CN115356789A - Plum rain period short-time strong precipitation grading early warning method - Google Patents
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Abstract
The invention discloses a plum-rain-period short-time strong precipitation grading early warning method which comprises the steps of obtaining key environment factors and dual-polarization radar characteristic quantities when different levels of short-time strong precipitation occur in the plum rain period by utilizing multi-source data such as reanalysis data, automatic station data, dual-polarization radar data and high-precision mode output data, calculating corresponding threshold values, establishing a forecasting model, carrying out magnitude division on output short-time strong precipitation grid point forecast values in a region according to a final forecast value obtained by the forecasting model and a divided short-time strong precipitation forecast level threshold range, and outputting a final short-time strong precipitation grading early warning product.
Description
Technical Field
The invention relates to the technical field of weather forecast, in particular to a plum raining period short-time strong precipitation grading early warning method.
Background
Short-term heavy rainfall is an expression form of strong convection weather, and often causes secondary disasters such as urban waterlogging, torrential flood, landslide, debris flow and the like. The short-time strong precipitation with the strength of 201.9mm/h appears in the extreme strong precipitation process which occurs in Henan in 7 months in 2021, and the extreme value of the rain strength in China since the weather record is created, which causes huge disasters, so the short-time strong precipitation is also the key point of research and attention of the weather department. In the period of 6 to 7 months every year, a quasi-static frontal rain zone is often formed in the Yangtze river-Huaihe river basin of China and continuous strong rainfall, namely plum rain, is generated.
In the period of plum rain, short-time strong precipitation with various intensities is often generated, which causes property and even life loss, and is one of the weather phenomena concerned by the public and local government departments. However, in the existing research at present, the methods for judging and forecasting the strong rainfall during the plum rain season are limited, and for the public, the methods have more and higher expectations for forecasting the strong rainfall. The public is concerned not only about whether strong precipitation occurs, but also about the intensity of the strong precipitation; because the magnitude of strong precipitation is larger in a short time, the disaster caused by the strong precipitation is more serious. Therefore, a staged early warning method for strong precipitation in a short time in a plum rain season is urgently needed to solve the problems.
Disclosure of Invention
The invention provides a plum rainy period short-time heavy precipitation grading early warning method which can divide the magnitude of heavy precipitation and output the final short-time heavy precipitation grading early warning to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme: a plum rain period short-time strong precipitation grading early warning method comprises the following steps:
s1, acquiring environmental physical quantities of different magnitudes of short-time strong rainfall in a plum rain period, characteristic quantities of a dual-polarization radar and a cyclone product;
s2, calculating and determining each key environment factor and a threshold value of the dual-polarization radar characteristic quantity;
s3, establishing a forecasting model by using an index weighting method based on each parameter threshold;
and S4, carrying out grading early warning on short-time strong rainfall by using a forecast equation, and outputting an identification product.
Preferably, in step S1, the magnitude of the short-term heavy precipitation is divided into: LL magnitude, LM magnitude and LH magnitude, wherein the LL magnitude precipitation is 20-50mm/h, the LM magnitude precipitation is 50-80mm/h, and the LH magnitude precipitation is more than 80 mm/h.
Preferably, in step S2, the key environmental factors include an equilibrium layer height ELC, a convection effective potential cap, a modified deep convection index MDCI, a storm intensity index SSI, and a lift index LI, and the characteristic quantity of the dual polarization radar includes a horizontal reflectivity factor
Differential reflectivity factor
Specific differential propagation phase shift
And correlation coefficient
The mesocyclone product M is identified by a dual-polarization radar.
Preferably, in step S3, a median value of each sensitive environmental factor is calculated, and the median value is used as a threshold.
Preferably, in step S2, a threshold value of the characteristic quantity of the dual polarization radar is determined based on the magnitude of the short-time heavy precipitation.
Preferably, in step S3, the parameter threshold further includes a 1-hour temperature change threshold
The method specifically comprises the following steps:
the method comprises the steps of obtaining the temperatures of all automatic stations in a selected area at two moments before and after, and then subtracting the temperature of each station at the previous moment from the temperature of each station at the next moment;
drawing a variable temperature evolution curve of the automatic station for 1 hour before and after the automatic station according to the obtained data;
and obtaining the 1-hour temperature-changing threshold value by using a probability analysis method.
Preferably, in step S3, the forecasting model is:
wherein the forecast rating is determined based on the Y value.
Compared with the prior art, the invention has the beneficial effects that: according to the method, the key environmental factors and the characteristic quantity of the dual-polarization radar when short-time strong precipitation of different magnitudes in the plum rainy period occurs are obtained by utilizing multi-source data such as reanalysis data, automatic station data, dual-polarization radar data and high-precision mode output data, corresponding threshold values are calculated, a forecasting model is built, magnitude division is carried out on output short-time strong precipitation grid point forecasting values in the region through the final forecasting values obtained by the forecasting model and the divided short-time strong precipitation forecasting grade threshold range, and final short-time strong precipitation grading early warning identification products are output.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a flow chart of the early warning method for the short-term strong precipitation in the plum rain season;
fig. 2 is a comparison graph of the short-time strong precipitation grading early warning effect according to the embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
Example (b): as shown in fig. 1, a plum raining period short-time heavy rainfall grading early warning method, wherein reanalysis data, automatic station data, dual-polarization radar data, high-precision mode output data and other multi-source data are utilized, in this embodiment, the utilized data include data of a basic station and an automatic station of Jiangsu province, 2011-2020 fifth generation atmosphere reanalysis data (ERA 5), data of a service weather radar of Jiangsu province and high-precision mode (PWRFS) output data; wherein, the basic station data is hourly precipitation data and is subjected to quality control; the automatic station data come from BUFR messages uploaded by the national-level automatic station, a program analyzes a minute-level file, and data such as temperature, air pressure and the like in the file are extracted and input into a database for query, statistics and calculation; ERA5 data is hourly data with spatial resolution of 0.25 ° x0.25 °; the PWRFS data is output in an hourly high-precision mode with a spatial resolution of 1kmx1 km.
The method comprises the following steps:
s1, acquiring environmental physical quantities of different magnitudes of short-time strong rainfall in a plum rain period, characteristic quantities of a dual-polarization radar and a cyclone product;
the dual-polarization radar characteristic quantity is CIRAD/SA dual-polarization service weather radar which is upgraded and modified by 7 parts of Jiangsu province. These 7 radars were deployed in Nanjing, nantong, saline city, xuzhou, liyunkong, changzhou, and Thai, respectively. The radar detection scanning mode adopts VCP-21, volume scanning is carried out once every 6 minutes averagely, each volume scanning comprises 9 layers of data, the elevation angles are respectively 0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees, the maximum detection distance of the echo intensity is 460 km, and the resolution ratio is 1 degree multiplied by 250 m. Acquisition of elevation angle of 0.5 degree by dual polarization radar
、
、
And
the equal polarization parameters and the mesocyclone products identified by the dual-polarization radar;
horizontal reflectivity factor
The calculation formula of (2) is as follows:
D h the size of the radar-detected particles in the horizontal direction, and N (D) the drop spectrum distribution of precipitation particles.
Differential reflectivity factor
The formula for (dB) is:
Specific differential propagation phase shift
The formula for (deg/km) is:
r m and r n Respectively the distances from the centers of two adjacent distance libraries in the precipitation area to the center of the radar;
(rn) with
Are the variation values of the two-way differential propagation phases obtained from the two adjacent distance bins, respectively.
zero time lag correlation coefficient
The correlation degree between the backscattering generated when the horizontally polarized wave and the vertically polarized wave simultaneously hit the same weather particle is reflected within the time interval of one pulse repetition period t after they are sampled simultaneously.
The mesocyclone (M) is obtained by mesocyclone products in the polarization radar, and meets the following main conditions: (1) The vertical extension of the vortex is at least 3km, and the height of the bottom of the vortex does not exceed 5km; (2) The rotating speeds at different distances from the radar station meet the two-dimensional characteristic strength threshold value; (3) If a vortex has to be detected in two consecutive sweeps, it is determined that a medium cyclone is generated.
Dividing the short-term strong precipitation into three magnitudes of 20-50mm/h (LL magnitude), 50-80mm/h (LM magnitude) and more than 80mm/h (LH magnitude), and calculating 16 environmental physical quantities (atmospheric water-reducing quantity, K index, sauter index, conditional convection stability index, zero-degree layer height, whole layer specific humidity, lifting condensation height, lifting layer height, CIN, CAPE, corrected deep convection index, storm intensity index, storm index, relative vorticity of storm, rough rational number and vertical wind shear) closely related to the occurrence of short-term strong precipitation events with different magnitudes by adopting hourly atmosphere reanalysis data of 0.25 degrees x0.25 degrees in the European center.
S2, calculating and determining each key environment factor and a threshold value of the dual-polarization radar characteristic quantity;
determination of threshold values for various key environmental factors
In this embodiment, data of the most recent moment before a strong precipitation event occurs is selected, and the correlation degree of 16 physical quantities calculated in S1 for distinguishing short-time strong precipitation with different magnitudes is evaluated by using a boxplot difference index, and the specific difference index is calculated as follows:
arranging each group of the 16 groups of physical quantities from large to small, and respectively calculating an upper edge, an upper quartile, a median, a lower quartile, a lower edge and an abnormal value; the median is taken as the threshold criterion for the key physical quantity.
The specific calculation method of the median is as follows: a set of data was divided equally into two and the number was taken in the middle.
If the length n of the original sequence is an odd number, the position of the median is (n + 1)/2;
if the original sequence length n is an even number, then the position of the median is n/2, n/2+1, and the value of the median is equal to the arithmetic mean of the numbers of the two positions;
the key physical quantities with different magnitudes can be effectively distinguished through the method, so that the following 5 key factors are obtained: equilibrium layer height (ELC), convection effective potential (CAPE), modified Deep Convection Index (MDCI), storm intensity index (SSI), and uplift index (LI). Calculating the median of 5 key factors, namely the physical quantity threshold of the short-time strong rainfall environment with different intensities, as shown in the following table:
wherein the basic environmental condition for different intensity precipitation is met when ELC and LI are below the threshold and the other 3 factors are above the threshold.
The obtained threshold values of the radar polarization parameters corresponding to the short-time strong precipitation with different intensities are shown in the following table:
in this embodiment, the parameters further include a 1-hour temperature change threshold: the temperature change within 1 hour can represent the strength of the ground cold pool, and has better indication significance for the movement of short-time heavy precipitation; therefore, the movement of the short-time heavy precipitation can be further judged by utilizing the temperature change within 1 hour; the calculation method for the 1 hour temperature change is as follows:
by acquiring the temperatures of all the automatic stations in the selected area at two moments before and after, and then subtracting the temperature of each station at the previous moment from the temperature of each station at the next moment:
wherein, in the process,
the temperature change for 1 hour is shown,
represents the temperature of the ith station, and t represents the current time;
drawing a variable temperature evolution curve of the automatic station for 1 hour before and after the automatic station according to the obtained data;
obtaining a variable temperature threshold value for 1 hour by using a probability analysis method;
in this embodiment, the temperature change of the upstream region before the occurrence of the short-time strong precipitation is 1 hour at 50mm/h as an example
Giving a certain value of short-time strong precipitation to the intensity and the intensity above in the forecasting model;
s3, establishing a forecasting model by using an index weighting method based on each parameter threshold;
and S4, carrying out grading early warning on short-time strong rainfall by using a forecast equation, and outputting an identification product.
The forecast is divided into four conditions based on the magnitude of the short-time strong precipitation, the forecast grade is 0, and no short-time strong precipitation occurs; forecasting the grade to be 1, and generating short-time strong precipitation with the strength of 20-50 mm/h; the forecast grade is 2, and short-time strong precipitation with the intensity of 50-80mm/h occurs; the forecast grade is 3, and short-time strong precipitation above 80mm/h intensity occurs; determining the occurrence levels of different short-time strong rainfall according to the forecast values in the forecast equation; the specific forecast values and corresponding forecast ratings are as follows:
when Y <5,shr =0;
when Y is more than or equal to 5 and less than 9, SHR =1;
when 9 is less than or equal to Y <16,SHR =2;
when Y is more than or equal to 16, SHR =3;
wherein, when the cyclone is medium, m =1; no mesowhirl exists, and m =0;
the temperature is changed to be less than-4 ℃ within 1 hour,
=1; if not, then,
=0。
referring to fig. 2, in a specific embodiment, a forecast area of 2 hours in the future is determined according to a 0-2 hour radar echo extrapolation product, observation data of each parameter in a forecast model in the forecast area is retrieved, a final forecast value is calculated according to a forecast equation established by the above operations, and a grid value of 1kmx1km is output; through the contrast inspection with actual precipitation process, the falling area of different magnitudes of short-time heavy precipitation can be better reflected by the short-time heavy precipitation grading early warning product.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A plum raining period short-time strong precipitation grading early warning method is characterized by comprising the following steps:
s1, acquiring environmental physical quantities of different magnitudes in a plum rain period during short-time strong precipitation, characteristic quantities of a dual-polarization radar and a medium-frequency cyclone product;
s2, calculating and determining each key environment factor and a threshold value of the dual-polarization radar characteristic quantity;
s3, establishing a forecasting model by using an index weighting method based on each parameter threshold;
and S4, carrying out graded early warning on the short-time heavy rainfall by using a forecast equation, and outputting an identification product.
2. The plum rain period short-time heavy precipitation grading early warning method as claimed in claim 1, characterized in that: in step S1, the magnitude of the short-term heavy precipitation is: LL magnitude, LM magnitude and LH magnitude, wherein the LL magnitude precipitation is 20-50mm/h, the LM magnitude precipitation is 50-80mm/h, and the LH magnitude precipitation is more than 80 mm/h.
3. The plum raining period short-time heavy precipitation grading early warning method as claimed in claim 1, characterized in that: in step S2, the key environmental factors include the height ELC of the balance layer, the effective convection potential cap, the modified deep convection index MDCI, the storm intensity index SSI, and the elevation index LI, and the characteristic quantity of the dual polarization radar includes the horizontal reflectivity factor
Differential reflectivity factor
Specific differential propagation phase shift
And correlation coefficient
The mesocyclone product M is identified by a dual-polarization radar.
4. The plum raining period short-time heavy precipitation grading early warning method as claimed in claim 3, characterized in that: in step S3, the median of each key environmental factor is calculated, and the median is used as a threshold.
5. The plum rain period short-time heavy precipitation grading early warning method as claimed in claim 1, characterized in that: in step S2, a threshold value of the characteristic quantity of the dual polarization radar is determined based on the magnitude of the short-time strong precipitation.
6. The plum rain period short-time heavy precipitation grading early warning method as claimed in claim 1, characterized in that: in step S3, the parameter threshold further includes a 1-hour temperature change threshold
The method specifically comprises the following steps:
the method comprises the steps of obtaining the temperatures of all automatic stations in a selected area at two moments before and after, and then subtracting the temperature of each station at the previous moment from the temperature of each station at the next moment;
drawing the obtained data into a variable temperature evolution curve before and after the automatic station for 1 hour;
and obtaining the 1-hour temperature-changing threshold value by using a probability analysis method.
7. The plum raining period short-time heavy precipitation grading early warning method as claimed in claim 1, characterized in that: in step S3, the prediction model is:
wherein the forecast rating is determined based on the Y value.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116068676A (en) * | 2023-03-07 | 2023-05-05 | 南京气象科技创新研究院 | General comprehensive evaluation method for rainfall forecast cross-magnitude |
| CN116430390A (en) * | 2023-06-13 | 2023-07-14 | 南京信息工程大学 | S-band dual-polarization radar quality control method for data assimilation |
| RU222061U1 (en) * | 2023-09-05 | 2023-12-11 | федеральное государственное бюджетное образовательное учреждение высшего образования "Нижегородский государственный технический университет им. Р.Е. Алексеева" (НГТУ) | Weather radar device for detecting polar mesocyclones |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108680920A (en) * | 2018-04-28 | 2018-10-19 | 深圳市雅码科技有限公司 | A kind of hazard weather identification early warning system and method based on dual polarization radar |
| CN110888186A (en) * | 2019-11-29 | 2020-03-17 | 天津大学 | Method for forecasting hail and short-time heavy rainfall based on GBDT + LR model |
| CN111665575A (en) * | 2020-05-27 | 2020-09-15 | 长江水利委员会水文局 | Medium-and-long-term rainfall grading coupling forecasting method and system based on statistical power |
| US20200348448A1 (en) * | 2019-04-30 | 2020-11-05 | ClimateAI, Inc. | Methods and systems for climate forecasting using artificial neural networks |
| CN114019514A (en) * | 2021-11-25 | 2022-02-08 | 浙江省气象台 | Thunderstorm strong wind early warning method, system, equipment and terminal |
| LU500953B1 (en) * | 2021-12-03 | 2022-06-03 | Xinjiang Inst Of Ecology And Geography Cas | Rainstorm disaster risk assessment method and system |
| CN114609698A (en) * | 2022-01-20 | 2022-06-10 | 成都信息工程大学 | Short-time strong precipitation probability forecasting method based on proportioning method idea and dichotomy |
| CN115049013A (en) * | 2022-07-05 | 2022-09-13 | 中国电建集团西北勘测设计研究院有限公司 | Short-term rainfall early warning model fusion method combining linearity and SVM |
-
2022
- 2022-10-08 CN CN202211221613.9A patent/CN115356789A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108680920A (en) * | 2018-04-28 | 2018-10-19 | 深圳市雅码科技有限公司 | A kind of hazard weather identification early warning system and method based on dual polarization radar |
| US20200348448A1 (en) * | 2019-04-30 | 2020-11-05 | ClimateAI, Inc. | Methods and systems for climate forecasting using artificial neural networks |
| CN110888186A (en) * | 2019-11-29 | 2020-03-17 | 天津大学 | Method for forecasting hail and short-time heavy rainfall based on GBDT + LR model |
| CN111665575A (en) * | 2020-05-27 | 2020-09-15 | 长江水利委员会水文局 | Medium-and-long-term rainfall grading coupling forecasting method and system based on statistical power |
| CN114019514A (en) * | 2021-11-25 | 2022-02-08 | 浙江省气象台 | Thunderstorm strong wind early warning method, system, equipment and terminal |
| LU500953B1 (en) * | 2021-12-03 | 2022-06-03 | Xinjiang Inst Of Ecology And Geography Cas | Rainstorm disaster risk assessment method and system |
| CN114609698A (en) * | 2022-01-20 | 2022-06-10 | 成都信息工程大学 | Short-time strong precipitation probability forecasting method based on proportioning method idea and dichotomy |
| CN115049013A (en) * | 2022-07-05 | 2022-09-13 | 中国电建集团西北勘测设计研究院有限公司 | Short-term rainfall early warning model fusion method combining linearity and SVM |
Non-Patent Citations (1)
| Title |
|---|
| 杨忠林,赵坤,徐坤等: "江淮梅雨期极端对流为物理特征的双偏振雷达观测研究", 《气象学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116068676A (en) * | 2023-03-07 | 2023-05-05 | 南京气象科技创新研究院 | General comprehensive evaluation method for rainfall forecast cross-magnitude |
| CN116068676B (en) * | 2023-03-07 | 2023-06-02 | 南京气象科技创新研究院 | General comprehensive evaluation method for rainfall forecast cross-magnitude |
| CN116430390A (en) * | 2023-06-13 | 2023-07-14 | 南京信息工程大学 | S-band dual-polarization radar quality control method for data assimilation |
| CN116430390B (en) * | 2023-06-13 | 2023-08-22 | 南京信息工程大学 | S-band dual-polarization radar quality control method for data assimilation |
| RU222061U1 (en) * | 2023-09-05 | 2023-12-11 | федеральное государственное бюджетное образовательное учреждение высшего образования "Нижегородский государственный технический университет им. Р.Е. Алексеева" (НГТУ) | Weather radar device for detecting polar mesocyclones |
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