CN112699197A - Quantitative analysis method for visibility navigation control data - Google Patents
Quantitative analysis method for visibility navigation control data Download PDFInfo
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- CN112699197A CN112699197A CN202011632563.4A CN202011632563A CN112699197A CN 112699197 A CN112699197 A CN 112699197A CN 202011632563 A CN202011632563 A CN 202011632563A CN 112699197 A CN112699197 A CN 112699197A
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Abstract
The invention relates to the technical field of meteorological data processing, and discloses a quantitative analysis method for visibility navigation control data. The method comprises the steps of obtaining the duration of visibility within a preset range in current port area control data; generating corresponding virtual visibility sites according to corresponding control ranges in port area control data; calculating the average visibility of the visibility continuous process in the current preset range; correspondingly setting the calculated average visibility and the virtual visibility stations; importing actual observation data of visibility stations at the same time as the control time, and forming grid point data through preset interpolation calculation; and calculating and interpolating actual observation data of each time visibility station according to preset interval time, storing interpolation calculation results according to a preset format, and forming a grid point data set. By adopting the method, a history sample of the visibility lattice points in the refined harbor area is established, and basic data is provided for the refined analysis and forecast of the sea fog.
Description
Technical Field
The invention relates to the technical field, in particular to a method for quantitatively analyzing visibility navigation control data.
Background
The low visibility at sea is mainly caused by sea fog, which is a dangerous weather phenomenon that the horizontal visibility is less than 1000m due to water drops or ice crystals generated by water vapor condensation in the low-level atmosphere above sea, shore and island. The low visibility has important influence on a plurality of aspects such as marine traffic, fishery production, port operation, sea-related tourism and the like. Statistics from the literature indicate that about 70% of marine disaster accidents are caused by sea fog.
Because of the lack of observation stations on the sea, sea fog is relatively deficient compared with the weather on the land, and only observation data of coastal weather stations, a small number of island stations, buoy stations and ships are available. The large-scale long-time continuous monitoring of the sea fog cannot be realized. In addition, sea fog has different characteristics in different regions, such as the advection fog of yellow Bohai sea, has obvious boundary characteristics distributed along a coastline in many cases, and has the characteristics of generation and expansion at night and retreat from the sea in the daytime. Thus the observation of fog at coastal sites is less than what actually occurs in the sea. With the development and application of the satellite remote sensing technology which has the advantages of being relatively quick, wide in coverage range, capable of continuously observing and the like, the occurrence, development and extinction of sea fog can be observed in real time, and particularly, the detection accuracy of fog is greatly improved due to the fact that a satellite millimeter wave radar appears. However, sea fog identification and detection based on a passive satellite remote sensor, whether a threshold value method, texture identification or a double-channel difference value method, have the difficulty in identifying and distinguishing sea fog from low cloud; a large amount of accurate sea fog and low cloud samples [7-8] can be obtained by using a satellite-borne millimeter wave radar, but the method is limited to the defects that only single-point detection can be carried out on sub-satellite points, the revisit period is long, and the like, and continuous detection on the sea fog in high time resolution and large space range cannot be realized. At present, on the ground, other unconventional monitoring devices also comprise a laser radar, a millimeter wave radar, a ceilometer, a microwave radiometer and the like, and can be used for fog judgment and quantitative inversion analysis, but due to the fact that the observation means have respective outstanding defects, the monitoring of fog, haze, downy rain, low cloud and the like is difficult to distinguish, and meanwhile, the problems of observation range, frequency and the like also exist.
In the harbor and the surrounding area, the terrain near the harbor is generally relatively complex, and thus, a small range of mist and the like are easy to appear. For these situations, effective observation methods and data are still relatively few. The existing sea fog monitoring system has the problems of small range, high hysteresis, large blind area and the like, and cannot well meet the weather guarantee service requirements of ship scheduling and berthing operation at ports.
