CN111950813A - Meteorological drought monitoring and predicting method - Google Patents
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Abstract
The invention discloses a meteorological drought monitoring and predicting method, and belongs to the technical field of meteorological drought monitoring and predicting. The method comprises the following steps: acquiring rainfall remote sensing image data in a certain time period from an authority website; converting the remote sensing image data into precipitation; forming a one-month-scale calculation time period by 30 consecutive days, adding the precipitation amounts of each day in the calculation time period to obtain the precipitation amount of the calculation time period, respectively calculating the precipitation amounts of the calculation time period and the calendar year in the same period, and calculating to obtain the monthly-scale precipitation amount interval percentage; a monthly-scale precipitation rate flat percentage profile of the target region is generated. The method can acquire the weather conditions and the temperature range of each day in the future according to the weather forecast information issued by the weather department, and calculate the expected rainfall interval percentage of each day in the future, thereby achieving the purpose of quantitative drought prediction of each day in the future.
Description
Technical Field
The invention belongs to the technical field of meteorological drought monitoring and prediction, and particularly relates to a meteorological drought monitoring and prediction method.
Background
Drought is a complex natural phenomenon which generally occurs worldwide, has a wide spread and long duration, and is one of the most serious natural disasters in agricultural production and human life. Due to the differences in the fields of interest, drought is generally classified into 4 categories: weather drought: abnormal water shortage due to unbalanced balance of precipitation and evaporation; agricultural drought: the water deficiency in the crop body caused by external environmental factors influences the normal growth and development of the crop; hydrologic drought: water shortage caused by unbalanced balance of precipitation and surface water or underground water; social and economic drought: water shortage caused by unbalanced water resource supply and demand of natural system drought and human socioeconomic system drought.
The type of drought contemplated by the present invention is meteorological drought. The general weather and drought indexes are based on precipitation, and the more common indexes include average percent precipitation, standard deviation of precipitation, Standard Precipitation (SPI) and Palmer indexes. The national standard weather drought in (GB/T20481-2017) explicitly stipulates a calculation method of each index and a division standard of the table in drought degree.
Currently, drought is monitored by mainly calculating the drought indexes through precipitation data acquired by meteorological observation stations, so that the aim of representing the drought degree is fulfilled. The drought prediction is mainly realized by precipitation prediction, and the prediction method can be divided into a drought transmission mathematical drought meter prediction method and a modern intelligent prediction method. Mainly comprises a regression drought-separating method, a Markov chain, a principal component drought-separating method, a power spectrum drought-separating method, a gray prediction method, a fuzzy prediction method, an artificial neural network, a 3S technology and a time sequence theory (AR/MA/ARMA/ARIMA model) method. The methods all need a large amount of monitoring data to support, and the operation process of some methods is very complicated.
At present, the meteorological department can perform drought monitoring according to observation site data, and issue weather forecasts of each region from six days to fourteen days in the future every day, including temperature range (lowest-highest temperature) every day and weather conditions (eyes, clouds, overcast, light rain and heavy rain). However, there is no clear quantitative index for the prediction of drought, and the prediction of the development of drought is usually only a qualitative description, for example, heavy rain is forecasted in the future days of a certain place, and the drought is expected to be slow; the high temperature is continued for one week in the future of a certain place, and the drought is expected to be aggravated.
