CN117077437A - Method for constructing and determining polar region sea surface net radiation model based on multi-source satellite - Google Patents
Method for constructing and determining polar region sea surface net radiation model based on multi-source satellite Download PDFInfo
- Publication number
- CN117077437A CN117077437A CN202311315756.0A CN202311315756A CN117077437A CN 117077437 A CN117077437 A CN 117077437A CN 202311315756 A CN202311315756 A CN 202311315756A CN 117077437 A CN117077437 A CN 117077437A
- Authority
- CN
- China
- Prior art keywords
- sea surface
- data
- net radiation
- sea
- remote sensing
- 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
- 230000005855 radiation Effects 0.000 title claims abstract description 189
- 238000000034 method Methods 0.000 title claims abstract description 82
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 84
- 238000010521 absorption reaction Methods 0.000 claims abstract description 35
- 238000004364 calculation method Methods 0.000 claims abstract description 29
- 238000005259 measurement Methods 0.000 claims abstract description 21
- 108091006146 Channels Proteins 0.000 claims description 72
- 230000008569 process Effects 0.000 claims description 15
- 238000002310 reflectometry Methods 0.000 claims description 15
- 238000012795 verification Methods 0.000 claims description 12
- 238000002834 transmittance Methods 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- 239000000758 substrate Substances 0.000 claims 2
- 230000004907 flux Effects 0.000 description 16
- 239000013535 sea water Substances 0.000 description 8
- 238000013528 artificial neural network Methods 0.000 description 5
- 238000010276 construction Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 238000012937 correction Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000005457 Black-body radiation Effects 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 230000002441 reversible effect Effects 0.000 description 3
- 238000000862 absorption spectrum Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 239000005457 ice water Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 230000001932 seasonal effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 241000196171 Hydrodictyon reticulatum Species 0.000 description 1
- 238000012952 Resampling Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 239000000443 aerosol Substances 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000008264 cloud Substances 0.000 description 1
- 230000002354 daily effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000003746 surface roughness Effects 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- 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)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Operations Research (AREA)
- Evolutionary Computation (AREA)
- Algebra (AREA)
- Geometry (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Radiation Pyrometers (AREA)
- Photometry And Measurement Of Optical Pulse Characteristics (AREA)
Abstract
The embodiment of the disclosure provides a method for constructing and determining a polar sea surface net radiation model based on a multi-source satellite, which is applied to the technical field of remote sensing quantitative inversion. The method comprises the steps of obtaining first satellite remote sensing data; extracting first condition data and first measurement data from first satellite remote sensing data; the first condition data comprises a yearly long-and-short day, a cloud mask, a relative azimuth angle, a solar zenith angle, sea Liu Yanmo and a sea ice range; the first measurement data comprise ice surface temperature, sea surface temperature, apparent albedo, water vapor absorption band channel, long wave infrared atmospheric window area channel bright temperature and clear sky index; and establishing a regional sea surface net radiation quantitative inversion model lookup table according to the first condition data, and establishing a high-latitude regional sea surface net radiation quantitative inversion model according to the first measurement and calculation data. The model can give a specific function form of the solution, has definite physical meaning, improves the sea ice net radiation inversion precision, and greatly improves the sea surface net radiation spatial resolution.
Description
Technical Field
The disclosure relates to the technical field of remote sensing quantitative inversion, in particular to a method for constructing and determining a polar sea surface net radiation model based on a multi-source satellite.
Background
Sea surface net radiation is the net energy that sea surfaces acquire through the downlink and uplink short wave and long wave radiation processes. As an important component of ocean heat flux, accurate estimation of net sea surface radiant flux is of great importance for the assessment of the energy balance of the earth system. However, compared to turbulent heat flux studies, there are very few studies at home and abroad on net radiant flux at sea. The traditional way of obtaining the sea surface radiation flux has limitation that sea surface radiation measured data are obtained through berthing buoy stations, but most of the berthing buoy stations are arranged at middle and low latitudes, and some buoys in polar regions are very sparse or do not provide sea surface net radiation related variables. And analyze the data product, for example: the sea surface radiant flux product provided by ERA5 of the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), the European Centre for Medium-Range Weather Forecasts (ECWMF) of NASA Global Modeling Assimilation Office (GMAO) has the problems of coarse spatial resolution and limited accuracy. Existing remote sensing sea surface radiant flux products, for example: the Japanese Ocean Flux Datasets with Use of Remote Sensing Observations version 3 (J-OFURO 3) has stopped updating, the product time can only be obtained by 12 months and 31 days in 2017, the historical product also does not provide radiant flux of sea ice areas, only covers global ice-free sea areas, and the situation when the underlying surface is sea ice is not considered. And there is also a major inconsistency between the sea surface radiation component data and the product.
The traditional sea surface net radiation inversion method adopts a radiation four-component method to directly calculate the downlink and uplink short wave radiation and long wave radiation components, but it is very difficult to accurately estimate all radiation components, and the precision of the sea surface net radiation is affected by error propagation and accumulation, so the precision is not high. At present, a shallow ANN (Artificial Neural Network) artificial neural network is adopted to construct a nonlinear regression model with regard to a technology for inverting sea surface net radiation based on satellite remote sensing, and the constructed model is usually presented in a black box form, and has no interpretation because of no analytic solution. Moreover, the ANN adopts a gradient descent algorithm to train an optimization model, and the non-convexity of an objective function of the ANN can cause difficulty in ensuring the forward qualification of the Heisen matrix in the training process. Compared with a polynomial regression method, the model constructed by the method can give a specific function form of a solution, generate polynomial coefficients and has definite physical meaning.
The existing sea ice net radiation inversion technology considers the inversion of sea ice mixing areas, but the model needs high-latitude sea ice net radiation as an input parameter. The sea ice net radiation of the same sea ice underlying surface, different longitude and latitude geographic positions and different sea ice densities are different. Similarly, the net radiation of the seawater is different from the net radiation of the seawater on the same seawater under-water pad surface. If the sea water net radiation fixed value of the research area and the sea ice net radiation fixed value of the high latitude area are simply input, the sea ice net radiation of different geographic positions is calculated inaccurately only by relying on sea ice concentration parameters, and atmospheric condition factors are not considered. The model is therefore less effective in high latitude areas, especially polar and very night situations.
