CN113050195B - Hourly resolution precipitation process identification method - Google Patents
Hourly resolution precipitation process identification method Download PDFInfo
- Publication number
- CN113050195B CN113050195B CN202110179775.XA CN202110179775A CN113050195B CN 113050195 B CN113050195 B CN 113050195B CN 202110179775 A CN202110179775 A CN 202110179775A CN 113050195 B CN113050195 B CN 113050195B
- Authority
- CN
- China
- Prior art keywords
- precipitation
- station
- index
- precipitation process
- rainfall
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 305
- 238000001556 precipitation Methods 0.000 title claims abstract description 250
- 238000004364 calculation method Methods 0.000 claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims description 30
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000012935 Averaging Methods 0.000 claims description 10
- 239000013256 coordination polymer Substances 0.000 claims description 7
- 238000000605 extraction Methods 0.000 claims description 7
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 4
- 238000009795 derivation Methods 0.000 claims description 3
- DDVBPZROPPMBLW-IZGXTMSKSA-N latrunculin A Chemical compound C([C@H]1[C@@]2(O)C[C@H]3C[C@H](O2)CC[C@@H](\C=C/C=C/CC\C(C)=C/C(=O)O3)C)SC(=O)N1 DDVBPZROPPMBLW-IZGXTMSKSA-N 0.000 claims description 3
- NSHPHXHGRHSMIK-IWQSFCKSSA-N latrunculin B Natural products C[C@H]1CC[C@@H]2C[C@@H](C[C@@](O)(O2)[C@@H]3CSC(=O)N3)OC(=O)C=C(C)/CCC=C/1 NSHPHXHGRHSMIK-IWQSFCKSSA-N 0.000 claims description 3
- DDVBPZROPPMBLW-UHFFFAOYSA-N latrunculin-A Natural products O1C(=O)C=C(C)CCC=CC=CC(C)CCC(O2)CC1CC2(O)C1CSC(=O)N1 DDVBPZROPPMBLW-UHFFFAOYSA-N 0.000 claims description 3
- -1 hydrogen Chemical class 0.000 claims 1
- 239000001257 hydrogen Substances 0.000 claims 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims 1
- 230000008901 benefit Effects 0.000 abstract description 5
- 230000002354 daily effect Effects 0.000 description 16
- 238000011160 research Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000010972 statistical evaluation Methods 0.000 description 4
- 238000013210 evaluation model Methods 0.000 description 3
- 230000002688 persistence Effects 0.000 description 2
- 230000002085 persistent effect Effects 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000007670 refining Methods 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- 241000283690 Bos taurus Species 0.000 description 1
- 241000819999 Nymphes Species 0.000 description 1
- 241000234314 Zingiber Species 0.000 description 1
- 235000006886 Zingiber officinale Nutrition 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 235000008397 ginger Nutrition 0.000 description 1
- 230000009916 joint effect Effects 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 230000005068 transpiration Effects 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- General Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Operations Research (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Computing Systems (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an identification method for an hour resolution precipitation process, which comprises the following steps: the method comprises the following steps of 1, identification method and 2, wherein the strength quantitative calculation method is a calculation method based on the combined action of three indexes of precipitation strength, duration and coverage range, and the method has the following advantages: the method forms a strength quantitative calculation method suitable for the hourly resolution precipitation process of China and the regional hourly resolution precipitation process, has high accuracy, has practical applicability and improves the pertinence and the service benefit of meteorological service; the method solves the practical problem of identifying the precipitation process from the small time resolution suitable for China.
Description
Technical Field
The invention belongs to the technical field of weather forecast, and particularly relates to an identification method for an hourly resolution precipitation process.
Background
At present, precipitation generated by a weather system is mainly in a process, precipitation forecast and service are carried out according to the process, precipitation has obvious regional difference, and the spatial and temporal distribution of the process precipitation is not uniform, so that the identification and the strength evaluation of the beginning and the ending of the precipitation process in the weather forecast service business are mainly artificially and subjectively identified and judged, and the subjectivity is strong.
Due to the highly disaster-causing characteristics of the strong rainfall process and the rainstorm process, students have more researches on defining and evaluating methods of the strong rainfall process and the rainstorm process, for example, when the space-time concentration of the strong rainfall process in China is researched by the ginger love force (2005), the strong rainfall process is divided into a heavy rainfall process and a rainstorm process, and when the zhao (2012) defines the continuous strong rainfall process in the river basin, the students indicate that at least 1 station generates more than or equal to 50mm of rainfall every day in 5 days continuously. Liangnu and Dingyi Hui (2013) research on strong precipitation process in flood season of Shanghai district in China, the situation that more than 3 stations are met in 11 basic stations and the daily precipitation is more than magnitude of heavy rain (10mm) is determined as strong precipitation, and strong precipitation events which are continuous or only separated by 1 day are taken as a precipitation process for statistics. Chen Qing (2014) defines that the station number of the designated days with the precipitation amount of more than or equal to 10mm in the strong precipitation process in Hunan is more than or equal to 30 as the starting date of the strong precipitation process, and the day before the station number of less than or equal to 20 as the ending date of the strong precipitation process. Wew Weak (2015) employed 1/3 where the daily precipitation exceeds the summer 85 percentile threshold for total number of sites as criteria for the strong precipitation process downstream in the Yangtze river. Selecting 3 indexes of an average precipitation amount, an extreme precipitation intensity value and a coverage range based on the daily precipitation data in the aged autumn (2006) to establish a Liaoning regional rainstorm process rapid evaluation model based on a distance function; the courtesy incense (2008) defines a comprehensive evaluation index of the rainstorm process of the Jilin by using 3 indexes of average rainfall of the rainstorm, the maximum daily rainfall of the area and the rainstorm range and carries out grade division; the shore Finland (2009) determines the identification index of the regional rainstorm process in the Hubei province by adopting rainstorm, heavy rainstorm and extra-large rainstorm station number, and establishes a regional rainstorm disaster risk estimation method based on a distance function; the method comprises the steps that indexes of average daily precipitation, maximum daily precipitation, coverage area and duration of a region are selected by Zheng nations (2011), and a probability statistical method is utilized to establish an upstream rainstorm event assessment model of the Chinese Huaihe river; in consideration of spatial continuity and time continuity of regional extreme events, Tu (2011) adopts a method of combining hierarchical clustering and Euclidean distance to objectively identify a rainstorm process in the eastern region of China, and a Qianweihong (2011) judges the persistence of single-station rainstorm and aggregates the rainstorm according to the adjacent distance of persistent heavy precipitation sites, so that regional heavy precipitation events are objectively identified. The Yuan-Hui-Min (2012) utilizes daily precipitation data, selects 4 indexes of average precipitation, precipitation intensity, coverage area and duration to calculate and divide the grade of the