The navigation control record provided by the port management department is a qualitative record of the influence of visibility on port shipping under the actual production condition. Because the influence area is the most direct area, the utilization of the data has a direct effect on the technical improvement of the weather guarantee service of the port. However, port control data is qualitative written description, so that it is difficult to directly apply to quantitative analysis and forecasting models of visibility, and a quantitative analysis method is needed.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a quantitative analysis method of visibility navigation control data, which can realize the quantitative analysis of navigation control data, and in order to achieve the purpose, the invention adopts the following technical scheme: a quantitative analysis method for visibility navigation control data comprises the following steps:
s1: acquiring port area control information, and acquiring the duration of visibility within a preset range in the current port area control information according to the control time in the port area control information and a preset visibility duration algorithm;
s2: generating corresponding virtual visibility sites according to corresponding control ranges in port area control data;
s3: calculating the average visibility in the continuous process of the visibility in the current preset range according to a preset visibility empirical formula; correspondingly setting the calculated average visibility and the virtual visibility stations;
s4: importing actual observation data of visibility sites in a preset peripheral range in a harbor district at the same time as the control time, and forming grid point data through preset interpolation calculation;
s5: according to the preset interval time, calculating and interpolating actual observation data of visibility stations in the preset peripheral range of the harbor area of each time, storing the interpolation calculation result according to a preset format, and forming a grid point data set.
Further, the step S2 of generating a corresponding virtual visibility site includes:
s21: dividing the port area into a plurality of preset control areas according to the corresponding control range in the port area control data;
s22: a plurality of marine virtual visibility stations are arranged in each preset control area;
s23: configuring geographical position information of the plurality of virtual visibility sites set in step S22;
s24: and generating corresponding virtual visibility sites according to the configured geographic position information of each marine virtual visibility site and the control area information in the control data.
Further, the empirical formula according to the preset visibility is as follows:
f(h)＝aebh+cedh
wherein, f (h) is the average visibility value in the continuous process, and h is the duration of the visibility in the preset range;
the parameter a is 599.9, b is-0.5078, c is 239.9, d is-0.02844;
when f (h) is greater than 500, f (h) is 500.
Further, in step S21, the selection of the plurality of marine virtual visibility stations set in each preset controlled area is:
acquiring the peripheral shape of a corresponding controlled water area in a preset controlled area;
acquiring a central position corresponding to the peripheral shape of a controlled water area;
and setting the acquired central position as a marine virtual visibility station.
The invention at least comprises the following beneficial effects: the method has the advantages that the quantitative analysis of the navigation management data is realized, the problem of rare sea fog observation data is solved, a refined harbor visibility grid point historical sample set is established by fusing the navigation management data and the shore visibility observation data, basic data are provided for the refined sea fog analysis and forecast, and the method is suitable for being widely applied to scientific research and actual business of meteorological guarantee services of harbor districts.
Drawings
FIG. 1 is a flow chart of a quantitative analysis method of visibility navigation control data according to the present invention.
Fig. 2 is a first preset time visibility station measured value provided in the first embodiment.
Fig. 3 is a calculated value of the visibility of the virtual station at the first preset time according to an embodiment.
FIG. 4 is a block diagram of the visibility contour line after merging the control data at the first predetermined time according to an embodiment.
Fig. 5 is a second preset time visibility station measured value provided in the second embodiment.
Fig. 6 is a third measured value of the visibility station at the preset time according to the second embodiment.
Fig. 7 is a sea fog scene inversion of the central weather station at the second preset time provided in the second embodiment.
Fig. 8 is a third preset-time central weather station sea fog scene inversion provided in example two.
FIG. 9 is a block diagram of the visibility contour line after fusing the control data at the second predetermined time according to the second embodiment.
FIG. 10 is a block diagram of the visibility contour line after merging the control data at the third predetermined time according to the second embodiment.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
Example one
According to the control record of the harbor district control data, a low visibility event occurs in the water area near the bridge of the gold pond in the harbor district in the afternoon (Table 1). The specific quantitative analysis comprises the following steps:
TABLE 12019 years 1 month 11 days Port control records
Reading control data to control the area to be a water area near the bridge of the gold pond; the duration of the low visibility event is calculated to be 2.17h at the regulated time of 15:20 to 17:30 pm.
