Sea-air turbulent heat flux co-inversion method and system

Abstract

The present application belongs to the technical field of heat flux remote sensing inversion, and relates to a sea-air turbulent heat flux collaborative inversion method and system. The method comprises the following steps: collecting observation data required for sea-air turbulent heat flux remote sensing inversion; preprocessing; selecting variables as model driving data sets; calculating sea-air turbulent wave-enthalpy ratio; constructing a sea-air turbulent heat flux collaborative inversion multivariate random forest model; determining the optimal hyperparameter combination to obtain the target sea-air turbulent heat flux collaborative inversion multivariate random forest model; and collaboratively inverting the sea-air turbulent heat flux by the collaborative inversion multivariate random forest model. The present application can solve the problem that the existing data-driven model ignores the collaborative change relationship among sea-air turbulent sensible heat flux, sea-air turbulent latent heat flux and sea-air turbulent wave-enthalpy ratio, resulting in an inaccurate sea-air turbulent wave-enthalpy ratio, and improve the estimation accuracy of the sea-air turbulent wave-enthalpy ratio.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat flux remote sensing inversion, and in particular, relates to a sea-air turbulent heat flux collaborative inversion method and system based on a multivariate random forest model. Background Art

[0002] The heat of the ocean and atmosphere profoundly influences the global meteorological and climate systems. As a major process at the air-sea interface, the air-sea turbulent heat flux, comprising both the incoming and latent heat fluxes, is crucial for understanding the interaction between the ocean and atmosphere, contributing to climate change projections and predictions of extreme weather events. However, the widely used global aerodynamic models based on the Monin-Obukhov similarity theory suffer from biased estimates of the incoming and latent heat fluxes due to their simple parameterization schemes and uncertainties in the model-driven data. This, in turn, leads to errors and uncertainties in the estimated Bowen ratio of the air-sea turbulence. Existing data-driven methods estimate the sea-air turbulent incoming heat flux or the sea-air turbulent latent heat flux separately. Although the estimation errors of each are reduced, they ignore the coordinated variation relationship between the sea-air turbulent incoming heat flux and the sea-air turbulent latent heat flux, which will still lead to unreasonable estimation results of the sea-air turbulent Bowen ratio, bringing certain uncertainties to global climate change projections, meteorological forecasts, and global energy and water cycle research. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a collaborative inversion method and system for sea-air turbulent heat flux based on a multivariate random forest model, aiming to solve the problem that the existing data-driven model ignores the coordinated variation relationship between sea-air turbulent incoming heat flux, sea-air turbulent latent heat flux and sea-air turbulent Bowen ratio, resulting in the estimated sea-air turbulent Bowen ratio being inaccurate, thereby introducing uncertainty into global climate change predictions, extreme weather event predictions and global energy and water cycle research.

[0004] In a first aspect, the present invention provides a method for collaborative inversion of sea-air turbulent heat flux, comprising:

[0005] Collect observational data required for remote sensing inversion of sea-air turbulent heat flux; the observational data include near-sea meteorological observation data, sea surface observation data, and sea-air turbulent heat flux observation data; sea-air turbulent heat flux includes sea-air turbulent current heat flux and sea-air turbulent latent heat flux;

[0006] Preprocess the collected observation data;

[0007] All available variables in the preprocessed observation data are sorted according to their contribution, and the variables are selected as the model-driven data set;

[0008] The sea-air turbulent Bowen ratio is calculated based on the sea-air turbulent current heat flux and the sea-air turbulent latent heat flux;

[0009] A multivariate random forest model for collaborative inversion of air-sea turbulent heat flux is constructed by taking all variables in the model-driven dataset as input, the air-sea turbulent Bowen ratio as the internal constraint, and the air-sea turbulent current heat flux, air-sea turbulent latent heat flux, and air-sea turbulent Bowen ratio as outputs.

[0010] The grid retrieval method is used to train the multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux, determine the optimal hyperparameter combination, and obtain the target multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux;

[0011] The target sea-air turbulent heat flux collaborative inversion multivariate random forest model is used to collaboratively invert the sea-air turbulent heat flux.

