阅读说明：本技术 一种雪质预报方法 (Snow quality forecasting method ) 是由 刘巍巍 于 2021-06-22 设计创作，主要内容包括：本发明公开了一种雪质预报方法,根据天气现象、温度、湿球温度和风速条件进行雪质条件预报。本申请技术方案综合考虑天气现象、温度、湿球温度和风速条件进行雪质条件反演,并结合滑雪场雪质影响因素的变化合理调整雪质预报所依据的气象信息,显著提高了雪质条件预报的准确性和科学性。(The invention discloses a snow condition forecasting method, which is used for forecasting snow conditions according to weather phenomena, temperature, wet bulb temperature and wind speed conditions. According to the technical scheme, the weather phenomenon, the temperature, the wet bulb temperature and the wind speed condition are comprehensively considered to carry out snow condition inversion, and the meteorological information according to the snow condition forecast is reasonably adjusted by combining the change of the snow influencing factors of the ski field, so that the accuracy and the scientificity of the snow condition forecast are remarkably improved.)

1. A snow condition forecasting method carries out snow condition forecasting according to weather phenomena, temperature, wet bulb temperature and wind speed conditions, and is characterized in that: the method comprises the following steps:

s1: acquiring weather data at the forecast time and within the past 6 hours;

s2: judging whether the forecast time is the morning business time or not, and selecting meteorological data according to the forecast time;

s3: selecting a wet bulb temperature judgment standard according to meteorological data;

s4: forecasting the snow condition at the forecasting moment according to the meteorological data and the wet bulb temperature judgment standard;

s5: repeating the steps S1-S4, and forecasting the snow conditions at other forecasting moments;

s6: transmitting and displaying the snow condition forecast information;

s7: and correcting the snow condition forecast information according to the real-time information feedback.

2. The snow quality prediction method according to claim 1, wherein in step S2, when the forecast time is in the morning hours and a certain time later than the morning hours, the forecast time and the weather data in the past 6 hours are selected; and selecting the meteorological data of the forecasting time when the forecasting time is later than the business hours of the morning for a certain time.

3. A snow forecast method according to claim 2, wherein said certain time is selected based on the number of people in the ski field at the current time, for example, when the number of people in the ski field at the forecast time is small, the certain time is selected to be 1 hour, when the number of people in the ski field at the forecast time is large, the certain time is selected to be half an hour, when the number of people in the ski field at the forecast time is medium, the certain time is selected to be 45 minutes.

4. A snow forecast method according to claim 3, characterized in that said number of persons in the ski field at the forecast moment is statistically predicted based on big data and the actual number of persons in the ski field.

5. The snow quality forecasting method according to claim 1, wherein in step S3, the weather phenomenon in the meteorological data at the forecasting time is snow, sleet, sunny, cloudy, fog, haze or floating dust, and when the condition is met, the wet bulb temperature judgment criterion is criterion one;

wherein the first condition is as follows: snowfall of magnitude or more occurs at the forecast time or within the past 6 hours;

the first standard is as follows: the wet bulb temperature is less than or equal to minus 10 ℃, the snow quality is dry snow, the wet bulb temperature is less than or equal to minus 7 ℃ below minus 10 ℃, the snow quality is powder snow, the wet bulb temperature is less than 2 ℃ below minus 7 ℃, the snow quality is soft snow, the wet bulb temperature is less than or equal to 2 ℃, and the snow quality is wet snow.

6. The snow quality forecasting method according to claim 1, wherein in step S3, the weather phenomenon in the weather data at the forecasting time is snow, rain and snow, and when the condition two is satisfied, the wet bulb temperature judgment criterion is criterion two;

wherein the second condition is: snowfall of magnitude below that of heavy snow occurs at the forecast time and within the past 6 hours;

the second standard is: the wet bulb temperature is less than 2 ℃, the snow is soft snow, the wet bulb temperature is more than or equal to 2 ℃, and the snow is wet snow.

7. The snow forecast method according to claim 1, wherein in step S3, the weather phenomenon in the weather data at the forecast time is sunny, cloudy, fog, haze or floating dust, or when the condition two is satisfied, the wet bulb judgment criterion is criterion three;

wherein the second condition is: snow falling with magnitude lower than that of heavy snow does not occur or does not occur within the forecast time and the past 6 hours;

the third standard is: the wet bulb temperature is more than or equal to 2 ℃, the snow quality is wet snow, the wet bulb temperature is less than 2 ℃ and the wind speed is more than or equal to 8m/s, the snow quality is ice surface snow, the wet bulb temperature is less than 2 ℃ and the wind speed is less than 8m/s, and the snow quality is soft snow.