Disclosure of Invention
The invention aims to provide a weather and drought monitoring and predicting method, which can acquire the weather conditions and the temperature range of each day in the future according to weather forecast information issued by a weather department and calculate the expected precipitation percentage of each day in the future so as to achieve the aim of carrying out quantitative drought prediction on each day in the future.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a meteorological drought monitoring and predicting method comprises the following steps:
s1, acquiring precipitation remote sensing image data in a certain time period from the Towei agency website;
s2, converting the remote sensing image data into precipitation;
s3, forming a one-month-scale calculation time period by 30 consecutive days, adding the precipitation of each day in the calculation time period to obtain the precipitation of the calculation time period, respectively calculating the precipitation of the calculation time period and the same period of the past year, and calculating the monthly-scale precipitation distance flat percentage according to the following formula:
wherein, PA is the percentage of precipitation in a certain period, and the unit is%; p is the precipitation in millimeters (mm) at the calculation time interval;
calculating the mean precipitation in millimeters (mm) at the same time in a time period; n is the number of the synchronous precipitation; pi is the precipitation in millimeters (mm) in the ith year of the calculation period;
s4, making a monthly-scale precipitation distance flat percentage distribution map of the target area;
s5, according to the standard of division of national standard weather drought in and the calculated PA meter, the distribution range of different drought in the distribution diagram and the area and the proportion of the different drought in the drought meter are obtained, so as to realize quantitative monitoring of the drought in the target area;
s6, acquiring weather forecast data of the target area and the surrounding areas thereof from a meteorological department, wherein the weather forecast data comprises weather conditions and a climate range of a plurality of days in the future;
s7, converting the weather conditions and the daily average temperature of each day into corresponding weather type drought totalizer and temperature drought totalizer according to the weather condition and drought totalizer lookup table and the daily average temperature and drought totalizer lookup table, and adding the weather type drought totalizer and the temperature drought totalizer to obtain the drought totalizer of each day in the target area and the peripheral area thereof;
s8, making a dry-land total planning map of the target area of each day according to the dry-land total planning of the target area and the peripheral area of the target area;
s9, adding the monthly-scale precipitation distance flat percentage PA of the Nth day and the drought counting meter of the (N + 1) th day to obtain a PA predictor of the (N + 1) th day; adding the PA predictor of the (N + 1) th day and the drought-sensitive chemical counter of the (N + 2) th day to obtain a PA predictor of the (N + 2) th day; and so on, respectively obtaining PA predictors of the N +3 … … in each day in the future;
s10, respectively counting the drought equal-lying and distribution range of each day of the drought in the future according to the standard divided by the national standard weather drought-lying, and the PA, thereby realizing quantitative prediction of the drought in the future.
Further, in step S1, the authority website is a national aviation and space service website.
Further, in step S4, the method for creating the monthly precipitation rate flat percentage distribution map includes the following steps:
a1, extracting precipitation image data of the target peripheral area, and cutting the data according to the vector boundary of the target peripheral area;
a2, converting the original image resolution into a higher spatial resolution, and smoothing the data of the converted image;
and A3, cutting the smoothed image according to the vector boundary of the target area to obtain a monthly-scale precipitation distance flat percentage distribution diagram of the target area.
Further, in step S8, the method for manufacturing the dry-land planning map of the target area includes the following steps:
b1, converting the vector diagram of the target region and the peripheral region thereof into a grid diagram, and setting the spatial resolution to be consistent with the monthly-scale precipitation rate flat percentage distribution diagram of the target region;
b2, setting the pixel meter of the grid image as a drought meter of the corresponding area, smoothing the grid image after the meter is assigned, and respectively obtaining a target area and a drought meter smoothing image of the peripheral area of the target area on each day;
and B3, cutting the drought-counting planning graph by using the vector boundary of the target area to obtain the drought-counting planning graph of the target area, and acquiring the drought-counting planning graph of the target area in each day by the method.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
the method calculates the rainfall distance flat percentage representing drought through remote sensing rainfall data, establishes a weather condition and drought situation meter lookup table and a daily average temperature and drought situation meter lookup table respectively, acquires the weather conditions (sunny and heavy rain) and the temperature range (lowest-highest temperature) of each day in the future according to weather forecast information issued by a meteorological department, and calculates the expected rainfall distance flat percentage of each day in the future, thereby achieving the purpose of quantitatively predicting the drought of each day in the future.
