CN112561140A - China four-season rainfall prediction method based on cooperative change of east Asia subtropical zone torrent and extreme torrent - Google Patents
China four-season rainfall prediction method based on cooperative change of east Asia subtropical zone torrent and extreme torrent Download PDFInfo
- Publication number
- CN112561140A CN112561140A CN202011397938.3A CN202011397938A CN112561140A CN 112561140 A CN112561140 A CN 112561140A CN 202011397938 A CN202011397938 A CN 202011397938A CN 112561140 A CN112561140 A CN 112561140A
- Authority
- CN
- China
- Prior art keywords
- torrent
- extreme
- prediction
- wind speed
- average
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000001556 precipitation Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims abstract description 5
- 238000012417 linear regression Methods 0.000 claims abstract description 4
- 238000010586 diagram Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 2
- 238000012360 testing method Methods 0.000 description 7
- 230000001932 seasonal effect Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 3
- 230000001869 rapid Effects 0.000 description 2
- 230000002195 synergetic effect Effects 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 238000000540 analysis of variance Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013277 forecasting method Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000012950 reanalysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000005437 stratosphere Substances 0.000 description 1
- 239000005436 troposphere Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Operations Research (AREA)
- Economics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Game Theory and Decision Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Algebra (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Development Economics (AREA)
- Evolutionary Biology (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a Chinese four-season rainfall prediction method based on cooperative change of subtropical zone torrent and extreme torrent, which is characterized in that four indexes, namely a regional average full wind speed value of a subtropical zone torrent active area, a regional average full wind speed value of an extreme torrent active area, a latitude average value of the maximum western wind in the subtropical zone torrent active area and a latitude average value of the maximum western wind in the extreme torrent active area, are subjected to standardization processing; calculating a correlation coefficient between the average precipitation of the prediction quarter and 48 groups of four indexes 12 months before the prediction quarter, wherein each index corresponds to 12 groups; selecting a month corresponding to the maximum value of the correlation coefficient as a prediction factor of a corresponding index; and processing the prediction factors by using a stepwise linear regression method to obtain a four-season rainfall model. The method can improve the accuracy of domestic four-season rainfall forecast in spring, summer, autumn and winter.
Description
Technical Field
The invention relates to a seasonal precipitation prediction method in a Chinese range, in particular to a seasonal precipitation prediction method based on cooperative change of intensity of an east Asia subtropical zone torrent and a polar front torrent, and belongs to the field of atmospheric science.
Background
The high altitude torrent refers to a narrow strong wind speed zone surrounding the latitude circle at the upper layer of the troposphere and the lower layer of the stratosphere in a medium latitude area, and the wind speed is generally more than 30 meters per second. The high altitude rush current comprises two branches: the location and intensity of the subtropical torrent and extreme torrent have significant seasonal differences and seasonal variations. Aiming at the rapid stream characterization method, the intensity index, the latitudinal direction position index and the longitudinal direction position index of the rapid stream can be selected. Research in recent years finds that the cooperative change of the two torrents can represent the common activities of cold air and hot air, and the climate state of a downstream area is more easily influenced. Therefore, from the viewpoint of cooperative change of the two torrent flows, the connection and modulation process between the high-altitude torrent flow and the atmospheric low-frequency far correlation in the middle and high latitude areas is recognized and understood, and the further influence on the climate effect in the downstream areas is of great significance.
The seasonal rainfall forecast in China is an important problem to be solved in the field of atmospheric science, and particularly, the summer rainfall forecast has certain uncertainty due to complexity. The construction of the prior forecasting method mostly focuses on strong signals of tropical oceans, such as ENSO, MKO and the like. However, in recent years, sea ice in high latitudes is continuously decreasing, corresponding to a significant arctic amplification phenomenon. Significant changes in tropical oceans and high latitude ocean ice can directly alter global heat distribution, resulting in changes in radial temperature gradients that affect both subtropical and extreme torrent flows in east asia. From another point of view, the synergistic effect of the two torrents can be said to be comprehensive performance of tropical sea and high-latitude thermal anomaly, and the high-altitude torrent is a stable system existing all the year round and is easier to capture signals than the tropical sea and the high-latitude sea ice, so that a forecasting model of four-season rainfall in China needs to be established from the point of synergistic effect of the two torrents.
Disclosure of Invention
The invention aims to solve the technical problem of improving the accuracy of domestic four-season rainfall forecast in spring, summer, autumn and winter.
