A method for calculating rapid droplet heat flux in super typhoons

Abstract

The present invention discloses a method for calculating the droplet heat flux of a super typhoon, which is applied to a coupled ocean-atmosphere-wave-sediment transport model system. The method comprises the following steps: calculating the white crown coverage W H1 , according to W H18 The wind speed function's segmented intervals were redefined, and the wind speed function was fitted using FASTEX data, and a new droplet heat flux was calculated. The inclusion of droplet heat flux in this study had little impact on the simulation of Mangkhut's path, but it significantly improved the simulation results of typhoon intensity, allowing the typhoon's horizontal low-pressure structure and wind field structure to develop more fully.

Description

Technical Field

[0001] The present invention belongs to the technical field of numerical calculation, and in particular relates to a method for calculating droplet heat flux of a super typhoon. Background Art

[0002] Tropical cyclones (TCs) are among the most severe weather systems, causing devastating loss of life and property in coastal areas. Accurately forecasting TC tracks and intensity is crucial. While advances in numerical forecast models have significantly improved TC track predictions over the past few decades, predicting typhoon intensity remains a significant challenge. The difficulties in predicting TC intensity stem primarily from the complex microphysical processes within TCs and the lack of clarity regarding the mechanisms by which air-sea interactions influence typhoons.

[0003] As the primary energy source for TCs, the ocean plays a crucial role in their generation and development. Accurately describing the energy exchange between the ocean and atmosphere is crucial for predicting TC intensity. Research has shown that TCs receive energy from the sea surface in the form of latent and sensible heat fluxes.

[0004] Numerous studies have revealed the role of heat transfer between the ocean and atmosphere in TC development. Emanual, based on both observations and numerical simulations, has proposed that heat transfer from the ocean is a primary factor in the development and maintenance of TCs. Based on this, Rotunno and Emanual demonstrated that wind-induced sea-surface heat exchange (WISHE) leads to an increase in the atmospheric boundary layer potential temperature. Heat received from the ocean surface is redistributed by cumulus convection, enhancing TC circulation and further increasing heat transfer and intensification. This role of heat flux in TC intensification is consistent with the observation that TCs are more likely to intensify when passing through regions of high surface heat flux but always decay after landfall.

[0005] The air-sea heat flux is crucial for the intensification of TCs. Accurately describing the air-sea-surface heat flux in numerical simulation systems is of great significance for improving TC forecasts. In current numerical models, the latent heat flux H at the air-sea interface is L，int and sensible heat flux H S，int Mainly calculated by the volume flux algorithm:

[0006] H L，int =ρL v C q U 10 Δq, (1)

[0007] H S，int =ρC p C h U 10 Δθ, (2)

[0008] where ρ is the air density; U 10 is the wind speed at 10m above sea level; L v is the latent heat of vaporization; C p is the specific heat capacity of air; C q and C h are the exchange coefficients of latent heat flux and sensible heat flux, respectively; Δq and Δθ are the differences in humidity and potential temperature between the air and the ocean, respectively. q and C h It can be shown by formula (3) and (4). Then, H L，int and H S，int According to the C in the numerical model q and C h (or z0, z t and z q ) is calculated from the bulk flux algorithm (Equation 4).

[0009]

[0010]

[0011] However, the method for calculating the interfacial heat flux introduced above is based on observational data under medium and low wind speed conditions, and the adaptability of the algorithm in high wind speed environments needs to be improved. Under high wind speed conditions caused by TC, waves are destroyed by high wind speeds, and a large amount of ocean droplets can be observed in the ocean boundary layer. Experts generally believe that these ocean droplets have a non-negligible effect on the heat flux between the atmosphere and the ocean, but the above scheme does not consider the impact of waves on the heat flux. The mechanism of how ocean droplets affect heat flux has been studied in depth, and the general consensus is that ocean droplets enhance the heat flux at the sea-air interface. After droplets are blown into the air from the warm sea surface, because their temperature is higher than that of the surrounding air, the water droplets release sensible heat to the surrounding air while cooling. At the same time, due to the humidity difference between the droplets and the surrounding air, the droplets will release latent heat to the surrounding air while evaporating until the droplets fall back into the ocean. Therefore, when waves are generated, the surface heat flux will be enhanced. Theoretical analysis and observations show that when U 10 When the speed reaches 12 m / s, the heat flux caused by ocean spray accounts for at least 10% of the total heat flux. L，T and H S，T It can be expressed as:

[0012] H L，T =H L，int +H L，sp (5)

[0013] H S，T =H S，int +HS，sp (6)

[0014] Among them, H L，int and H S，int represent the latent heat flux and sensible heat flux of interface exchange, respectively, and H L，sp and H S，sp represent the latent heat flux and sensible heat flux caused by droplets, respectively.