[0012] In a second aspect, the present invention provides a sea-air turbulent heat flux collaborative inversion system, comprising a collection unit, a preprocessing unit, a selection unit, a first processing unit, a model building unit, a model training unit, and a second processing unit;

[0013] A collection unit is used to collect observation data required for remote sensing inversion of sea-air turbulent heat flux; the observation data include near-sea meteorological observation data, sea surface observation data, and sea-air turbulent heat flux observation data; the sea-air turbulent heat flux includes sea-air turbulent current heat flux and sea-air turbulent latent heat flux;

[0014] A preprocessing unit, used for preprocessing the collected observation data;

[0015] The selection unit is used to sort all available variables in the preprocessed observation data according to their contribution and select variables as the model-driven data set;

[0016] The first processing unit is used to calculate the sea-air turbulent Bowen ratio based on the sea-air turbulent incoming heat flux and the sea-air turbulent latent heat flux;

[0017] A model building unit is used to construct a multivariate random forest model for collaborative inversion of air-sea turbulent heat flux, taking all variables in the model-driven dataset as input, the air-sea turbulent Bowen ratio as an internal constraint, and the air-sea turbulent current heat flux, air-sea turbulent latent heat flux, and the air-sea turbulent Bowen ratio as outputs;

[0018] A model training unit is used to train a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux using a grid retrieval method, determine the optimal hyperparameter combination, and obtain a target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux;

[0019] The second processing unit is used to collaboratively invert the sea-air turbulent heat flux using a target sea-air turbulent heat flux collaborative inversion multivariate random forest model.

[0020] On the basis of the above technical solution, the present invention can also be improved as follows.

[0021] Furthermore, the near-sea surface observation data include sea surface air temperature, sea surface air specific humidity and sea surface wind speed, and the sea surface observation data include sea surface temperature and sea surface specific humidity.

[0022] Furthermore, the collected observation data are preprocessed, including: performing height correction on the collected observation data; averaging the observation data with a sampling time frequency higher than the set time length, determining the validity of the observation data based on the proportion of high-frequency data within the set time length, and discarding the observation data that is not valid; deleting the sea-air observation data and the sea-air turbulent heat flux observation data under ship maneuvering and plume conditions; deleting the sea-air observation data and the sea-air turbulent heat flux observation data with a sea-air turbulent wave ratio less than a first set value or greater than a second set value; and calculating the temperature gradient and the specific humidity gradient.

[0023] Furthermore, the sea-air turbulent Bowen ratio is calculated based on the sea-air turbulent current heat flux and the sea-air turbulent latent heat flux, including:

[0024] Assume SHF is the sea-air turbulent heat flux, LHF is the sea-air turbulent latent heat flux, β is the sea-air turbulent Bowen ratio, then the sea-air turbulent Bowen ratio is:

[0025]

[0026] Furthermore, a multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux is constructed, including: assuming SHF represents the sea-air turbulent incoming heat flux, LHF represents the sea-air turbulent latent heat flux, β represents the sea-air turbulent Bowen ratio, the multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux is expressed as BrTHF(SHF, LHF, β), f() represents the functional relationship of the multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux, T s represents the sea surface temperature of the ocean, q s represents the surface specific humidity of the ocean surface, T a Indicates the sea surface air temperature at a set altitude, q a Indicates the specific humidity of sea surface air at a set altitude, W s Indicates the sea surface wind speed at the set height, diff T Indicates the calculated temperature gradient, diff q represents the calculated specific humidity gradient, then:

[0027] BrTHF(SHF,LHF,β)=f(T s,q s ,T a ,q a ,W s ,diff T ,diff q ).

[0028] Furthermore, a grid retrieval method is used to train a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux, determine the optimal hyperparameter combination, and obtain a target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux, including: presetting multiple sets of hyperparameter combinations, the hyperparameters include the number of decision trees, the minimum number of sample splits for each regression tree node, and the minimum number of samples for leaf nodes, and determining the range of each hyperparameter by comparing the accuracy of each multivariate random forest model for collaborative inversion of sea-air turbulent heat flux under the preset multiple sets of hyperparameter combinations, and then using the grid retrieval method to train the optimal hyperparameter combination to obtain the optimal target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux.