In order to solve the technical problem, the Chinese four-season rainfall prediction method based on cooperative change of the subtropical zone torrent and the extreme torrent in east Asia comprises the following steps of standardizing four indexes, namely a regional average full wind speed value of a subtropical zone torrent active area, a regional average full wind speed value of an extreme torrent active area, a latitude average value of the maximum western wind in the subtropical zone torrent active area and a latitude average value of the maximum western wind in the extreme torrent active area; calculating a correlation coefficient between the average precipitation of the prediction quarter and 48 groups of four indexes 12 months before the prediction quarter, wherein each index corresponds to 12 groups; selecting a month corresponding to the maximum value of the correlation coefficient as a prediction factor of a corresponding index; and processing the prediction factors by using a stepwise linear regression method to obtain a four-season rainfall model.
In the above method, the four indexes are normalized by,
In the above method, the predictor is processed by y ═ β0+β1x1+β2x2+β3x3+β4x4+ε，
Wherein y is the normalized average monthly rainfall in China quarterly, beta1，β2，β3，β4Is a regression coefficient, x1，x2，x3，x4Is a four-exponential predictor, and epsilon is a random error that follows a normal distribution.
In the method, the method for screening the subtropical zone active area and the extreme torrent active area comprises the following steps of calculating a global area range by using 300hPa full wind speed day by day or 6 hours to obtain a wind speed large value center, marking the wind speed large value center with a central wind speed value of more than or equal to 30m/s or a central wind speed value of more than 8 grid points on the periphery as a torrent center, respectively corresponding to the subtropical zone active area and the extreme torrent active area by using a torrent center distribution diagram of at least 10 years, and recording latitude and longitude ranges of the subtropical zone active area and the extreme torrent active area.
In the above method, the regression coefficient β is determined according to the least square method1，β2，β3，β4Estimate b of1,b2,b3,b4To obtain
For x1，x2，x3，x4The linear regression equation of (1):
wherein
Represents an estimate of y; e is the error estimate or residual.
And finally, based on analysis of variance, checking the regression equation to determine the credibility of the method in different areas.
The method for forecasting the rainfall in the four seasons of China based on the cooperative change of the torrent of the subtropical zone and the torrent of the extreme point in east Asia in the scheme establishes a forecasting model aiming at the rainfall in the China range, provides a new thought for forecasting the rainfall in China, and has certain practical significance.
Drawings
FIG. 1 is a flow chart of a method of practicing the present invention;
FIG. 2 is a graph showing the rapid nuclear distribution in month 1 based on ERA-Interim reanalysis data, taking 1979-2014 as an example of a 300hPa full wind speed field in month 1, wherein the shaded area indicates that the value is greater than 24 times; the solid and dashed boxes represent the active regions of the east asian subtropical rapids (25-30 ° N, 80-104 ° E) and the polar front rapids (50-60 ° N, 80-100 ° E), respectively;
FIG. 3 shows the time evolution of std-pj-int, std-sj-int, std-pj-lat and std-sj-lat of month 1 standardized in 1979-;
FIG. 4 shows the precipitation sequences observed (solid line) and forecasted (dotted line) in summer at Nanjing station (upper) and Sanxia station (lower) in 1980-;
FIG. 5 is a diagram showing a distribution of primary factors in a prediction model of each station through a reliability test.
Detailed Description
With reference to fig. 1 to 5, taking a summer rainfall model in 1980-2014 as an example, the prediction object is average domestic rainfall in 1980-2011 in summer, the prediction factor reverses each index of subtropical torrent and extreme torrent for 12 months from the forecast season to the front, and finally, the test year using 2012-2014 in the last three years as the prediction model is described in detail.
The positions of the torrent cores in different months are determined, the calculation is carried out by utilizing the full wind speed of 300hPa day by day or 6 hours, the centers of the wind speeds in different months are searched in the range of 10-70 degrees N and 60-160 degrees W in the Pacific region of east Asia, and if the centers meet the following two conditions, the centers are marked as torrent centers. The central wind speed value is more than or equal to 30 m/s; and secondly, the wind speed values of 8 grid points around the center are all smaller than the wind speed value of the center.
And determining large-value centers corresponding to low latitude and high latitude respectively corresponding to active areas of the east Asia subtropical torrent and the extreme torrent by utilizing the torrent center distribution map for years, wherein the optimal distribution map is more than 30 years, and recording latitude and longitude ranges of the active areas. The rush current kernel distribution for 1 month, see fig. 2.