[0015] To quantitatively assess the impact of ocean spray on latent and sensible heat fluxes, Andreas et al. proposed a parameterized algorithm for calculating ocean spray heat flux. This algorithm calculates the heat flux of ocean droplets of different radii separately and then integrates the total heat flux over the droplet radius. Subsequently, Andreas et al. made several improvements to this scheme and proposed a fast algorithm, where the spray flux is calculated as follows:

[0016]

[0017] H S，sp =ρ w C w (T s -T eq，100 )V S (u * ) (8)

[0018] In formula (7), ρ w is the density of seawater, L v is the latent heat of vaporization, τ f，50 represents the time that a droplet with an initial radius of 50 mm remains in the air, r(τ f，50 ) represents the radius of the droplet when it falls back into the water. In formula (8), C w stands for specific heat, T s represents the sea surface temperature, T eq，100 is the temperature at which the droplet with an initial radius of 100 mm reaches equilibrium with the environment; V L and V S is the wind speed function of the body friction velocity. Andreas et al. fitted the wind speed function obtained by using more than 4,000 sets of measured data as follows:

[0019] V S =3.92×10 -8 ,0≤u * ≤0.1480 (9)

[0020]

[0021] V L =1.76×10 -9 ,0≤u *≤0.1358 (11)

[0022]

[0023] By using this algorithm (referred to as AN15 algorithm, corresponding to equation (12)), Andreas et al. calculated 0<U 10 The latent heat and sensible heat flux caused by droplets in the range of <40m / s are found when U 10 When the velocity reaches about 25 m / s, the magnitude of the latent heat and sensible heat flux caused by the droplets exceeds the corresponding interface heat flux. 10 As the wind speed increases, the proportion of heat flux caused by droplets in the total heat flux increases. This shows that in high-wind speed environments, such as TC, the heat flux caused by droplets will play a dominant role. However, when the present invention applied the AN15 algorithm to the coupled COAWST model to simulate Super Typhoon Mangkhut (2018), the model reported an error and crashed. This shows that the AN15 algorithm has problems with its applicability in high-wind speed conditions. Summary of the Invention

[0024] Through experimental and theoretical analysis, this paper identifies the reasons why the AN15 algorithm is inapplicable in high-wind environments. Addressing these shortcomings, we propose an improved rapid ocean wave heat flux algorithm, designated YJ22, suitable for typhoon conditions. We then explore the impact of droplet heat flux on severe TCs. Based on prior research, this paper is the first to use the coupled COAWST model to investigate the impact of droplet heat flux on Super Typhoon Mangkhut.

[0025] This paper first introduces the observational data FASTEX used to improve the AN15 algorithm and describes the coupled model COAWST. Then, based on over 2,000 sets of observational data, an improved droplet-induced heat flux algorithm, YJ22, suitable for high wind speed conditions is proposed. The differences between the new and old algorithms are compared. The improved fast algorithm is then applied to the coupled model to analyze the impact of droplet heat flux on super tropical cyclones.

[0026] The present invention discloses a method for calculating the droplet heat flux of a super typhoon, which is applied to a coupled ocean-atmosphere-wave-sediment transport model system and includes the following steps:

[0027] Calculate the white crown coverage W H18 , according to W H18 The segmented intervals of the wind speed function are defined, and the wind speed function is obtained by fitting the FASTEX data, and the droplet heat flux is calculated. The droplet heat flux calculation method is as follows:

[0028]

[0029] H S，sp =ρ w C w (T s -T eq，100 )V S (u * )

[0030]

[0031]

[0032] Among them, H S，sp is the droplet sensible heat flux, H L，sp is the droplet latent heat flux, f n Represents the droplet generation spectrum function within the unit white crown area, u * Represents the friction velocity, and its value characterizes the magnitude of the turbulent pulsation velocity at the sea-air interface. V L To calculate the wind speed function of droplet latent heat flux, V S To calculate the wind speed function of droplet sensible heat flux, r(τ f，50 ) represents the radius of the droplet when it falls back into the water, ρ w is the density of seawater, L v is the latent heat of vaporization, C w stands for specific heat, T s represents the sea surface temperature, T eq，100 is the temperature at which a droplet with an initial radius of 100 mm reaches equilibrium with its environment.

[0033] Furthermore, the total droplet heat flux is directly calculated using droplets with radii of 50 μm and 100 μm.

[0034] Furthermore, only the atmosphere model and the wave model are activated to provide variables for the calculation of the heat flux caused by ocean waves.

