Water vapor traceability and quantitative contribution evaluation method in strong rain and snow process

Abstract

The invention relates to a strong rain and snow process water vapor traceability and quantitative contribution evaluation method, which comprises the following steps: aiming at a certain strong rain and snow process, establishing an accumulated rainfall falling area map and an hourly rainfall distribution map, determining a process strong rainfall falling area and a strong rainfall time period, establishing a water vapor tracking scheme, and determining a tracking area and tracking time; determining a track source of an air mass point causing the strong rain and snow process by using an HYSPLIT tracking mode; all air mass point track results obtained through tracking are screened, and contributive air mass points are obtained; classifying the screened air mass point tracks to obtain contributive air mass point tracks; according to a symbol of dq in the air mass point track, identifying a water vapor source place in the strong rain and snow process; and quantitatively confirming the average specific humidity evolution of the screened air mass points and the final relative water vapor contribution of each water vapor source to the strong rain and snow process. The method can provide a reliable decision basis for traffic, electric power, agriculture and other departments in a target area.

Description

TECHNICAL FIELD

[0001] The application relates to a water vapor tracing method for a strong snow and rain process, in particular to a water vapor tracing and quantitative contribution evaluation method for a strong snow and rain process. BACKGROUND

[0002] In recent years, extreme weather events occur frequently, which have a significant impact on the social economy. In particular, extreme snowfall events are usually accompanied by extremely low temperature and strong snow, which has a disastrous impact on transportation, agriculture, industry and other fields. Therefore, extreme snowfall events have always been the focus of research and business work of meteorological departments. However, the accuracy of the current routine forecast of extreme snowfall events still needs to be improved. Therefore, it is crucial to deeply understand the formation mechanism of extreme snowfall events and build an effective prediction method.

[0003] Sufficient water vapor transport is one of the necessary conditions for the occurrence of extreme precipitation events, especially under the influence of the East Asian winter monsoon, the dry and cold air from the north invades the east of China frequently, and the occurrence of winter extreme snowfall events is more so. The terrain of a certain region in China located in the east of Eurasia is complex, and has unique topographic distribution and water vapor source characteristics. Therefore, exploring the water vapor source of extreme snowfall events in a certain region of China and deeply understanding its formation mechanism are of great significance to significantly improve the spatial and temporal accuracy of extreme snowfall prediction. The research on water vapor transport and source has been relatively extensive, but the analysis of water vapor transport characteristics of extreme snowstorm process in a certain region is still insufficient. Most studies use the Euler method, and the water vapor flux calculated by the instantaneous atmospheric wind field is instantaneous, which leads to the relatively simple water vapor transport channel finally identified, and it is difficult to quantitatively evaluate the actual contribution rate of each water vapor source to the rainfall and snowfall. SUMMARY

[0004] The purpose of the present application is to provide a strong snow and rain process water vapor tracing and quantitative contribution evaluation method to solve the problem that the prediction accuracy of extreme rainfall and snowfall events needs to be improved by traditional methods, so as to improve the prediction accuracy and timeliness of strong snow and rain process, and enhance the pertinence and effectiveness of disaster prevention and reduction work.

[0005] The purpose of the present application is achieved as follows: A strong snow and rain process water vapor tracing and quantitative contribution evaluation method, comprising the following steps: S1, for a certain strong snow and rain process in a target region, according to the hourly precipitation observation data of the national automatic weather station covering the strong snow and rain process in the target region, a cumulative precipitation falling area map and an hourly precipitation distribution map of the strong snow and rain process are established, and the process heavy precipitation falling area and the heavy precipitation period in the period are determined.

[0006] S2, according to the determined process heavy precipitation area and heavy precipitation period, establish the water vapor tracking scheme of this heavy rain and snow process, determine the tracking area and tracking time of HYSPLIT tracking mode.

[0007] S3, using the fifth generation atmospheric reanalysis grid data or other refined grid data released by the European Center for Medium-Range Weather Forecasts as the initial field of HYSPLIT tracking mode to determine the trajectory source of air particles leading to this heavy rain and snow process.

[0008] S4, classify the screened air particle trajectories to obtain the air particle trajectories that contribute to this heavy rain and snow process.

[0009] S5, according to the sign of dq in the air particle trajectory that contributes to this heavy rain and snow process, identify the water vapor source of this heavy rain and snow process; according to the administrative division and geographical area, divide the water vapor source of this heavy rain and snow process.

[0010] S6, using the water vapor source attribution algorithm, quantitatively confirming the average specific humidity evolution of the screened air particles and the final relative water vapor contribution of each water vapor source to this heavy rain and snow process.

[0011] Further, the water vapor tracking scheme in step S2 is: according to the water vapor transport characteristics, determine the height layers of 700 hPa, 850 hPa, 925 hPa and 950 hPa as the trajectory tracking height layer, take the process heavy precipitation area determined in step S1 as the trajectory tracking area, select the air particles at different height layers in the trajectory tracking area as the starting particles of trajectory tracking, take the starting time of the heavy rain period determined in step S1 as the starting time of the trajectory tracking area, and perform 10-day backward trajectory tracking, the position of the trajectory point is output once per hour.

[0012] Further, the specific operation mode of step S4 includes the following sub-steps: S4-1, using the K-means cost function minimization principle to classify the air particle trajectories obtained by tracking; the cost function of K-means J is: Wherein, S i represents the set of all points belonging to the i th cluster; c i is the center point of the i th cluster; x j is the position point of an air particle belonging to the cluster.

[0013] S4-2 Randomly select several cluster centers as initial centers, assign each air particle trajectory to the nearest cluster center, and calculate the average position of all trajectories in each cluster as the new center of the cluster. Repeat this operation until the cluster center no longer changes or the maximum number of iterations is reached.

[0014] S4-3 Confirm the number of air particle clusters using the elbow rule k : For each k value, calculate and record the corresponding cost function J , draw the relationship between k value and the corresponding cost function J , find the "elbow" point in the curve, which is the point where the cost function J decreases significantly, and the k value corresponding to this point is the recommended optimal cluster number, which is also the final air particle trajectory class number.

[0015] S4-4 According to the following two screening conditions, screen out air particles that contribute to heavy precipitation from the tracking results of HYSPLIT tracking mode: (1) The initial relative humidity of air particles is greater than 80%; (2) The specific humidity difference of the starting point position at two consecutive times is less than 0.1 g / kg; The trajectory of the screened air particles is the air particle trajectory that contributes to heavy precipitation.

[0016] Further, the calculation method of the final relative water vapor contribution of each water vapor source in step S6 includes the following sub-steps: S6-1 Screen all air particles with initial relative humidity greater than 80% at the starting point position.

[0017] S6-2 Calculate the precipitation at the starting point position according to the following formula: Where, P sfc represents surface precipitation, g is the acceleration of gravity, represents the water vapor reduction of the initial position air particle, Δ p is the vertical range of the air particle, and is calculated every 1 hour.

[0018] S6-3 Reverse track along the air particle trajectory until the air particle humidity is less than the threshold .

[0019] S6-4 For the water vapor absorption process, record the contribution score f m corresponding to each air particle absorption of water vapor: wherein, m is n the water vapor absorption amount of the air particle at the previous moment of the moment, q m is m the water vapor absorption amount of the air particle at the moment, n the water vapor absorption amount of the air particle at the moment, q n is n the air specific humidity of the air particle at the moment.

[0020] S6-5 for the precipitation process, the total loss of water vapor is Δ q r , then the water vapor amount reduced corresponding to each moment is: S6-6 according to the calculation result, update the water vapor absorption contribution score of each moment.

[0021] S6-7 record the water vapor change amount of the air particle at each moment, and then calculate the water vapor contribution according to the division of the water vapor source, to obtain the water vapor contribution of each water vapor source at the initial moment is: wherein, Δ q z is the total water vapor absorption amount of the air particle in the first z water vapor source.

[0022] Through the research on the water vapor transport characteristics in the extreme snowstorm process, the relative water vapor contribution of each water vapor source of the target region to the snowfall can be quantitatively confirmed, thereby the spatiotemporal accuracy of the snowstorm forecast can be correspondingly improved, reliable decision basis can be provided for the transportation, power, agricultural and other departments of the target region, and the waste of emergency resources caused by forecast deviation can be correspondingly reduced. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is the hourly precipitation distribution diagram of a certain region from 0 o'clock on November 6, 2021 to 0 o'clock on November 9.

[0024] Figure 2 is k the relationship diagram of the value and the corresponding cost function J .

[0025] Figure 3 is the relative contribution diagram of each water vapor source; wherein, (a) is the average specific humidity evolution of the particles along the trajectory and the contribution diagram of each water vapor source which contributes to the snowfall of the target region; (b) is the relative water vapor contribution diagram of different regions when the water vapor in the particles is released. DETAILED DESCRIPTION

[0026] The application will be further described in detail below with reference to the accompanying drawings.

[0027] Taking an extreme snowfall event in a certain area from November 6 to 9, 2021 as an example, the water vapor tracing and quantitative contribution evaluation method for the strong snowfall process of the application includes the following steps: S1, for the extreme snowfall process, according to the hourly precipitation observation data of the national automatic weather station covering the starting period of the extreme snowfall process in a certain area, the cumulative precipitation falling area graph in the duration of the extreme snowfall process is established, and the hourly precipitation distribution graph as shown in the figure is determined. Figure 1 The process strong precipitation falling area in the duration of the extreme snowfall process is the area range of 39 o N-44 o N and 120 o E-125 o E, and the strong precipitation period is from 7:00 on November 7 to 0:00 on November 8.

[0028] S2, according to the determined process strong precipitation falling area and strong precipitation period, the water vapor tracking scheme of the extreme snowfall event is established, and the specific scheme is: according to the water vapor transport characteristics, the height layers of 700 hPa, 850 hPa, 925 hPa and 950 hPa are determined as the trajectory tracking height layers, the process strong precipitation falling area is determined as the trajectory tracking area, the air particles at different height layers in the trajectory tracking area are selected as the starting particles for trajectory tracking, the strongest precipitation time 7:20 is selected as the starting time of the trajectory tracking area, and 10 days of backward trajectory tracking is carried out. The tracking time period is from 20:00 on November 7 to 20:00 on October 28, and the position of the trajectory point is output once an hour.

[0029] S3, using the fifth generation atmospheric reanalysis grid data or other refined grid data published by the European Centre for Medium-Range Weather Forecasts as the initial field of HYSPLIT tracking mode, and screening all air particle trajectory results obtained by tracking, to obtain the trajectory source of air particles that contribute to the strong snowfall process, and determining that the air particles that cause the extreme snowfall process are from the higher layer atmosphere of Eurasia continent and the lower layer atmosphere of North Pacific.

[0030] S4, using K-means clustering method to minimize the cost function as the target, and classifying the screened air particle trajectories according to the elbow rule. The specific operation mode includes the following sub-steps: S4-1, considering that HYSPLIT mode may cause confusion of water vapor trajectory, using K-means cost function minimization principle to classify the air particle trajectories obtained by tracking, and the cost function of K-means J can be expressed as: in, S i Representative belongs to the i The set of all points in a cluster; c i It is the first i The center point of each cluster; x j It is the location of an air particle belonging to this cluster.

[0031] S4-2 Randomly selects several cluster center points as initial center points. Assigns the trajectory of each air particle to the nearest cluster center point. Calculates the average position of all trajectories within each cluster and uses this average position as the new center point for that cluster. Repeat this process until the cluster center points no longer change or the maximum number of iterations is reached. The goal of K-means is to find... i For each cluster, minimize this cost function. J .

[0032] S4-3 Using the elbow rule to determine the number of air particle clusters k For each k Values, calculate and record the corresponding cost function. J Draw Figure 2 shown k Value and corresponding cost function J From the relationship diagram, find the "elbow" point in the curve, which is the cost function. J The point where the rate of descent slows down significantly, this point corresponds to k This value represents the suggested optimal number of clusters, and also the final number of air particle trajectory categories. From Figure 2 As can be seen from the relationship curve, with k As the value gradually increases, the cost function J The value gradually decreased. When k After the value reaches 3, the cost function J The rate of decrease slowed significantly, thus determining the number of air particle clusters. k =3. Therefore, the recommended optimal number of clusters is 3. Similarly, the final air particle trajectory categories in this process are divided into three categories.

[0033] S4-4 Based on the following two screening criteria, select air particles that contribute to heavy precipitation events from the tracking results of the HYSPLIT tracking model: (1) The initial relative humidity of the air particles is greater than 80%; (2) The difference in specific humidity between two consecutive moments at the starting point is less than 0.1 g / kg; The selected air particle trajectories are those that contribute to the heavy precipitation process.

[0034] The particle trajectory distribution leading to this extreme snowstorm process can be represented as follows according to three categories: The first category of trajectory is the northward path, the particles are located in the west of New Land Island ten days ago, pass through the west of a province in the northeast three days ago, and move to the heavy snow area clockwise after entering the BHS region, accounting for 39%.

[0035] The second category of trajectory is the northeast path, the particles originate from the northwest corner of the Okhotsk Sea, move to the Sea of Japan along the southwest direction, and then enter the heavy snow area clockwise through the Yellow Sea, accounting for 9%.

[0036] The third category of trajectory is the northwest path, the particles are located west of the Ural Mountains ten days ago, move to the Sea of Japan along the southeast direction, and then enter the heavy snow area through the Yellow Sea, accounting for 52%.

[0037] S5, according to the sign of dq in the classified air particle trajectory, identify the water vapor source of this extreme snowstorm process. According to the administrative division and geographical region, the water vapor source of this extreme snowstorm process is divided into ten water vapor sources. The ten water vapor sources are EURA, NC, SC, NP, WNP, JS, YS, BHS, ES and SCS.

[0038] S6, using the water vapor source attribution algorithm, quantitatively confirming the average specific humidity evolution of the screened air particles and the final relative water vapor contribution of each water vapor source.

[0039] The calculation method of the final relative water vapor contribution of each water vapor source includes the following sub-steps: S6-1, all air particles with relative humidity greater than 80% at the starting point position are screened.

[0040] S6-2, the precipitation at the starting point position is calculated according to the following formula: wherein, P sfc represents the surface precipitation, g is the gravitational acceleration, represents the water vapor reduction of the initial position air particle, Δ p is the vertical range of the air particle, calculated every 1 hour.

[0041] S6-3, reverse tracking along the air particle trajectory until the air particle humidity is less than the threshold .

[0042] S6-4, for the water vapor absorption process, record the contribution score corresponding to each air particle absorbing water vaporf m is: wherein, m is n the previous time of the time, Δ q m is m the time to n the water vapor absorption amount from the time to q n is n the air specific humidity at the time.

[0043] S6-5 For the precipitation process, the total loss of water vapor is Δ q r , then the water vapor amount reduced corresponding to each time is: S6-6 According to the calculation results, update the water vapor absorption contribution score of each time.

[0044] S6-7 Record the water vapor change amount of the air particle at each time, and then calculate the water vapor contribution according to the division of the water vapor source, to obtain the water vapor contribution of each water vapor source at the initial time is: wherein, Δ q z is the total water vapor absorbed by the air particle in the first z water vapor source.

[0045] As Figure 3 (a) shows the relative contribution of the above ten water vapor sources and the water vapor source of the unknown area, the average specific humidity evolution of the particles along the trajectory and the contribution of each water vapor source which contribute to this extreme snowstorm process. In the figure, 0d corresponds to 20:00 (UTC) on November 7, 2021. The light gray area in the lower left corner represents the water vapor source of the unknown area. The upper boundary of the area represents the total specific humidity. Figure 3 (b) gives the relative water vapor contribution of different regions when the water vapor is released in the particle (0d), which shows the regions with relative contribution higher than 1%.

[0046] Through the evaluation method of the present application, the relative contribution of each water vapor source can be quantitatively obtained, from Figure 3(b) It can be clearly seen that the Sea of Japan (JS) contributed the most to this extreme snowstorm, accounting for 39.7%, followed by North China (NC), accounting for 33.3%, the Yellow Sea (YS), accounting for 15%, and the Bohai Sea (BHS), accounting for 8.4%. Although the tracked particles did not originate from the above-mentioned areas, most of them passed through these areas during their journey to the snowfall area. In general, the substantial moistening of the particles along the way, especially when passing through the sea, is a necessary condition for the occurrence of this heavy snowfall.

Claims

1. A method for strong snow and rain process water vapor tracing and quantitative contribution evaluation, characterized in that, The method comprises the following steps: S1, for a certain heavy snow process in a target area, according to the hourly precipitation observation data of the national automatic weather station covering the heavy snow process in the target area, a cumulative precipitation area graph and an hourly precipitation distribution graph of the heavy snow process are established, and a process heavy precipitation area and a heavy precipitation period in the period are determined; S2, according to the determined process heavy precipitation area and heavy precipitation period, a water vapor tracking scheme of the heavy snow process is established, and a tracking area and a tracking time of the HYSPLIT tracking mode are determined; S3, the fifth generation atmospheric reanalysis grid data or other refined grid data published by the European Medium-Range Weather Forecast Center is used as the initial field of the HYSPLIT tracking mode to determine the trajectory source of the air mass points leading to the heavy snow process; S4, the screened air mass point trajectories are classified to obtain air mass point trajectories that contribute to the heavy snow process; S5, according to the sign of dq in the air mass point trajectories that contribute to the heavy snow process, the water vapor source of the heavy snow process is identified; and the water vapor source of the heavy snow process is divided according to the administrative division and geographical area; S6, the average specific humidity evolution of the screened air mass points and the final relative water vapor contribution of each water vapor source to the heavy snow process are quantitatively confirmed by using a water vapor source attribution algorithm.

2. The method according to claim 1, characterized in that, The water vapor tracking scheme in step S2 is: according to the water vapor transport characteristics, the height layers of 700 hPa, 850 hPa, 925 hPa and 950 hPa are determined as the trajectory tracking height layers, the process heavy precipitation area determined in step S1 is taken as the trajectory tracking area, the air mass points at different height layers in the trajectory tracking area are selected as the starting particles for trajectory tracking, the starting time of the heavy precipitation period determined in step S1 is taken as the starting time of the trajectory tracking area, and 10 days of backward trajectory tracking is performed, and the position of the trajectory point is output once per hour.

3. The method of claim 1, wherein the method further comprises: The specific operation mode of step S4 comprises the following sub-steps: S4-1 utilizes the K-means cost function minimization principle to classify the air particle trajectories obtained by tracking; the cost function of K-means is: J J =∑i≠j∈Ck∥xi-xj∥2 ， wherein, S i a set of points representing all points belonging to the i cluster; c i is a center point of the i cluster; x j is a position point of an air particle belonging to the cluster. S4-2, randomly select a plurality of cluster center points as initial center points, assign each air mass point trajectory to the center point of the nearest cluster, and calculate the average position of all trajectories in each cluster as the new center point of the cluster; repeat the operation until the center points of the clusters no longer change or the maximum number of iterations is reached; S4-3 Confirming the number of air particle cluster by elbow rule k : For each k value, calculate and record the corresponding cost function J , draw the relation graph of k value and corresponding cost function J , find the "elbow" point in the curve, which is the point where the speed of cost function J decrease significantly slows down, and the k value corresponding to this point is the suggested optimal cluster number, which is also the final air particle trajectory category number; S4-4, according to the following two screening conditions, the air mass points that contribute to the heavy precipitation process are screened from the tracking results of the HYSPLIT tracking mode: The initial relative humidity of the air mass point is greater than 80%; The specific humidity difference of the starting point position at two consecutive times is less than 0.1 g / kg; The trajectory of the screened air mass point is the air mass point trajectory that contributes to the heavy precipitation process.

4. The method of claim 1, wherein the method further comprises: The calculation mode of the final relative water vapor contribution of each water vapor source in step S6 comprises the following sub-steps: S6-1, all air mass points with a relative humidity greater than 80% at the starting point position are screened; S6-2, the precipitation at the starting point position is calculated according to the following formula: ， wherein P sfc denotes the surface precipitation, g is the acceleration of gravity, denotes the water vapor reduction of the air particle in the initial position, Δ p is the vertical range of the air particle, calculated every 1 hour; S6-3 backtracks along the air particle motion trajectory until the air particle humidity is less than a threshold ; S6-4 For the water vapor absorption process, record the contribution score corresponding to each time the air particle absorbs water vapor f m is: ， wherein, m is n the time immediately preceding the time, q m is m the time immediately preceding the time, n the amount of water vapor absorption from the time, q n is n the air specific humidity at the time; S6-5 For the precipitation process, the total loss of water vapor is Δ q r Then the amount of water vapor reduced for each time is: ， S6-6, the water vapor absorption contribution score at each time is updated according to the calculation result; S6-7 records the water vapor variation of air particles at each moment, and then calculates the water vapor contribution according to the division of the water vapor source, to obtain the water vapor contribution of each water vapor source at the initial moment is: ， where Δ q z is the total water vapor absorbed by the air particles at the first z water vapor source.

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