[0029] Furthermore, when training the optimal hyperparameter combination, the cost function of each regression tree node is determined by the Mahalanobis distance; let cost tree Represents the cost function of each regression tree node, y pre_i represents the predicted sea-air turbulent heat flux, y mea_i represents the observed sea-air turbulent heat flux, Σ i -1 Represents the inverse matrix of the covariance matrix of the output response matrix; when i=1, y mea_1 represents the observed value of the sea-air turbulent heat flux, y pre_1 represents the predicted value of the sea-air turbulent heat flux; when i = 2, y mea_2 represents the observed value of the sea-air turbulent latent heat flux, y pre_2 represents the predicted value of the sea-air turbulent latent heat flux; when i = 3, y mea_3 represents the observed value of the sea-air turbulence Bowen ratio, y pre_3 represents the predicted value of the sea-air turbulence Bowen ratio; the cost function of the sea-air turbulence heat flux collaborative inversion multivariate random forest model with the sea-air turbulence Bowen ratio as the internal constraint is expressed as:

[0030]

[0031] The beneficial effect of the present invention is that the present invention can solve the problem that the existing data-driven model ignores the coordinated change relationship between the sea-air turbulent incoming heat flux, the sea-air turbulent latent heat flux and the sea-air turbulent Bowen ratio, resulting in the estimated sea-air turbulent Bowen ratio being inaccurate and introducing uncertainty into global climate change predictions, extreme weather event predictions and global energy and water cycle research, reduce the deviation in the estimation of the sea-air turbulent incoming heat flux and the sea-air turbulent latent heat flux, and improve the accuracy of the sea-air turbulent Bowen ratio estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the collaborative inversion method for sea-air turbulent heat flux provided in Example 1 of the present invention;

[0033] Figure 2 Schematic diagram of the sea-air turbulent heat flux collaborative inversion system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0035] Example 1

[0036] As an example, Figure 1 As shown, to solve the above technical problems, this embodiment provides a sea-air turbulent heat flux collaborative inversion method, including:

[0037] Collect observational data required for remote sensing inversion of sea-air turbulent heat flux; the observational data include near-sea meteorological observation data, sea surface observation data, and sea-air turbulent heat flux observation data; sea-air turbulent heat flux includes sea-air turbulent current heat flux and sea-air turbulent latent heat flux;

[0038] Preprocess the collected observation data;

[0039] All available variables in the preprocessed observation data are sorted according to their contribution, and the variables are selected as the model-driven data set;

[0040] The sea-air turbulent Bowen ratio is calculated based on the sea-air turbulent current heat flux and the sea-air turbulent latent heat flux;

[0041] A multivariate random forest model for collaborative inversion of air-sea turbulent heat flux is constructed by taking all variables in the model-driven dataset as input, the air-sea turbulent Bowen ratio as the internal constraint, and the air-sea turbulent current heat flux, air-sea turbulent latent heat flux, and air-sea turbulent Bowen ratio as outputs.

[0042] The grid retrieval method is used to train the multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux, determine the optimal hyperparameter combination, and obtain the target multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux;

[0043] The target sea-air turbulent heat flux collaborative inversion multivariate random forest model is used to collaboratively invert the sea-air turbulent heat flux.

[0044] Optionally, the near-sea surface observation data include sea surface air temperature, sea surface air specific humidity and sea surface wind speed, and the sea surface observation data include sea surface temperature and sea surface specific humidity.

[0045] Optionally, the collected observation data are preprocessed, including: performing altitude correction on the collected observation data; averaging the observation data with a sampling time frequency higher than the set time length, determining the validity of the observation data based on the proportion of high-frequency data within the set time length, and discarding the observation data that is not valid; deleting the sea-air observation data and the sea-air turbulent heat flux observation data under ship maneuvering and plume conditions; deleting the sea-air observation data and the sea-air turbulent heat flux observation data with a sea-air turbulent wavelength ratio less than a first set value or greater than a second set value; and calculating the temperature gradient and the specific humidity gradient.

[0046] Specifically, the preprocessing process includes, for example: correcting the sea surface air temperature, sea surface air specific humidity and sea surface wind speed collected at an observation height of 11-25m to the sea surface air temperature, sea surface air specific humidity and sea surface wind speed at an altitude of 10m; correcting the sea surface temperature and sea surface specific humidity collected at a measurement depth of 0.05-5m to the sea surface temperature and sea surface specific humidity of the ocean surface; averaging the observation data with a sampling time frequency higher than 1 hour as 1 hour of observation data, and if the high-frequency observation data within 1 hour is not less than 75%, the 1 hour of observation data is valid, otherwise it is discarded; performing quality control on the observation data, deleting meteorological, oceanographic observation data and sea-air turbulent heat flux observation data under ship maneuvering and plume conditions, and deleting meteorological, oceanographic observations and sea-air turbulent heat flux observations with a sea-air turbulent Bowen ratio less than -1 or greater than 2 that are beyond the sea-air interaction range; and calculating the temperature gradient and specific humidity gradient.