rainstorm process in a recurrence period, and establishes a comprehensive evaluation model of the rainstorm process on the basis of weight analysis; wuzheng Ling (2012) determines the rainstorm assessment indexes and the assessment vectors in the Chinese Haihe river basin, and utilizes the Euclidean distance function method to realize the rainstorm grade assessment of the Chinese Haihe river basin. Thunderstorm (2012) determines regional rainstorm process of Chinese yellow river midstream river dragon region based on the areas of 25mm and 50mm rain regions; the Zhouyan (2014) determines the regional rainstorm process standard of the Fujian province by adopting rainstorm, heavy rain and intermediate rain station numbers, and adopts a comprehensive weighted construction regional rainstorm process comprehensive intensity quantitative evaluation method by adopting 4 factors including duration, influence range, process accumulated precipitation and maximum daily precipitation of the regional rainstorm process; the Wangchun school (2016) adopts the superposition ratio of the concentrated rainstorm station number, the heavy rain station number and the continuous day heavy rain station number as the identification index of the regional rainstorm process of the Sichuan basin in China; the bovine nymph (2017) judges the regional rainstorm process of the east region of 95-degree E in China in an subjective and objective combination mode, and carries out comprehensive evaluation by adopting an effective rainstorm accumulation method. The five red rains (2019) define the standard of the regional rainstorm process of the Guangdong China, and a comprehensive strength evaluation method of the regional rainstorm process of the Guangdong China based on 4 indexes of duration, rainstorm range, maximum daily rainfall and maximum process rainfall is constructed. The leaf palace show (2019) adopts the number of neighboring stations of the rainstorm and the center distance of the rainstorm district to determine an objective identification method of the Chinese regional rainstorm process, and a comprehensive intensity evaluation model of the regional rainstorm process is constructed based on the average intensity, duration and average range of the rainstorm process. The method is characterized in that the Linelan (2020) establishes a regional persistent strong precipitation process definition index by adopting a method of sliding average, percentile and point-surface correlation analysis based on ground daily precipitation data and aiming at embodying the regionality, the persistence and the disaster causing of the strong precipitation process.
The above-mentioned prior art and developments with regard to precipitation processes are thus known:
1. the method is concentrated on two aspects of a strong rainfall process and a rainstorm process, does not consider a strong rainfall process, a medium rainfall process or a weak rainfall process, and cannot meet the requirements for identification and evaluation of the rainfall process in weather forecast service;
2. the rainstorm or heavy rain is defined according to the national standard GB/T28592-2012 precipitation level and appears in the eastern region of China, so that study regions of students are mostly fixed regions or administrative regions of the eastern region of China, which are completely inapplicable to the western region of China, do not reflect the region difference and the space-time distribution characteristic of the precipitation of China, and do not have universality of China;
3. the heavy rainfall process and the heavy rainfall process researched by students are based on a daily scale, the rainfall process is not identified and quantitatively evaluated from a small-time scale at present, but the fine requirement of weather service needs to be accurately evaluated;
4. as the short-time strong rainfall which is very easy to cause disasters, the evaluation method of the rainfall process does not consider the short-time strong rainfall, and the matching degree of the rainfall with the disasters is influenced.
Disclosure of Invention
The invention aims to provide a method for identifying an hourly resolution precipitation process, which can overcome the technical problems, and comprises the following steps:
step 1.1, when identifying the station precipitation process, considering the regional difference of the station, the disaster critical threshold and the extraction method of the station threshold suitable for China, determining the threshold of the station extraction by a percentile method, wherein the identification of the station precipitation process simultaneously meets the following two conditions: a: the station precipitation process is defined as that the precipitation amount reaches 80 percentile values in the order from small to large when the precipitation amount reaches 1 hour to the historical hour, and is defined as that the precipitation amount does not reach 80 percentile values at any moment for more than 8 continuous hours; b: the daily precipitation of the site reaches a 90-percentile value in the sequence from small to large of the historical daily precipitation;
step 1.2, when the regional rainfall process is identified, the complexity of the Chinese rainfall process is considered, and the regional rainfall process not only covers the large-scale rainfall process of the south and north China, but also only appears at the middle and lower reaches of the Yangtze river in China or the Jingjin Ji in China, and even appears in small regions of a single city (district) in China. Under the condition of meeting the Chinese applicability, from the aspect of Chinese national weather service, the method is divided into two conditions of a large area and a small area, wherein the large area refers to the south, the north or any area which is larger than or equal to the area 1/3 of China, and the distance between sites is considered during process identification; the small area refers to a Chinese basin, 32 provinces (autonomous regions, directly administered cities) or any area smaller than 1/3 of Chinese area, the distance between stations is not considered during process identification, and in order to ensure that the objectively identified precipitation process and the weather system precipitation process have high matching degree, three parameter values with the best matching degree with the precipitation process recorded by a Chinese central weather station according to the weather system are found out by adjusting parameters through continuous tests, namely adjusting the distance between adjacent stations in the precipitation process, the minimum station number or proportion of precipitation recorded at a single time and the maximum interval time of two moments belonging to the one-time precipitation process, wherein the three parameter values specifically comprise the following steps:
step 1.2.1, large area: starting to define that the station number reaching the entry threshold value and the distance between the adjacent stations is less than 900 kilometers accounts for 5% or more or 40 stations in the evaluation area, ending to define that the station number reaching the entry threshold value and the distance between the adjacent stations is less than 900 kilometers accounts for less than 5% or more or less than 40 stations in the evaluation area, when the distance between the stations is calculated, assuming that the earth is a sphere, the radius is the average radius of the earth (R is 6371.004 kilometers), when the Longitude of 0 degree is taken as the reference, the earth surface distance between any two points on the surface of the earth can be calculated according to the Longitude and Latitude of the Longitude of 0 degree, the Longitude and Latitude of the point A is (LonA, LatA), the Longitude and Latitude of the point B is (LonB, LatB), the Longitude and Longitude of 0 degree is taken as the positive value of east Longitude (Longitude), the Longitude of west warp (-Longitude), and the Latitude of 90-Latitude are taken in north (90-Latitude), the south Latitude is 90+ Latitude value (90+ Latitude), the processed points A and B are counted as (MLonA, MLatA) and (MLonB, MLatB), and the following formula for calculating the distance between the two points can be obtained by triangle derivation:
C＝sin(MLatA)*sin(MLatB)*cos(MLonA-MLonB)+cos(MLatA)*cos(MLatB)……(1),
Distance＝R*Arccos(C)*Pi/180……(2),
in the above formula (2), R and Distance are expressed in kilometers;
step 1.2.2, small area: the number of stations reaching the entry threshold value accounts for 10% or more or 40 or more of the stations in the evaluation area at the beginning, and the number of stations reaching the entry threshold value for more than 8 continuous hours accounts for less than 10% or more or less than 40 of the stations in the evaluation area at the end;
and 2, carrying out an intensity quantitative calculation method, namely a calculation method based on the combined action of three indexes including precipitation intensity, duration and coverage range on the basis of precipitation process intensity, and carrying out research on precipitation process intensity evaluation based on hour resolution by improving three index definitions and algorithms of precipitation intensity, duration and coverage range for refining time resolution to hours:
step 2.1, precipitation strength index:
the rainfall intensity is one of disaster-causing factors of the rainstorm disaster, particularly the disaster-causing risk of strong rainfall in short duration of 1 hour, 3 hours and 6 hours is larger, the influence of rainfall is considered, and the maximum rainfall R in 1 hour in the process is considered when the rainfall intensity index is calculated1max3 hours of sliding for maximum precipitation RSliding 3maxMaximum precipitation R in 6-hour slidingSlip 6maxAnd process hour average precipitation RProcess averagingThe four elements of the magnitude are,
wherein,
in the formula (3), RProcess averagingIs the average precipitation per hour of the process; h is the number of site process hours;
by taking the space-time distribution characteristics, the regional differences and the disaster threshold of precipitation into consideration, the hourly precipitation data of 2410 China national weather observation stations in 1951-2018 are utilized, and the hourly precipitation, the 3-hour sliding precipitation and the 6-hour sliding precipitation are arranged from small to large, and the corresponding values of 90%, 95%, 98%, 99%, 99.5%, 99.8%, 99.9%, 99.95%, 99.98%, 99.99% and 100% of percentiles are extracted to form an hourly precipitation intensity matrix R1Sliding 3 hours precipitation intensity matrix R 36 hours of sliding precipitation intensity matrix R6The formula is as follows:
in formula (4), i is 2410, and j is 11; i is the number of sites; j is the number of percentile values; r isijIs the hour precipitation value corresponding to the jth percentile of the ith station; rr (rr) ofijIs the 3-hour precipitation value corresponding to the jth percentile of the ith site; rrrijIs a 6-hour precipitation quantity value corresponding to the jth percentile of the ith station; in order to unify the four elements for describing the rainfall intensity into a comparable dimension, an index division method is adopted, the station difference is considered, and an hourly rainfall intensity matrix R is adopted1Sliding 3 hours precipitation intensity matrix R3Sliding 6 hours precipitation intensity matrix R6Divided by site index, as shown in table 1:
TABLE 1 precipitation Strength index partitioning
The maximum amount of precipitation R at each site for 1 hour was found by referring to column 1 of Table 11maxAverage hourly precipitation RProcess averagingCorresponding 1 hour maximum precipitation intensity index I1maxAnd process hour average precipitation intensity index IProcess averagingIn comparison with column 2, the maximum precipitation R for 3 hours of slippage at each station is found3maxCorresponding maximum rainfall intensity index I in 3 hours of sliding3maxIn comparison with column 3, the maximum precipitation R for 6 hours at each station was found6maxCorresponding maximum rainfall intensity index I of 6 hours of sliding6maxThe joint action of the elements is integrated, and the station precipitation intensity index is calculated according to the following formula:
in the formula (5), I is a precipitation strength index; a. b, c and d are weight coefficients, areas with different weight coefficients a, b, c and d can be properly adjusted according to different disaster-causing factors, and the weight of the weight during the large-area precipitation process is 0.25;
step 2.2, duration index:
the duration of the station precipitation process is from the beginning to the end of the precipitation process, the duration is defined as the hours of the recording process, and the formula is as follows:
in the formula (6), T is an effective precipitation time index; k is a rainfall attenuation index, h is the hours of the input process, regarding the attenuation index K, different seasons in different regions are different, the attenuation index K has a close relation with the local disaster prevention and reduction capacity, and the value K is 0.8 by taking day as a unit, and the value of the method is 0.98 by taking hour as a unit;
step 2.3, coverage index:
coverage area (C)P) The ratio of the station N to the station N in the evaluation area in the precipitation process is recorded as the hour precipitation, and the formula is as follows:
in the formula (7), N is the number of the stations recorded in the precipitation process, and N is the total number of the stations in the evaluation area;
considering the correspondence with the magnitude of the intensity, duration of the precipitation, the precipitation coverage index (C) is defined as:
C＝10*CP……(8)，
in the formula (8), CPIs the coverage area;
step 2.4, calculating the comprehensive strength index in the precipitation process:
according to the method for calculating the comprehensive intensity index of the station and area rainfall process, which is provided by the invention, the comprehensive intensity index of the station rainfall process is the combined action of the rainfall intensity and the duration, and the comprehensive intensity index RSI of the station rainfall process is calculated by the following formula:
RSI＝I*T……(9)，
in formula (9), I is a precipitation intensity index; t is a duration index;
the comprehensive strength of the regional precipitation process is the combined action of 3 indexes including precipitation strength, coverage area and duration, and the comprehensive strength index (RPI) of the regional precipitation process is calculated by the following formula:
in the formula (10), RSI is a comprehensive strength index of a station precipitation process, n is the number of stations recorded in the precipitation process, and C is a coverage range index;
step 2.5, strength grade division in the precipitation process:
extracting and evaluating the rainfall process of different area ranges of 32 provinces (China autonomous region and China direct administration city) of China, south and north with China mountain-Huaihe as boundary and provincial level administrative division, applying a formula (9) and a formula (10) to calculate the rainfall process comprehensive intensity index of 1981 plus 2018 China, China south, China north and China 32 provinces (China autonomous region and China direct administration city), analyzing the probability density distribution of the intensity index of single-station and regional rainfall process, dividing the rainfall process into five levels of extreme, extra-strong, strong and strong according to the occurrence probability of about 1%, 5%, 10%, 15% and 69%, dividing the intensity levels of single-station and regional rainfall process into five levels as shown in table 2, dividing the single-station rainfall process comprehensive intensity index (RSI) into five index ranges according to four nodes of 30, 50, 90 and 150, the comprehensive intensity index (RPI) of the regional precipitation process is divided into five index ranges according to four nodes of 80, 180, 400 and 800, and the five index ranges respectively represent medium, strong, extra-strong and extreme levels.