[0035] Furthermore, the Perdue Lin scheme is used for microphysical processes, the RRTMG scheme is used for both longwave and shortwave radiation, the Kain-Fritsch scheme is used for the outermost and sub-outer cumulus convection, the MYNN2.5 scheme is used for boundary layer parameterization, the MYNN scheme is used for near-surface processes, the Unified Noah scheme is used for land surface processes, and the COARE3.5 scheme is used for heat flux parameterization.

[0036] Furthermore, the white crown coverage W H18 The calculation of is as follows:

[0037]

[0038] The beneficial effects of the present invention are as follows:

[0039] (1) The inclusion of droplet heat flux has little effect on the simulation of the path of Mangkhut, but the inclusion of droplet heat flux can significantly improve the simulation results of typhoon intensity (MSLP and Vmax).

[0040] (2) The addition of droplet heat flux will make the typhoon's horizontal low-pressure structure and wind field structure develop more fully. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Flowchart of the present invention;

[0042] Figure 2 (a) The droplet heat flux calculated by the AN15 algorithm under typical tropical cyclone conditions changes with U 10 (b) The white crown coverage in the AN15 algorithm changes with U 10 changes;

[0043] Figure 3 W under different wind speed conditions H18 With W F94 Performance comparison of the solutions

[0044] Figure 4 New and old wind speed functions V L and V S Comparison of the wind speeds of the original model and the modified model; the circles represent the values calculated by the original model and the modified model, and the curves represent the fitted wind speed functions obtained using a cubic polynomial fit. (a) and (c) represent the low to medium wind speed range; (b) and (d) represent the low to high wind speed range.

[0045] Figure 5 Comparison of the computational performance of the YJ22 and AN15 algorithms based on the FASTEX dataset. (a) and (c) show how the latent heat flux and sensible heat flux calculated by the AN15 scheme differ from the measured data as wind speed changes; (b) and (d) show how the latent heat flux and sensible heat flux calculated by the YJ22 scheme differ from the measured data as wind speed changes. The red line represents the fitted curve of the difference between all observed data and the model data.

[0046] Figure 6 The droplet heat flux calculated by the YJ22 algorithm under typical tropical cyclone conditions varies with U 10 changes;

[0047] Figure 7 The path errors of Typhoon Mangkhut's moving paths simulated by five sets of experiments compared with the JTWC optimal path data;

[0048] Figure 8Comparison of typhoon intensity simulated by five sets of experiments with JTWC optimal path data, (a) reflecting the comparison of minimum sea level pressure (MSLP), (b) reflecting the comparison of maximum wind speed at 10m height (Vmax);

[0049] Figure 9 Time-dependent changes in azimuthal sea level pressure (hPa) for the five simulated experiments. The horizontal axis represents the distance from the typhoon center, and the vertical axis represents the simulated time. (a) Results for the L0_S0 experiment, (b) for the L1_S0 experiment, (c) for the L0_S1 experiment, (d) for the L1_S1 experiment, and (e) for the L2_S2 experiment.

[0050] Figure 10 Time-varying radial distributions of the azimuthally averaged tangential and radial winds simulated in the five experimental groups. The horizontal axis represents the distance from the typhoon center, and the vertical axis represents the simulation time. (a) is the result of the L0_S0 experiment, (b) is the result of the L1_S0 experiment, (c) is the result of the L0_S1 experiment, (d) is the result of the L1_S1 experiment, and (e) is the result of the L2_S2 experiment. DETAILED DESCRIPTION

[0051] The present invention will be further described below with reference to the accompanying drawings, but the present invention is not limited in any way. Any changes or substitutions made based on the teachings of the present invention fall within the scope of protection of the present invention.

[0052] The data and models used in this invention are briefly introduced as follows:

[0053] FASTEX dataset

[0054] In the present invention, the Frontier and Atlantic Storm Track Experiment (FASTEX) data set will be used to test the effect of the new algorithm YJ22. The FASTEX data set contains a large number of observations of sea-air interface fluxes, which has made a great contribution to the study of sea-air exchange. This data set includes multiple sets of reliable measured data of sensible heat flux, latent heat flux, wind speed, sea surface temperature, air temperature, humidity and significant wave height. The above data are necessary for proposing and improving the AN15 algorithm. The data used in the present invention include 2435 sets of eddy covariance measurements of heat flux. The wind speed range of this data set is 0-30m / s.

[0055] Model description and configuration

[0056] Air-sea heat flux has an important impact on the evolution and intensification of tropical cyclones. A single atmospheric model cannot simulate the process of air-sea interaction. Therefore, the present invention uses the coupled ocean-atmosphere-wave-sediment transport (COAWST) model system to explore the impact of heat flux on Super Typhoon Mangkhut. The model system was developed and maintained by Warner et al. The model system includes independent atmospheric models, ocean models and wave models. The coupled model relies on the Model Coupling Toolkit (MCT) to exchange variables between different models. In the experiment of the present invention, the atmospheric model and the wave model are activated to provide variables for calculating the heat flux caused by ocean waves. Since the ocean model contributes very little to the calculation of the heat flux caused by ocean waves, the present invention does not activate the ocean model to improve the efficiency of the experiment.