Calculating the torrent index, namely calculating monthly data in 1979-2014, and representing the average total wind speed value of the area of the subtropical torrent active area as the subtropical torrent intensity in the torrent active area of the corresponding month, wherein the average total wind speed value is marked as sj-int; representing the average full wind speed value of the area of the active region of the pole front torrent as the pole front torrent intensity, and marking the value as pj-int; representing the average value of the latitude where the maximum western wind is found in the active region of the subtropical zone torrent as the index of the longitudinal position of the subtropical zone torrent, and marking the index as sj-lat; and representing the average value of the latitude where the maximum western wind is found in the extreme torrent active region as the meridional position index of the extreme torrent, and marking the index as pj-lat.
The statistics are performed based on month-by-month data, so that the four indices (sj-int, pj-int, sj-lat, pj-lat) include 12 (months) × 4 (pieces) ═ 48 sets of sequences.
48 groups of sequences of each month are standardized,
Taking the year-by-year evolution of std-sj-int, std-sj-lat, std-pj-int and std-pj-lat of 1 month as an example, see FIG. 3 for details. The time evolution sequences of the four rapid indexes in 1979-2014 of 1-12 months by months are obtained in the same way.
Chinese summer rainfall forecasting model based on subtropical zone torrent and extreme torrent indexes
Selecting a Nanjing station (longitude 118.48 degrees E, latitude 32.00 degrees N) and a three gorges station (longitude 111.18 degrees E, latitude 30.42 degrees N) as examples, and obtaining forecasting models of the two stations respectively as y (236.07 +20.84 XX) based on a plurality of rush current indexes1(9 months of the previous year), y is 156.84+12.79 xX1(12 months earlier) +23.19 XX3(9 months in the previous year), wherein the first forecasting factors selected firstly by the Nanjing station and the Sanxia station are the extreme torrent intensity index of 9 months in the previous year and the subtropical torrent intensity index of 9 months in the previous year respectively.
The predicted summer precipitation sequence is shown in dashed lines in fig. 4, and the complex correlation coefficients in the two prediction models are 0.42 and 0.52, respectively, and both pass the 95% confidence test. The prediction test is carried out in 2011-2014, and the test qualification rates of the Nanjing station and the Sanxia station are both 100%.
The method is popularized to the whole country, sites of which models pass the credibility test are marked, and as shown in figure 5, the fact that modeling of most regions in the whole country passes the credibility test can be seen, and the feasibility and the effect of the method are proved. Because the summer precipitation time evolution is different for each site across the country, the resulting prediction model is also different based on different time series. In addition, the first factors in the site prediction models, namely the main rapid flow prediction factors selected in the stepwise regression model, are also shown in fig. 5.
Claims (4)
1. The Chinese four-season rainfall prediction method based on cooperative change of the east Asia subtropical torrent and the extreme torrent is characterized by comprising the following steps of: standardizing four indexes, namely, the average total wind speed value of the area of the subtropical zone torrent active area, the average total wind speed value of the area of the extreme torrent active area, the average latitude value of the maximum western wind in the subtropical zone torrent active area and the average latitude value of the maximum western wind in the extreme torrent active area; calculating a correlation coefficient between the average precipitation of the prediction quarter and 48 groups of four indexes 12 months before the prediction quarter, wherein each index corresponds to 12 groups; selecting a month corresponding to the maximum value of the correlation coefficient as a prediction factor of a corresponding index; and processing the prediction factors by using a stepwise linear regression method to obtain a four-season rainfall model.
2. The method for predicting four-season rainfall in China based on cooperative change of the subtropical torrent and the extreme torrent in east Asia according to claim 1, wherein: the four indexes are normalized by
Where n is the total number of samples, xiIs any one of the exponential sequences of the DNA,
is the average of any exponential sequence.
3. The method for predicting four-season rainfall in China based on cooperative change of the subtropical torrent and the extreme torrent in east Asia according to claim 1, wherein: the method for processing the prediction factor is that y is beta0+β1x1+β2x2+β3x3+β4x4+ ε, wherein y is the normalized average monthly precipitation in China, β1，β2，β3，β4Is a regression coefficient, x1，x2，x3，x4Is a four-exponential predictor, and epsilon is a random error that follows a normal distribution.