[0047] Optionally, the air-sea turbulence Bowen ratio is calculated based on the air-sea turbulence current heat flux and the air-sea turbulence latent heat flux, including:

[0048] Assume SHF is the sea-air turbulent heat flux, LHF is the sea-air turbulent latent heat flux, β is the sea-air turbulent Bowen ratio, then the sea-air turbulent Bowen ratio is:

[0049]

[0050] Optionally, a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux is constructed, including: let SHF represent sea-air turbulent heat flux, LHF represent sea-air turbulent latent heat flux, β represent sea-air turbulent Bowen ratio, and the multivariate random forest model for collaborative inversion of sea-air turbulent heat flux is expressed as BrTHF(SHF , LHF,β), f() represents the functional relationship of the multivariate random forest model for the coordinated inversion of sea-air turbulent heat flux, T s represents the sea surface temperature of the ocean, q s represents the surface specific humidity of the ocean surface, T a Indicates the sea surface air temperature at a set altitude, q a Indicates the specific humidity of sea surface air at a set altitude, W s Indicates the sea surface wind speed at the set height, diff T Indicates the calculated temperature gradient, diff q represents the calculated specific humidity gradient, then:

[0051] BrTHF(SHF,LHF,β)=f(T s ,q s ,T a ,q a ,W s ,diff T ,diff q ).

[0052] In actual application, the setting height of sea surface air temperature, sea surface air specific humidity and sea surface wind speed is selected as 10m.

[0053] Optionally, a grid retrieval method is used to train a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux to determine the optimal hyperparameter combination and obtain a target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux, including: presetting multiple sets of hyperparameter combinations, the hyperparameters include the number of decision trees, the minimum number of sample splits for each regression tree node and the minimum number of samples for leaf nodes, and the range of each hyperparameter is determined by comparing the accuracy of each multivariate random forest model for collaborative inversion of sea-air turbulent heat flux under the preset multiple sets of hyperparameter combinations, and then the grid retrieval method is used to train the optimal hyperparameter combination to obtain the optimal target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux.

[0054] Optionally, in training the optimal hyperparameter combination, the cost function of each regression tree node is determined by the Mahalanobis distance; let cost tree represents the cost function of each regression tree node, y pre_i represents the predicted sea-air turbulent heat flux, y mea_i represents the observed sea-air turbulent heat flux, y i -1 represents the inverse of the covariance matrix of the output response matrix; when i = 1, y mea_1 represents the observed value of the sea-air turbulent sensible heat flux, y pre_1 represents the predicted value of the sea-air turbulent sensible heat flux; when i = 2, y mea_2 represents the observed value of the sea-air turbulent latent heat flux, y pre_2 represents the predicted value of the sea-air turbulent latent heat flux; when i = 3, y mea_3 represents the observed value of the sea-air turbulent Bowen ratio, y pre_3 represents the predicted value of the sea-air turbulent Bowen ratio; the cost function of the sea-air turbulent heat flux co-inversion multivariate random forest model with the sea-air turbulent Bowen ratio as an intrinsic constraint is represented as:

[0055]

[0056] The present application can solve the problem that the existing data-driven model ignores the co-variation relationship of the sea-air turbulent sensible heat flux, the sea-air turbulent latent heat flux and the sea-air turbulent Bowen ratio, resulting in inaccurate estimation of the sea-air turbulent Bowen ratio, introducing uncertainty into global climate change prediction, extreme weather event prediction and global energy and water cycle research, reducing the bias in the estimation of the sea-air turbulent sensible heat flux and the sea-air turbulent latent heat flux, and improving the accuracy of the estimation of the sea-air turbulent Bowen ratio.