[0057] WRF atmospheric model

[0058] The WRF-ARW model is an atmospheric model adopted in the COAWST model system. The model is configured as a non-hydrostatic, fully compressible atmospheric model with various physical parameterization schemes. The boundary and initial conditions we used in this invention are from the FNL data provided by NCEP, with a spatial resolution of 0.25°×0.25° and a time resolution of 6 hours. Three bidirectional nested grids are used in this invention. The three grids are grids d01, d02 and d03 from outside to inside. The horizontal resolution of d01 is 27 km, and the time integration step is 90s. The horizontal resolution of grid d02 is 9 km, and its time integration step is 30s. The horizontal resolution of d03 is 3 km, and the integration time step is 10s. It should be noted that the d03 grid automatically tracks the movement of the typhoon, and its position is calculated every 15 minutes. In this study, the Perdue Lin scheme was used for microphysical processes, the RRTMG scheme was used for both longwave and shortwave radiation, the Kain-Fritsch scheme was used for the outermost and sub-outermost cumulus convection, and the cumulus convection parameterization scheme was not used for the innermost layer due to its higher resolution. The MYNN2.5 scheme was used for the boundary layer parameterization, the MYNN scheme was used for the near-surface surface, the Unified Noah scheme was used for the land surface process, and the COARE3.5 scheme was used for the heat flux parameterization.

[0059] SWAN mode

[0060] SWAN is the ocean wave model used in the COAWST model system. SWAN provides the variables necessary to calculate ocean wave heat flux, such as sea surface temperature and significant wave height. SWAN then exchanges these variables to WRF via MCT to simulate the heat flux caused by ocean waves. The 10m wind field simulated by WRF serves as the forcing field for the SWAN model. In this paper, WaveWatchIII data is used as the boundary field data. SWAN has a horizontal resolution of 9 km and a time integration step of 180 seconds.

[0061] The development process of the AN15 algorithm and its problems

[0062] The ocean droplet heat flux algorithm proposed by Andreas et al. is one of the most widely used droplet heat flux schemes. The present invention adopts the latest version of the scheme, AN15, proposed in 2015.

[0063] To find out why the AN15 algorithm cannot be used to simulate super-strong TC, the present invention reviewed the process of proposing the scheme. Based on their proposed microphysical model, Andreas proposed the latent heat and sensible heat flux of droplets at a certain radius length as follows:

[0064]

[0065]

[0066] Here, r0 represents the radius of the droplet when it is generated, and dF / dr0 is the droplet generation function, which represents the number of droplets generated per unit area per unit time at the sea surface. Integrating Equations (13) and (14) with respect to the droplet radius yields the sum of the latent and sensible heat fluxes of the entire ocean droplet. Based on observational data, droplets with radii between 1.6 and 500 microns have a dominant influence on the droplet heat flux, so the upper and lower limits of the integration are set at 1.6 and 500 microns.

[0067]

[0068]

[0069] Due to the large number of approximations used in the calculation process and the uncertainty of the parameters in this parameterization scheme, especially the uncertainty of the droplet generation function dF / dr0, the results calculated by formulas (15) and (16) are and It cannot be used as the final droplet heat flux. The actual droplet latent heat flux H L，sp and sensible heat flux H S，sp The forms are written as:

[0070]

[0071]

[0072] Where α, β and γ are small adjustment coefficients, the purpose of which is to make the heat flux calculated by AN15 match the observed value as closely as possible.

[0073] According to the observation data, Andreas found that the droplets with radii of 50 microns and 100 microns were respectively and It is instructive that the total droplet heat flux can be directly calculated using droplets with radii of 50 μm and 100 μm:

[0074]

[0075] H S，sp

[0076] The above is the derivation process of the AN15 algorithm, which corresponds to formulas (19) and (20). Wind speed function V L and V S Corresponding to formulas (9)-(12).