TABLE 2 comprehensive intensity ratings for single station and regional precipitation processes
The method has the following advantages:
1. the method provides an identification method of the hourly resolution precipitation process, defines the starting and ending conditions of the station, large area and small area precipitation processes, establishes a quantitative calculation method of the comprehensive intensity of the hourly resolution precipitation process, and defines the intensity grade division standard of the precipitation process;
2. the method defines the full-scope precipitation process from the hour resolution, considers the distance between adjacent stations, embodies the characteristics of flaking and neutrality of the precipitation process, enhances the matching degree with the weather system, realizes the definition and the objective extraction rationality and scientificity of the precipitation process, improves the usability, can identify the precipitation process in the Chinese range, and has the time precision reaching the hour resolution;
3. the method combines the regional difference, the space distribution characteristics during rainfall and the disaster threshold, realizes the rainfall process identification method suitable for any site and area (south north China, China drainage basin and China province) in east China and west China, south China and north China from a small time resolution, has Chinese universality, and can be selectively used in meteorological service according to requirements;
4. the method forms the intensity quantitative calculation method of the rainfall process with the small resolution suitable for the sites and the regions in China, has high precision, is suitable for the sites and the regions in China, has the advantages that the detection and demonstration display results are all in line with the actual situation, is oriented to the business application requirements, qualitatively divides the intensity of the rainfall process into weak, medium, strong, extra strong and extreme 6 grades, has practicability, can also carry out historical comparison, and improves the pertinence and the service benefits of the meteorological service;
5. according to the method, the maximum rainfall amount in 1 hour, the maximum rainfall amount in 3 hours in sliding, the maximum rainfall amount in 6 hours in sliding and the average rainfall amount in hour are simultaneously considered when the rainfall intensity is set, the attenuation influence of the transpiration and evaporation effects on rainfall is considered in the duration, the effective rainfall time is set, the rainfall intensity and the duration are set, the degree of coincidence with the disaster is improved, and the possible disaster-causing situation can be analyzed from the comprehensive intensity index of the rainfall process;
6. the method solves the problem that the method is suitable for identifying the rainfall process from the hour resolution in China in a practical manner, realizes the weather system matching, and identifies the full-range rainfall process from the hour resolution, including the weak rainfall process, the medium rainfall process, the strong rainfall process, the extra-strong rainfall process and the extreme rainfall process.
Drawings
FIG. 1 is a schematic diagram of an hourly resolution precipitation process identification method according to the present invention;
FIG. 2 is a schematic view of an hourly resolution precipitation process assessment software interface for the method of the present invention;
FIG. 3 is a schematic diagram illustrating the statistical evaluation of rainfall process in southern regions according to the method of the present invention;
FIG. 4 is a schematic illustration of statistical evaluation of rainfall process in northern areas according to the method of the present invention;
FIG. 52011 is a diagram of statistical evaluation of rainfall process in the middle and lower reaches of Yangtze river in China;
fig. 62016 is a schematic diagram illustrating statistical evaluation of rainfall process at stations in the middle and downstream Yangtze river from 30/6/7/5/06.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. As shown in fig. 1, the method of the present invention comprises the following steps:
step 1.1, when identifying the station precipitation process, considering the regional difference of the station, the disaster critical threshold and the extraction method of the station threshold applicable to China, determining the threshold extracted by the station by a percentile method, wherein the two conditions are simultaneously met when identifying the station precipitation process: a: the station precipitation process is defined as that precipitation in 1 hour reaches 80 percent values from small to large when the precipitation in historical hours, and is defined as that no time reaches 80 percent values continuously for more than 8 hours; b: sorting 90 percent values of station daily precipitation from small to large when the station daily precipitation reaches the historical daily precipitation;
step 1.2, when the regional rainfall process is identified, the complexity of the Chinese rainfall process is considered, and the regional rainfall process not only covers the large-scale rainfall process of the south and north China, but also only appears at the middle and lower reaches of the Yangtze river in China or the Jingjin Ji in China, and even appears in small regions of a single city (district) in China. Under the condition of meeting the Chinese applicability, from the aspect of Chinese national weather service, the method is divided into two conditions of a large area and a small area, wherein the large area refers to the south, the north or any area which is larger than or equal to the area 1/3 of China, and the distance between sites is considered during process identification; the small area refers to a Chinese watershed, 32 provinces (autonomous regions and directly administered cities) or any area smaller than 1/3 of Chinese area, the distance between stations is not considered during process identification, and in order to ensure that the objectively identified precipitation process and the weather system precipitation process have high matching degree, the parameters are adjusted through continuous tests, namely the distance between adjacent stations in the precipitation process, the minimum station number or proportion of precipitation input at a single time and the maximum interval time of two moments belonging to one precipitation process are adjusted, and the three parameter values with the best matching degree with the precipitation process input by a Chinese central weather station according to the weather system are found out, and the method specifically comprises the following steps:
step 1.2.1, large area: starting to define that a recording threshold is reached and the number of stations with the distance between adjacent stations being less than 900 kilometers accounts for 5% or more or 40 or more of the number of stations in the evaluation area, ending to define that the recording threshold is reached for more than 12 hours continuously and the number of stations with the distance between adjacent stations being less than 900 kilometers accounts for less than 5% or more or less than 40 of the number of stations in the evaluation area, when the distance between the stations is calculated, assuming that the earth is a sphere, the radius is the average radius of the earth (R: 6371.004 kilometers), when the Longitude and the Latitude of any two points on the surface of the sphere are taken as a reference, the earth surface distance between the two points can be calculated according to the Longitude and the Latitude of the earth, the Longitude and the Latitude of the point A are (Lo, LatA), the Longitude and the Longitude of the point B are (LonB, LatB), the Longitude of the east Longitude is taken as a positive value (Longiude), the west Longitude is taken as a negative value (-Longitude), and the north is taken as a 90-Latitude value, the south Latitude is 90+ Latitude value (90+ Latitude), the processed points A and B are counted as (MLonA, MLatA) and (MLonB, MLatB), and the following formula for calculating the distance between the two points can be obtained by triangle derivation:
C＝sin(MLatA)*sin(MLatB)*cos(MLonA-MLonB)+cos(MLatA)*cos(MLatB)……(1)，
Distance＝R*Arccos(C)*Pi/180……(2)，
in the above formula (2), R and Distance are expressed in kilometers;
step 1.2.2, small area: the number of stations reaching the entry threshold value accounts for 10% or more or 40 or more of the stations in the evaluation area at the beginning, and the number of stations reaching the entry threshold value for more than 8 continuous hours accounts for less than 10% or more or less than 40 of the stations in the evaluation area at the end;
and 2, carrying out an intensity quantitative calculation method, namely a calculation method based on the combined action of three indexes of precipitation intensity, duration and coverage range of precipitation process intensity, and carrying out research based on the intensity evaluation of the precipitation process with hour resolution by improving the three index definitions and algorithms of precipitation intensity, duration and coverage range for refining the time resolution to hour:
step 2.1, rainfall intensity index:
the rainfall intensity is one of disaster-causing factors of the rainstorm disaster, particularly the disaster-causing risk of strong rainfall in short duration of 1 hour, 3 hours and 6 hours is larger, the influence of rainfall is considered, and the maximum rainfall R in 1 hour in the process is considered when the rainfall intensity index is calculated1max3-hour maximum precipitation R in slidingSlip 3maxMaximum precipitation R in 6-hour slidingSlip 6maxAnd process hour average precipitation RProcess averagingThe four elements of the magnitude are,
wherein,
in the formula (3), RProcess averagingIs the average precipitation per process hour; h is the number of site process hours;
by taking the space-time distribution characteristics, the regional differences and the disaster threshold of precipitation into consideration, the hourly precipitation data of 2410 China national weather observation stations in 1951-2018 are utilized, and the hourly precipitation, the 3-hour sliding precipitation and the 6-hour sliding precipitation are arranged from small to large, and the corresponding values of 90%, 95%, 98%, 99%, 99.5%, 99.8%, 99.9%, 99.95%, 99.98%, 99.99% and 100% of percentiles are extracted to form an hourly precipitation intensity matrix R1Sliding 3 hours precipitation intensity matrix R 36 hours of sliding precipitation intensity matrix R6The formula is as follows:
in the formula (4), i 2410, j 11, i is the station number, j is the percentile number, rijIs the hour precipitation value rr corresponding to the jth percentile of the ith stationijIs the 3-hour precipitation value, rrr, corresponding to the jth percentile of the ith stationijIs a 6-hour precipitation quantity value corresponding to the jth percentile of the ith station, and adopts an exponential division method to reduce the hour by considering station difference in order to unify four elements for describing precipitation strength to a comparable dimensionWater strength matrix R 13 hours of sliding precipitation intensity matrix R3Sliding 6 hours precipitation intensity matrix R6Divided by site index as shown in table 1:
TABLE 1 precipitation Strength index partitioning
The maximum amount of precipitation R at 1 hour at each site was determined by referring to column 1 of Table 11maxAverage hourly precipitation RProcess averagingCorresponding 1 hour maximum precipitation intensity index I1maxAnd process hour average precipitation intensity index IProcess averagingIn comparison with column 2, the maximum precipitation R for 3 hours of slippage at each station is found3maxCorresponding maximum rainfall intensity index I in 3 hours of sliding3maxIn comparison with column 3, the maximum precipitation R for 6 hours at each station was found6maxCorresponding maximum rainfall intensity index I of 6 hours of sliding6maxAnd (3) integrating the combined action of the elements and calculating the station rainfall intensity index, wherein the formula is as follows:
in the formula (5), I is a precipitation intensity index; a. b, c and d are weight coefficients, different areas with the weight coefficients a, b, c and d can be properly adjusted according to different disaster-causing elements, and the weight average is 0.25 when the large-area precipitation process is calculated;
step 2.2, duration index:
the duration of the station precipitation process is from the beginning to the end of the precipitation process, the duration is defined as the hours of the logging process, and the formula is as follows:
in the formula (6), T is an effective precipitation time index; k is a rainfall attenuation index, h is the number of hours in the recording process, regarding the attenuation index K, different seasons in different regions are different, and the attenuation index K has close relation with the local disaster prevention and reduction capability, and the value K is 0.8 by taking day as a unit, and the value of the method is 0.98 by taking hour as a unit;
step 2.3, coverage index:
coverage (C)P) The ratio of the station N to the station N in the evaluation area in the precipitation process is recorded as the hour precipitation, and the formula is as follows:
in the formula (7), N is the number of the stations recorded in the precipitation process, and N is the total number of the stations in the evaluation area;
considering the correspondence with the magnitude of the intensity, duration of the precipitation, the precipitation coverage index (C) is defined as:
C＝10*CP……(8)，
in the formula (8), CPIs the coverage area;
step 2.4, calculating the comprehensive strength index of the precipitation process:
