KR102222541B1 - System and Method for accumulated solar irradiance using estimation of daytime temperature on the slpoe - Google Patents

Abstract

The present invention relates to a system and method for estimating the daytime temperature of a complex terrain slope every hour by using the accumulated insolation for an hour, and more particularly, to receive the neighborhood forecast and observed temperature provided by the Meteorological Agency, and the insolation effect. It provides a system and method for simulating the daytime temperature of the slope through the correction of. Accordingly, the present invention has the effect of not only being able to simulate the hourly temperature of a complex terrain such as a mountainous region, but also calculating a crop model using the hourly temperature or the duration of low and high temperatures.

Description

System and Method for accumulated solar irradiance using estimation of daytime temperature on the slpoe}

The present invention relates to a system and method for estimating the daytime temperature of a complex terrain slope every hour by using the accumulated insolation for an hour, and more particularly, to receive the neighborhood forecast and observed temperature provided by the Meteorological Agency, and the insolation effect. It is to provide a system and method for simulating the daytime temperature of the slope through correction of the slope.

Due to the recent climate change, abnormal high temperature phenomena such as heat waves and tropical nights are prolonged, and it is predicted that direct damage to farm household income such as physiological disturbance of crops and resulting decrease in yield, poor quality and coloration of fruits will increase. Climate change and extreme weather occur in a variety of spaces, but farmers experience the effect on a small local scale such as farms and orchards. Therefore, as a countermeasure against extreme weather in the agricultural sector, an early warning system for weather hazards has been established and operated.

The early warning system is a system that quantifies and delivers predicted weather hazards to farms based on the growth stages of crops grown, such as topographic conditions and soil characteristics, for each farm, and uses forecasts for each region and neighborhood of the Meteorological Administration. The Meteorological Agency's neighborhood forecast provides temperature, precipitation, precipitation probability, snowfall, sky conditions, humidity, wind direction, wind speed, etc. at a resolution of 5x5km, but this has a problem that does not reflect changes in temperature due to complex topography at a local scale.

In the case of the highest temperature, a large difference from the actual temperature can occur due to the difference in altitude and the insolation according to the slope direction and inclination, which directly or indirectly affects the damage to the crops, so the need for high-resolution and reliable forecast data is on the rise. Has become.

The maximum daily temperature is also affected by the difference in elevation, but is affected by the difference in the amount of light received due to the relationship between the slope and the position of the sun. When detailed with spatial statistics based on information from the Meteorological Agency, it undergoes a process of correcting the elevation deviation by applying the temperature lapse rate as much as the deviation between the representative altitude of the grid point and the altitude of an arbitrary point. However, in this method of estimating temperature, there is an error range that cannot be explained only by correcting the altitude above sea level. The error between the estimated temperature value and the actual observed value obtained by performing only elevation correction is large, especially during the daytime on a clear day. This is because if the incident angle of solar radiation reaching the earth surface varies according to the position of the sun, the slope direction, and the inclination, the net radiation energy received by the earth surface also changes.

Therefore, research is being conducted to quantify the observed solar radiation effect, and in general, a relationship between the deviation of the received light amount of the slope with respect to the horizontal plane and the temperature deviation of the slope with respect to the horizontal plane for 4 hours from 11:00 am to 3:00 pm is derived. It was done by estimating the maximum daily temperature. Recently, a method of estimating the maximum daily temperature by combining the solar radiation effect and the advection effect due to wind speed based on the standard light-receiving index presented by supplementing the BioSIM model has been disclosed.

Due to this method, the agricultural meteorological disaster early warning system generates the actual temperature and forecast at 3 pm every day, and these data are used to predict the growth stage and high temperature damage of various crops. However, in order to calculate a crop model using the hourly temperature or the duration of low and high temperatures, temperature information for each time period is required. The current insolation effect is optimized to simulate the maximum temperature or 3 p.m. temperature of a complex terrain such as a mountainous area, and a separate quantitative formula is required to simulate the insolation effect during the daytime from 6 p.m. to 2 p.m. To.

Korean Patent Registration No. 1,689,441 (Registered on Dec. 19, 2016) Republic of Korea Patent Registration No. 1,040,621 (2011. 06. 03. Registration)

The present invention is to solve the problems of the prior art described above, and in order to calculate the crop model or the duration of low and high temperatures, it is an object of the present invention to estimate the daytime temperature at each time of the slope from data from nearby weather stations, and to provide the same. .

The object of the present invention is not limited to the above-mentioned object, and other objects and advantages of the present invention that are not mentioned can be understood by the following description, and will be more clearly understood by examples of the present invention. In addition, it will be easily understood that the objects and advantages of the present invention can be realized by the means shown in the claims and combinations thereof.

In order to achieve the above object, the present invention is based on a data receiving module for receiving a neighborhood forecast and an observation temperature provided by the Meteorological Agency, and a neighborhood forecast and observation temperature collected by the data receiving module. It includes a temperature simulation module that simulates the temperature (T).

Here, in order to simulate the daytime temperature (T), the temperature simulation module applies the solar radiation effect to a value obtained by adding the product of the deviation of the altitude above sea level and the temperature lapse rate to the background temperature.