Disclosure of Invention
The present disclosure provides a method for constructing and determining a polar sea surface net radiation model based on a multi-source satellite.
According to a first aspect of the present disclosure, a method for constructing a polar sea surface net radiation model based on a multi-source satellite is provided. The method comprises the following steps:
acquiring first satellite remote sensing data; the first satellite remote sensing data are historical multi-source satellite remote sensing data and products of the regional area;
extracting first condition data and first measurement data from the first satellite remote sensing data; the first condition data comprise yearly long-pending days, cloud mask data, relative azimuth angles, solar zenith angles, sea Liu Yanmo and sea ice range data; the first measurement data comprise ice surface temperature, sea surface temperature, top albedo of an atmosphere layer, brightness and clear sky indexes; the bright temperature comprises a water vapor absorption band channel bright temperature and a long-wave infrared atmospheric window area channel bright temperature;
establishing a regional sea surface net radiation quantitative inversion model lookup table according to the first condition data, and establishing a regional sea surface net radiation quantitative inversion model according to the first measurement and calculation data;
the quantitative inversion model of the net radiation of the sea surface in the polar region is as follows:
，
wherein,expressing net radiation prediction results of the sea surface of the polar region; />The method is expressed as a polar sea surface net radiation quantitative inversion model function under different conditions in a lookup table; DOY represents the yearly product day; CLM represents cloud mask data; />Representing the relative azimuth; day, light are determined from the zenith angle of the sun; ice, sea are determined from sea Liu Yanmo and sea ice range data;representing the top albedo of the atmosphere; SST stands for sea surface temperature; IST represents ice temperature; />Expressed as the bright temperature of the water vapor absorption band channel, +.>The bright temperature of the long-wave infrared atmospheric window area channel is expressed; CI represents a clear sky index.
In accordance with aspects and any one of the possible implementations described above, there is further provided an implementation in which the relative azimuth angle is calculated from an observed azimuth angle and a solar azimuth angle;
in the process of constructing a polar sea surface net radiation quantitative inversion model, the method comprises the following steps:
the relative azimuth angles are divided according to the intervals of 0-30, 30-60, 60-90, 90-120, 120-150 and 150-180 and are used as one of the condition parameters for constructing the quantitative inversion model of the net radiation of the sea surface of the polar region.
In accordance with aspects and any one of the possible implementations described above, there is further provided an implementation in which the atmospheric top albedo is calculated from an atmospheric top apparent reflectance as follows:
，
wherein,indicating the apparent reflectivity of the top of the atmosphere, +.>Representing the radiance value of the solar reflection channel, +.>Represents the reciprocal of the distance to earth, +.>Representing solar irradiance of solar reflection channel, +.>Representing the zenith angle of the sun in radian +.>Indicating the top albedo of the atmosphere.
In the aspect and any possible implementation manner described above, there is further provided an implementation manner, wherein a calculation formula of the channel brightness temperature of the water vapor absorption band and the channel brightness temperature of the long-wave infrared atmospheric window area is as follows:
，
wherein,expressed as the bright temperature of the water vapor absorption band channel, +.>Represents the central wavelength of the water vapor absorption channel, < >>Represents the radiance value of the channel of the water vapor absorption band, h represents the Planck constant and is +.>C represents the constant rate of light in vacuum, the value +.>K represents a Boltzmann constant, the value is +.>，C 1 The calculation result is +.>，C 2 The calculation result is +.>。
In the aspect and any possible implementation manner as described above, there is further provided an implementation manner, wherein the calculation formula of the clear sky index is as follows:
,
wherein,represents sea surface downlink short wave radiation, +.>Represents the solar constant at the top of the atmosphere, the value is +.>，/>Representing the zenith angle of the sun>Represents the reciprocal of the distance to earth, +.>Represents the atmospheric transmittance, < >>Represents atmospheric layer top down short wave radiation, < ->Indicates sunset time angle, < >>Indicate latitude>The unit is radian, DOY is the annual product day,indicating the apparent reflectivity of the near infrared water vapor absorption channel, < >>Indicating the apparent reflectance of the near infrared atmospheric window channel.
Aspects and any one of the possible implementations as described above, further providing an implementation, the method further including:
in the obtained historical multisource satellite remote sensing data and products of the polar region, the same geographic position of the polar region corresponds to a plurality of instantaneous observation values of satellites at different moments, and when space-time matching is carried out on the satellite remote sensing data and products of the satellite remote sensing data and the products of the satellite remote sensing data, a nearest neighbor interpolation method is spatially adopted, and a measurement value method of matching adjacent moments is temporally adopted, so that matching processing of multisource data is carried out; when the data products are matched with the data products under different projection coordinate systems, the data products are unified into the same projection coordinate system through projection conversion, and then matching is realized by adopting a nearest neighbor interpolation method; similarly, when matching with the lattice point data product, the nearest neighbor interpolation method is adopted to realize matching.
Aspects and any one of the possible implementations as described above, further providing an implementation, the method further including:
setting the time step of 24 hours in 1 day as 1 hour, inputting a verification sample set at a corresponding moment into a polar region sea surface net radiation quantitative inversion model, and outputting a verification sample sea surface net radiation inversion value;
calculating average deviation between the verification sample sea surface net radiation inversion value and the polar sea surface net radiation true value in a subsection manner;
correcting the sea surface net radiation inversion result according to the calculated average deviation;
the verification sample set is obtained by carrying out data segmentation on historical multi-source satellite remote sensing data and products.
According to a second aspect of the present disclosure, a method of determining polar sea surface net radiation based on a multi-source satellite is provided. The method comprises the following steps:
acquiring second satellite remote sensing data; the second satellite remote sensing data are multisource satellite remote sensing data and products of the region to be detected;
extracting second condition data and second measurement data from the second satellite remote sensing data;
adapting the second condition data with a polar region sea surface net radiation quantitative inversion model lookup table to determine a corresponding pre-constructed polar region sea surface net radiation prediction model; wherein the polar region sea surface net radiation quantitative inversion model lookup table is constructed according to any of the methods of the first aspect described above;
inputting the second measuring and calculating data into the polar region sea surface net radiation prediction model, and outputting sea surface net radiation prediction results.