8. A snow quality forecasting method according to claim 1, wherein in step S3, the weather phenomenon in the weather data at the forecasting time is flying dust, sand storm, strong wind, sleet or hail, and the snow quality is snow on ice.

9. The snow forecast method according to claim 1, wherein in step S3, the weather phenomenon in the meteorological data is other types of rainfall, the temperature is less than or equal to 0 ℃, the snow is ice snow, the temperature is greater than 0 ℃, and the snow is wet snow.

10. The snow forecast method according to claim 1, wherein in step S6, the snow condition forecast information is displayed by GOSKI, ski removal APP, chinese air net, or the like; preferably, in step S7, the two-dimensional code is put in a snow field, and snow quality and other weather elements are fed back in real time by snow field workers to correct the live condition and forecast data.

Technical Field

The invention relates to the field of meteorological prediction, in particular to a snow quality forecasting method.

Background

Snow conditions are one of the most important factors affecting the skiing experience of a ski resort, and a total of 500 skies worldwide are defined into 5 types of snow, i.e., dry snow, pink snow, soft snow, wet snow and ice snow, according to the usual classification of snow quality by ski enthusiasts and the survey or field investigation of the influence of weather on snow conditions in countries such as china, japan, korea, usa, canada, swiss, france, austria, italy, sweden, finland, new zealand, australia, and the like, and according to the snow standards and requirements of the skiing association or institutional competition in various countries. The snow powder is the most popular snow for the skiing lovers, the snow is mainly natural snow, and the best dry snow powder is soft and powdery and has floating feeling when sliding on the snow; the soft snow is mostly the snow quality after the snow channel is compacted by the snow press after the artificial snow is made, and is the common snow quality in the domestic ski field; the wet snow is in a slurry state, the sliding resistance is large, and the snow is mostly formed in high-humidity weather; snow on ice mostly melts in windy weather or at a high temperature in the daytime, is solidified to form a snow accumulation state again after being cooled at night, and slides and is difficult to control speed and direction. In the prior art, the method for forecasting the snow quality is less, the forecasting accuracy is lower, and the guiding function of a ski field and a skier is still unsatisfactory.

Therefore, a more accurate and perfect snow quality forecasting method is needed to provide more accurate forecasting information for skiers and skiers in ski fields.

Disclosure of Invention

The invention aims to provide a snow quality forecasting method aiming at the defects in the prior art.

In order to achieve the purpose, the invention adopts the following scheme:

based on the snow type, the research on the technical means and the inversion method of the snow forecast is carried out based on the weather condition without considering the sliding factors of the skiers. According to public shared data of artificial snow making in Europe and America and other countries, based on a wet bulb temperature evaluation system, the influence of weather phenomena, temperature, humidity, wet bulb temperature, wind speed conditions and snow field business hours on snow conditions is comprehensively considered, snow quality forecasting algorithm research is carried out, and finally 5 kinds of snow conditions of dry snow, powder snow, soft snow, wet snow and ice surface snow are forecasted.

And comprehensively considering weather phenomena, temperature, wet bulb temperature and wind speed conditions to carry out snow condition inversion. In addition, there is a certain correlation between the business hours of the snow field and the snow quality, generally, the snow quality in the morning is related to the snowfall state accumulated at the current time and in the non-business hours at night, and the correlation between the snow quality and the snowfall state in the past time is not large because tourists repeatedly tread the snow passage in other business hours, and is determined only by the weather state in the current time.

A snow condition forecasting method carries out snow condition forecasting according to weather phenomena, temperature, wet bulb temperature and wind speed conditions, and is characterized in that: the method comprises the following steps:

s1: acquiring weather data at the forecast time and within the past 6 hours;

s2: judging whether the forecast time is the morning business time or not, and selecting meteorological data according to the forecast time;

s3: selecting a wet bulb temperature judgment standard according to meteorological data;

s4: forecasting the snow condition at the forecasting moment according to the meteorological data and the wet bulb temperature judgment standard;

s5: repeating the steps S1-S4, and forecasting the snow conditions at other forecasting time;

s6: transmitting and displaying the snow condition forecast information;

s7: and correcting the snow condition forecast information according to the real-time information feedback.

Further, in step S2, when the forecast time is in the morning hours and a certain time later than the morning hours, selecting the forecast time and the weather data in the past 6 hours; and selecting the meteorological data of the forecasting time when the forecasting time is later than the business hours of the morning for a certain time.