Based on the neighborhood forecast and the observed temperature collected by the data receiving module, the temperature simulation module that simulates the daytime temperature from 6 o'clock to 20:00 on the slope estimates the cumulative amount of insolation on the slope using Equations 1 and 2.

[Equation 1]

는 적산 일사량,

는 일사량 직달성분, r은 태양광선과 경사면이 이루는 각,

는 태양의 천정각이다.here,

Is the accumulated insolation,

Is the direct component of insolation, r is the angle between the sunlight and the slope,

Is the zenith angle of the sun.

[Equation 2]

는 태양고도,

는 태양 방위각, A는 경사도(Slope), B는 경사향(Aspect)이다. Here, Si is the accumulated insolation, So is the direct component of insolation,

Is the solar altitude,

Is the solar azimuth, A is the slope, and B is the aspect.

과 같고,

은 배경기온 값,

은 해발고도 편차,

은 기온감률이다.Based on the neighborhood forecast and observed temperature collected by the data receiving module, in order to simulate the daytime temperature (T) every hour in the temperature simulation module that simulates the daytime temperature from 06:00 to 20:00 on the slope, the background temperature, elevation deviation, and An equation that becomes a basic premise is generated by using the temperature decrease rate, and an hourly insolation effect is added to reduce the error of the hourly daytime temperature (T). Here, the basic premise is

Is the same as,

Is the background temperature value,

Is the deviation in elevation,

Is the rate of decrease in temperature.

The hourly insolation effect is performed by selecting one of a method of correcting by applying Equation 3 as an insolation effect, and a method of correcting by applying Equation 4 as an insolation effect.

[Equation 3]

[Equation 4]

Here, x is the deviation of insolation, and y is the deviation of temperature.

Based on the neighborhood forecast and the observed temperature collected by the data receiving module, the temperature simulation module that simulates the daytime temperature from 06:00 to 20:00 on the slope does not apply the solar radiation effect at 06:00, but performs correction by applying only the temperature lapse rate. , From 07 o'clock to 16:00 o'clock, the correction by applying Equation 3 as an insolation effect to the altitude deviation correction result according to the hourly temperature decrease rate, and from 17:00 to 18:00 mathematics as a solar radiation effect on the altitude deviation correction result according to the hourly temperature decrease rate. Correction by applying Equation 4 is performed, and only the elevation deviation correction according to the hourly temperature decrease rate is performed from 19:00 to 20:00.

In addition, the present invention is based on the data reception step of receiving the neighborhood forecast and observation temperature provided by the meteorological agency in the data reception module, and the neighborhood forecast and observation temperature collected in the data reception step, It includes a temperature simulation step of simulating the temperature T in the temperature simulation module.

In the temperature simulation step, in order to simulate the daytime temperature (T), the insolation effect is applied to the value obtained by adding the product of the deviation of the altitude above sea level and the lapse rate of the background temperature.

The temperature simulation step (S2) that simulates the daytime temperature (T) of the slope from 06:00 to 20:00 based on the neighborhood forecast and observed temperature collected in the data receiving step is the integration of the slope by Equation 1 and Equation 2. Estimate the amount of insolation.

[Equation 1]

는 적산 일사량,

는 일사량 직달성분, r은 태양광선과 경사면이 이루는 각,

는 태양의 천정각이다.here,

Is the accumulated insolation,

Is the direct component of insolation, r is the angle between the sunlight and the slope,

Is the zenith angle of the sun.

[Equation 2]

는 적산 일사량,

는 일사량 직달성분,

는 태양고도,

는 태양 방위각, A는 경사도(Slope), B는 경사향(Aspect)이다. here,

Is the accumulated insolation,

Is the direct component of insolation,

Is the solar altitude,

Is the solar azimuth, A is the slope, and B is the aspect.

이고,

은 배경기온 값,

은 해발고도 편차,

은 기온감률이다.The temperature simulation step (S2), which simulates the daytime temperature (T) from 06:00 to 20:00 on the slope, based on the neighborhood forecast and observed temperature collected in the data reception step, is to simulate the daytime temperature (T) every hour. Using the temperature, elevation deviation, and temperature decrease rate, a basic premise is generated, and an hourly insolation effect is added to reduce the error of the hourly daytime temperature (T). The equation that becomes the premise is

ego,

Is the background temperature value,

Is the deviation in elevation,

Is the rate of decrease in temperature.

The hourly insolation effect is performed by selecting one of a method of correcting by applying Equation 3 as an insolation effect, and a method of correcting by applying Equation 4 as an insolation effect.

[Equation 3]

[Equation 4]

Here, x is the deviation of insolation, and y is the deviation of temperature.

The temperature simulation step (S2), which simulates the daytime temperature (T) from 6 o'clock to 20:00 on the slope, based on the neighborhood forecast and observed temperature collected in the data reception stage, does not apply the solar radiation effect at 06 o'clock, and the temperature decrease rate. The correction is performed by applying only, and from 7 o'clock to 16:00, the altitude deviation correction result according to the hourly temperature decrease rate is corrected by applying Equation 3 as an insolation effect, and from 17:00 to 18:00, the altitude deviation according to the hourly temperature decrease rate is corrected. Correction is performed by applying Equation 4 as an insolation effect to the result, and only the elevation deviation correction according to the hourly temperature decrease rate is performed from 19:00 to 20:00.