Secondly, according to the geographical characteristics of the water areas near the bridge of the golden pond, 5 marine virtual visibility stations are established in advance, wherein 4 points are arranged on the north and south water areas near the two ends of the bridge to form a quadrangle including the bridge of the golden pond and the surrounding water areas, and the other 1 point is arranged near the center of the bridge.
And thirdly, substituting the duration time into the empirical formula through the established empirical formula (formula 1) of the duration time of the low visibility process and the average visibility of the process, calculating the average visibility of the whole process to be 245m, and setting the visibility value of each time of the whole process of the 5 virtual stations to be the value.
And fourthly, importing actual observation data of visibility stations around the same time harbor area, wherein the precision of the actual observation data is 0.05 degrees within the ranges of 121.5-122.8 degrees E and 29.4-30.2 degrees N, and forming high-precision lattice data with the spatial resolution of about 500m through interpolation calculation.
Fifthly, starting from 15:20 to 17:20 (inclusive), and performing calculation interpolation for each time at an interval of 10 minutes, and then storing the result as a lattice point data set according to a uniform universal format.
The quantitative analysis of visibility navigation control data provided by the embodiment is shown in fig. 2 by the live site of visibility of shoreside and island stations and interpolation analysis. Therefore, the visibility values of 3 stations (a new hong Kong station, a middle oil wharf station and a Jintang harbor station) around the bridge in the Jintang are all over 2500 m. In addition, it can be seen from a comparison of several of the stations of fig. 2 where the fog occurs with surrounding stations: the visibility of a small dry station (K9614) is 230m, the visibility of a peripheral pluronic station (58570) is 9983m, and the distance between the two stations is only 4.8km, which shows that the coverage range of the marine cluster fog can be small. And comparing the control records, and analyzing only by actually measured site data, so that the mass fog of the water area near the bridge of the pond cannot be monitored.
Applying a quantitative analysis method of control data, adding the calculated virtual visibility sites as shown in FIG. 3, fusing the live and virtual site arrays, and performing interpolation as shown in FIG. 4. The comparison shows that the coverage and the strength of the mist close to the bridge of the gold pond are better reflected by the graph of fig. 4.
Example two
The present embodiment provides the second embodiment: take the example that a large area of sea fog occurs in the core harbor region in 6.6.6.2020. From the control record, it was found that, from time 02 to time 04, 40 minutes, sea fog occurred in the north (long stem mouth to mud coating mouth) and south (shores in the west and Buddha river (Meishan) and the channel of shores in the west) areas, and then declined to the east and south areas, and the channel of shores was recovered to normal until time 08, 40 minutes. According to the control record information, quantitative analysis operation steps are the same as those of the embodiment, and are different from the virtual visibility stations set in the step II.
From the visibility station monitoring data at the port area, it can be seen that as shown in fig. 5 and 6: the visibility low-value area of 02:50 (figure 5) covers the main water area of the harbor area and is consistent with the control record. When 08, the visibility of the harbor area is good, the maximum value of the visibility station at the periphery of the shrimp facing channel is greater than 7km, the minimum value is less than 500m, the visibility of the shrimp station closest to the shrimp facing channel is 1971m, the visibility conditions of the shrimp facing channel and the surrounding water area are analyzed in an interpolation mode, and the coverage area is obviously small.
Sea fog scene inversion products of central weather station: 2 hours and 50 minutes (fig. 7), the entire harbor area and the surrounding sea area are not indicated with sea fog, but with cloud coverage. Obviously, the presence of low clouds masks the sea fog, making it unrecognizable. The view at 08 (fig. 8) covers the middle and south of the harbor area, and the water area in the southeast of the harbor area has dense fog, which approximately corresponds to the recorded shrimp facing channel fog, but the coverage is obviously large.
