CN118011524A - Wave height forecasting method based on sea surface wind direction and wind speed forecasting - Google Patents
Wave height forecasting method based on sea surface wind direction and wind speed forecasting Download PDFInfo
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Abstract
The invention discloses a wave height forecasting method based on sea surface wind direction and wind speed forecasting, which comprises the steps of matching an observation point for a wave height point to be forecasted according to a principle of closest distance, extracting a wave height observation value at the current moment of the observation point, and wind direction observation values and wind speed observation values at the current moment and a plurality of integral point moments before the current moment to form a historical sample; training the nonlinear prediction model by using all historical samples, wherein the wind direction and the wind speed observation value are used as prediction factors and the wave height observation value is used as a prediction object during training, and the wave height prediction model is obtained after the training is finished; extracting wind direction forecast values and wind speed forecast values of a plurality of integral points at the current forecast moment of the wave height point to be forecasted to form a forecast sample; inputting the forecasting sample into a wave height forecasting model, and outputting a wave height value at the current forecasting moment by the wave height forecasting model as a wave height forecasting value; the method has the advantages that the wave height can be automatically, objectively and accurately calculated, and further, risk forecast and early warning can be provided for marine shipping and marine operation safety.
Description
Technical Field
The invention relates to a sea wave forecasting technology, in particular to a sea wave height forecasting method based on sea surface wind direction and wind speed forecasting.
Background
The natural disasters can be formed by the strong wind and the strong waves independently, and many times, the two natural disasters occur simultaneously, so that the degree of the disasters is increased. The ocean wave is generally referred to as wave generated by wind in the ocean, and comprises stormy waves, surging waves and near-shore waves. The wind wave is caused by the wind, and the surge is the wave left by the wind wave in fact, and the wave is generally caused by the wind, so that the common language of no wind and no wave generation exists. The surge wave is slower than the wind wave, and has higher continuity in a certain time, while the wind wave is greatly influenced by wind, and if the wind direction or the wind speed suddenly changes, the wind wave also changes greatly. The growth of wind waves is related to three main factors of wind speed, wind time and wind distance, and is also related to the topography of the sea area, the water depth condition, the influence of ocean currents and the like in the propagation process.
The wind wave at a certain moment in a certain sea area is related to the wind tight cut in a period of time in the earlier stage, and in general, the more stable the wind direction is, the larger the wind speed is, and the larger the wind wave is. The relationship between wind speed and wave height is more linear, while the relationship between wind direction and wave height is more nonlinear, so if the effect of the previous wind direction and wind speed on the wave is considered, a nonlinear model is necessary.
Although the ocean numerical mode can forecast the change of sea waves at present, the ocean numerical mode has the problems of wave-current coupling and difficulty in accurately describing coastlines, so that local strong current areas such as boundary currents, coasts and the like can be caused, larger errors can occur in output results, and the weather numerical mode has higher accuracy in forecasting the atmosphere. The wind is considered to be the main factor of wind wave generation and elimination in a short time, the sea surface wind direction and wind speed forecast based on the meteorological numerical mode is considered, the wave height forecast model is designed to forecast the change of the wave height of the sea wave, and the risk forecast and the early warning are provided for ocean shipping and offshore operation, so that the method has higher economic benefit and social benefit.
Disclosure of Invention
The invention aims to solve the technical problem of providing a wave height forecasting method based on sea surface wind direction and wind speed forecasting, which can automatically, objectively and accurately calculate the wave height and further can provide risk forecasting and early warning for marine shipping and offshore operation safety.