According to the present invention, it is possible to provide a system and method capable of estimating a more accurate daytime temperature on a slope by acquiring and correcting neighborhood forecast data from nearby weather stations.

Therefore, not only can the hourly temperature of a complex terrain such as a mountainous area be simulated, but also a crop model using the hourly temperature or the duration of low and high temperatures can be calculated.

In addition to the above-described effects, specific effects of the present invention will be described together with explanation of specific matters for carrying out the present invention.

1 is a schematic diagram of a system for estimating daytime temperature on a slope using integrated solar radiation according to the present invention.
2 is a flow chart of a method for estimating daytime temperature on a slope using integrated solar radiation according to the present invention.
3 is a diagram showing a target area for the implementation of the present invention.
4 is a location image provided by the Meteorological Administration and an actual photographed image of the location.
5 is a view comparing the deviation of insolation and temperature between AWS07 and AWS04 under a no-wind time condition.
6 is an observation scatter plot (left) and an estimated scatter plot (right) according to insolation and temperature deviation.
7 is a bar graph of the mean error and mean squared error for each region during daylight hours.
8 is a bar graph of the mean error over time and the root mean square mean error for 11 observation points.
9 is a relationship diagram of the temperature observed at 15:00 in 11 regions, comparing the conventional method A, B to which Equation 4 is applied, and C to which Equation 3 is applied.

The above-described objects, features, and advantages will be described later in detail with reference to the accompanying drawings, and accordingly, a person of ordinary skill in the art to which the present invention pertains will be able to easily implement the technical idea of the present invention. In describing the present invention, if it is determined that a detailed description of known technologies related to the present invention may unnecessarily obscure the subject matter of the present invention, a detailed description will be omitted. Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used to indicate the same or similar elements.

1 is a schematic diagram of a system for estimating daytime temperature on a slope using integrated solar radiation according to the present invention.

Referring to FIG. 1, the present invention includes a data receiving module 100 for receiving neighborhood forecast and observed temperature data provided by the Meteorological Agency, and a temperature simulation module 200 for simulating daytime temperature on a slope based on the received data. do.

The data receiving module 100 receives the neighborhood forecast and observation temperature data provided by the Meteorological Agency. In addition, observations of insolation in hourly units of weather stations with the nearest or similar latitude are collected. Here, the neighborhood forecast is information including temperature, precipitation, probability of precipitation, snowfall, sky conditions, humidity, wind direction, wind speed, etc. at a resolution of 5x5km.

The temperature simulation module 200 simulates the daytime temperature from 06:00 to 20:00 on the slope based on the neighborhood forecast and observed temperature collected by the data receiving module.

In addition, in order to simulate the daytime temperature, the accumulated solar radiation is calculated, and the accumulated solar radiation can be expressed by Equation 1 or Equation 2 below.

[Equation 1]

는 적산 일사량,

는 일사량 직달성분, r은 태양광선과 경사면이 이루는 각,

는 태양의 천정각이다.here,

Is the accumulated insolation,

Is the direct component of insolation, r is the angle between the sunlight and the slope,

Is the zenith angle of the sun.

[Equation 2]

는 적산 일사량,

는 일사량 직달성분,

는 태양고도,

는 태양 방위각, A는 경사도(Slope), B는 경사향(Aspect)이다. here,

Is the accumulated insolation,

Is the direct component of insolation,

Is the solar altitude,

Is the solar azimuth, A is the slope, and B is the aspect.

In addition, in order to simulate the hourly daytime temperature (T), a prerequisite formula that becomes the basic premise is generated by using the background temperature, the elevation deviation and the temperature decrease rate, and the hourly insolation effect is reduced to reduce the error of the hourly daytime temperature (T). Add.

로 표현 가능하고,

은 배경기온 값,

은 해발고도 편차,

은 기온감률이다.Basically, the equation to simulate the daytime temperature (T) is

Can be expressed as,

Is the background temperature value,

Is the deviation in elevation,

Is the rate of decrease in temperature.

Here, the solar radiation effect is added to the above prerequisite equation, and the solar radiation effect is corrected by selecting one of a method of correcting by applying Equation 3 as a solar effect, or a method of correcting by applying Equation 4 as a solar effect. .

[Equation 3]

[Equation 4]

Here, x is the deviation of insolation, and y is the deviation of temperature.

In order to apply Equation 3 and Equation 4 as a solar radiation effect during the daytime, conditions that can be applied for each time are different. Therefore, in the present invention, the correction is performed by applying only the temperature lapse rate without applying the solar radiation effect at 06:00, and the correction by applying Equation 3 as the solar radiation effect to the elevation deviation correction result according to the temperature decrease rate every hour from 07:00 to 16:00. And, from 17 o'clock to 18 o'clock, correction by applying Equation 4 as an insolation effect to the result of correcting the altitude deviation according to the hourly temperature decrease rate, and from 19 o'clock to 20 o'clock, only the altitude deviation correction according to the hourly temperature decrease rate is performed.