[0077] In order to test the adaptability of the AN15 algorithm under strong tropical cyclone conditions, the present invention tested the ocean droplet heat flux calculated by the AN15 algorithm under typical tropical cyclone conditions (air temperature at 10m height is 26℃, relative humidity is 90%, sea surface temperature is 28℃, sea level pressure is 950hPa, and seawater salinity is 34psu). L，sp and H S，sp represent the latent heat and sensible heat fluxes caused by ocean droplets, H L，int and H S，int are the latent heat and sensible heat fluxes at the air-sea interface, respectively. Figure 2 As shown in (a), H L，sp and H S，sp They all show the characteristics of increasing with the increase of wind speed, and the growth rate is getting faster and faster. 10 =80m / s, H L，sp More than 10000W / m 2 , H S，sp More than 2000W / m 2 , which far exceeds the normal magnitude of sea surface heat flux, which is generally on the order of several hundred W / m 2 , the latent heat flux generally does not exceed 2000W / m 2 Therefore, the AN15 algorithm is10 The heat flux calculated under the environment of >50m / s is obviously too large. 10 The calculated heat flux of >50m / s will overestimate the value of droplet heat flux. 10 >50m / s is very common in TC environment, so the AN15 algorithm cannot be used to improve the numerical simulation of TC.

[0078] The new droplet heat flux algorithm YJ22 proposed in this paper

[0079] In order to consider the impact of droplet heat flux on TC in a high wind speed environment, the present invention finds out the problems existing in the AN15 algorithm and proposes a new droplet heat flux algorithm YJ22 suitable for high wind speed environments.

[0080] After analysis, the present invention found that the problem of the AN15 algorithm at high wind speeds comes from the droplet generation function dF / dr0 it uses. The dF / dr0 used by the AN15 algorithm is as follows:

[0081]

[0082] Where W is the white crown coverage function, f n Represents the droplet generation spectrum function per unit white crown area. In the AN15 scheme, W is expressed as follows (the white crown coverage in the AN15 scheme is called W F94 ):

[0083]

[0084] Because W F94 It is proposed based on observation data under medium and low wind speeds, so the performance of this scheme in high wind speed environments remains to be tested. Figure 2 (b) shows W F94 Follow U 10 As can be seen from the changes in W F94 With U 10 It is worth noting that when U 10 After exceeding 40m / s, W F94 The value of U exceeds 1. 10 =80m / s, W F94 The value even reached 12. However, the physical meaning of the white canopy coverage ratio is the ratio of the white canopy coverage area to the sea surface area, and its upper limit can only be 1. Therefore, the white canopy coverage ratio in AN15 is extremely unreasonable. This unreasonable white canopy coverage ratio causes the AN15 solution to seriously overestimate the droplet heat flux value in high wind speed environments.

[0085] In order to solve the problem of the applicability of the AN15 scheme under high wind speeds, the natural way is to choose a more reasonable white crown coverage parameterization scheme to replace the W in AN15. F94 Based on recent satellite observations of high wind speeds, Hwang proposed a new W parameterization scheme (abbreviated as W H18 ):

[0086]

[0087] Figure 3 Shows W under different wind speed conditions H18 With W F94 The performance comparison of the schemes. It can be seen that even in U 10 >40m / s high wind speed area, W H1 Still provides reasonable white crown coverage, and its performance is far better than W F94 Therefore, the present invention uses W H18 Replaced the W in the AN15 solution F94 , an attempt is made to optimize the applicability of the droplet heat flux algorithm at high wind speeds by changing the white crown coverage.

[0088] Andreas et al. found that droplets with radii of 50 and 100 μm have a significant effect on H L，sp and H S，sp The calculation of the wind speed function V has a good guiding significance, and the wind speed function V is proposed. L and V S To avoid integration and speed up the calculation efficiency of the algorithm. The algorithm includes the errors and inapplicability caused by white crown coverage and other factors into the wind speed function. Next, the present invention imitates this solution and proposes a new wind speed function V based on FASTEX data. L and V S .

[0089] According to W H18 The present invention divides the wind speed range into two parts: (i) 0≤U 10 ≤35m / s is the low to medium wind speed range; (ii) U 10 >35m / s is the high wind speed range. The wind speed range of FASTEX is from 0 to 30m / s, so the present invention uses these measurements to fit the wind speed function in medium and low wind speed environments.

[0090] At 0≤U 10 ≤35m / s, the present invention also according to W H18 The wind speed function is further divided into three subsections. After many experiments, the cubic polynomial fitting can provide the best fitting results. Figure 4(a) and (c) show the comparison between the new wind function and the original wind function under low to medium wind speed conditions. It can be seen that the value of the new wind function is smaller than the original one. The specific fitting results are shown in Equations (22) and (23).

[0091] However, due to the lack of observations in the high wind speed range, the present invention adopts another method to calculate the wind function at high wind speeds. As shown in equations (19) and (20), Andreas incorporates all uncertainties in the spray induction algorithm (including the white canopy coverage) into the wind speed function. Therefore, the present invention uses the original wind speed function to remove the original white canopy coverage W F94 , multiplied by the new white crown coverage W H1 , and obtain the new wind speed function under high wind speed. Figure 4 (b) and (d) reflect the comparison between the new wind speed function and the original wind speed function. It is not difficult to find that under high wind speed conditions, the magnitude of the new wind speed function is much smaller than the original one. The new wind function V L and V S As shown below.