[0057] Embodiment 2

[0058] Based on the same principle as the method shown in Embodiment 1 of the present application, as shown in FIG. 2, the present application also provides a sea-air turbulent heat flux co-inversion system in the embodiment, which comprises a collection unit, a preprocessing unit, a selection unit, a first processing unit, a model construction unit, a model training unit and a second processing unit. Figure 1 The collection unit is used to collect the observation data required for sea-air turbulent heat flux remote sensing inversion; the observation data includes near-surface meteorological observation data, sea surface observation data and sea-air turbulent heat flux observation data; the sea-air turbulent heat flux includes sea-air turbulent sensible heat flux and sea-air turbulent latent heat flux.

[0059] The preprocessing unit is used to preprocess the collected observation data.

[0060]

[0061] ​The selection unit is used to sort all available variables in the preprocessed observation data according to their contribution and select variables as the model-driven data set;

[0062] The first processing unit is used to calculate the sea-air turbulent Bowen ratio based on the sea-air turbulent incoming heat flux and the sea-air turbulent latent heat flux;

[0063] A model building unit is used to construct a multivariate random forest model for collaborative inversion of air-sea turbulent heat flux, taking all variables in the model-driven dataset as input, the air-sea turbulent Bowen ratio as an internal constraint, and the air-sea turbulent current heat flux, air-sea turbulent latent heat flux, and the air-sea turbulent Bowen ratio as outputs;

[0064] A model training unit is used to train a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux using a grid retrieval method, determine the optimal hyperparameter combination, and obtain a target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux;

[0065] The second processing unit is used to collaboratively invert the sea-air turbulent heat flux using a target sea-air turbulent heat flux collaborative inversion multivariate random forest model.

[0066] Optionally, the near-sea surface observation data include sea surface air temperature, sea surface air specific humidity and sea surface wind speed, and the sea surface observation data include sea surface temperature and sea surface specific humidity.

[0067] Optionally, the collected observation data are preprocessed, including: performing altitude correction on the collected observation data; averaging the observation data with a sampling time frequency higher than the set time length, determining the validity of the observation data based on the proportion of high-frequency data within the set time length, and discarding the observation data that is not valid; deleting the sea-air observation data and the sea-air turbulent heat flux observation data under ship maneuvering and plume conditions; deleting the sea-air observation data and the sea-air turbulent heat flux observation data with a sea-air turbulent wavelength ratio less than a first set value or greater than a second set value; and calculating the temperature gradient and the specific humidity gradient.

[0068] Optionally, the air-sea turbulence Bowen ratio is calculated based on the air-sea turbulence current heat flux and the air-sea turbulence latent heat flux, including:

[0069] Assume SHF is the sea-air turbulent heat flux, LHF is the sea-air turbulent latent heat flux, β is the sea-air turbulent Bowen ratio, then the sea-air turbulent Bowen ratio is:

[0070]

[0071] Optionally, a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux is constructed, including: let SHF represent sea-air turbulent heat flux, LHF represent sea-air turbulent latent heat flux, β represent sea-air turbulent Bowen ratio, and the multivariate random forest model for collaborative inversion of sea-air turbulent heat flux is expressed as BrTHF(SHF , LHF,β), f() represents the functional relationship of the multivariate random forest model for the coordinated inversion of sea-air turbulent heat flux, T s represents the sea surface temperature of the ocean, q s represents the surface specific humidity of the ocean surface, T a Indicates the sea surface air temperature at a set altitude, q a Indicates the specific humidity of sea surface air at a set altitude, W s Indicates the sea surface wind speed at the set height, diff T Indicates the calculated temperature gradient, diff q represents the calculated specific humidity gradient, then:

[0072] BrTHF(SHF,LHF,β)=f(T s ,q s ,T a ,q a ,W s ,diff T ,diff q ).

[0073] Optionally, a grid retrieval method is used to train a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux to determine the optimal hyperparameter combination and obtain a target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux, including: presetting multiple sets of hyperparameter combinations, the hyperparameters include the number of decision trees, the minimum number of sample splits for each regression tree node and the minimum number of samples for leaf nodes, and the range of each hyperparameter is determined by comparing the accuracy of each multivariate random forest model for collaborative inversion of sea-air turbulent heat flux under the preset multiple sets of hyperparameter combinations, and then the grid retrieval method is used to train the optimal hyperparameter combination to obtain the optimal target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux.