Further, the predetermined time may be selected based on the number of people in the ski field at the current time, for example, when the number of people in the ski field at the forecast time is small, such as 1/3 less than the maximum capacity, the predetermined time is selected to be 1 hour, when the number of people in the ski field at the forecast time is large, such as 2/3 greater than the maximum capacity, the predetermined time is selected to be half an hour, when the number of people in the ski field at the forecast time is medium, such as 1/3 to 2/3 with the maximum capacity, the predetermined time is selected to be 45 minutes.

Further, the number of people in the skiing field at the forecast moment is predicted based on big data statistics and the number of actual people in the skiing field.

Further, in step S3, the weather phenomenon in the forecast time weather data is snow, rain, snow, fine, cloudy, fog, haze or floating dust, and when the condition is met, the wet bulb temperature judgment criterion is a first criterion;

wherein the first condition is as follows: snowfall of magnitude or more occurs at the forecast time or within the past 6 hours;

the first standard is as follows: the wet bulb temperature is less than or equal to minus 10 ℃, the snow quality is dry snow, the wet bulb temperature is less than or equal to minus 7 ℃ below minus 10 ℃, the snow quality is powder snow, the wet bulb temperature is less than 2 ℃ below minus 7 ℃, the snow quality is soft snow, the wet bulb temperature is less than or equal to 2 ℃, and the snow quality is wet snow.

Further, in step S3, the weather phenomenon in the forecast time weather data is snow, rain and snow, and when the condition two is satisfied, the wet bulb temperature determination criterion is criterion two;

wherein the second condition is: snowfall of magnitude below that of heavy snow occurs at the forecast time and within the past 6 hours;

the second standard is: the wet bulb temperature is less than 2 ℃, the snow is soft snow, the wet bulb temperature is more than or equal to 2 ℃, and the snow is wet snow.

Further, in step S3, when the weather phenomenon in the weather data at the forecast time is sunny, cloudy, fog, haze or floating dust, or the condition two is satisfied, the wet bulb determination criterion is criterion three;

wherein the second condition is: snow falling with magnitude lower than that of heavy snow does not occur or does not occur within the forecast time and the past 6 hours;

the third standard is: the wet bulb temperature is more than or equal to 2 ℃, the snow quality is wet snow, the wet bulb temperature is less than 2 ℃ and the wind speed is more than or equal to 8m/s, the snow quality is ice surface snow, the wet bulb temperature is less than 2 ℃ and the wind speed is less than 8m/s, and the snow quality is soft snow.

Further, in step S3, the weather phenomenon in the forecast time weather data is raise dust, sand storm, strong wind, sleet or hail, and the snow is ice surface snow;

further, in step S3, the weather phenomenon in the meteorological data is other types of rainfall, the temperature is less than or equal to 0 ℃, the snow is ice snow, the temperature is greater than 0 ℃, and the snow is wet snow.

Further, in step S6, the snow condition forecast information is displayed by GOSKI, ski removal APP, chinese air net, and the like.

Further, in step S7, a two-dimensional code is put in the snow field, and snow quality and other weather elements are fed back in real time by snow field workers to correct the actual situation and forecast data.

Compared with the prior art, the invention has the advantages that: the technical scheme of the application comprehensively considers weather phenomena, temperature, wet bulb temperature and wind speed conditions to carry out snow condition inversion, reasonably adjusts meteorological information according to snow condition forecast by combining changes of snow condition influence factors of a ski field, and selects more effective wet bulb temperature judgment standards under different meteorological information conditions, so that the accuracy and the scientificity of the snow condition forecast are obviously improved, meanwhile, big data statistics is reasonably utilized, factors influencing the snow condition forecast are accurately judged, for example, the number of people in the ski field is forecasted, the change trend based on the actual number of people in the ski field can be accurately forecasted based on the big data statistics, the forecast data is adjusted in real time according to the changes of the actual number of people, and more accurate basis is provided for the selection of the forecast influence factors; in addition, the technical scheme of the application is additionally provided with an information feedback updating mechanism, so that weather elements such as snow quality and the like are fed back in real time, live condition and forecast data are corrected, and the accuracy of the forecast is further guaranteed.

Drawings

Fig. 1 is a block diagram of an implementation of the snow quality forecasting method.

Fig. 2 is information displayed by the application of the WAP site of the chinese weather network.

Fig. 3 is a schematic view of a snow field feedback interface.