[0092]

[0093]

[0094] The new wind speed function V L and V S By applying it to formulas (19) and (20), we can obtain a set of droplet heat flux algorithms suitable for high wind speed conditions. This algorithm is called YJ22 in the present invention.

[0095] Next, the present invention uses the measured FASTEX data set to test the performance of the YJ22 algorithm and the original AN15 algorithm in calculating the droplet heat flux, as shown in the following example: Figure 5 shown. Figure 5 The horizontal axis represents the neutral stable wind speed at a height of 10m, and the vertical axis represents the difference between the measured heat flux data in FASTEX and the heat flux data calculated by the algorithm. The red line in the figure represents the fitting curve of the data points in the figure, and the black line represents the difference between the observed heat flux data and the heat flux data calculated by the algorithm. It is easy to understand that the better the algorithm effect, the closer the heat flux calculated by the algorithm is to the measured value, and the closer the fitting curve is to the black line. Figure 5 (a) and (b) show that Figure 5 The red fitting curve in (a) gradually deviates from the black line as the wind speed increases; Figure 5 The fitting curve in (b) is closer to the black line. N10>15m / s, it is found that in this area, the latent heat flux calculated by the YJ22 algorithm is closer to the real data, which shows the performance of the YJ22 scheme in the higher wind speed range. Figure 5 (c) and Figure 5 (d) It shows that the YJ22 algorithm also optimizes the calculation of sensible heat flux. Figure 5 From the trend shown, it can be concluded that as the wind speed continues to increase, the performance of the YJ22 scheme will be much better than that of the AN15 scheme. Through the above analysis, the heat flux values calculated by the YJ22 algorithm are closer to the actual observed values than those calculated by the AN15 algorithm, especially in U N10 The results support the hypothesis of the present invention, that is, W F94 The solution will affect the applicability of the AN15 solution under high wind speeds.

[0096] The YJ22 algorithm is used in typical tropical cyclone conditions (with Figure 2 The performance of the YJ22 algorithm under the same conditions as (a) is shown in Figure 6. It can be seen that the performance of the YJ22 algorithm at high wind speeds has been greatly improved, and it can also give a reasonable H at high wind speeds. S，sp and H L，sp The results show that the algorithm can be used in numerical simulation experiments of strong TCs. The influence of droplet heat flux on tropical cyclones is explored.

[0097] The impact of droplet heat flux on typhoons

[0098] This experiment is based on the coupled model COAWST, and the 2018 super typhoon "Mangosteen" is selected as the experimental case. In order to study the impact of droplet heat flux on TC simulation, the total latent heat flux H between the ocean and the atmosphere is calculated. L，T and H S，T The coefficients a and b were added to the equation. Five sets of numerical experiments were conducted. The five sets of experiments used different combinations of a and b, as shown in Table 1.

[0099] H L，T =H L，int +aH L，sp (5)

[0100] H S，T =H S，int +bH S，sp (6)

[0101] Table 1. Numerical simulation experiment settings

[0102] EXP ID EXP name a b 1 L0_S0 0 0 2 L1_S0 1 0 3 L0_S1 0 1 4 L1_S1 1 1 5 L2_S2 2 2

[0103] Experiment L0_S0 is a control experiment. This experiment does not consider the influence of droplet heat flux and only uses the sea-air interface heat flux in the experiment. Experiment L1_S0 only considers the droplet latent heat flux H. L，sp The influence of droplet sensible heat flux H is not considered S，sp The experiment L0_S1 only considers the droplet heat flux H S，sp The influence of droplet latent heat flux H is not considered L，sp The influence of H L，sp and H S，sp The effect of H L，sp and H S，sp The comparison between the experiment L0_S0 and the experiment L1_S1 is the key point. By comparing these two groups of experiments, we can analyze the effect of adding droplet heat flux on typhoon simulation. In addition, by comparing the results of experiments L0_S0, L1_S0, L0_S1 and L1_S1, we can study the effect of H L，sp and H S，sp Impact on the simulation of Typhoon Mangkhut; by comparing the results of L0_S0, L1_S1 and L2_S2, we can more clearly study the impact of the overall droplet heat flux on the simulation of Typhoon Mangkhut.