According to the polar region sea surface net radiation model construction and determination method based on the multi-source satellite, based on the atmospheric radiation transmission mode theory, the situation that sea ice under-polar region cushion surface and sea water cushion surface have different roughness, albedo and emissivity, sea-gas interaction and corresponding radiation transmission process are different is considered, and a polar region sea surface net radiation quantitative inversion model is constructed by taking a satellite remote sensing data water vapor channel, atmospheric transmittance, ice surface temperature, an optimized clear sky index and the like as parameters. The constructed model can give out a specific function form of the solution, has definite physical meaning, is suitable for quantitative inversion of the sea surface net radiation in a high-latitude region, improves the inversion precision of the sea ice net radiation, and greatly improves the spatial resolution of the sea surface net radiation.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. For a better understanding of the present disclosure, and without limiting the disclosure thereto, the same or similar reference numerals denote the same or similar elements, wherein:
FIG. 1 illustrates a flow chart of a method of model construction of multi-source satellite based polar sea surface net radiation in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a flow chart of a method of determining multi-source satellite based polar sea surface net radiation in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates a schematic construction of a polar sea surface net radiation quantification inversion model in accordance with an embodiment of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the disclosure, are within the scope of the disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In the method, a model constructed based on multisource satellite remote sensing data and a product quantitative inversion polar region sea surface net radiation method is provided, a specific function form of a solution can be given, the model has clear physical meaning, the method is suitable for quantitative inversion of sea surface net radiation in a high-latitude polar region, sea ice net radiation inversion accuracy is improved, and sea surface net radiation spatial resolution is greatly improved.
Fig. 1 shows a flowchart of a model building method 100 for multi-source satellite-based polar sea surface net radiation, in accordance with an embodiment of the present disclosure. The method 100 comprises the following steps:
in step 110, first satellite remote sensing data is obtained.
The first satellite remote sensing data are regional historical multi-source satellite remote sensing data and products.
And step 120, extracting first condition data and first measurement data from the first satellite remote sensing data.
The first condition data comprise yearly long-pending days, cloud mask data, relative azimuth angles, solar zenith angles, sea Liu Yanmo and sea ice range data; the first measurement data comprise ice surface temperature, sea surface temperature, top albedo of an atmosphere layer, brightness and clear sky indexes; the bright temperature comprises the bright temperature of a water vapor absorption band channel and the bright temperature of a long-wave infrared atmospheric window area channel.
And 130, establishing a regional sea surface net radiation quantitative inversion model lookup table according to the first condition data, and establishing a regional sea surface net radiation quantitative inversion model according to the first measurement and calculation data.
Wherein the valid sample parameters are screened based on a look-up table.
The quantitative inversion model of the net radiation of the sea surface in the polar region is as follows:
，
wherein,expressing net radiation prediction results of the sea surface of the polar region; />The method is expressed as a polar sea surface net radiation quantitative inversion model function under different conditions in a lookup table; DOY represents the yearly product day; CLM represents cloud mask data; />Representing the relative azimuth; day, light are determined from the zenith angle of the sun; ice, sea are determined from sea Liu Yanmo and sea ice range data;representing the top albedo of the atmosphere; SST stands for sea surface temperature; IST represents ice temperature; />Expressed as the bright temperature of the water vapor absorption band channel, +.>The bright temperature of the long-wave infrared atmospheric window area channel is expressed; CI represents a clear sky index.
The following describes the setting process of each parameter to the construction of the final polar region sea surface net radiation quantitative inversion model in detail by combining a specific embodiment and a schematic construction diagram of the polar region sea surface net radiation quantitative inversion model shown in fig. 3, and specifically includes the following steps:
(1) Taking the North Pole as an example of a research area, satellite remote sensing data and products passing through the border area are automatically screened by judging whether the latitude of model parameter data is within the range of 66-90 degrees.
(2) Remote sensing sea surface radiant flux product Clouds and the Earth Radiant Energy System Synoptic Radiation Fluxes and Clouds Edition A (CERES) Single Scanner Footprint (SSF) Terra-Aqua Ed4A (CERES SSF) contains Terra, aqua and S-NPP satellites measuring short wave radiation and long wave radiation reflected by the sun and emitted by the earth TOA (atmospheric layer roof, top of atmosphere) on orbit to the earth' S surface with a sub-satellite point spatial resolution of 20 km for CERES SSF. Therefore, CERES SSF is selected as a sea surface net radiation prediction parameter for model modeling. The net radiation flux at sea level is calculated as follows:
,
wherein,represents sea surface net radiation, < >>Represents short wave net radiation, < >>Indicating long wave net radiation->Representing downlink short wave radiation>Representing the upstream short wave radiation>Representing downstream long wave radiation, ">Representing the uplink long wave radiation in units of. Because CERES SSF does not directly provide sea surface net radiation flux, provides sea surface short wave net radiation and sea surface long wave net radiation, after invalid values are removed, the sea surface net radiation is obtained by adding the sea surface net radiation flux and the sea surface short wave net radiation according to a sea surface net radiation flux calculation formula. The scan time is also resolved into a millisecond count with a start time of 1970-01-00:00:00 universal time.
(3) And (3) performing quality control on the calculated CERES SSF sea surface net radiation flux data by using a three-sigma (three-sigma) method, calculating the mean value and standard deviation of the sea surface net radiation flux data, and eliminating the value of which the difference from the mean value is larger than 3 times of the standard deviation in the data.
(4) The sea surface uplink long wave radiation refers to the sea surface receiving atmospheric reverse radiation can not completely absorb the long wave radiation emitted outwards and the long wave radiation emitted by the sea surface itself. The calculation formula is as follows:
,
wherein,indicating sea surface upward long wave radiation. />Is constant and takes the value +.>。/>Represents sea level emissivity, ++>Represents sea surface temperature, in k, (-)>Indicating sea surface downlink long wave radiation. According to the formula, the sea surface up-going long wave radiation is mainly influenced by sea surface temperature and sea surface emissivity, and the magnitude of the sea surface emissivity is greatly dependent on the type of the sea surface underlying surface and the sea surface roughness physical parameters. Thus, sea surface temperature (SST, sea Surface Temperature) data of GHRSST (The Group for High-Resolution Sea Surface Temperature) with a spatial resolution of 1 km is selected, invalid values are culled and the data is clipped to arctic study area size. Meanwhile, the data of the analyzed sea ice concentration (SIC, sea Ice Concentration) is not high in accuracy and cannot be used as model parameters.