Detailed Description

Examples

A snow condition forecasting method carries out snow condition forecasting according to weather phenomena, temperature, wet bulb temperature and wind speed conditions, and is characterized in that: the method comprises the following steps:

s1: acquiring weather data at the forecast time and within the past 6 hours;

s2: judging whether the forecast time is the morning business time or not, and selecting meteorological data according to the forecast time;

s3: selecting a wet bulb temperature judgment standard according to meteorological data;

s4: forecasting the snow condition at the forecasting moment according to the meteorological data and the wet bulb temperature judgment standard;

s5: repeating the steps S1-S4, and forecasting the snow conditions at other forecasting time;

s6: transmitting and displaying the snow condition forecast information;

s7: and correcting the snow condition forecast information according to the real-time information feedback.

Specifically, taking the morning business hours of the ski resort as an example, the forecast time is 8 current times,

s1: acquiring meteorological data at the current moment of 8 and within the past 6 hours;

s2: and judging that the current time is the morning business time, and selecting meteorological data of 8 points and within the past 6 hours, wherein the meteorological data comprises weather phenomena, temperature, wet bulb temperature and the like.

S3: in the selected meteorological data, the instant weather phenomenon of 8 points is clear, and excessive snow occurs in the past 6 hours, namely the condition one is met, so that the wet bulb temperature judgment standard is selected as the standard one, and the standard one is as follows: the wet bulb temperature is less than or equal to minus 10 ℃, the snow quality is dry snow, the wet bulb temperature is less than or equal to minus 7 ℃ below minus 10 ℃, the snow quality is powder snow, the wet bulb temperature is less than 2 ℃ below minus 7 ℃, the snow quality is soft snow, the wet bulb temperature is less than or equal to 2 ℃, and the snow quality is wet snow.

S4: according to the wet bulb temperature data of 8 points, namely 8 ℃, the condition that the snow quality of 8 points is forecasted to be powder snow can be known.

S5: the snow condition with the forecast time of 9 points is forecast as follows: acquiring meteorological data at the forecast time 9 and within the past 6 hours; judging that the forecast time is within 1 hour later than the business time of the morning, and selecting the forecast time and meteorological data within the last 6 hours according to the fact that the forecasted number of people in the ski resort is small (1/3 with less than the maximum capacity); in the selected meteorological data, the weather forecast phenomenon of 9 points is cloudy, and excessive snow occurs in the past 6 hours, namely the condition I is met, so that the wet bulb temperature judgment standard is selected as the standard I, and the snow condition forecast of 9 points is known as snow powder according to the wet bulb temperature data of 9 points, namely-7 ℃.

The snow condition with the forecast time of 10 points is forecast as follows: acquiring meteorological data at the forecast time of 10 and within the past 6 hours; judging that the forecast time is 2 hours later than the business time of the morning, and selecting meteorological data of the forecast time according to the medium forecasted population in the ski resort; in the selected meteorological data, the weather phenomenon of 10 points is big snow, namely the condition one is met, so the wet bulb temperature judgment standard is selected as the standard one, and the condition that the snow quality of 10 points is forecasted to be soft snow can be obtained according to the wet bulb temperature data of 10 points, namely the temperature is minus 5 ℃.

The snow condition with the forecast time of 12 points is forecast as follows: acquiring meteorological data at the forecast time of 12 points and within the past 6 hours; judging that the forecast time is 4 hours later than the business time of the morning, and selecting meteorological data of the forecast time according to more forecasted people in the ski resort; in the selected meteorological data, the weather forecast phenomenon at 12 points is clear, so the wet bulb temperature judgment standard is selected as the third standard, and the condition forecast of the snow at 12 points is wet snow according to the wet bulb temperature data at 12 points of 4 ℃.

The snow condition with the forecast time of 14 points is forecast as follows: acquiring meteorological data at the forecast time 14 and within the past 6 hours; judging that the forecast time is 6 hours later than the business time of the morning, and selecting meteorological data of the forecast time according to the medium forecasted population in the ski resort; in the selected meteorological data, the forecast weather phenomenon of 14 points is cloudy, so the wet bulb temperature judgment standard is selected as the third standard, and the snow condition of 12 points can be forecast to be ice snow according to the wet bulb temperature data of 14 points, wherein the temperature is 1 ℃ and the wind speed is more than or equal to 8 m/s.

S6: the snow condition forecast information is transmitted and displayed, and the display is shown in fig. 2 by taking a Chinese weather net as an example;

s7: two-dimensional codes are put in a snow field, snow workers perform real-time feedback of weather factors such as snow quality and the like, live and forecast data are corrected, and a snow field feedback interface is shown in fig. 3.

While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.