[0104] The impact of droplet heat flux on typhoon tracks

[0105] Figure 7 Comparison of typhoon paths simulated by five sets of experiments with the best JTWC best path data. Figure 7 This figure shows the path errors of the five simulated typhoons compared to the JTWC optimal path data. It can be seen that the typhoon path errors of the five simulated experiments are not significant. This paper focuses on the LO_S0 and L1_S1 schemes. The difference in the typhoon path errors simulated by these two schemes is minimal, indicating that droplet heat flux has no significant impact on typhoon path simulation.

[0106] The impact of droplet heat flux on typhoon intensity

[0107] The minimum sea surface pressure (MSLP) and the maximum wind speed at 10 m (Vmax) are commonly used parameters for evaluating typhoon intensity. The present invention uses these two parameters to evaluate the impact of droplet heat flux on typhoon intensity.

[0108] Figure 8 Comparison of five sets of simulated typhoon intensities and optimal path data. Figure 8(a) shows the variation of the minimum sea level pressure (MSLP) over the entire simulation timeframe. Similarly, we first focus on the comparison between Experiments L0_S0 and L1_S1. As can be seen from the figure, the MSLP simulated by Experiment L1_S1 is closer to the optimal path data than that of Experiment L0_S0. This indicates that the influence of droplet heat flux improves the MSLP simulation results and optimizes the simulation of typhoon intensity.

[0109] By comparing the results of experiments L0_S0, L1_S1, and L2_S2, it can be seen that the addition of droplet heat flux can significantly change the typhoon's intensification speed and maximum intensity. The greater the droplet heat flux amplification factor, the faster the typhoon intensifies and the stronger its maximum intensity. By comparing the results of experiments L0_S0, L1_S0, and L0_S1, it can be seen that the influence of droplet heat flux on the change of typhoon intensity is dominated by H L，sp Compared with the simulation results of experiment L0_S0, the typhoon intensification speed and maximum intensity of L1_S0 are significantly enhanced. However, the experimental results of experiment L0_S1 and experiment L0_S0 are not significantly different, indicating that the addition of H S，sp The impact on the typhoon intensity simulation results is not significant.

[0110] Figure 8 (b) reflects the variation of the maximum wind speed Vmax at 10 m throughout the simulation time range. The increasing trend of Vmax basically corresponds to the weakening trend of MSLP. The differences among the experimental groups are basically consistent with the MSLP and are not repeated here.

[0111] The impact of droplet heat flux on typhoon structure

[0112] In order to analyze the effect of droplet heat flux on the radial structure of typhoons, we plotted the azimuthally averaged sea level pressure (SLP) and 10m wind speed versus simulation time ( Figure 9 and Figure 10 The horizontal axis represents the radial distance from the typhoon center, and the vertical axis represents the simulation time. Because the typhoon structure was not clear in the first 24 hours, only the results from hours 24 to 120 are plotted. Figure 9 It clearly shows the horizontal development of the typhoon's low-pressure structure, and the changes in the typhoon's central pressure and Figure 8 (a) shows that the development of MSLP is basically the same. As can be seen from the figure, the faster the typhoon strengthens, the faster the low-pressure structure develops in the horizontal direction. By comparing the results of experiments L0_S0, L1_S1 and L2_S2, it can be seen that the addition of droplet heat flux can make the low-pressure structure of the typhoon develop more fully. By comparing the results of L0_S0 and L1_S0, it can be seen that HL，sp The addition of H has a significant promoting effect on the development of typhoon low pressure structure. By comparing the results of L0_S0 and L0_S1, it can be seen that considering H alone S，sp It has little impact on the development of the typhoon low-pressure structure.

[0113] Figure 10 The radial distribution of the azimuthally averaged tangential wind and radial wind at a height of 10m is shown over time. The tangential wind is represented by color, and the radial wind is represented by contour lines. Focus on the results of L0_S0 and L1_S1. By comparing the experiments L0_S0 and L1_S1, it can be seen that the addition of droplet heat flux will not only enhance the tangential wind field at the bottom of the typhoon, but also make the wind field structure of the typhoon in the radial direction more fully developed. Similarly, by comparing typhoons L0_S0, L1_S0 and L0_S1, we can see that the addition of H alone will not only enhance the tangential wind field at the bottom of the typhoon, but also make the wind field structure of the typhoon in the radial direction more fully developed. L，sp It can make the typhoon wind field develop more fully, while considering H alone S，sp However, the impact on the typhoon wind field is not obvious.

[0114] Compared with the prior art, the present invention has the following beneficial effects:

[0115] 1. The present invention first analyzes the currently widely used droplet heat flux algorithm AN15 scheme. Since the white crown coverage W used in this scheme is F94 It cannot be applied in high wind speed environments, which leads to problems in the applicability of the AN15 scheme under high wind speeds, and therefore it cannot be used in the numerical simulation of TC.