(5) And adding ice temperature parameters based on the sea ice underlying surface quantitative inversion model. Taking time efficiency of quantitative inversion of net radiation of Sea surface in a region and Sea surface in a model modeling process and a model prediction process into consideration, and drawing display of quantitative inversion results, selecting Ice surface temperature (IST, ice Surface Temperature) data and Sea Ice range (SIE, sea Ice extension) data of which the spatial resolution is 4km and the projection type is Lanbert azimuth equal-Area projection (Lambert Azimuthal Equal-Area), removing invalid values and multiplying the invalid values by a scale factor by a Terra and Aquary satellite MODIS load Ice surface temperature product Sea Ice Extent and IST Daily L Global 4km EASE-Grid (MOD 29E1D/MYD29E 1D).
(6) Clear sky Index (CI) is the ratio of sea surface downlink short wave radiation to atmospheric layer top downlink short wave radiation, the range of the value is 0-1, sea surface solar radiation quantity is related to regional latitude and seasonal factors, and the capability of a quantitative inversion model for explaining the change in clear sky conditions and atmospheric turbidity in one day can be enhanced by inputting the independent variable. Because the polar region satellite remote sensing sea surface downlink short wave radiation with the same spatial scale and time scale is not available in the model prediction process, the calculation formula of the clear sky index parameterization is improved as follows:
,
in the formula (i),by sea surface downlink short wave radiation is meant total solar radiation reaching the sea surface, including direct solar radiation through the atmosphere, atmospheric scattered solar radiation, and scattered solar radiation reaching the sea surface after multiple scattering between the atmosphere and the sea surface. />Represents the solar constant at the top of the atmosphere, the value is +.>。/>Representing the zenith angle of the sun>Represents the reciprocal of the distance to earth, +.>Represents the atmospheric transmittance, < >>Represents atmospheric layer top down short wave radiation, < ->Indicates sunset time angle, < >>Indicate latitude>The declination of the sun is expressed in radians, and DOY is the annual product day.
The sea surface solar radiation quantity is related to the atmospheric moisture content and the cloud layer thickness, and the total moisture in the atmosphere is related to the transmittance of the moisture absorption spectrum channel, so that the atmospheric transmittance is calculated by adopting the ratio of the apparent reflectivities of the near infrared atmospheric window channel and the moisture absorption band channel. In the formula (i),indicating the apparent reflectivity of the near infrared water vapor absorption channel, < >>Indicating the apparent reflectance of the near infrared atmospheric window channel.
The sea surface solar radiation quantity is related to the atmospheric moisture content and the cloud layer thickness, and the total moisture in the atmosphere is related to the transmittance of the moisture absorption spectrum channel, so that the atmospheric transmittance is calculated by adopting the ratio of the apparent reflectivities of the near infrared atmospheric window channel and the moisture absorption band channel. In the formula, the apparent reflectivity of the near infrared water vapor absorption channel is expressed, and the apparent reflectivity of the near infrared atmospheric window channel is expressed.
Therefore, FY-3D wind cloud satellite No. three MERSI-II load data with the spatial resolution of 1 km and 5 minutes are selected, the apparent reflectivities of solar zenith angles, latitudes and near infrared atmospheric window area channels and vapor absorption channel channels after radiation calibration are read, and the improved clear sky index is calculated, so that the problem that downlink short wave radiation of regional satellite remote sensing sea surface cannot be obtained in the model prediction process is solved, and the spatial resolution of the clear sky index is improved to 1 km.
(7) The difference in satellite observation angles can lead to the difference in the lengths of the atmospheric radiation transmission paths under given satellite incidence conditions. The edge pixels of the satellite image scan line are farther from the satellite than at the point below the satellite. The intensity of the edge pixel radiation received by the sensor has distortion phenomenon along with the increase of the zenith angle of the satellite and the influence of the curvature of the earth. Therefore, FY-3D wind cloud satellite No. three MERSI-II load data with the spatial resolution of 1 km and 5 minutes is selected, and the zenith angle is read and observedIn order to improve the quantitative inversion accuracy of the model, the +.>Is removed. The scan time is read and resolved simultaneously as a millisecond count with a start time of 1970-01-00:00:00 universal time.
(8) The sea surface downlink long wave radiation refers to the reverse radiation of the atmosphere, which is emitted outwards to reach the sea surface after water vapor, other gases and aerosol and cloud in the atmosphere absorb solar radiation. Because of the unavailability of the regional satellite remote sensing sea surface downlink long wave radiation in the model prediction process, in order to capture the low-layer atmospheric reverse radiation, a water vapor absorption band channel and a long wave infrared atmospheric window region channel of FY-3D wind cloud satellite No. three MERSI-II load data with the spatial resolution of 1 km and 5 minutes are selected as model modeling and inversion parameters, the atmospheric top radiance value is calculated through calibration, and finally the bright temperature is generated.
The DN (Digital Number) gray scale value of the satellite heat emission channel is multiplied by the slope and intercept scaling calculation to generate the radiance value. Calculating the brightness temperature on the satellite according to the inverse function of the Planck blackbody radiation law formula, wherein the brightness temperature calculation formula is as follows:
，
in the method, in the process of the invention,expressed as the bright temperature of the water vapor absorption band channel, in k. />Represents the center wavelength of the channel in +.>。Represents the channel radiance value in +.>. h represents the Planck constant, the value +.>C represents the constant rate of light in vacuum, the value +.>K represents a Boltzmann constant, and has a value of，C 1 The calculation result is +.>，C 2 The calculation result is。
Namely, the DN (Digital Number) gray scale value of the satellite water vapor absorption band channel is multiplied by the slope and intercept calibration to generate the radiance value of the water vapor absorption band channel. According to the inverse function of the Planck blackbody radiation law formula, calculating the bright temperature of a water vapor absorption band channel on a satellite, wherein the bright temperature of the water vapor absorption band channel is calculated according to the following formula:
，
in the method, in the process of the invention,expressed as the bright temperature of the water vapor absorption band channel, in k. />Represents the central wavelength of the water vapor absorption channel, and the unit is +.>。/>Represents the channel radiance value of the water vapor absorption band, and the unit is +.>. h represents the Planck constant, the value +.>C represents the constant rate of light in vacuum, the value +.>K represents a Boltzmann constant, the value is +.>，C 1 The calculation result is +.>，C 2 The calculation result is +.>。
Namely, the DN (Digital Number) gray scale value of the satellite long-wave infrared atmospheric window area channel is multiplied by the slope and intercept scaling calculation to generate the radiance value of the long-wave infrared atmospheric window area channel. Calculating the bright temperature of a long-wave infrared atmospheric window area channel on a satellite according to the inverse function of a Planck blackbody radiation law formula, wherein the bright temperature of the long-wave infrared atmospheric window area channel is calculated according to the following formula:
，
in the method, in the process of the invention,expressed as the bright temperature of the long-wave infrared atmospheric window area channel, and the unit is k. />Represents the central wavelength of a long-wave infrared atmospheric window area channel, and the unit is +.>。/>The radiation brightness value of the long-wave infrared atmospheric window area channel is represented, h represents the Planck constant, and the value is +.>C represents the constant rate of light in vacuum, the value +.>K represents a Boltzmann constant, the value is +.>，C 1 The calculation result is +.>，C 2 The calculation result is +.>。
It should be noted that in the polar night mode, since the top albedo and the clear sky index of the atmosphere cannot be used, the calculated bright temperature of the water vapor absorption band channel and the bright temperature of the long-wave infrared atmospheric window area channel need to be normalized to be changed into dimensionless data as the final bright temperature.