Drawings
FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a plot of the drought in Daiyuan county in one embodiment of the present invention;
FIG. 3 is a graph of the results of a drought prediction in accordance with an embodiment of the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
As shown in fig. 1-3, a method for monitoring and predicting weather drought includes the following steps:
and S1, acquiring precipitation remote sensing image data in a certain time period from the Towei institution website. In this embodiment, the authority website is a National aviation and Space navigation Agency (NASA) service website, the Precipitation remote sensing data is a GPM satellite Precipitation data product, a Global Precipitation observation satellite (GPM) is an international satellite network system for Global Precipitation observation, is created by the National aviation and Space navigation Agency (NASA) and the Japan aviation Exploration Agency (JAXA), and is operated by the international Space research institute including the french aviation Agency, the indian Space research organization, and the european weather satellite development organization. The GPM carries the latest double-frequency radar system and improves the performance of a microwave radiometer, the detection capability of weak precipitation and solid precipitation is improved, and research results of numerous scholars show that precipitation data detected by the GPM have higher precision and meet the global precipitation monitoring requirement. GPM data can be downloaded from the NASA service website, and the data products are divided into 3 parts. In this embodiment, a3 rd fused precipitation product IMERG (Integrated Multi-satellite retrieval for GPM) is adopted, a data file is in a geotif format, data covers the world, time resolution is day, spatial resolution is 0.1 ° × 0.1 °, coverage is the world, and data delay time is 12hours (Latency 12hours, NRT/LATE RUN).
S2, the image pixel of the acquired GPM IMERG data is downloaded and counted as precipitation rate, and the unit is 0.1 mm/d; converting the daily precipitation rate into precipitation amount by a formula P ═ V multiplied by 0.1;
s3, forming a one-month-scale calculation time period by 30 consecutive days, adding the precipitation of each day in the calculation time period to obtain the precipitation of the calculation time period, respectively calculating the precipitation of the calculation time period and the same period of the past year, and calculating the monthly-scale precipitation distance flat percentage according to the following formula:
wherein, PA is the percentage of precipitation in a certain period, and the unit is%; p is the precipitation in millimeters (mm) at the calculation time interval;
s4, making a monthly-scale precipitation distance flat percentage distribution map of the target area;
s5, according to the standard of division of weather drought in (GB/T20481-2017) and the calculated PA meter, the distribution range of different drought in the distribution diagram and the area and the proportion of the different drought in the drought meter are obtained, so that the quantitative monitoring of the drought in the target area is realized;
s6, acquiring weather forecast data of the target area and the surrounding areas thereof from a meteorological department, wherein the weather forecast data comprises weather conditions and a climate range of a plurality of days in the future;
s7, according to the weather condition and drought counting look-up table (see table 1), converting the weather conditions of each day into corresponding counts, wherein the average counts of the weather conditions in a day are taken, and the average counts are called weather type drought counting counts.
TABLE 1 weather and drought variation lookup table
The daily average temperature is obtained by averaging the maximum and minimum daily temperatures in the weather forecast, and is converted into a corresponding meter, namely a temperature drought meter, according to a daily average temperature and drought meter lookup table (see table 2).
TABLE 2 mean daily temperature and drought variation lookup table
| Average temperature in day (. degree. C.) | Endow it with |
| ≥35 | -4 |
| 34.9-30 | -2 |
| 29.9-25 | -1.33 |
| 24.9-15 | -1 |
| 14.9-10 | -0.8 |
| <10 | -0.67 |
And adding the weather type drought meter and the temperature drought meter to obtain a drought meter for each day in the target area and the peripheral area thereof.
S8, making a dry-land total planning map of the target area of each day according to the dry-land total planning of the target area and the peripheral area of the target area;
s9, adding the monthly-scale precipitation distance flat percentage PA of the Nth day and the drought counting meter of the (N + 1) th day to obtain a PA predictor of the (N + 1) th day; adding the PA predictor of the (N + 1) th day and the drought-sensitive chemical counter of the (N + 2) th day to obtain a PA predictor of the (N + 2) th day; and so on, respectively obtaining PA predictors of the N +3 … … in each day in the future;
s10, respectively counting the drought equal-lying and distribution range of each day in the future according to the standard of division of weather drought-lying (GB/T20481-2017) by a PA meter, and realizing quantitative prediction of the drought-lying of each day in the future. Wherein, PA < -40 > is no drought, PA < -40 > and ≧ PA < -60 > is light drought, PA < -60 > and ≧ PA < -80 > is medium drought, PA < -80 > and ≧ PA < -95 > is heavy drought, PA < -95 > and ≧ PA < -100 > is super drought
The method for manufacturing the precipitation rate distribution diagram comprises the following steps:
a1, extracting precipitation image data of the target peripheral area, and performing data clipping according to the vector boundary of the target peripheral area by using an ENVI/IDL software data clipping (Subset) function in the embodiment;
a2, converting the original image resolution into a higher spatial resolution, and smoothing the data of the converted image; in this embodiment, since the spatial resolution of the image is 0.1 ° (about 10 km), the spatial span of each pixel is large, and it is difficult to present the precipitation quantization details in a small area range in the 0.1 ° spatial resolution image. Thus, the image resolution is converted from 0.1 ° to a higher spatial resolution, and the converted image is subjected to data smoothing. The image resolution conversion and data smoothing are performed in the ENVI/IDL software.
And A3, cutting the smoothed image according to the vector boundary of the target area to obtain a monthly-scale precipitation distance flat percentage distribution diagram of the target area.
In step S8, the method for manufacturing the dry-on-design distribution map of the target area includes the following steps:
and B1, converting the vector diagram of the target region and the peripheral region thereof into a grid diagram, wherein the spatial resolution is set to be consistent with the monthly-scale precipitation percentage distribution diagram of the target region, and the vector diagram is converted into the grid diagram by using ArcGIS software.
B2, setting the pixel meter of the grid image as a drought meter of the corresponding area, smoothing the grid image after the meter is assigned, and respectively obtaining a target area and a drought meter smoothing image of the peripheral area of the target area on each day; in the present embodiment, the grid map after the assigning is smoothed by the same smoothing method as the above-described step a 2.
And B3, cutting the drought-counting planning graph by using the vector boundary of the target area to obtain the drought-counting planning graph of the target area, and acquiring the drought-counting planning graph of the target area in each day by the method.
Taking the Ruyuan county of Guangdong province as an example, the drought monitoring is carried out on 26 th 11 th in 2019, and the drought prediction is carried out on 6 days from 27 th 11 th to 2 nd 12 th in 11 th.
(1) Downloading a global GPM IMERG NRT/LATE RUN daily synthetic data product from 8/6/2000 to 26/11/2019;
(2) extracting precipitation rate product files in the data products, and converting the precipitation rate in each data file into precipitation amount by adopting a formula P which is V multiplied by 0.1;
(3) adding the precipitation of 30 days in total from 28 days in 10 months to 26 days in 11 months in 2019 to obtain the total precipitation in the current time period; and (4) respectively adding the precipitation in the same time period (from 28 days in 10 months to 26 days in 11 months) in the past year (2000-2018) to respectively obtain the total precipitation in the same time period in the past year. And calculating the monthly scale precipitation rate flat percentage of 11, 26 and 2019 according to a precipitation rate flat percentage calculation formula.
(4) And (3) when the milk source county in Guangdong province and the Hunan province are in a boundary, in order to acquire data of the milk source peripheral area, the monthly-scale precipitation rate is cut from the flat percentage by using the province vector boundary of the Guangdong province and the Hunan province.
(5) And converting the image resolution obtained by the cutting from 0.1 degree to 0.005 degree, and performing data smoothing on the converted 0.005 degree image. Image resolution conversion and data smoothing are both carried out in ENVI/IDL software, wherein the resolution conversion uses a Resize module, and a nearest neighbor method is selected for data resampling; the data smoothing uses the IDL "SMOOTH" function, and uses boxcar average to perform data operation, and the size of the conversion core is 30 × 30.