[0116] 2. The present invention uses a more reasonable white crown coverage calculation scheme W H18 To replace the original W F94 Scheme, according to W H18 The segmented interval of the wind speed function was redefined, and a new wind speed function was fitted using FASTEX data. This led to a new droplet heat flux algorithm, YJ22, suitable for high wind speed environments. Both FASTEX data and ideal experiments have proven that the YJ22 algorithm performs significantly better than the AN15 algorithm.

[0117] 3. The present invention successfully applied the YJ22 scheme to the coupled COAWST model to simulate the super typhoon "Mangosteen". By comparing the results of five groups of experiments, the present invention reached the following conclusions:

[0118] (1) The addition of droplet heat flux has little effect on the simulation of the path of "Mangosteen".

[0119] (2) The addition of droplet heat flux can significantly improve the simulation results of typhoon intensity (MSLP and Vmax).

[0120] (3) The addition of droplet heat flux will make the typhoon's horizontal low-pressure structure and wind field structure develop more fully.

[0121] As used herein, the word "preferred" is intended to serve as an example, instance, or illustration. Any aspect or design described herein as "preferred" is not necessarily to be construed as advantageous over other aspects or designs. Rather, the use of the word "preferred" is intended to present concepts in a concrete manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X employs A or B" is intended to mean any of the naturally inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied in any of the foregoing examples.

[0122] Moreover, although the present disclosure has been shown and described with respect to one or implementation, those skilled in the art will think of equivalent variations and modifications based on reading and understanding of this specification and the accompanying drawings. The present disclosure includes all such modifications and variations and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the above-mentioned components (such as elements, etc.), the terms used to describe such components are intended to correspond to any component (unless otherwise indicated) that performs the specified function of the component (such as it is functionally equivalent), even if structurally different from the disclosed structure that performs the function in the exemplary implementation of the present disclosure shown herein. In addition, although the specific features of the present disclosure have been disclosed with respect to only one of several implementations, such features can be combined with one or other features of other implementations that can be desired and advantageous for a given or specific application. Moreover, insofar as the terms "including", "having", "containing" or their variations are used in specific embodiments or claims, such terms are intended to be included in a manner similar to the term "comprising".

[0123] The functional units in the embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or multiple or more units may be integrated into a single module. The aforementioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The aforementioned storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc. The aforementioned devices or systems may execute the storage method in the corresponding method embodiment.

[0124] In summary, the above embodiment is one implementation method of the present invention, but the implementation method of the present invention is not limited to the described embodiment. Any other changes, modifications, substitutions, combinations, and simplifications that deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for calculating droplet heat flux for super typhoons, applied to a coupled ocean-atmosphere-wave-sediment transport model system, characterized in that: The following steps are involved: Calculate the white crown coverage W H18 , according to W H18 The segmented intervals of the wind speed function are defined, and the wind speed function is obtained by fitting the FASTEX data, and the droplet heat flux is calculated. The droplet heat flux calculation method is as follows: H S，sp =ρ w C w (T s -T eq，100 )V S (u * ) Among them, H s,sp is the droplet sensible heat flux, H L,sp is the droplet latent heat flux, f n Represents the droplet generation spectrum function within the unit white crown area, u * Represents the friction velocity, and its value characterizes the magnitude of the turbulent pulsation velocity at the sea-air interface. V L To calculate the wind speed function of droplet latent heat flux, V S To calculate the wind speed function of droplet sensible heat flux, r(τ f,50 ) represents the radius of the droplet when it falls back into the water, ρ w is the density of seawater, L v is the latent heat of vaporization, C w stands for specific heat, T s represents the sea surface temperature, T eq,100 is the temperature at which a droplet with an initial radius of 100 mm reaches equilibrium with its environment.

2. The method for calculating droplet heat flux for super typhoons according to claim 1, characterized in that: The total droplet heat flux is calculated directly using droplets with radii of 50 μm and 100 μm.

3. The method for calculating droplet heat flux for super typhoons according to claim 1, characterized in that: Only the atmosphere model and the wave model are activated to provide variables for the calculation of the heat flux caused by ocean waves.

4. The method for calculating droplet heat flux for super typhoons according to claim 1, characterized in that: The Perdue Lin scheme was used for microphysical processes, the RRTMG scheme was used for both longwave and shortwave radiation, the Kain-Fritsch scheme was used for the outermost and sub-outer cumulus convection, the MYNN2.5 scheme was used for boundary layer parameterization, the MYNN scheme was used for near-surface processes, the Unified Noah scheme was used for land surface processes, and the COARE3.5 scheme was used for heat flux parameterization.

5. The method for calculating droplet heat flux for super typhoons according to claim 1, characterized in that: White crown coverage W H18 The calculation of is as follows:

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