4. The method for predicting four-season rainfall in China based on cooperative change of the subtropical torrent and the extreme torrent in east Asia according to claim 1, wherein: the method for screening the active regions of the sub-tropical torrent and the extreme torrent comprises the following steps of calculating a global area range by using 300hPa full wind speed day by day or 6 hours to obtain a wind speed large value center, marking the wind speed large value center with a central wind speed value of more than or equal to 30m/s or a central wind speed value of more than 8 grid points around as a torrent center, respectively corresponding the wind speed large value centers corresponding to low latitude and high latitude to the active regions of the sub-tropical torrent and the extreme torrent by using a torrent center distribution diagram of at least 10 years, and recording the latitude and longitude ranges of the active regions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011397938.3A CN112561140A (en) | 2020-12-04 | 2020-12-04 | China four-season rainfall prediction method based on cooperative change of east Asia subtropical zone torrent and extreme torrent |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011397938.3A CN112561140A (en) | 2020-12-04 | 2020-12-04 | China four-season rainfall prediction method based on cooperative change of east Asia subtropical zone torrent and extreme torrent |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112561140A true CN112561140A (en) | 2021-03-26 |
Family
ID=75047883
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011397938.3A Pending CN112561140A (en) | 2020-12-04 | 2020-12-04 | China four-season rainfall prediction method based on cooperative change of east Asia subtropical zone torrent and extreme torrent |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112561140A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113627690A (en) * | 2021-09-03 | 2021-11-09 | 中国人民解放军国防科技大学 | Method for predicting seasonal precipitation in southern China |
-
2020
- 2020-12-04 CN CN202011397938.3A patent/CN112561140A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113627690A (en) * | 2021-09-03 | 2021-11-09 | 中国人民解放军国防科技大学 | Method for predicting seasonal precipitation in southern China |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110058328B (en) | Multi-mode combined downscaling prediction method for northeast summer rainfall | |
| Allen et al. | A severe thunderstorm climatology for Australia and associated thunderstorm environments | |
| CN114019579B (en) | High space-time resolution near-surface air temperature reconstruction method, system and equipment | |
| Ngo-Duc et al. | Monthly adjustment of Global Satellite Mapping of Precipitation (GSMaP) data over the VuGia–ThuBon River Basin in Central Vietnam using an artificial neural network | |
| CN110598290A (en) | Method and system for predicting future hydropower generation capacity of basin considering climate change | |
| Gámiz-Fortis et al. | Potential predictability of an Iberian river flow based on its relationship with previous winter global SST | |
| CN110781458B (en) | Method for predicting surface solar irradiance based on mixed regression model | |
| CN112114384A (en) | Power transmission line icing occurrence probability forecasting method | |
| Xu et al. | Determinants and identification of the northern boundary of China’s tropical zone | |
| CN114443627A (en) | Land and ocean temperature restoration method and system and computer equipment | |
| Zhu et al. | Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21· 7” extreme rainfall event in Henan Province, China | |
| Barton et al. | Variability in wintertime position and strength of the North Pacific jet stream as represented by re‐analysis data | |
| CN114444020A (en) | Air temperature forecast correction system for power grid tower point | |
| CN112561140A (en) | China four-season rainfall prediction method based on cooperative change of east Asia subtropical zone torrent and extreme torrent | |
| Dong et al. | Simulation of United States mesoscale convective systems using GFDL’s new high-resolution general circulation model | |
| Li et al. | A precipitation forecast model with a neural network and improved GPT3 model for Japan | |
| Łupikasza et al. | Climatology of Air-Mass and Frontal Extreme Precipitation | |
| Kuo et al. | Association of Taiwan's October rainfall patterns with large-scale oceanic and atmospheric phenomena | |
| Sun et al. | Corrected ERA5 precipitation by machine learning significantly improved flow simulations for the third pole basins | |
| Du et al. | Attribution of the record-breaking consecutive dry days in winter 2017/18 in Beijing | |
| Flocas et al. | Evaluation of maximum and minimum temperature of NCEP-NCAR reanalysis data over Greece | |
| CN112330075A (en) | China four-season air temperature prediction method based on cooperative change of subtropical zone torrent and extreme torrent | |
| CN109886497B (en) | Ground air temperature interpolation method based on latitude improved inverse distance weighting method | |
| Javadinejad et al. | Modeling the effects of climate change on probability of maximum rainfall and on variations in storm water in the Zayandeh Rud River | |
| Lin et al. | Comparative Study on Rainfall Rate Digital Map in China Land Area |
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 |