(6) And (4) cutting the smoothed data according to a boundary vector diagram of the Ruyuan county to obtain a distribution diagram of the monthly-scale precipitation rate from the Ruyuan county in 11 and 26 days in 2019. According to the standard of division of weather drought in (GB/T20481-2017), drought distribution is prepared as shown in figure 2, and the results show that all the counties in Ruyuan county are Zhonghan in 11 and 26 days in 2019.
(7) Weather forecast information of Ruyuan county and neighboring counties around the county (Yizhang, Lizhou, Yangshan, Yinde, Wujiang, Qujiang, Zhen, Renzhi, Lechang) from 11 months to 12 months and 2 days in 2019 is obtained from a weather department website, and is shown in Table 3.
Dairy Source and surrounding county weather forecast information in Table 3.2019 from 27/11/12/2/12/month
(8) Converting the weather conditions of each day into weather type drought counting meters according to the weather conditions and drought counting meter lookup table (table 1); the average temperature of each day was calculated, and the dry chemical indicator of the temperature of each day was obtained from the daily average temperature and dry chemical indicator look-up table (table 2). The results are shown in Table 4.
Dairy origin and county drought in table 4.2019 from 27/11/12/2/12/4
(9) The vector maps of milk origin and county were converted into raster maps with a spatial resolution of 0.005 ° using ArcGIS software.
(10) The grids of each county are respectively assigned to the drought total accumulation meters corresponding to each day, and the data smoothing is carried out on the assigned images by adopting a 'SMOOTH' function of ENVI/IDL software (the parameter setting is the same as the method in the step (5), the data operation is carried out by using boxcar average, and the counting and changing kernel size is 30x 30).
(11) Cutting the smoothed image by using the vector boundary of the milk source county to obtain a milk source drought counting distribution diagram of each day;
(12) adding the month-scale precipitation rate flat percentage distribution graph of 11, 26 and 11 months in 2019 with the drought-on-scale planning distribution graph of 11, 27 and obtaining a month-scale precipitation rate flat percentage prediction graph of 11, 27 and 11 days; adding the monthly-scale precipitation percentage flat percentage prediction graph of 11 months and 27 days with the drought-on-scale planning distribution graph of 11 months and 28 days to obtain a monthly-scale precipitation percentage flat percentage prediction graph of 11 months and 28 days; and the like, respectively obtaining a monthly-scale precipitation percentage prediction graph from 11 months, 29 days to 12 months, 2 days.
(13) According to the standard of division of weather drought in (GB/T20481-2017), different drought area ratio on-condition in a monthly-scale rainfall distance flat percentage prediction graph of each day is respectively calculated in a drought mode (table 5), a drought distribution graph (figure 3) is created, and the quantitative prediction of the drought in 11-12-2 days in Daiyuan county is realized.
TABLE 5 drought area ratio (%)
The results were classified as drought: according to the drought monitoring result (figure 2) and the drought prediction result (figure 3), the drought in 11 months and 26 days in 2019 in the whole county of Dayuanshan county is the middle drought, the drought in 11 months and 27 days to 12 months and 2 days is predicted to gradually increase, the heavy drought continuously expands from south to north, but the drought is always kept between the middle drought and the heavy drought.
Drought in 11 months and 27 days will begin to occur from the southwest of the town of Luoyang, and the area where drought occurs is expected to occupy 4.2% of the area of the county;
the heavy drought is expanded to a large township in 28 days at 11 months, and the area of the heavy drought is predicted to be increased to 12.0 percent;
the heavy drought develops to the town of Dongpeng in 29 months at 11 months, and the area of the heavy drought is predicted to expand to 23.3 percent;
the drought is extended to Dai town, one six towns and Yangxi towns in 11 months and 30 days, and the area of the drought is predicted to be 45.1 percent;
the heavy drought develops to Bibei town, Daqiaotown and Guitou town within 12 months and 1 day, and the heavy drought area is predicted to be 69.5 percent;
the drought in 12 months and 2 days continues to develop, and the area of the drought is estimated to reach 82.2%.
Setting basis of lookup table count:
according to the weather drought situation (GB/T20481-2017), the critical value of the occurrence of the drought situation is judged to be-40% according to the monthly-scale precipitation rate average Percentage (PA), namely PA < -40% is no drought, and 100% < PA < -40% is indicated to be the occurrence of the drought situation.
According to the understanding of the development of drought in daily life, the drought can be eliminated by a large rain field, the drought condition can be converted into no drought, and the corresponding average percentage of monthly-scale precipitation is more than-40%. Under the assumption of extreme conditions, the monthly-scale precipitation range percentage is-100% in the original drought, after a heavy rain, the drought is eliminated, the monthly-scale precipitation range percentage is calculated as-40%, and the monthly-scale precipitation range percentage is calculated as 60%. According to the rainfall standard of China weather administration (see table 6), the 'heavy rain' is 25-49.9mm in 24 hours, and the average rainfall of the heavy rain is 37.5mm according to the calculation, namely the average rainfall is more than 37.5mm and corresponds to the 60% monthly-scale rainfall interval percentage.
The monthly-scale precipitation distance flat percentage meter for respectively calculating the rest precipitation intensity is based on the fact that the precipitation amount of 37.5mm corresponds to 60% monthly-scale precipitation distance flat percentage meter. The average precipitation of the rainstorm is 75mm, which is equivalent to the precipitation of 2 heavy rains, and the corresponding monthly-scale precipitation is 60% 75/37.5-120% in percentage by weight. The average precipitation of heavy rains is 175%, and the average percent of the corresponding monthly precipitation is 60% 175/37.5, 280% by proportion. The average precipitation from heavy rainstorm to extra heavy rainstorm is 237.5mm, and the corresponding monthly scale precipitation is 380% by percentage calculation. The average precipitation amount of the medium rain is 17.5mm, and the precipitation amount of the medium rain can reach more than 37.5mm only by 3 times of rainfall, so that the monthly-scale precipitation corresponding to the medium rain at each time is counted by 20% from the average percent. The average rainfall amount of light rain and gust rain is 5mm, 8 rains are needed for eliminating drought, and the monthly-scale rainfall corresponding to each rainfall moves 7.5 percent from the average percent.
Monthly-scale precipitation versus average percentage gauges for transitional precipitation intensity take the average of two corresponding gauges of upper and lower precipitation intensity, for example: the monthly-scale precipitation from low to medium rain is calculated as the average of the corresponding low and medium rain counts, i.e., (7.5% + 20%)/2 ═ 13.75%; the monthly scale precipitation range average percentages of medium to heavy rain, heavy rain to heavy rain and extra heavy rain are respectively calculated and calculated to be 40%, 90%, 200% and 480%.
In addition, the precipitation amount of sunny, cloudy and cloudy days in the weather type is 0, but the sky clouds of different weather conditions cover different degrees, so that the condition difference of surface water evaporation is caused, and the influence degree on drought is different; meanwhile, the air temperature is also an important factor influencing the water loss of the earth surface. According to the experience of drought monitoring experts, corresponding assumptions are made for the types of sunny, cloudy and cloudy weathers and different temperature conditions (see table 7):
look-up tables, table 1 and table 2, are formed according to the weather type and the air temperature, respectively.
TABLE 6
TABLE 7 hypothetical conditions responsible for drought
TABLE 1 weather and drought variation lookup table
| Weather type | Endow it with | Weather type | Endow it with |
| All-weather | -1.33 | Heavy rain | 60 |
| Cloudy | -1 | Heavy to heavy rain | 90 |
| Yin (kidney) | 0 | Storm rain | 120 |
| Gust of rain | 7.5 | Rainstorm to heavy rainstorm | 200 |
| Light rain | 7.5 | Heavy rainstorm | 280 |
| Rain in the small to medium range | 13.75 | Heavy rainstorm to extra heavy rainstorm | 380 |
| Medium rain | 20 | Extra-large heavy rain | 480 |
| Moderate to heavy rain | 40 |
TABLE 2 mean daily temperature and drought variation lookup table
The above description is directed to the preferred embodiment of the present invention, but the present invention is not limited thereto, and all modifications and variations made by the present invention within the technical spirit of the present invention shall fall within the scope of the present invention.