according to the method for calculating the comprehensive intensity index of the station and area rainfall process, which is provided by the invention, the comprehensive intensity index of the station rainfall process is the combined action of the rainfall intensity and the duration, and the comprehensive intensity index RSI of the station rainfall process is calculated by the following formula:
RSI＝I*T……(9)，
in formula (9), I is a precipitation intensity index; t is a duration index;
the comprehensive strength of the regional precipitation process is the combined action of 3 indexes including precipitation strength, coverage area and duration, and the comprehensive strength index (RPI) of the regional precipitation process is calculated by the following formula:
in the formula (10), RSI is a comprehensive strength index of a site precipitation process, n is the number of sites recorded in the precipitation process, and C is a coverage range index;
step 2.5, intensity grade division in the precipitation process:
extracting and evaluating the rainfall process of different area ranges of 32 provinces (China autonomous region and China direct administration city) of China, south and north with China mountain-Huaihe as a boundary and province level administrative districts, calculating the comprehensive strength index of the rainfall process of China, China south, China north and China 32 provinces (China autonomous region and China direct administration city) in 1981 plus 2018 by applying a formula (9) and a formula (10), analyzing the probability density distribution of the strength index of the single-station and area rainfall process, dividing the rainfall process into five levels of extreme, extra-strong, strong and medium according to the occurrence probability of about 1%, 5%, 10%, 15% and 69%, dividing the strength levels of the single-station and area rainfall process into five levels as shown in a table 2, dividing the comprehensive strength index (RSI) of the single-station rainfall process into five index ranges according to four nodes of 30, 50, 90 and 150, the comprehensive intensity index (RPI) of the regional precipitation process is divided into five index ranges according to four nodes of 80, 180, 400 and 800, and respectively represents medium, strong, extra-strong and extreme levels.
TABLE 2 comprehensive intensity ratings for single station and regional precipitation processes
As shown in fig. 1, the method of the present invention comprises three steps:
(1) the rainfall process is defined so as to realize the objective automatic extraction of the rainfall process of the hourly resolution of any site and area in the Chinese range;
(2) the intensity quantitative calculation method realizes the comprehensive intensity quantitative calculation of the small-resolution precipitation process of any station, large area and small area;
(3) and (3) grading, aiming at the requirements of business application, the precipitation process is divided into 6 grades of weak, medium, strong, extra-strong and extreme so as to improve the practicability of the business.
The time (accurate to hours) of beginning and ending of the rainfall process in southern China, northern China in 2013 and the rainfall process in the middle and downstream areas of Yangtze river in 2011 China, the coverage proportion, the comprehensive intensity index and the grade are calculated by using an hour resolution rainfall process identification method, as shown in figures 3-6, and as shown in figure 6, the comprehensive intensity index and the grade of the rainfall process in the middle and downstream areas of Yangtze river in China from 2016, 6, 30, 20 to 7, 5 and 06, the results can be directly applied to the weather forecast service business.
The hour scale rainfall process statistics and accurate evaluation of the method are used for representing one of important methods of strong, weak, rich and little rainwater, are important weather forecast service works and products which are needed urgently, are also used for case warehousing, archiving and historical comparison of historical rainfall processes, are key research indexes for accurate risk evaluation and influence estimation of the rainstorm disaster, establish a foundation for hour scale dynamic risk evaluation of the rainstorm disaster and provide a basis for disaster evolution characteristic analysis.
The Chinese regional scope of the invention is subject to the official domain of China.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the present disclosure should be covered within the scope of the present invention claimed.
Claims (1)
1. An hour resolution precipitation process identification method is characterized by comprising the following steps:
the method comprises the following steps that 1, in order to finely identify the precipitation process according to the hourly resolution, the precipitation process is classified and identified in different spatial scales and facing sites, large areas and small areas in the Chinese range, the matching degree of the precipitation process and a weather system is increased, and the precipitation process of the large area is accurately identified on the basis of the proximity principle; the method comprises the following steps:
step 1.1, when identifying the station precipitation process, considering the regional difference of the station, the disaster critical threshold and the extraction method of the station threshold suitable for China, determining the threshold of the station extraction by a percentile method, wherein the identification of the station precipitation process simultaneously meets the following two conditions: a: the station precipitation process is defined as that precipitation in 1 hour reaches 80 percent values from small to large when the precipitation in historical hours, and is defined as that no time reaches 80 percent values continuously for more than 8 hours; b: sorting 90 percent values of station daily precipitation from small to large when the station daily precipitation reaches the historical daily precipitation;
step 1.2, when the regional precipitation process is identified, the complexity of the Chinese precipitation process is considered, and the regional precipitation process not only covers the large-scale precipitation process in the south and north China, but also has a small-region precipitation process only appearing in the middle and lower reaches of the Yangtze river in China or Jingjin Ji in China, and even in a single urban area in China; under the condition of meeting the Chinese applicability, from the aspect of Chinese national weather service, the large area and the small area are divided, wherein the large area refers to the south, the north or any area which is more than or equal to the area 1/3 of China, and the distance between sites is considered during process identification; the small area refers to a Chinese watershed, 32 provinces and municipalities, a directly-governed city or any area smaller than 1/3 of Chinese area, the distance between stations is not considered during process identification, and in order to ensure that the objectively-identified precipitation process and the weather system precipitation process have high matching degree, the three parameter values with the best matching degree with the precipitation process recorded by a weather system by continuously testing and adjusting parameters, namely adjusting the distance between adjacent stations recorded in the precipitation process, the minimum station number or proportion recorded in precipitation for a single time and the maximum interval time of two moments belonging to one precipitation process are found out through continuously testing and adjusting the parameters, wherein the three parameter values specifically comprise the following steps:
step 1.2.1, large area: starting to define that a recording threshold is reached and the number of stations with the distance between adjacent stations being less than 900 kilometers accounts for 5% or more or 40 or more of the number of stations in the evaluation area, ending to define that the recording threshold is reached for more than 12 hours continuously and the number of stations with the distance between adjacent stations being less than 900 kilometers accounts for less than 5% or more or less than 40 of the number of stations in the evaluation area, when the distance between the stations is calculated, assuming that the earth is a sphere, the radius is the average radius R of the earth 6371.004 kilometers, when the Longitude and the Latitude of 0 degree are taken as a reference, the earth surface distance between any two points on the surface of the sphere can be calculated according to the Longitude and the Latitude of the earth, the Longitude and the Latitude of the point a are (long, LatA), the Longitude and the Latitude of the point B are (long B, LatB), the Longitude of the east Longitude is taken as a positive value (long itude), the west Longitude is taken as a negative value (-long Latitude), and the north is taken as a 90-long value (90-Latitude), the south Latitude is 90+ Latitude value (90+ Latitude), the processed points A and B are counted as (MLonA, MLatA) and (MLonB, MLatB), and the following formula for calculating the distance between the two points can be obtained by triangle derivation:
C＝sin(MLatA)*sin(MLatB)*cos(MLonA-MLonB)+cos(MLatA)*cos(MLatB)……(1)，
Distance＝R*Arccos(C)*Pi/180……(2)，
in the above formula (2), R and Distance are expressed in kilometers;
step 1.2.2, small area: the number of stations reaching the entry threshold value accounts for 10% or more or 40 or more of the stations in the evaluation area at the beginning, and the number of stations reaching the entry threshold value for more than 8 continuous hours accounts for less than 10% or more or less than 40 of the stations in the evaluation area at the end;
step 2, an intensity quantitative calculation method, namely a calculation method based on the combined action of three indexes of precipitation intensity, duration and coverage range of precipitation process intensity, wherein the time resolution is refined to be hours, three index definitions and algorithms of precipitation intensity, duration and coverage range are improved, and precipitation process intensity evaluation based on the hour resolution is carried out; the method comprises the following steps:
step 2.1, precipitation strength index:
the rainfall intensity is one of disaster factors of the rainstorm disaster, and the maximum rainfall R in 1 hour in the process is considered during the calculation of the rainfall intensity index1max3-hour maximum precipitation R in slidingSliding 3maxMaximum precipitation R in 6-hour slidingSlip 6maxAnd process hourly average precipitation RProcess averagingThe four elements of the magnitude are,
wherein,
in the formula (3), RProcess averagingIs the average precipitation per hour of the process; h is the number of site process hours;
consider to decreaseThe time-space distribution characteristics, the regional differences and the disaster threshold of water are utilized, 2410 hour rainfall data of China weather observation stations in 1951-2018 are utilized, the hour rainfall, the sliding 3-hour rainfall and the sliding 6-hour rainfall are arranged from small to large, and corresponding values of 90%, 95%, 98%, 99%, 99.5%, 99.8%, 99.9%, 99.95%, 99.98%, 99.99% and 100% are extracted to form an hour rainfall intensity matrix R1Sliding 3 hours precipitation intensity matrix R3Sliding 6 hours precipitation intensity matrix R6The formula is as follows:
in formula (4), i is 2410, j is 11; i is the number of sites; j is the number of percentile values; r is a radical of hydrogenijIs the hour precipitation value corresponding to the jth percentile of the ith station; rr ofijIs the 3-hour precipitation value corresponding to the jth percentile of the ith site; rrrijIs a 6-hour precipitation value corresponding to the jth percentile of the ith site; in order to unify the four elements for describing the rainfall intensity into a comparable dimension, an index division method is adopted, the station difference is considered, and an hourly rainfall intensity matrix R is adopted13 hours of sliding precipitation intensity matrix R36 hours of sliding precipitation intensity matrix R6Dividing according to site indexes;
step 2.2, duration index:
the duration of the station precipitation process is defined as the hours of the logging process from the beginning to the end of the precipitation process, and the formula is as follows:
in equation (6): t is an effective precipitation time index; k is the rainfall attenuation index, h is the hours of the recording process, and K takes the value of 0.98;
step 2.3, coverage index:
coverage area CPDefinition ofRecording the proportion of the station N in the precipitation process to the station N in the evaluation area for the hour precipitation, wherein the formula is as follows:
in equation (7): n is the number of stations recorded in the precipitation process, and N is the total number of stations in the evaluation area;
considering the consistency with the magnitude of the intensity and duration of the precipitation, the precipitation coverage index C is defined as:
C＝10*CP……(8)，
in the formula (8), CPIs the coverage area;
step 2.4, calculating the comprehensive strength index in the precipitation process:
the comprehensive intensity index of the station rainfall process is the combined action of the rainfall intensity and the duration, the comprehensive intensity index RSI of the station rainfall process is calculated, and the formula is as follows:
RSI＝I*T……(9)，
in formula (9), I is a precipitation strength index; t is a duration index;
the comprehensive strength of the regional precipitation process is the combined action of 3 indexes including precipitation strength, coverage area and duration, and the comprehensive strength index RPI of the regional precipitation process is calculated by the following formula:
in the formula (10), RSI is a comprehensive strength index of a station precipitation process, n is the number of stations recorded in the precipitation process, and C is a coverage range index;