(9) The sea surface uplink short wave radiation refers to solar radiation reflected by sea surface, and the sea surface solar radiation quantity can be determined by sunlight radiation intensity. The reflectivity on the satellite comprises sea surface reflectivity and atmospheric path radiation, and can be used for representing the ratio of sea surface reflected radiation to incident radiation to a certain extent and representing the absorption and reflection capacity of sea surface to solar radiation. Therefore, near infrared channels of FY-3D wind cloud satellite No. three MERSI-II load data with the spatial resolution of 1 km and 5 minutes are selected as model modeling and inversion parameters, atmospheric layer top radiance values are calculated through calibration, and finally the atmospheric layer top albedo is generated.
And multiplying the DN (Digital Number) gray value of the satellite solar reflection channel by the slope and intercept scaling calculation to generate the radiance value. The calculation formulas of the apparent reflectivity and the albedo of the top of the atmosphere layer are as follows:
，
wherein,the apparent reflectivity of the top of the atmosphere layer is shown, and the method is dimensionless. />Represents the radiance value of a solar reflection channel, and the unit is。/>Represents the reciprocal of the distance to earth, +.>Representing solar irradiance of solar reflection channel in units of。/>Representing the zenith angle of the sun in radian +.>The top albedo of the atmosphere is represented, and the method is dimensionless.
And (3) establishing sea surface net radiation quantitative inversion model lookup tables under different bedding surfaces of sea ice and sea water of the polar region under all weather conditions and under the condition of considering polar day and night on the basis of space-time matched multi-source satellite data and products by adopting a multi-polynomial regression parameterization method based on an atmospheric radiation transmission model, and finally establishing a sea surface net radiation inversion model suitable for the polar region of high latitude and calculating residual errors.
(10) When the multi-source satellite data is matched with the product, the problem that the spatial scale and the time scale of the multi-source satellite data are inconsistent needs to be solved. The FY-3D polar orbit satellite revisits the same region of the polar region at least 8 times every day, so that the same geographic position corresponds to a plurality of instantaneous observation values at different moments, and when the FY-3D polar orbit satellite performs space-time matching with a discrete point CERES SSF Terra-Aqua Ed4A sea surface net radiation data product under the same projection coordinate system, the nearest neighbor interpolation method is spatially adopted, so that the problems that the numerical value of the satellite data product is changed by the resampling interpolation method, and the quantitative inversion result is inaccurate due to different information in the lifting scale conversion process can be avoided. The method of matching the measured values of the adjacent moments is also adopted in time. And setting the pixel scanning millisecond counting time difference absolute value of data matching to be smaller than or equal to 3300 seconds according to priori knowledge of multi-source satellite orbit intersection, wherein the spatial distance longitude and latitude difference absolute value is smaller than. When the ice surface temperature data products of MOD29E1D/MYD29E1D under different projection coordinate systems are matched, the same projection coordinate system is unified through projection conversion, and then the nearest neighbor interpolation method is adopted to realize the matching. Similarly, when matching with the lattice GHRST sea surface temperature data product, the nearest neighbor interpolation method is adopted to realize matching.
(11) The sea level solar radiation is also related to the number of sunshine hours and seasonal factors. Polar daytime and polar night in the polar region alternate, after spring, the direct solar point moves from the equator north, polar daytime occurs in the north pole, polar night occurs in the south pole, and the polar daytime range in the north and south region expands thereafter. Until the summer reaches the day, the direct solar radiation point is at the northern back line, the polar day and polar night range of the polar region is maximum, the polar day appears in the northern circle, and the polar night appears in the southern circle. After the summer reaches the date, the direct solar radiation point is shifted in the south, and the polar daytime and polar night range of the polar region is gradually reduced. By autumn, the sun is directly incident on the equator, and the polar day and polar night phenomenon of the polar region disappears. After autumn, the direct solar point moves south, the north pole appears in the polar night, the south pole appears in the polar day, and the polar day and polar night range of the south-north polar region is enlarged. Until winter, the direct solar radiation point is in the return line of south, the polar day and polar night range of the polar region reaches the maximum, polar night appears in the northern circle, and polar day appears in the southern circle. After winter, the direct solar radiation point moves north, and the polar day and night range of the polar region is gradually reduced. Until spring, the sun is directly irradiated to the equator, and the polar day and polar night phenomena of the polar region disappear. Therefore, taking the north pole as a research area, taking the time points of the polar regions of the 3 month 21 day and the 9 month 23 day at which the polar days and the polar night appear as one of the condition parameters for constructing the polar region sea surface net radiation quantitative inversion model lookup table.