Claims (4)
1. A meteorological drought monitoring and predicting method is characterized by comprising the following steps:
s1, acquiring precipitation remote sensing image data in a certain time period from the Towei agency website;
s2, converting the remote sensing image data into precipitation;
s3, forming a one-month-scale calculation time period by 30 consecutive days, adding the precipitation of each day in the calculation time period to obtain the precipitation of the calculation time period, respectively calculating the precipitation of the calculation time period and the same period of the past year, and calculating the monthly-scale precipitation distance flat percentage according to the following formula:
wherein, PA is the percentage of precipitation in a certain period, and the unit is%; p is the precipitation in millimeters (mm) at the calculation time interval;
calculating the mean precipitation in millimeters (mm) at the same time in a time period; n is the number of the synchronous precipitation; pi is the precipitation in millimeters (mm) in the ith year of the calculation period;
s4, making a monthly-scale precipitation distance flat percentage distribution map of the target area;
s5, according to the standard of division of national standard weather drought in and the calculated PA meter, the distribution range of different drought in the distribution diagram and the area and the proportion of the different drought in the drought meter are obtained, so as to realize quantitative monitoring of the drought in the target area;
s6, acquiring weather forecast data of the target area and the surrounding areas thereof from a meteorological department, wherein the weather forecast data comprises weather conditions and a climate range of a plurality of days in the future;
s7, converting the weather conditions and the daily average temperature of each day into corresponding weather type drought totalizer and temperature drought totalizer according to the weather condition and drought totalizer lookup table and the daily average temperature and drought totalizer lookup table, and adding the weather type drought totalizer and the temperature drought totalizer to obtain the drought totalizer of each day in the target area and the peripheral area thereof;
s8, making a dry-land total planning map of the target area of each day according to the dry-land total planning of the target area and the peripheral area of the target area;
s9, adding the monthly-scale precipitation distance flat percentage PA of the Nth day and the drought counting meter of the (N + 1) th day to obtain a PA predictor of the (N + 1) th day; adding the PA predictor of the (N + 1) th day and the drought-sensitive chemical counter of the (N + 2) th day to obtain a PA predictor of the (N + 2) th day; and so on, respectively obtaining PA predictors of the N +3 … … in each day in the future;
s10, respectively counting the drought equal-lying and distribution range of each day of the drought in the future according to the standard divided by the national standard weather drought-lying, and the PA, thereby realizing quantitative prediction of the drought in the future.
2. The weather drought monitoring and forecasting method according to claim 1, wherein in step S1, the authority website is the United states national aviation and aerospace agency service website.
3. The weather drought monitoring and prediction method as claimed in claim 2, wherein in step S4, the method for generating the monthly-scale precipitation rate flat percentage distribution map comprises the following steps:
a1, extracting precipitation image data of the target peripheral area, and cutting the data according to the vector boundary of the target peripheral area;
a2, converting the original image resolution into a higher spatial resolution, and smoothing the data of the converted image;
and A3, cutting the smoothed image according to the vector boundary of the target area to obtain a monthly-scale precipitation distance flat percentage distribution diagram of the target area.
4. The weather drought monitoring and prediction method according to claim 2, wherein in step S8, the method for making the drought-saturation planning map of the target area comprises the following steps:
b1, converting the vector diagram of the target region and the peripheral region thereof into a grid diagram, and setting the spatial resolution to be consistent with the monthly-scale precipitation rate flat percentage distribution diagram of the target region;
b2, setting the pixel meter of the grid image as a drought meter of the corresponding area, smoothing the grid image after the meter is assigned, and respectively obtaining a target area and a drought meter smoothing image of the peripheral area of the target area on each day;
and B3, cutting the drought-counting planning graph by using the vector boundary of the target area to obtain the drought-counting planning graph of the target area, and acquiring the drought-counting planning graph of the target area in each day by the method.
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