Through the fusion analysis of the control data, the visibility change on the water area can be reflected in space and time at the same time (figures 9 and 10). In particular, in case 08 (fig. 10), the distribution range and intensity of visibility low-value regions on the water area of the shrimp facing the navigation channel are enhanced by increasing the virtual visibility data.
The invention discloses a quantitative analysis method of visibility navigation control data, which comprises the steps of establishing an empirical formula of sea fog duration and visibility by analyzing observation data of automatic visibility stations around seas, islands and ports, establishing offshore virtual visibility stations by contrasting visibility standards of navigation control and geographical characteristics of ports and channels, and calculating visibility values of the virtual visibility stations by substituting control duration in the navigation control data into the empirical formula. And finally, importing actual observation data of peripheral visibility stations at the same time, and forming high-precision grid point data through interpolation calculation.
The quantification and the digitization of control data are realized, and the problem of rare visibility observation data on port water areas, especially the observation loss of small-range cluster fog, is effectively solved; compared with the current mainstream live sea fog monitoring and analyzing method, the reliability of the live visibility data in the harbor area, and the accuracy and refinement degree of time and space distribution can be effectively improved. The method provides basic data for the subsequent analysis of the harbor sea fog forming mechanism and the establishment of a forecasting model, and is suitable for being widely applied to scientific research and actual business of harbor weather guarantee service.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (4)
1. A quantitative analysis method for visibility navigation control data is characterized by comprising the following steps:
s1: acquiring port area control information, and acquiring the duration of visibility within a preset range in the current port area control information according to the control time in the port area control information and a preset visibility duration algorithm;
s2: generating corresponding virtual visibility sites according to corresponding control ranges in port area control data;
s3: calculating the average visibility in the continuous process of the visibility in the current preset range according to a preset visibility empirical formula; correspondingly setting the calculated average visibility and the virtual visibility stations;
s4: importing actual observation data of visibility sites in a preset peripheral range in a harbor district at the same time as the control time, and forming grid point data through preset interpolation calculation;
s5: according to the preset interval time, calculating and interpolating actual observation data of visibility stations in the preset peripheral range of the harbor area of each time, storing the interpolation calculation result according to a preset format, and forming a grid point data set.
2. The method for quantitatively analyzing visibility flight control data according to claim 1, wherein the step S2 of generating corresponding virtual visibility sites comprises the steps of:
s21: dividing the port area into a plurality of preset control areas according to the corresponding control range in the port area control data;
s22: a plurality of marine virtual visibility stations are arranged in each preset control area;
s23: configuring geographical position information of the plurality of virtual visibility sites set in step S22;
s24: and generating corresponding virtual visibility sites according to the configured geographic position information of each marine virtual visibility site and the control area information in the control data.
3. The quantitative analysis method for visibility navigation control data according to claim 1, wherein the empirical formula according to the preset visibility is as follows:
f(h)＝aebh+cedh
wherein, f (h) is the average visibility value in the continuous process, and h is the duration of the visibility in the preset range;
the parameter a is 599.9, b is-0.5078, c is 239.9, d is-0.02844;
when f (h) is greater than 500, f (h) is 500.
4. The quantitative analysis method for visibility flight control data as claimed in claim 2, wherein in step S21, the selection of the plurality of marine virtual visibility stations in each preset control area is:
acquiring the peripheral shape of a corresponding controlled water area in a preset controlled area;
acquiring a central position corresponding to the peripheral shape of a controlled water area;
and setting the acquired central position as a marine virtual visibility station.
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| CN107749093A (en) * | 2017-09-01 | 2018-03-02 | 上海海事大学 | A kind of Vessel's Description information data structure of optimization and its transmission and recording method |
| CN108227041A (en) * | 2017-12-27 | 2018-06-29 | 中国海洋大学 | Horizontal visibility forecasting procedure based on website measured data and model results |
| US20200225672A1 (en) * | 2018-06-18 | 2020-07-16 | Zoox, Inc. | Occulsion aware planning and control |
| US20200351236A1 (en) * | 2019-05-03 | 2020-11-05 | EMC IP Holding Company LLC | Discovering a host in a storage network |
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