a short time, and realizes the quick estimation of ADRE. Compared with the traditional radiation transmission mode, the method has higher calculation efficiency.
2. The calculation accuracy is high. The method of the invention adopts multi-characteristic data, establishes a comprehensive and accurate ADRE inversion model, and can more accurately estimate the ADRE. Meanwhile, inversion capability and stability of the model can be improved by considering the multi-feature value range.
3. The application range is wide. The method of the invention can process aerosol data with large range and high space-time resolution, and is applicable to global, regional and local ADRE research. Meanwhile, the method can be applied to aerosol scenes with different types and complexity, and has wider application prospect.
In conclusion, the method has the advantages of high calculation efficiency, high calculation precision, wide application range and the like, and is a novel ADRE inversion method.
Some of the block diagrams and/or flowchart illustrations are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations of blocks in the block diagrams and/or flowchart illustrations, 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, or other programmable data processing apparatus, such that the instructions, when executed by the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. Furthermore, the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.
While the foregoing is directed to embodiments of the present invention, other and further details of the invention may be had by the present invention, it should be understood that the foregoing description is merely illustrative of the present invention and that no limitations are intended to the scope of the invention, except insofar as modifications, equivalents, improvements or modifications are within the spirit and principles of the invention.

Claims (9)

1. An aerosol instantaneous short wave direct radiation effect inversion method is characterized by comprising the following steps of:
step S1, acquiring numerical distribution conditions of a plurality of initial features required for calculating ADRE based on aerosol data of an AERONET ground station and a CALIOP satellite, wherein the initial features comprise aerosol optical characteristics and vertical distribution features;
s2, according to the numerical distribution condition, obtaining the influence degree of each initial feature on ADRE;
step S3, different feature combinations of the initial features are determined according to the numerical distribution condition and the influence degree, and the instantaneous short wave atmosphere layer top ADRE and the atmosphere layer bottom ADRE under the different feature combinations are calculated based on an SBDART radiation transmission mode to form a sample data set;
s4, constructing a non-uniform multidimensional lookup table for calculating instantaneous ADRE according to the sample data set;
step S5, correcting the non-uniform multidimensional lookup table according to the aerosol data;
and S6, inputting remote sensing data to be inverted, calling a corrected non-uniform multidimensional lookup table, and calculating a corresponding aerosol instantaneous short wave direct radiation effect by adopting cubic spline interpolation.
2. The aerosol short wave direct radiation effect inversion method according to claim 1, wherein in step S1, the aerosol optical characteristics comprise: aerosol optical thickness, single scattering albedo, asymmetry factor, angstrom wavelength index, solar zenith angle, earth surface albedo;
the vertical distribution feature includes: aerosol layer bottom height and aerosol layer thickness.
3. The aerosol short wave direct radiation effect inversion method according to claim 2, wherein the numerical distribution condition comprises a value range and a reference value; the step S1 specifically includes:
step S11, setting respective value ranges and reference values of aerosol optical thickness, single scattering albedo, asymmetry factor and Angstrom wavelength index;
step S12, setting respective value ranges and reference values of solar zenith angles and earth surface albedo;
step S13, setting the respective value ranges and reference values of the bottom height and the thickness of the aerosol layer.
4. The aerosol short wave direct radiation effect inversion method according to claim 1, wherein said step S2 specifically comprises:
s21, analyzing the influence degree of the aerosol layer bottom height change on ADRE;
s22, analyzing local sensitivity of ADRE to each initial characteristic;
step S23, analyzing global sensitivity of ADRE to each initial feature;
and step S24, according to the local sensitivity and the global sensitivity, the influence degree of each initial feature on the ADRE is obtained.
5. The aerosol short wave direct radiation effect inversion method according to claim 4, wherein the global sensitivity of ADRE to each initial feature is analyzed as follows:
hypothesis modelHas the following advantages ofdInput features, sobol sample based generationN×2dIs used for the sampling matrix of the (c),Nthe number of samples for sampling;
front of the sample matrixdThe columns are arranged as a matrix A, the backdThe columns are arranged as a matrix B, and the matrix A and the matrix B are respectivelyN×dIs a new matrix of (a);
the first of the matrix AiColumn matrix B ofiColumn replacement, construction of newN×dMatrix AB i ，i=1,2,3,…,dForming intodNew matrices;
matrices A, B and AB i Is summed up withN×(d+2) samples, using the modelYCalculating a sample correspondence valuef(A)、f(B) Andf(AB i )；
the total effect index is calculated according to the following formula:
，
in the method, in the process of the invention,；i=1,2,…,d；j=i+1,i+2,…,d；X i represent the firstiA plurality of input features;E Xi represent the firstiConditional expectation of the individual input features;Var Xi represent the firstiConditional variance of the individual input features;S Ti representation modelYFor the firstiA total effect index of the individual input features;Var(Y) Representation modelYIs a desired value of (2); and determining the global sensitivity of ADRE to each initial characteristic according to the total effect index.
6. The aerosol short wave direct radiation effect inversion method according to claim 1, wherein the step S3 specifically comprises:
step S31, determining the value range and the step length of each initial feature according to the numerical distribution condition and the influence degree;
step S32, dividing each initial feature into a plurality of sub-features based on the value range and the step length, and forming a plurality of feature combinations by the sub-features of the initial features;
step S33, calculating an instantaneous short wave atmosphere top ADRE and an atmosphere bottom ADRE under each of the feature combinations based on the SBDART radiation transmission mode, to form a sample data set.
7. The aerosol short wave direct radiation effect inversion method according to claim 2, wherein said step S5 specifically comprises:
step S51, performing space-time matching on aerosol data of the AERONET ground station and the CALIOP satellite to obtain the bottom height and the aerosol layer thickness of the AERONET ground station;
step S52, inputting aerosol optical characteristics and albedo data of the AERONET ground station into a constructed non-uniform multi-dimensional lookup table, and calculating a corresponding ADRE inversion result;
and step S53, comparing the ADRE inversion result with the actual value of the instantaneous ADRE, and determining parameters of the non-uniform multi-dimensional lookup table to obtain a corrected non-uniform multi-dimensional lookup table.
8. The aerosol short wave direct radiation effect inversion method according to claim 7, wherein said step S51 specifically comprises:
spatial matching: taking the AERONET ground station as a center, taking the radius 30 km as a circular area, and extracting the bottom height and the thickness of the aerosol layer in the circular area when the satellite passes;
time matching: and in the circular area, extracting the corresponding bottom height and thickness of the aerosol layer within +/-3 hours of the satellite transit time and the AERONET ground station data by taking the satellite transit time as a reference.
9. The aerosol short wave direct radiation effect inversion method according to claim 7, wherein said step S53 comprises:
measuring the coincidence degree of the ADRE inversion result and the actual value of the instantaneous ADRE by adopting a decision coefficient, a root mean square error and an average absolute error;
and adjusting parameters of the non-uniform multidimensional lookup table according to the anastomosis degree.
CN202310836603.4A 2023-07-10 2023-07-10 Inversion method for aerosol instantaneous short wave direct radiation effect Active CN116561487B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310836603.4A CN116561487B (en) 2023-07-10 2023-07-10 Inversion method for aerosol instantaneous short wave direct radiation effect

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310836603.4A CN116561487B (en) 2023-07-10 2023-07-10 Inversion method for aerosol instantaneous short wave direct radiation effect

Publications (2)

Publication Number Publication Date
CN116561487A true CN116561487A (en) 2023-08-08
CN116561487B CN116561487B (en) 2023-09-19

Family

ID=87496881

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310836603.4A Active CN116561487B (en) 2023-07-10 2023-07-10 Inversion method for aerosol instantaneous short wave direct radiation effect

Country Status (1)

Country Link
CN (1) CN116561487B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109974665A (en) * 2019-04-04 2019-07-05 东北师范大学 It is a kind of for the aerosol remote sensing inversion method and system that lack short-wave infrared data
US20210318253A1 (en) * 2019-05-29 2021-10-14 University Of Electronic Science And Technology Of China Method for retrieving atmospheric aerosol based on statistical segmentation
CN116337701A (en) * 2023-03-14 2023-06-27 武昌首义学院 Urban high-resolution aerosol optical thickness inversion method based on double-star networking

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109974665A (en) * 2019-04-04 2019-07-05 东北师范大学 It is a kind of for the aerosol remote sensing inversion method and system that lack short-wave infrared data
US20210318253A1 (en) * 2019-05-29 2021-10-14 University Of Electronic Science And Technology Of China Method for retrieving atmospheric aerosol based on statistical segmentation
CN116337701A (en) * 2023-03-14 2023-06-27 武昌首义学院 Urban high-resolution aerosol optical thickness inversion method based on double-star networking

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
胡斯勒图;施建成;李明;王天星;尚华哲;雷永荟;姬大彬;闻建光;阳坤;陈良富;: "基于卫星数据的地表下行短波辐射估算:方法、进展及问题", 中国科学:地球科学, no. 07, pages 27 - 42 *

Also Published As

Publication number Publication date
CN116561487B (en) 2023-09-19

Similar Documents

Publication Publication Date Title
CN111651941B (en) Global ionosphere electron total content prediction algorithm
Engerer Minute resolution estimates of the diffuse fraction of global irradiance for southeastern Australia
Besharat et al. Empirical models for estimating global solar radiation: A review and case study
Lei et al. Cloud cover over the Tibetan Plateau and eastern China: a comparison of ERA5 and ERA-Interim with satellite observations
Kang et al. A new characterization and classification method for daily sky conditions based on ground-based solar irradiance measurement data
CN109948281B (en) Direct dispersion separation modeling method based on weather type effective identification and combined prediction
Kong et al. Explicit calculations of wet‐bulb globe temperature compared with approximations and why it matters for labor productivity
Kursinski et al. A method to deconvolve errors in GPS RO-derived water vapor histograms
CN116449331B (en) Dust particle number concentration estimation method based on W-band radar and meteorological satellite
Anagnostou et al. Rainfall estimation from TOGA radar observations during LBA field campaign
Blumberg et al. Quantifying the accuracy and uncertainty of diurnal thermodynamic profiles and convection indices derived from the Atmospheric Emitted Radiance Interferometer
CN113108918A (en) Method for inverting air temperature by using thermal infrared remote sensing data of polar-orbit meteorological satellite
Lu et al. Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques
Huang et al. Non-stationary statistical modeling of extreme wind speed series with exposure correction
CN116561487B (en) Inversion method for aerosol instantaneous short wave direct radiation effect
Hao et al. Capability of TMPA products to simulate streamflow in upper Yellow and Yangtze River basins on Tibetan Plateau
Driesse et al. Improving common PV module temperature models by incorporating radiative losses to the sky
Rudié et al. System Advisor Model performance modeling validation report: Analysis of 100 sites
CN114280694B (en) Rapid radiation transmission method and system based on meteorological satellite spectrum imager
CN113742929B (en) Data quality evaluation method for grid point weather condition
Kesavavarthini et al. Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms
Carrió et al. Empirical determination of the covariance of forecast errors: An empirical justification and reformulation of hybrid covariance models
Ishibashi Improvement of accuracy of global numerical weather prediction using refined error covariance matrices
Aksu et al. Frequency analysis based on Peaks-Over-Threshold approach for GPM IMERG precipitation product
CN113435119B (en) Global ocean surface brightness temperature determination method and system

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