The technical scheme adopted for solving the technical problems is as follows: a wave height forecasting method based on sea surface wind direction and wind speed forecasting is characterized by comprising the following steps:
Step 1: for one wave height to-be-forecasted point of any sea area, defining the wave height to-be-forecasted point as a current wave height forecast point; then, according to the principle of closest distance, matching an observation point for the current wave height forecast point, wherein the observation point has a wind direction observation value, a wind speed observation value and a wave height observation value at the whole point moment in a historical time;
Step 2: defining the current whole point time to be processed of the observation point as the current time;
Step 3: the current moment is the H-th integral moment, the wind direction observation value and the wind speed observation value of each of T+1 integral moments from the H-T integral moment to the current moment are extracted, the wave height observation value of the current moment is extracted, and the extracted wave height observation value, the T+1 wind direction observation values and the T+1 wind speed observation values form a historical sample; wherein, the initial value of H is T+1, and the value of T is 12-24;
step 4: taking the next whole point time to be processed of the observation point as the current time, and returning to the step 3 to continue execution until all whole point times within a period of history time of the observation point are processed, so as to obtain a plurality of history samples;
Step 5: training a nonlinear forecasting model based on deep learning by using all historical samples, wherein a wind direction observation value and a wind speed observation value are taken as forecasting factors during training, a wave height observation value is taken as a forecasting object, and a wave height forecasting model based on wind direction and wind speed is obtained after training is finished;
Step 6: aiming at the current wave height forecasting point, defining the whole point time to be forecasted as the current forecasting time; then, acquiring respective wind direction forecast values and wind speed forecast values of the first T integral points at the current forecast moment, and forming a forecast sample by the acquired T wind direction forecast values and the T wind speed forecast values; and inputting the forecasting sample into a wave height forecasting model, and outputting the wave height value at the current forecasting moment by the wave height forecasting model as a wave height forecasting value.
In the step 1, a period of history time is at least one year.
In the step 3, the value of T is 16.
In the step 5, the nonlinear prediction model based on deep learning is constructed by adopting an artificial neural network or a support vector machine.
In the step 6, if the current wave height forecasting point is not on the forecasting grid of the weather numerical forecasting mode, for any one of the previous T integer points at the current forecasting time, the U wind speed and the V wind speed forecasting on the forecasting grid at the integer point time are interpolated to the current wave height forecasting point in space by bilinear interpolation, and then converted into the wind direction forecasting value and the wind speed forecasting value at the integer point time.
Compared with the prior art, the invention has the advantages that:
According to the method, experience or experiment is carried out on a specific forecasting sea area according to the main influencing factors of sea wave extinction, the time span T of wind direction and forward wind speed required by forecasting wave height is determined according to the historical observation data of wind direction, wind speed and wave height, a historical sample is formed by acquiring the historical observation data, a wave height forecasting model based on wind direction and wind speed is obtained through training of the historical sample, a wind direction forecasting value and a wind speed forecasting value of a point to be forecasted of wave height in T hours forward based on the sea surface wind direction and wind speed forecasting of a weather numerical forecasting mode are obtained, and the wave height forecasting model is input, so that the wave height forecasting value can be automatically, objectively and accurately forecasted, risk forecasting and early warning are provided for marine shipping and offshore operation, the effects of risk decision and pest avoidance are achieved on marine shipping safety, and higher economic benefit and social benefit are achieved.
Drawings
Fig. 1 is a block diagram of a general implementation of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the embodiments of the drawings.
The invention provides a wave height forecasting method based on sea surface wind direction and wind speed forecasting, which is generally implemented as shown in a block diagram in figure 1 and comprises the following steps:
Step 1: for one wave height to-be-forecasted point of any sea area, defining the wave height to-be-forecasted point as a current wave height forecast point; then, according to the principle of nearest distance, an observation point is matched for the current wave height forecast point, wherein the observation point has a wind direction observation value, a wind speed observation value and a wave height observation value at the whole point moment in a historical time (such as at least one year).
Step 2: the current point time of the observation point to be processed is defined as the current time.
Step 3: the current moment is the H-th integral moment, the wind direction observation value and the wind speed observation value of each of T+1 integral moments from the H-T integral moment to the current moment are extracted, the wave height observation value of the current moment is extracted, and the extracted wave height observation value, the T+1 wind direction observation values and the T+1 wind speed observation values form a historical sample; wherein, the initial value of H is T+1, the value of T is 12-24, and the specific value of T can be set according to experience or the optimal principle of test results, for example, the value of T is 16.
Step 4: and taking the next whole point time to be processed of the observation point as the current time, and returning to the step 3 to continue execution until all whole point times within a period of history time of the observation point are processed, so as to obtain a plurality of history samples.
Step 5: and training the nonlinear prediction model based on deep learning by using all historical samples, wherein the wind direction observation value and the wind speed observation value are taken as prediction factors during training, the wave height observation value is taken as a prediction object, and the wave height prediction model based on wind direction and wind speed is obtained after training is finished.
Here, the nonlinear prediction model based on deep learning is constructed by using an artificial neural network or a support vector machine.