2 is a flow chart of a method for estimating daytime temperature on a slope using integrated solar radiation according to the present invention.

Referring to FIG. 2, the present invention is based on the data receiving step (S1) of receiving the neighborhood forecast and the observed temperature provided by the meteorological agency in the data receiving module, and the neighborhood forecast and the observed temperature collected in the data receiving step (S1). It includes a temperature simulation step (S2) for simulating the daytime temperature (T) of the slope from 06:00 to 20:00.

In the data receiving step (S1), the neighborhood forecast and observation temperature data provided by the meteorological agency are received. In addition, observations of insolation in hourly units of weather stations with the nearest or similar latitude are collected. Here, the neighborhood forecast is information including temperature, precipitation, probability of precipitation, snowfall, sky conditions, humidity, wind direction, wind speed, etc. at a resolution of 5x5km.

In the temperature simulation step (S2), based on the neighborhood forecast and the observed temperature collected in the temperature data reception step (S1), the daytime temperature of the slope from 06:00 to 20:00 is simulated.

In addition, in order to simulate the daytime temperature, the accumulated solar radiation is calculated, and the accumulated solar radiation can be expressed by Equation 1 or Equation 2 below.

[Equation 1]

는 적산 일사량,

는 일사량 직달성분, r은 태양광선과 경사면이 이루는 각,

는 태양의 천정각이다.here,

Is the accumulated insolation,

Is the direct component of insolation, r is the angle between the sunlight and the slope,

Is the zenith angle of the sun.

[Equation 2]

는 태양고도,

는 태양 방위각, A는 경사도(Slope), B는 경사향(Aspect)이다. Here, Si is the accumulated insolation, So is the direct component of insolation,

Is the solar altitude,

Is the solar azimuth, A is the slope, and B is the aspect.

In addition, in order to simulate the hourly daytime temperature (T), a prerequisite formula that becomes the basic premise is generated by using the background temperature, the elevation deviation and the temperature decrease rate, and the hourly insolation effect is reduced to reduce the error of the hourly daytime temperature (T). Add.

로 표현 가능하고,

은 배경기온 값,

은 해발고도 편차,

은 기온감률이다.Basically, the equation to simulate the daytime temperature (T) is

Can be expressed as,

Is the background temperature value,

Is the deviation in elevation,

Is the rate of decrease in temperature.

Here, the solar radiation effect is added to the above prerequisite equation, and the solar radiation effect is corrected by selecting one of a method of correcting by applying Equation 3 as a solar effect, or a method of correcting by applying Equation 4 as a solar effect. .

[Equation 3]

[Equation 4]

Here, x is the deviation of insolation, and y is the deviation of temperature.

In order to apply Equation 3 and Equation 4 as a solar radiation effect during the daytime, conditions that can be applied for each time are different. Therefore, in the present invention, the correction is performed by applying only the temperature lapse rate without applying the solar radiation effect at 06:00, and the correction by applying Equation 3 as the solar radiation effect to the elevation deviation correction result according to the temperature decrease rate every hour from 07:00 to 16:00. And, from 17 o'clock to 18 o'clock, correction by applying Equation 4 as an insolation effect to the result of correcting the altitude deviation according to the hourly temperature decrease rate, and from 19 o'clock to 20 o'clock, only the altitude deviation correction according to the hourly temperature decrease rate is performed.

Here, the temperature simulation simulates the temperature only when the average wind speed for an hour is less than 0.2m/s.

In order to verify the present invention, analysis and verification were performed as follows.

3 is a diagram showing a target area for the implementation of the present invention.

Referring to FIG. 3, Akyang-myeon, Hadong-gun, Gyeongsangnam-do, which is a target for the implementation of the present invention, is a farming and mountain village area south of Mt. The present invention was carried out as a background. Weather data including temperature, wind speed, and solar radiation observed from the unmanned meteorological network installed at 11 points in the altitude range of 50 to 510 m above sea level in the region were collected, and the period was 2 years observed from October 2014 to March 2017. It is data for 6 months.

The location of FIG. 3 is shown in Table 1 below, and when the features of each terrain are summarized, it can be represented by the following example. AWS01 is 212m altitude, inclination is 21 °, the oblique direction is 267 °, latitude is 35.220 °, 127.648 ° hardness. In addition, AWS04, which is a standard point for the implementation of the present invention, has an altitude of 328m, an inclination of 24 ° , an inclination of 265 ° , a latitude of 15.166 ° , and a longitude of 127.733 ° , and AWS07 has an altitude of 286m and an inclination of 17. ° , the slope direction is 145 ° , the latitude is 35.171 ° , and the longitude is 127.693 ° .

Site Altitude(m) Slope(°) Aspect(°) Latitude Longitude AWS01 212 21 267 35.220 127.648 AWS02 95 10 259 35.172 127.719 AWS03 241 16 275 35.178 127.725 AWS04 328 24 265 15.166 127.733 AWS05 267 15 110 35.186 127.708 AWS06 98 9 167 35.182 127.718 AWS07 286 17 145 35.171 127.693 AWS08 230 20 100 35.121 127.683 AWS09 122 16 126 35.157 127.686 AWS10 57 7 156 35.157 127.699 AWS11 508 4 4 35.456 127.526

4 is a location image provided by the Meteorological Administration and an actual photographed image of the location.