(12) Binary Yun Yanmo (CLM, cloud Mask) data of a Little-end (Little Endian) byte order is read as a condition parameter for extracting pixels marked as Clear sky (Clear) and Cloudy (Cloud). Reading an observation azimuth angle and a sun azimuth angle of the observation geometric information, and calculating a relative azimuth angle, wherein the formula is as follows:
，
wherein,indicating the azimuth angle of the sun>Representing the azimuth angle of observation. Relative azimuth angle->The interval division according to 0-30, 30-60, 60-90, 90-120, 120-150, 150-180 is also used as a condition parameter in modeling and inversion of the model. Reading sun zenith angle->Will be in the model modeling process/>Meanwhile, the method is used as a condition parameter for dividing pixels in daytime and at night. The DayNightFlag indicates whether the satellite remote sensing data pixels are identification codes of daytime or night, and when the satellite remote sensing data pixels are equal to 1, the satellite remote sensing data pixels are indicated as the night. In some embodiments, it is also possible to pass the solar zenith angle alone,>as a conditional parameter for the division of the black night pixels. Sea Liu Yanmo and matched Sea Ice range (SIE) data are read, and a Sea identification code and a Sea Ice identification code are extracted as condition parameters for dividing the polar Sea subsurface into Sea Ice and Sea water. The finally formed model lookup table comprises a regional sea surface net radiation quantitative inversion model under 96 conditions, namely, classifying the model into 2 classes according to the year product day of 3 months, 21 days and 9 months, 23 days, classifying the model into 2 classes according to cloud mask clear sky or cloud, classifying the model into 6 classes according to relative azimuth angles, classifying the model into 2 classes in daytime or night, classifying the model into sea ice or sea water, and classifying the model into 96 classes in total.
(13) And screening effective sample parameters based on the constructed model lookup table, carrying out Normalization (Normalization) optimization treatment on the sample parameters, removing dimensions, accelerating model calculation, improving model precision and reducing model overfitting. And (3) data segmentation is carried out, the data is segmented into a training data set, a verification data set and a test data set. And adjusting the degree of polynomial regression model parameters to generate polynomial characteristics. And constructing a polynomial regression model formula, fitting to generate a statistical regression coefficient among parameters, wherein any local optimal solution is a global optimal solution as the objective function is a convex function.
(14) Setting the time step length of 24 hours of 1 day as 1 hour, inputting effective verification sample parameters at corresponding moments into a quantitative inversion model, and calculating the deviation between the CERES regional sea surface net radiation value and the model predicted regional sea surface net radiation value in a segmented mode to obtain an average deviation, wherein the calculation formula is as follows:
，
expressed as mean deviation, follow a normal distribution, +.>Represents the net radiation truth value of the sea surface in the polar region, ++>The sea surface net radiation inversion value of the verification sample is represented, and n represents the number of the verification samples.
Inputting the preprocessed multisource satellite remote sensing data and product model parameters into a pre-constructed quantitative inversion model of the sea-surface net radiation of the polar region, generating a sea-surface net radiation result, correcting the inversion result of the model according to the average deviation calculation result, and performing precision evaluation.
The final polar region sea surface net radiation quantitative inversion model formula is expressed as follows:
，
wherein,expressing net radiation prediction results of the sea surface of the polar region; />The method is expressed as a polar sea surface net radiation quantitative inversion model function under different conditions in a lookup table; DOY represents the yearly product day; CLM represents cloud mask data; />Representing the relative azimuth; day, light are determined from the zenith angle of the sun; ice, sea are determined from sea Liu Yanmo and sea ice range data;representing the top albedo of the atmosphere; SST stands for sea surface temperature; IST represents ice temperature; />Expressed as the bright temperature of the water vapor absorption band channel, +.>The bright temperature of the long-wave infrared atmospheric window area channel is expressed; CI represents a clear sky index.
The condition parameters comprise yeartime DOY of MERSI-II load data of 5 minutes, cloud mask CLM data and relative azimuth angleSolar zenith angle->And->The marker, sea Liu Yanmo and the matched MOD29E1D/MYD29E1D sea ice range data are substituted into a model lookup table, and a pre-constructed polar sea surface net radiation quantitative inversion model is matched.
(16) The ice surface temperature of MOD29E1D/MYD29E1D, the GHRST sea surface temperature and the calculated top albedo and bright temperature of the MERSI-II load atmospheric layer are input into a matched regional sea surface net radiation quantitative inversion model after being subjected to normalization treatment, a regional sea surface net radiation result with the spatial resolution of 0.01 DEG and 5 minutes is generated through inversion, and the average deviation at the corresponding moment is added for correction, wherein the correction formula is as follows:
,
expressed as mean deviation, follow a normal distribution, +.>Indicating the result after correction of the net radiation of the sea surface in the polar region, < >>And (5) expressing the net radiation prediction inversion result of the sea surface of the polar region.
(17) Finally, root mean square error (Rmse), average deviation (Bias) and R square (R) 2 ) And the precision index carries out precision evaluation on the net radiation deviation correction result of the sea surface of the polar region.
Fig. 2 illustrates a flow chart of a method 200 of determining multi-source satellite-based polar sea surface net radiation in accordance with an embodiment of the present disclosure. The method 200 comprises the following steps:
step 210, obtaining second satellite remote sensing data.
The second satellite remote sensing data are multisource satellite remote sensing data and products of the region to be detected.
And 220, extracting second condition data and second measurement data from the second satellite remote sensing data.
Step 230, adapting the second condition data with a polar region sea surface net radiation quantitative inversion model lookup table to determine a corresponding pre-constructed polar region sea surface net radiation prediction model.
Wherein the polar sea surface net radiation quantitative inversion model lookup table is constructed according to method 100.
And 240, inputting the second measurement data into the polar region sea surface net radiation prediction model, and outputting a sea surface net radiation prediction result.
In some embodiments, multi-source satellite remote sensing data of the region to be measured is obtained, and second condition data and second measurement data are extracted from the multi-source satellite remote sensing data of the region to be measured; the second condition data comprise annual product date, cloud mask data, relative azimuth angle, solar zenith angle, sea Liu Yanmo and sea ice range data; the second measurement data comprise ice surface temperature, sea surface temperature, atmospheric layer top albedo, brightness and clear sky index; the bright temperature comprises the bright temperature of a water vapor absorption band channel and the bright temperature of a long-wave infrared atmospheric window area channel.
And according to the second condition data, accurately adapting a pre-constructed polar region sea surface net radiation prediction model from a polar region sea surface net radiation quantitative inversion model lookup table, and then inputting second measurement and calculation data to obtain a sea surface net radiation prediction result. The sea surface net radiation prediction model of the polar region is constructed based on more fine conditions under a plurality of parameters, and the polar region is more attached to the polar region, so that the predicted sea surface net radiation result is more accurate.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (8)
1. The method for constructing the polar region sea surface net radiation model based on the multi-source satellite is characterized by comprising the following steps of:
acquiring first satellite remote sensing data; the first satellite remote sensing data are historical multi-source satellite remote sensing data and products of the regional area;
extracting first condition data and first measurement data from the first satellite remote sensing data; the first condition data comprise yearly long-pending days, cloud mask data, relative azimuth angles, solar zenith angles, sea Liu Yanmo and sea ice range data; the first measurement data comprise ice surface temperature, sea surface temperature, top albedo of an atmosphere layer, brightness and clear sky indexes; the bright temperature comprises a water vapor absorption band channel bright temperature and a long-wave infrared atmospheric window area channel bright temperature;
establishing a regional sea surface net radiation quantitative inversion model lookup table according to the first condition data, and establishing a regional sea surface net radiation quantitative inversion model according to the first measurement and calculation data;
the quantitative inversion model of the net radiation of the sea surface in the polar region is as follows:
，
wherein,expressing net radiation prediction results of the sea surface of the polar region; />The method is expressed as a polar sea surface net radiation quantitative inversion model function under different conditions in a lookup table; DOY represents the yearly product day; CLM represents cloud mask data; />Representing the relative azimuth; day, light are determined from the zenith angle of the sun; ice, sea are determined from sea Liu Yanmo and sea ice range data; />Representing the top albedo of the atmosphere; SST stands for sea surface temperature; IST represents ice temperature; />Expressed as the bright temperature of the water vapor absorption band channel, +.>The bright temperature of the long-wave infrared atmospheric window area channel is expressed; CI represents a clear sky index.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the relative azimuth angle is calculated according to the observation azimuth angle and the sun azimuth angle;
in the process of constructing a polar sea surface net radiation quantitative inversion model, the method comprises the following steps:
the relative azimuth angles are divided according to the intervals of 0-30, 30-60, 60-90, 90-120, 120-150 and 150-180 and are used as one of the condition parameters for constructing the quantitative inversion model of the net radiation of the sea surface of the polar region.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the atmospheric top albedo is calculated according to the apparent reflectivity of the atmospheric top, and the formula is as follows:
，
wherein,indicating the apparent reflectivity of the top of the atmosphere, +.>Representing the radiance value of the solar reflection channel, +.>Represents the reciprocal of the distance to earth, +.>Representing solar irradiance of solar reflection channel, +.>Representing the zenith angle of the sun in radian +.>Indicating the top albedo of the atmosphere.
4. The method of claim 1, wherein the calculation formula for the bright temperature of the vapor absorption band channel is as follows:
，
wherein,expressed as the bright temperature of the water vapor absorption band channel, +.>Represents the central wavelength of the water vapor absorption channel, < >>Represents the radiance value of the channel of the water vapor absorption band, h represents the Planck constant and is +.>C represents the constant rate of light in vacuum, the value +.>K represents a Boltzmann constant, the value is +.>，C 1 The calculation result is +.>，C 2 The calculation result is +.>。
5. The method according to claim 1, wherein the calculation formula of the clear sky index is as follows:
,
wherein,represents sea surface downlink short wave radiation, +.>Represents the solar constant at the top of the atmosphere, the value is +.>，/>Representing the zenith angle of the sun>Represents the reciprocal of the distance to earth, +.>Represents the atmospheric transmittance, < >>Represents atmospheric layer top down short wave radiation, < ->Indicates sunset time angle, < >>Indicate latitude>The expression of solar declination, the unit is radian, DOY is the annual holiday,/day>Indicating the apparent reflectivity of the near infrared water vapor absorption channel, < >>Indicating the apparent reflectance of the near infrared atmospheric window channel.
6. The method according to claim 1, wherein the method further comprises:
in the obtained historical multisource satellite remote sensing data and products of the polar region, the same geographic position of the polar region corresponds to a plurality of instantaneous observation values of satellites at different moments, and when space-time matching is carried out on the satellite remote sensing data and products of the satellite remote sensing data and the products of the satellite remote sensing data, a nearest neighbor interpolation method is spatially adopted, and a measurement value method of matching adjacent moments is temporally adopted, so that matching processing of multisource data is carried out; when the data products are matched with the data products under different projection coordinate systems, the data products are unified into the same projection coordinate system through projection conversion, and then matching is realized by adopting a nearest neighbor interpolation method; similarly, when matching with the lattice point data product, the nearest neighbor interpolation method is adopted to realize matching.
7. The method according to claim 1, wherein the method further comprises:
setting the time step of 24 hours in 1 day as 1 hour, inputting a verification sample set at a corresponding moment into a polar region sea surface net radiation quantitative inversion model, and outputting a verification sample sea surface net radiation inversion value;
calculating average deviation between the verification sample sea surface net radiation inversion value and the polar sea surface net radiation true value in a subsection manner;
correcting the sea surface net radiation inversion result according to the calculated average deviation;
the verification sample set is obtained by carrying out data segmentation on historical multi-source satellite remote sensing data and products.
8. A method for determining net radiation in a polar sea area based on a multi-source satellite, comprising:
acquiring second satellite remote sensing data; the second satellite remote sensing data are multisource satellite remote sensing data and products of the region to be detected;
extracting second condition data and second measurement data from the second satellite remote sensing data;
adapting the second condition data with a polar region sea surface net radiation quantitative inversion model lookup table to determine a corresponding pre-constructed polar region sea surface net radiation prediction model; wherein the polar region sea surface net radiation quantitative inversion model lookup table is constructed according to the method of any one of claims 1-7;
inputting the second measuring and calculating data into the polar region sea surface net radiation prediction model, and outputting sea surface net radiation prediction results.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311315756.0A CN117077437B (en) | 2023-10-12 | 2023-10-12 | Method for constructing and determining polar region sea surface net radiation model based on multi-source satellite |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311315756.0A CN117077437B (en) | 2023-10-12 | 2023-10-12 | Method for constructing and determining polar region sea surface net radiation model based on multi-source satellite |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117077437A true CN117077437A (en) | 2023-11-17 |
| CN117077437B CN117077437B (en) | 2024-01-23 |
Family
ID=88717260
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311315756.0A Active CN117077437B (en) | 2023-10-12 | 2023-10-12 | Method for constructing and determining polar region sea surface net radiation model based on multi-source satellite |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117077437B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117554300A (en) * | 2024-01-10 | 2024-02-13 | 中国科学院、水利部成都山地灾害与环境研究所 | Remote sensing space downscaling method for mountain land surface albedo site observation |
| CN117725375A (en) * | 2024-02-08 | 2024-03-19 | 中国科学院空天信息创新研究院 | Satellite remote sensing data reconstruction method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102338869A (en) * | 2011-06-20 | 2012-02-01 | 北京师范大学 | Inversion method and system of downlink shortwave radiation and photosynthetically active radiation data |
| KR20190037855A (en) * | 2017-09-29 | 2019-04-08 | 한국전자통신연구원 | Outgoing longwave radiatioin retrieval method using water vapor channel and infrared channel |
| KR20200059085A (en) * | 2018-11-20 | 2020-05-28 | 서울대학교산학협력단 | A Method for Sea Surface Temperature Retrieval using Surface Drifter Temperature Data and Satellite Infrared Images |
| CN111273376A (en) * | 2020-02-10 | 2020-06-12 | 北京师范大学 | Downscaling sea surface net radiation determination method, system, equipment and storage medium |
| CN112861355A (en) * | 2021-02-09 | 2021-05-28 | 北京师范大学 | Sea surface net radiation estimation method and system |
-
2023
- 2023-10-12 CN CN202311315756.0A patent/CN117077437B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102338869A (en) * | 2011-06-20 | 2012-02-01 | 北京师范大学 | Inversion method and system of downlink shortwave radiation and photosynthetically active radiation data |
| KR20190037855A (en) * | 2017-09-29 | 2019-04-08 | 한국전자통신연구원 | Outgoing longwave radiatioin retrieval method using water vapor channel and infrared channel |
| KR20200059085A (en) * | 2018-11-20 | 2020-05-28 | 서울대학교산학협력단 | A Method for Sea Surface Temperature Retrieval using Surface Drifter Temperature Data and Satellite Infrared Images |
| CN111273376A (en) * | 2020-02-10 | 2020-06-12 | 北京师范大学 | Downscaling sea surface net radiation determination method, system, equipment and storage medium |
| CN112861355A (en) * | 2021-02-09 | 2021-05-28 | 北京师范大学 | Sea surface net radiation estimation method and system |
Non-Patent Citations (1)
| Title |
|---|
| 胡婷婷;申双和;师春香;武艳;吴增巍;: "20年时间序列海表温度数据集反演和评价", 气象科技, no. 04 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117554300A (en) * | 2024-01-10 | 2024-02-13 | 中国科学院、水利部成都山地灾害与环境研究所 | Remote sensing space downscaling method for mountain land surface albedo site observation |
| CN117554300B (en) * | 2024-01-10 | 2024-03-19 | 中国科学院、水利部成都山地灾害与环境研究所 | Remote sensing space downscaling method for mountain land surface albedo site observation |
| CN117725375A (en) * | 2024-02-08 | 2024-03-19 | 中国科学院空天信息创新研究院 | Satellite remote sensing data reconstruction method and device, electronic equipment and storage medium |
| CN117725375B (en) * | 2024-02-08 | 2024-05-14 | 中国科学院空天信息创新研究院 | Satellite remote sensing data reconstruction method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117077437B (en) | 2024-01-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117077437B (en) | Method for constructing and determining polar region sea surface net radiation model based on multi-source satellite | |
| Schillings et al. | Operational method for deriving high resolution direct normal irradiance from satellite data | |
| McClain | Multiple atmospheric-window techniques for satellite-derived sea surface temperatures | |
| Minnis et al. | Cloud detection in nonpolar regions for CERES using TRMM VIRS and Terra and Aqua MODIS data | |
| Zhang et al. | An operational approach for generating the global land surface downward shortwave radiation product from MODIS data | |
| Kang et al. | Development of an observation processing package for data assimilation in KIAPS | |
| CN108256186B (en) | Pixel-by-pixel atmospheric correction method for online calculation lookup table | |
| CN116519557B (en) | Aerosol optical thickness inversion method | |
| Mouhamet et al. | Improving the Heliosat-2 method for surface solar irradiation estimation under cloudy sky areas | |
| Tan et al. | Evaluating precipitable water vapor products from Fengyun-4A meteorological satellite using radiosonde, GNSS, and ERA5 Data | |
| Li et al. | An Operational Split-Window Algorithm for Generating Long-Term Land Surface Temperature Products from Chinese Fengyun-3 Series Satellite Data | |
| Prata | Satellite-derived evaporation from Lake Eyre, South Australia | |
| Hawkinson et al. | A comparison of GOES sounder–and cloud lidar-and radar-retrieved cloud-top heights | |
| Zo et al. | Analysis of solar radiation on the surface estimated from GWNU solar radiation model with temporal resolution of satellite cloud fraction | |
| CN115452167A (en) | Satellite remote sensor cross calibration method and device based on invariant pixel | |
| Nahmani et al. | Sensitivity of GPS tropospheric estimates to mesoscale convective systems in West Africa | |
| Roy et al. | Impact of tropical convective conditions on solar irradiance forecasting based on cloud motion vectors | |
| Lefèvre et al. | Description of the software Heliosat-2 for the conversion of images acquired by Meteosat satellites in the visible band into maps of solar radiation available at ground level | |
| CN115222837A (en) | True color cloud picture generation method and device, electronic equipment and storage medium | |
| Jiang et al. | Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data | |
| Minnis et al. | 2.7 SEASONAL AND DIURNAL VARIATIONS OF CLOUD PROPERTIES DERIVED FOR CERES FROM VIRS AND MODIS DATA | |
| Valor et al. | Determination and Evaluation of Surface Solar Irradiance With the MAGIC-Heliosat Method Adapted to MTSAT-2/Imager and Himawari-8/AHI Sensors | |
| Kashyap et al. | Model for estimation of global horizontal irradiance in the presence of dust, fog, and clouds | |
| Chrysoulakis et al. | GIS based estimation and mapping of local level daily irradiation on inclined surfaces | |
| He et al. | Spatial and Temporal Differences in Surface Albedo over Different Underlying Surfaces in the Badain Fijaran Desert, China |
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 |