Step 6: aiming at the current wave height forecasting point, defining the whole point time to be forecasted as the current forecasting time; then, acquiring respective wind direction forecast values and wind speed forecast values of the first T integral points at the current forecast moment, and forming a forecast sample by the acquired T wind direction forecast values and the T wind speed forecast values; and inputting the forecasting sample into a wave height forecasting model, and outputting the wave height value at the current forecasting moment by the wave height forecasting model as a wave height forecasting value.
In step 6, if the current wave height forecasting point is not on the forecasting grid of the weather numerical forecasting mode, for any one of the previous T whole point moments of the current forecasting time, the U wind speed (east-west wind speed) and the V wind speed (north-south wind speed) on the forecasting grid at the whole point moment are forecasted and interpolated to the current wave height forecasting point in space by bilinear interpolation, and then converted into a wind direction forecasting value and a wind speed forecasting value at the whole point moment.
The Zhejiang province meteorological department puts in the mountain station, the wenzhou station, the reef station, the shrimp stand out of the gate station and the sea gate station 5 buoy stations in the Zhejiang coast successively from 2010, and the observation project comprises wave height (namely effective wave height), sea surface wind direction and wind speed. Based on the observation data of 5 buoy stations in coastal areas of Zhejiang province in 2020-2022, a wave height forecasting model is established. The wave height forecast value is calculated and checked by adopting fine grid mode sea surface wind direction and wind speed forecast data of the middle European weather forecast center (ECMWF) of 2023, and the result shows that: the wave height forecast value is well matched with the actual measurement value, root mean square errors of 24, 48 and 72 hours forecast are smaller than 0.5 meter, absolute deviation values are smaller than 0.1 meter, and the method has good forecast effect.
Claims (5)
1. A wave height forecasting method based on sea surface wind direction and wind speed forecasting is characterized by comprising the following steps:
Step 1: for one wave height to-be-forecasted point of any sea area, defining the wave height to-be-forecasted point as a current wave height forecast point; then, according to the principle of closest distance, matching an observation point for the current wave height forecast point, wherein the observation point has a wind direction observation value, a wind speed observation value and a wave height observation value at the whole point moment in a historical time;
Step 2: defining the current whole point time to be processed of the observation point as the current time;
Step 3: the current moment is the H-th integral moment, the wind direction observation value and the wind speed observation value of each of T+1 integral moments from the H-T integral moment to the current moment are extracted, the wave height observation value of the current moment is extracted, and the extracted wave height observation value, the T+1 wind direction observation values and the T+1 wind speed observation values form a historical sample; wherein, the initial value of H is T+1, and the value of T is 12-24;
step 4: taking the next whole point time to be processed of the observation point as the current time, and returning to the step 3 to continue execution until all whole point times within a period of history time of the observation point are processed, so as to obtain a plurality of history samples;
Step 5: training a nonlinear forecasting model based on deep learning by using all historical samples, wherein a wind direction observation value and a wind speed observation value are taken as forecasting factors during training, a wave height observation value is taken as a forecasting object, and a wave height forecasting model based on wind direction and wind speed is obtained after training is finished;
Step 6: aiming at the current wave height forecasting point, defining the whole point time to be forecasted as the current forecasting time; then, acquiring respective wind direction forecast values and wind speed forecast values of the first T integral points at the current forecast moment, and forming a forecast sample by the acquired T wind direction forecast values and the T wind speed forecast values; and inputting the forecasting sample into a wave height forecasting model, and outputting the wave height value at the current forecasting moment by the wave height forecasting model as a wave height forecasting value.
2. The wave height forecasting method based on sea surface wind direction and wind speed forecasting according to claim 1, wherein in the step 1, a period of history time is at least one year.
3. The wave height forecasting method based on sea surface wind direction and wind speed forecasting according to claim 1, wherein in the step 3, the value of T is 16.
4. The wave height forecasting method based on sea surface wind direction and wind speed forecasting according to claim 1, wherein in the step 5, the nonlinear forecasting model based on deep learning is constructed by adopting an artificial neural network or adopting a support vector machine.
5. The wave height forecasting method based on sea surface wind direction and wind speed forecasting according to claim 1, wherein in the step 6, if the current wave height forecasting point is not on the forecasting grid of the weather numerical forecasting mode, for any one of the previous T integer moments of the current forecasting moment, the U wind speed and the V wind speed forecasting on the forecasting grid at the integer moment are interpolated to the current wave height forecasting point in space by bilinear interpolation, and then converted into the wind direction forecasting value and the wind speed forecasting value at the integer moment.
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