[0074] Optionally, when training the optimal hyperparameter combination, the cost function of each regression tree node is determined by the Mahalanobis distance; let cost tree Represents the cost function of each regression tree node, y pre_i represents the predicted sea-air turbulent heat flux, y mea_i represents the observed sea-air turbulent heat flux, ∑ i -1 Represents the inverse of the covariance matrix of the output response matrix; when i = 1, y mea_1 represents the observed value of the sea-air turbulent heat flux, ypre_1 represents the predicted value of the sea-air turbulent sensible heat flux; y mea_2 represents the observed value of the sea-air turbulent latent heat flux, y pre_2 represents the predicted value of the sea-air turbulent latent heat flux; y mea_3 represents the observed value of the sea-air turbulent Bowen ratio, y pre_3 represents the predicted value of the sea-air turbulent Bowen ratio; the cost function of the sea-air turbulent heat flux collaborative inversion multivariate random forest model with the sea-air turbulent Bowen ratio as the intrinsic constraint is represented as:

[0075]

[0076] The above merely describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. The collaborative inversion method of sea-air turbulent heat flux is characterized by: include: Collect observational data required for remote sensing inversion of sea-air turbulent heat flux; Observational data include near-sea meteorological observation data, sea surface observation data, and sea-air turbulent heat flux observation data; The sea-air turbulent heat flux includes the sea-air turbulent current heat flux and the sea-air turbulent latent heat flux; Preprocess the collected observation data; All available variables in the preprocessed observation data are sorted according to their contribution, and the variables are selected as the model-driven data set; The sea-air turbulent Bowen ratio is calculated based on the sea-air turbulent current heat flux and the sea-air turbulent latent heat flux; A multivariate random forest model for collaborative inversion of air-sea turbulent heat flux is constructed by taking all variables in the model-driven dataset as input, the air-sea turbulent Bowen ratio as the internal constraint, and the air-sea turbulent current heat flux, air-sea turbulent latent heat flux, and air-sea turbulent Bowen ratio as outputs. The grid retrieval method is used to train the sea-air turbulent heat flux collaborative inversion multivariate random forest model, determine the optimal hyperparameter combination, and obtain the target sea-air turbulent heat flux collaborative inversion multivariate random forest model, including using the grid retrieval method to train the optimal hyperparameter combination and obtain the optimal target sea-air turbulent heat flux collaborative inversion multivariate random forest model; when training the optimal hyperparameter combination, the cost function of each regression tree node is determined by the Mahalanobis distance; set represents the cost function of each regression tree node, represents the predicted sea-air turbulent heat flux, represents the observed sea-air turbulent heat flux, Represents the inverse matrix of the covariance matrix of the output response matrix; when i=1, represents the observed value of the sea-air turbulent heat flux, represents the predicted value of the sea-air turbulent heat flux; when i=2, represents the observed value of the sea-air turbulent latent heat flux, represents the predicted value of the sea-air turbulent latent heat flux; when i=3, represents the observed value of the sea-air turbulence Bowen ratio, represents the predicted value of the sea-air turbulence Bowen ratio; the cost function of the sea-air turbulence heat flux collaborative inversion multivariate random forest model with the sea-air turbulence Bowen ratio as the internal constraint is expressed as: ； The target sea-air turbulent heat flux collaborative inversion multivariate random forest model is used to collaboratively invert the sea-air turbulent heat flux.

2. The sea-air turbulent heat flux collaborative inversion method according to claim 1, characterized in that: Near-sea surface meteorological observation data include sea surface air temperature, sea surface air specific humidity and sea surface wind speed, while sea surface observation data include sea surface temperature and sea surface specific humidity.

3. The sea-air turbulent heat flux collaborative inversion method according to claim 1, characterized in that: The collected observation data are preprocessed, including: performing altitude correction on the collected observation data; averaging the observation data with a sampling time frequency higher than the set time length, determining the validity of the observation data based on the proportion of high-frequency data within the set time length, and discarding the observation data that is not valid; deleting the sea-air observation data and sea-air turbulent heat flux observation data under ship maneuvering and plume conditions; deleting the sea-air observation data and sea-air turbulent heat flux observation data with a sea-air turbulent wave ratio less than a first set value or greater than a second set value; and calculating the temperature gradient and specific humidity gradient.

4. The sea-air turbulent heat flux collaborative inversion method according to claim 1, characterized in that: The sea-air turbulent Bowen ratio is calculated based on the sea-air turbulent current heat flux and the sea-air turbulent latent heat flux, including: set up is the sea-air turbulent heat flux, is the sea-air turbulent latent heat flux, is the sea-air turbulence Bowen ratio, then the sea-air turbulence Bowen ratio is: 。 5. The sea-air turbulent heat flux collaborative inversion method according to claim 1, characterized in that: Construct a multivariate random forest model for collaborative inversion of sea-air turbulent heat flux, including: represents the sea-air turbulent heat flux, represents the sea-air turbulent latent heat flux, represents the sea-air turbulence Bowen ratio, and the sea-air turbulence heat flux collaborative inversion multivariate random forest model is expressed as , Represents the functional relationship of the multivariate random forest model for the collaborative inversion of sea-air turbulent heat flux, represents the sea surface temperature of the ocean, represents the surface specific humidity of the ocean surface, Indicates the sea surface air temperature at the set altitude, Indicates the specific humidity of sea surface air at a set altitude. Indicates the sea surface wind speed at the set altitude, represents the calculated temperature gradient, represents the calculated specific humidity gradient, then: 。 6. The sea-air turbulent heat flux collaborative inversion method according to claim 1, characterized in that: A multivariate random forest model for collaborative inversion of sea-air turbulent heat flux is trained using a grid retrieval method to determine the optimal hyperparameter combination and obtain a target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux, including: presetting multiple sets of hyperparameter combinations, where the hyperparameters include the number of decision trees, the minimum number of sample splits for each regression tree node, and the minimum number of samples for leaf nodes. The range of each hyperparameter is determined by comparing the accuracy of each multivariate random forest model for collaborative inversion of sea-air turbulent heat flux under the preset multiple sets of hyperparameter combinations, and then the optimal hyperparameter combination is trained using a grid retrieval method to obtain the optimal target multivariate random forest model for collaborative inversion of sea-air turbulent heat flux. 7.Sea-air turbulent heat flux collaborative inversion system, characterized by: It includes a collection unit, a pre-processing unit, a selection unit, a first processing unit, a model building unit, a model training unit and a second processing unit; A collection unit is used to collect observation data required for remote sensing inversion of sea-air turbulent heat flux; the observation data includes near-sea meteorological observation data, sea surface observation data, and sea-air turbulent heat flux observation data; The sea-air turbulent heat flux includes the sea-air turbulent current heat flux and the sea-air turbulent latent heat flux; A preprocessing unit, used for preprocessing the collected observation data; The selection unit is used to sort all available variables in the preprocessed observation data according to their contribution and select variables as the model-driven data set; The first processing unit is used to calculate the sea-air turbulent Bowen ratio based on the sea-air turbulent incoming heat flux and the sea-air turbulent latent heat flux; A model building unit is used to construct a multivariate random forest model for collaborative inversion of air-sea turbulent heat flux, taking all variables in the model-driven dataset as input, the air-sea turbulent Bowen ratio as an internal constraint, and the air-sea turbulent current heat flux, air-sea turbulent latent heat flux, and the air-sea turbulent Bowen ratio as outputs; The model training unit is used to train the sea-air turbulent heat flux collaborative inversion multivariate random forest model using a grid retrieval method, determine the optimal hyperparameter combination, and obtain the target sea-air turbulent heat flux collaborative inversion multivariate random forest model, including using the grid retrieval method to train the optimal hyperparameter combination to obtain the optimal target sea-air turbulent heat flux collaborative inversion multivariate random forest model; when training the optimal hyperparameter combination, the cost function of each regression tree node is determined by the Mahalanobis distance; set represents the cost function of each regression tree node, represents the predicted sea-air turbulent heat flux, represents the observed sea-air turbulent heat flux, Represents the inverse matrix of the covariance matrix of the output response matrix; when i=1, represents the observed value of the sea-air turbulent heat flux, represents the predicted value of the sea-air turbulent heat flux; when i=2, represents the observed value of the sea-air turbulent latent heat flux, represents the predicted value of the sea-air turbulent latent heat flux; when i=3, represents the observed value of the sea-air turbulence Bowen ratio, represents the predicted value of the sea-air turbulence Bowen ratio; the cost function of the sea-air turbulence heat flux collaborative inversion multivariate random forest model with the sea-air turbulence Bowen ratio as the internal constraint is expressed as: ； The second processing unit is used to collaboratively invert the sea-air turbulent heat flux using a target sea-air turbulent heat flux collaborative inversion multivariate random forest model.

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