Referring to FIG. 4, A of FIG. 3 is a picture of the AWS07 point described above, and B is a picture of the AWS04 point described above. Here, A is a trend slope and B is a west slope. AWS04 and AWS07 have similar altitudes, but the slope direction is opposite, and there are no obstacles around, making it an environment suitable for solar radiation observation.

)을 계산하였다. 또한, 낮 기온을 모의하는 과정에서 일사량이 관측되지 않는 산간지역에 대한 일사효과를 적용하기 위해, 수평면 일사량으로부터 경사면 일사량을 추정하여 활용하였다. 이를 위해, 연구대상 지역에서 가장 가깝고 위도가 유사한 진주기상대의 시간단위 일사량 관측 자료를 수집하였다. 여기서, 진주기상대와 악양면 집수역은 약 9km 가량 떨어져 있는 바, 구름과 같은 장애물로 인한 일사량 차이가 발생할 수 있다. 따라서, 본 발명의 실시에서는 진주기상대를 중심으로 하는 하늘 상태와 악양계곡의 하늘 상태가 동일한 경우로 한정하여 실시하였다. 하늘 상태는 맑음(1), 구름 조금(2), 구름 많음(3), 흐림(4)인 총 4단계로 표현된 기상청 KLAPS(KMA local analysis and prediction system)의 하늘상태 5x5km 격자자료를 활용하였으며, 대상 집수역마다 공간으로 평균된 값을 대푯값으로 간주하여 분석을 실시하였다.Therefore, in the present invention, in order to simulate (derive) the hourly insolation effect based on the relationship between the insolation deviation due to the difference in terrain characteristics and the temperature deviation, the accumulated insolation amount for each hour of AWS04 and AWS07 (

) Was calculated. In addition, in order to apply the solar radiation effect to the mountainous area where no solar radiation is observed in the process of simulating the daytime temperature, the slope solar radiation was estimated and utilized from the horizontal solar radiation. To this end, the observation data of the time unit of insolation at the Jinju Meteorological Zone, which is the closest to the study area and has a similar latitude, were collected. Here, since Jinju Meteorological Zone and Akyang-myeon catchment area are about 9 km apart, a difference in insolation due to obstacles such as clouds may occur. Therefore, in the implementation of the present invention, the sky state centered on the Jinju Meteorological Zone and the sky state of Akyang Valley were the same. The sky condition was expressed in four stages: sunny (1), a little cloud (2), a lot of clouds (3), and cloudy (4).The 5x5km grid data of the KMA local analysis and prediction system (KLAPS) of the Meteorological Administration was used. , Analysis was conducted by considering the spatially averaged value for each target catchment area as a representative value.

Insolation deviation and quantitative formula derivation

In order to derive the effect of insolation on the temperature, the relationship between the hourly temperature deviation and the accumulated insolation deviation for the previous hour was expressed in a quantitative formula using the observed insolation and the estimated insolation on the slope. Since the heating effect due to insolation is offset by the wind, the average wind speed for an hour from October 2014 to March 2017 was limited to the case of less than 0.2m/s.

당 기온 변화량을 도출하였다. 기온 감률은 매시 계산하였으며, 하늘 상태를 0 내지 10의 범위의 운량 값으로 변환하여 기온감률 산출에 활용하였다. Observed insolation was calculated at the points AWS04 and AWS07 for one hour accumulated insolation for each time period during the daytime, and the deviation value between the two points (a trend slope value-a west slope value) was calculated. The altitude of the two meteorological observation points is 286m and 267m, respectively, and after correcting the observed temperature by the hourly temperature lapse rate based on the altitude of 300m, and calculating the temperature deviation, insolation deviation 1

The amount of change in temperature was derived. The temperature lapse rate was calculated every hour, and the sky condition was converted into a cloud value in the range of 0 to 10 and used to calculate the temperature lapse rate.

)은 아래의 수학식 1과 같이 경사면 일사량(

)로 변환된다.Therefore, the accumulated insolation on the mountain slope is estimated based on the insolation on the horizontal plane according to the slope, the direction of the inclination, and the position of the sun. When the zenith angle of the sun at point i is θ, and the angle between the sunlight incident on the slope and the slope is r, the direct component of the solar radiation reaching the horizontal plane of the point i (

) Is the insolation of the slope as shown in Equation 1 below (

).

[Equation 1]

를 태양고도,

를 태양의 방위각으로 설정하면,

는

와 같으며, 경사도(A)와 경사향(B)을 통해 해당 지점에 대한 매시간 적산 경사면의 일사량을 추정할 수 있다.Also,

The solar altitude,

If you set to the sun's azimuth,

Is

It is the same as, and the insolation amount of the accumulated slope for the corresponding point can be estimated through the slope (A) and the slope direction (B).

[Equation 2]

)은 일사효과 정량식 도출 및 모형 검증에 이용한다.A (slope) and B (slope) for each observation point are averaged from the 25th cell from the grid containing the observation point in the slope and slope raster calculated from the DEM of 30 x 30m grid resolution. Assign a value. Calculated slope insolation (

) Is used to derive a quantitative formula for the solar radiation effect and to verify the model.

For derivation of quantitative expressions, linear and nonlinear regression analysis is performed with a statistical program, SAS (Statistical analysis systtem, SAS Institute Inc.). The solar deviation is set to x and the temperature deviation is set to y, and the appropriate parameters for Table 2 below are estimated, and the equation with the smallest sum of squares error (SSE) is selected.

Table 2 below derives the equation for the relationship between the insolation deviation and the temperature deviation.

Case Equation Sum of squares Equation 3 Equation 4 Log Function

310.1 196.4 Hyperbolic

310.1 191.6 Exponential

547.9 215.2 Exp. Saturation

310.1 192.0 Maxima

310.1 192.3 Linear

310.2 236.9

Verification

For verification of the present invention, the quantitative equations derived for points AWS04 and AWS07 are verified using other weather observation values in the study area. From January 2015 to December 2017 from January 2015 to December 2017, we collected temperature data from the Meteorological Administration's synopsis and disaster prevention weather observation network, and produced the background virtual temperature distribution with a 270m grid resolution, and GIS programs (ArcGIS, ESRI Inc, USA). ) Is used to interpolate the temperature value through the Inverse Distrance Squared Weighting (IDSW) method for meteorological observation points within a radius of 50 km for each grid point. In addition, a virtual terrain was produced using the same spatial interpolation method as the background temperature as the altitude value of the meteorological administration's synopsis and the disaster prevention weather observation point. After checking the virtual topographic value for each verification point in the study area, calculating the deviation from the actual altitude, and then multiplying the hourly temperature lapse rate to calculate the amount of temperature change due to the altitude deviation, and adding this to the background temperature, so that there is no insolation effect. The temperature was estimated. The hourly temperature was corrected by calculating the hourly insolation effect using the equation selected in Table 2 above, using the deviation value between the insolation at each verification point and the insolation in the horizontal plane calculated from the horizontal plane insolation at the Jinju Meteorological Zone. The final estimated value was compared with the actual temperature and the degree of error improvement before and after the application of the solar radiation effect was confirmed. In the case of 15 o'clock temperature, the reliability of the model was compared. The derived quantitative equation of the solar radiation effect was obtained under the conditions of clear breeze, and was limited to the case where the average wind speed for an hour was less than 0.2m/s. I did it.

Results and Discussion

Insolation-temperature relationship

5 is a view comparing the deviation of insolation and temperature between AWS07 and AWS04 under a no-wind time condition.

로 나타났다. 오전에는 태양빛을 먼저 받는 동향사면의 기온이 급격하게 상승하여 일사 편차와 기온 편차가 모두 양(+)의 값을 나타냈고, 기온 편차는 분석기간 중 최대 7.6℃까지 발생하였다. 반면, 오후에는 오전에 비해 일사편차가 상대적으로 작거나 다소 음(-)의 값을 나타내었고, 기온 편차도 비교적 미미한 수준에 머물렀다.Referring to FIG. 5, the deviation of the accumulated insolation for 1 hour between AWS07 and AWS04, which are representative observation points of the trend slope and the west slope, under the condition of clear breeze is -0.2 to 0.8.

Appeared as. In the morning, the temperature of the trend slope, which receives sunlight first, rose sharply, so that both the insolation deviation and the temperature deviation were positive (+), and the temperature deviation occurred up to 7.6℃ during the analysis period. On the other hand, in the afternoon, the insolation deviation was relatively small or slightly negative (-) compared to the morning, and the temperature deviation remained at a relatively insignificant level.

6 is an observation scatter plot (left) and an estimated scatter plot (right) according to insolation and temperature deviation.

Referring to FIG. 6, the graph on the left is a positive value and a temperature deviation corresponding to the observed solar deviation between the trend slope and the west slope. In the case of Table 2 above, when quantitative equations were derived for each case, the linear regression equation was finally selected because all of them were close to a straight line and there was no significant difference in the SSE value.

[Equation 3]

On the other hand, when the insolation of the slope was estimated from the horizontal insolation of the Jinju Meteorological Zone and the insolation deviation between the trend slope and the west-hyang slope was calculated, the relationship between the insolation deviation and the temperature deviation was close to an exponential function.

[Equation 4]

Model reliability

7 is a bar graph of the mean error and mean squared error for each region during the daylight hours.

In FIG. 7, bar A is a result of performing only altitude correction with an hourly decrease in temperature, bar B is a result of applying Equation 4, and bar C is a result of applying Equation 3.

The method with the smallest hourly temperature estimation error (RMSE) during the daytime (from 06:00 to 20:00) for 11 observation points in the study area limited to breeze conditions is the insolation effect by applying Equation 4 derived based on the estimated insolation on the slope. Was. At this time, the average ME was -0.54℃ and RMSE was 1.47℃, and the result was better in terms of the planarity of the estimated temperature (ME value) than simply performing altitude correction with an hourly decrease in temperature (ME -0.7, RMSE 1.5). Is shown.

The result of applying Equation 3, which is the insolation effect based on the observed insolation deviation, is the average ME -0.16℃ and RMSE 1.55℃. The underestimation trend slightly disappeared, but the RMSE increased more than the case where only temperature lapse correction was performed. . However, it was found that the biased error of A can be somewhat improved by applying the temperature rise of the solar radiation effect to both B and C.

8 is a bar graph of the mean error over time and the root mean square mean error for 11 observation points.

Referring to FIG. 8, A denotes an underestimation error of a highly corrected temperature, B denotes a result of applying Equation 4, and C denotes a result of applying Equation 3. Comparing the ME of the temperature corrected for the solar radiation effect by the method of Equation 3 and Equation 4 above during the day from 06:00 to 20:00 with the result of applying the simple temperature lapse rate every hour, from sunrise to 4 p.m. in the upper graph of FIG. The underestimation error of the ground elevation corrected temperature was all reduced due to the solar radiation effect of Equation 3 to Equation 4, and the linear regression equation of Equation 3 improves the existing knitting than the hyperbolic equation of Equation 4 The effect was found to be more excellent. On the other hand, between 5 pm and 7 pm, there was a tendency to overestimate in the linear regression equation.At sunset, the amount of temperature change due to insolation deviation per unit area is relatively small. It is judged that is excessively simulated. In addition, when the solar effect was applied to Equation 4, the overall decrease in ME was small compared to Equation 3, but the overestimating error between 5 pm and 7 pm was found to be insignificant.

Referring to the graph below of FIG. 8, it can be seen that the RMSE is reduced through the solar radiation effect correction from 8 am to 4 pm. Among them, from 11 am to 3 pm, the method of correcting the solar radiation effect through Equation 3 is RMSE 1.29~1.45, and the method through Equation 4 is RMSE 1.37~1.52. Able to know. In the case of 8 am and 4-6 pm, the RMSE 1.29~1.83 for using Equation 3, and RMSE 0.88~1.79 for Equation 4, showing that the error when using Equation 4 was small. On the other hand, it was found that the results of performing only altitude correction were more suitable for 6 to 7 am and 7 to 8 pm. In particular, in the case of sunrise, although the bias (ME) of the estimation error was adjusted to reflect the insolation effect, the daily estimation error (RMSE) was rather increased. At sunrise and sunset, at least the solar altitude is very low, so the insolation deviation can be calculated relatively large. If the insolation on a slope is not accurate, the insolation effect may be erroneously simulated. Since the current insolation calculation method does not reflect the shading effect of direct sunlight caused by the opposite slope, in some cases, there is a possibility that the amount of insolation at sunrise and sunset calculated within the complex terrain is higher than the actual amount.

ME RMSE A Existing model -1.20 2.01 B Equation 4 -0.64 1.37 C Equation 3 -0.28 1.29

Table 3 above shows the average error of the temperature estimate at 3 pm and the error of the root mean at 11 points from January 2015 to December 2017.

Referring to Table 3, in the case of the temperature at 3 pm, when Equations 3 and 4 were used than the conventional method, the temperature estimation error was found to be more improved. Looking at A, the case of applying the solar radiation effect of the previous study, an underestimation error of about -1.2℃ on the average ME was found. On the other hand, when Equation 3 and Equation 4 were applied for the same period, ME was -0.28°C and -0.64°C, respectively. If the temperature (graph mismatch) is simulated at 3 pm based on the previous study, an error of about 2.01°C RMSE occurs (A), but when the solar radiation effect is replaced by Equation 4 (B), the RMSE decreases to 1.37°C. Equation 3 removed a considerable part of the tendency to underestimate the temperature at 3 pm, and the RMSE was 1.29°C.

9 is a relationship diagram of the temperature observed at 15:00 in 11 regions, comparing the conventional method A, B to which Equation 4 is applied, and C to which Equation 3 is applied.

Referring to FIG. 9, it is a one-to-one correspondence between the measured values and estimated values at 3 pm for 11 verification points. When the temperature is higher than 25°C, the tendency of underestimation in the existing A increases, but Equation 4(B) and math This phenomenon could be alleviated by the method of Equation 3(C). If the temperature exceeds 30℃ significantly, crops and fruits may be damaged due to high temperatures in areas subject to high insolation. If the midday temperature prediction is performed in the same manner as in A, it is highly likely that the high temperature will not be expected as much as it actually is. . Therefore, it is more accurate to apply the methods of Equation 4(B) and Equation 3(C) as a solar radiation effect in order to forecast the detailed daytime temperature based on the neighborhood forecast and determine the high temperature harm.

As described above with reference to the drawings illustrated for the present invention, the present invention is not limited by the embodiments and drawings disclosed in the present specification, and various by a person skilled in the art within the scope of the technical idea of the present invention. It is obvious that transformations can be made. In addition, even if not explicitly described and described the effects of the configuration of the present invention while describing the embodiments of the present invention, it is natural that the predictable effects of the configuration should also be recognized.

100: data receiving module 200: temperature simulation module
S1: Data reception step S2: Temperature simulation step

Claims (10)

A data receiving module for receiving a neighborhood forecast and an observation temperature provided by the Meteorological Agency;
A temperature simulation module that simulates the daytime temperature (T) of the slope from 06:00 to 20:00 based on the neighborhood forecast and the observed temperature collected by the data receiving module; Including,
The temperature simulation module applies an insolation effect to a value obtained by adding the product of the deviation of altitude above sea level and the temperature lapse rate to the background temperature in order to simulate the daytime temperature (T),
Based on the neighborhood forecast and the observed temperature collected by the data receiving module, the temperature simulation module that simulates the daytime temperature from 06:00 to 20:00 on the slope estimates the accumulated insolation on the slope by Equations 1 and 2,
Based on the neighborhood forecast and the observed temperature collected by the data receiving module, the temperature simulation module that simulates the daytime temperature from 6 o'clock to 20:00 on the slope is to simulate the daytime temperature (T) every hour, and the background temperature (

), elevation deviation (

) And temperature lapse rate (

) To become a basic premise (

), and add an hourly insolation effect to reduce the error of the hourly daytime temperature (T),
The hourly insolation effect is a system for estimating daytime temperature on a slope using a cumulative insolation that is performed by selecting any one of a method of correcting by applying Equation 3 as an insolation effect and a method of correcting by applying Equation 4 as an insolation effect.
[Equation 1]

here,

Is the accumulated insolation,

Is the direct component of insolation, r is the angle between the sunlight and the slope,

Is the zenith angle of the sun.
[Equation 2]

here,

Is the accumulated insolation,

Is the direct component of insolation,

Is the solar altitude,

Is the solar azimuth, A is the slope, and B is the aspect.
[Equation 3]

[Equation 4]

Here, x is the deviation of insolation and y is the deviation of temperature. The method of claim 1,
Based on the neighborhood forecast and the observed temperature collected by the data receiving module, the temperature simulation module that simulates the daytime temperature from 06:00 to 20:00 on the slope is
At 06 o'clock, the solar radiation effect is not applied, and only the temperature lapse rate is applied.
From 07 o'clock to 16:00 o'clock, correction by applying Equation 3 as an insolation effect to the altitude deviation correction result according to the hourly temperature decrease rate,
From 17 o'clock to 18 o'clock, the correction by applying Equation 4 as an insolation effect to the altitude deviation correction result according to the hourly temperature decrease rate is performed,
From 19:00 to 20:00, it is a system for estimating the daytime temperature on a slope using accumulated insolation that only corrects the elevation deviation according to the hourly temperature decrease rate. delete delete delete A data receiving step (S1) of receiving a neighborhood forecast and an observation temperature provided by the meteorological agency in the data receiving module;
A temperature simulation step (S2) of simulating the daytime temperature (T) of the slope from 06:00 to 20:00 in the temperature simulation module based on the neighborhood forecast and the observed temperature collected in the data receiving step; Including,
In the temperature simulation step, in order to simulate the daytime temperature (T), an insolation effect is applied to a value obtained by adding the product of the deviation of the altitude above sea level and the decrease in temperature to the background temperature,
The temperature simulation step (S2) of simulating the daytime temperature (T) from 6 o'clock to 20:00 on the slope based on the neighborhood forecast and the observed temperature collected in the data receiving step is Estimate the accumulated solar radiation,
Based on the neighborhood forecast and the observed temperature collected in the data receiving step, the temperature simulation step (S2) of simulating the daytime temperature (T) of the slope from 06:00 to 20:00 is
To simulate the hourly daytime temperature (T), the background temperature (

), elevation deviation (

) And temperature lapse rate (

) To become a basic premise (

), and add an hourly insolation effect to reduce the error of the hourly daytime temperature (T),
The hourly insolation effect is a method of estimating the daytime temperature of a slope using the accumulated insolation performed by selecting any one of a method of correcting by applying Equation 3 as a solar radiation effect or a method of correcting by applying Equation 4 as an insolation effect.
[Equation 1]

here,

Is the accumulated insolation,

Is the direct component of insolation, r is the angle between the sunlight and the slope,

Is the zenith angle of the sun.
[Equation 2]

here,

Is the accumulated insolation,

Is the direct component of insolation,

Is the solar altitude,

Is the solar azimuth, A is the slope, and B is the aspect.
[Equation 3]

[Equation 4]

Here, x is the deviation of insolation, and y is the deviation of temperature. The method of claim 6,
Based on the neighborhood forecast and the observed temperature collected in the data receiving step, the temperature simulation step (S2) of simulating the daytime temperature (T) of the slope from 06:00 to 20:00 in the temperature simulation module is
At 06 o'clock, the solar radiation effect is not applied, and only the temperature lapse rate is applied.
From 07 o'clock to 16:00 o'clock, correction by applying Equation 3 as an insolation effect to the altitude deviation correction result according to the hourly temperature decrease rate,
From 17 o'clock to 18 o'clock, the correction by applying Equation 4 as an insolation effect to the altitude deviation correction result according to the hourly temperature decrease rate is performed,
A method of estimating the daytime temperature on a slope using accumulated solar radiation that only corrects the elevation deviation according to the hourly temperature decrease rate from 19:00 to 20:00. delete delete delete

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