step 2.5, intensity grade division in the precipitation process:
extracting and evaluating the precipitation process of 32 provinces in different regional ranges of the south, north and province level administrative districts in China, taking the Qinling mountain-Huaihe river of China as a boundary, calculating the precipitation process comprehensive strength indexes of China, south China, north China and 32 provinces in 1981 and 2018 by applying a formula (9) and a formula (10), analyzing the probability density distribution of the strength indexes of single-station and regional precipitation processes, the precipitation process is divided into five levels of extreme, extra-strong, strong and medium according to the occurrence probability of about 1%, 5%, 10%, 15% and 69%, the comprehensive strength index of the single-station precipitation process is divided into five index ranges according to four nodes of 30, 50, 90 and 150, and the comprehensive strength index of the regional precipitation process is divided into five index ranges according to four nodes of 80, 180, 400 and 800, and respectively represents the medium, strong, extra-strong and extreme levels.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110179775.XA CN113050195B (en) | 2021-02-07 | 2021-02-07 | Hourly resolution precipitation process identification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110179775.XA CN113050195B (en) | 2021-02-07 | 2021-02-07 | Hourly resolution precipitation process identification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113050195A CN113050195A (en) | 2021-06-29 |
| CN113050195B true CN113050195B (en) | 2022-07-15 |
Family
ID=76509029
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110179775.XA Active CN113050195B (en) | 2021-02-07 | 2021-02-07 | Hourly resolution precipitation process identification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113050195B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114677059B (en) * | 2022-05-26 | 2022-08-23 | 水利部交通运输部国家能源局南京水利科学研究院 | Method and system for comprehensively evaluating precision of inversion precipitation product by integrating time-space indexes |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101349767A (en) * | 2008-09-05 | 2009-01-21 | 国家卫星气象中心 | High resolution precipitation data processing method |
| CN109946762A (en) * | 2019-03-06 | 2019-06-28 | 重庆邮电大学移通学院 | A kind of method and system based on probability distribution Short-term Forecast precipitation |
| CN110555557A (en) * | 2019-08-30 | 2019-12-10 | 北京师范大学 | Regional small-scale rainstorm disaster early warning method and device and electronic equipment |
| CN110928973A (en) * | 2019-11-30 | 2020-03-27 | 内蒙古蒙草生命共同体大数据有限公司 | Precipitation condition assessment method |
| WO2020229753A1 (en) * | 2019-05-15 | 2020-11-19 | Hd Rain | Precipitation measurement method and device |
| CN111983732A (en) * | 2020-07-27 | 2020-11-24 | 南京信息工程大学 | Rainfall intensity estimation method based on deep learning |
-
2021
- 2021-02-07 CN CN202110179775.XA patent/CN113050195B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101349767A (en) * | 2008-09-05 | 2009-01-21 | 国家卫星气象中心 | High resolution precipitation data processing method |
| CN109946762A (en) * | 2019-03-06 | 2019-06-28 | 重庆邮电大学移通学院 | A kind of method and system based on probability distribution Short-term Forecast precipitation |
| WO2020229753A1 (en) * | 2019-05-15 | 2020-11-19 | Hd Rain | Precipitation measurement method and device |
| CN110555557A (en) * | 2019-08-30 | 2019-12-10 | 北京师范大学 | Regional small-scale rainstorm disaster early warning method and device and electronic equipment |
| CN110928973A (en) * | 2019-11-30 | 2020-03-27 | 内蒙古蒙草生命共同体大数据有限公司 | Precipitation condition assessment method |
| CN111983732A (en) * | 2020-07-27 | 2020-11-24 | 南京信息工程大学 | Rainfall intensity estimation method based on deep learning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113050195A (en) | 2021-06-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shan et al. | Characteristics of dry-wet abrupt alternation events in the middle and lower reaches of the Yangtze River Basin and the relationship with ENSO | |
| CN112070286B (en) | Precipitation forecast and early warning system for complex terrain river basin | |
| Caillouet et al. | Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871 | |
| Javelle et al. | Evaluating flash-flood warnings at ungauged locations using post-event surveys: a case study with the AIGA warning system | |
| CN102201034B (en) | Method for estimating nonpoint source pollution load of village adjacent to river | |
| CN102999694B (en) | A kind of mountain region disaster takes place frequently district's Risk Evaluation of Debris Flow method | |
| Viviano et al. | Combined use of caffeine and turbidity to evaluate the impact of CSOs on river water quality | |
| CN108269016B (en) | Small watershed torrential flood disaster risk analysis method based on information diffusion | |
| CN114201922A (en) | Dynamic landslide sensitivity prediction method and system based on InSAR technology | |
| CN111639810B (en) | Rainfall forecast precision assessment method based on flood prevention requirements | |
| CN111639865B (en) | High-speed rail line meteorological disaster occurrence risk analysis method | |
| CN109255100A (en) | A kind of Urban Rain inversion algorithm based on microwave attenuation characteristic response fingerprint recognition | |
| Cheng et al. | Analyzing hazard potential of typhoon damage by applying grey analytic hierarchy process | |
| CN111027810A (en) | Mountain torrent disaster distribution driving force evaluation method in super-large area | |
| CN115689293B (en) | Urban waterlogging toughness assessment method based on pressure-state-response framework | |
| Zhong et al. | Discriminating the precipitation phase based on different temperature thresholds in the Songhua River Basin, China | |
| CN113050195B (en) | Hourly resolution precipitation process identification method | |
| Lai et al. | Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: a case study of an urban storm in Beijing | |
| Hu et al. | A modified regional L-moment method for regional extreme precipitation frequency analysis in the Songliao River Basin of China | |
| Men et al. | Study on the spatial pattern of rainfall erosivity based on geostatistics in Hebei Province, China | |
| CN112330150A (en) | Early-stage judging and identifying method for expandability of large-area debris flow disasters | |
| Lee et al. | Development of groundwater level monitoring and forecasting technique for drought analysis (I)-groundwater drought monitoring using standardized groundwater level index (SGI) | |
| Fathizad et al. | Watershed-level rainfall erosivity mapping using GIS-based geostatistical modeling | |
| CN114548559A (en) | Dynamic early warning method for mountain torrent disasters based on early rainfall | |
| Joshi et al. | Spatio-temporal trend detection of rainfall for climate change assessment in Ahmedabad-Gandhinagar District of Gujarat State, India |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |