Water vapor source and quantitative contribution evaluation method for heavy snow and rain process

Abstract

The application relates to a water vapor tracing and quantitative contribution evaluation method for a strong rain and snow process, and comprises the following steps: for a strong rain and snow process, a cumulative precipitation falling area graph and an hourly precipitation distribution graph are established, a strong precipitation falling area and a strong precipitation period are determined, a water vapor tracking scheme is established, and a tracking area and a tracking time are determined; the HYSPLIT tracking mode is used to determine the trajectory source of air particles leading to the strong rain and snow process; all air particle trajectory results obtained through tracking are screened to obtain contributing air particles; the screened air particle trajectories are classified to obtain the contributing air particle trajectories; the water vapor source of the strong rain and snow process is identified according to the sign of dq in the air particle trajectory; the average specific humidity evolution of the screened air particles and the final relative water vapor contribution of each water vapor source to the strong rain and snow process are quantitatively confirmed. The application can provide reliable decision-making basis for the transportation, power, agriculture and other departments of the target region.

Description

TECHNICAL FIELD

[0001] The application relates to a water vapor tracing method for a strong rain and snow process, in particular to a water vapor tracing and quantitative contribution evaluation method for a strong rain and snow 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 terrain 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 temporal and spatial 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 rain and snow 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, so as to improve the prediction accuracy and timeliness of strong rain and snow processes and enhance the pertinence and effectiveness of disaster prevention and reduction work.

[0005] The purpose of the present application is achieved as follows:

[0006] A strong rain and snow process water vapor tracing and quantitative contribution evaluation method comprises the following steps:

[0007] S1, for a certain strong rain and snow process in a target region, according to the hourly precipitation observation data of the national automatic weather station covering the strong rain and snow process in the target region, a cumulative precipitation falling area map and an hourly precipitation distribution map of the strong rain and snow process are established, and the process heavy precipitation falling area and the heavy precipitation period in the period are determined.

[0008] 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.

[0009] 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.

[0010] S4, classify the screened air particle trajectories to obtain the air particle trajectories that contribute to this heavy rain and snow process.

[0011] 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.

[0012] 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.

[0013] 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.

[0014] Further, the specific operation mode of the step S4 includes the following sub-steps:

[0015] 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:

[0016]

[0017] Wherein, S i represent 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.

[0018] S4-2 Randomly select several cluster center points as initial center points, assign each air particle trajectory to the nearest cluster center point, and calculate the average position of all trajectories in each cluster as the new center point of the cluster; repeat this operation until the cluster center points no longer change or the maximum number of iterations is reached.

[0019] 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 a graph of 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.

[0020] S4-4 According to the following two screening conditions, screen out air particles that contribute to the strong precipitation process from the tracking results of the HYSPLIT tracking model:

[0021] (1) The initial relative humidity of the air particle is greater than 80%;

[0022] (2) The specific humidity difference of the starting point position at two consecutive times is less than 0.1 g / kg;

[0023] The trajectory of the screened air particle is the trajectory of the air particle that contributes to the strong precipitation process.

[0024] Further, the calculation method of the final relative water vapor contribution of each water vapor source in step S6 includes the following sub-steps:

[0025] S6-1 Screen all air particles with initial point position relative humidity greater than 80%.

[0026] S6-2 Calculate the precipitation at the starting point position according to the following formula:

[0027]

[0028] where, P sfc represents the surface precipitation, g is the acceleration of gravity,

[0029] 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.

[0030] S6-3 reversely tracks along the air particle motion trajectory until the air particle humidity is less than a threshold value .

[0031] S6-3 reversely tracks along the air particle motion trajectory until the air particle humidity is less than a threshold value f m is:

[0032]

[0033] wherein, m is n the previous moment of the moment, Δ q m is m the water vapor absorption amount from the moment to n the moment, q n is n the air specific humidity at the moment.

[0034] S6-5 for the precipitation process, the total lost water vapor is Δ q r , then the water vapor amount reduced at each moment is:

[0035]

[0036] S6-6 updates the water vapor absorption contribution score at each moment according to the calculation result.

[0037] S6-7 records the water vapor change amount of the air particle 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:

[0038]

[0039] wherein, Δ q z is the total water vapor absorbed by the air particle in the first z water vapor source.

[0040] By researching 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, so that the spatial and temporal accuracy of the snowstorm forecast can be correspondingly improved, reliable decision basis can be provided for the transportation, power, agriculture and other departments of the target region, and the waste of emergency resources caused by the forecast deviation can be correspondingly reduced. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is the hourly precipitation distribution diagram of a certain region from 0:00 on November 6, 2021 to 0:00 on November 9.

[0042] Figure 2 is k Value and corresponding cost function J Relationship diagram.

[0043] Figure 3 is the relative contribution map of each water vapor source; wherein (a) is the average specific humidity evolution along the trajectory of the particles that contribute to the snowfall in the target area and the contribution map of each water vapor source; (b) is the relative water vapor contribution map of different regions when the water vapor in the particles is released. DETAILED DESCRIPTION

[0044] The application will be further described in detail below with reference to the accompanying drawings.

[0045] 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:

[0046] 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 map in the duration of the extreme snowfall process is established, and the hourly precipitation distribution map as shown in Figure 1 The process heavy precipitation falling area in the duration of the extreme snowfall process is determined to be the area range of 39 o N-44 o N and 120 o E-125 o E, and the heavy precipitation period is from 00 on November 7 to 00 on November 8.

[0047] S2, according to the determined process heavy precipitation falling area and heavy precipitation period, the water vapor tracing 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 heavy 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 of the trajectory tracking, the strongest precipitation moment 20 on November 7 is selected as the starting moment of the trajectory tracking area, and 10 days of backward trajectory tracking is carried out, the tracking time period is from 20 on November 7 to 20 on October 28, and the position of the trajectory point is output once an hour.

[0048] S3. Using the fifth-generation atmospheric reanalysis grid data or other refined grid data released by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the initial field for the HYSPLIT tracking model, and screening all the air particle trajectory results obtained from the tracking, the trajectory sources of the air particles that ultimately contributed to this heavy rain and snow event were determined, and the air particles that caused this extreme snowfall event originated from the upper atmosphere of Eurasia and the lower atmosphere of the North Pacific.

[0049] S4. Using the K-means clustering method with the goal of minimizing the cost function, and based on the elbow rule, classify the trajectories of the filtered air particles. The specific operation includes the following sub-steps:

[0050] S4-1 Considering that the HYSPLIT model may lead to chaotic water vapor trajectories, the K-means cost function minimization principle is used to classify the tracked air particle trajectories. The K-means cost function... J It can be represented as:

[0051]

[0052] 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.

[0053] 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 .

[0054] 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 kThis 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.

[0055] 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:

[0056] (1) The initial relative humidity of the air particles is greater than 80%;

[0057] (2) The difference in specific humidity between two consecutive moments at the starting point is less than 0.1 g / kg;

[0058] The selected air particle trajectories are those that contribute to the heavy precipitation process.

[0059] The particle trajectory distribution that caused this extreme blizzard can be represented by three categories as follows:

[0060] The first type of trajectory is a northerly path. Ten days ago, the particles were located in the western part of Novaya Zemlya. Three days ago, they passed through the western part of a northeastern province and entered the BHS region. After that, they turned clockwise and moved to the area of ​​heavy snowfall. This type of trajectory accounts for 39% of the total number of particles.

[0061] The second type of trajectory is the northeast path, with particles originating from the northwest corner of the Sea of ​​Okhotsk, moving southwest to the Sea of ​​Japan, and then turning clockwise through the Yellow Sea into the area of ​​heavy snowfall, accounting for 9%.

[0062] The third type of trajectory is the northwest path. Ten days ago, the particle was located west of the Ural Mountains, moved southeast to the Sea of ​​Japan, and then entered the heavy snowfall area via the Yellow Sea, accounting for 52%.

[0063] S5. Based on the symbols of dq in the classified air particle trajectories, identify the water vapor source region for this extreme snowstorm. Divide the water vapor source regions for this extreme snowstorm according to administrative divisions and geographical regions, establishing ten water vapor source regions. These ten water vapor source regions are: Eurasia (EURA), North China (NC), South China (SC), North Pacific (NP), Northwest Pacific (WNP), Sea of ​​Japan (JS), Yellow Sea (YS), Bohai Sea (BHS), East China Sea (ES), and South China Sea (SCS).

[0064] S6, using the water vapor source attribution algorithm, quantitatively confirm the average specific humidity evolution of the screened air particles and the final relative water vapor contribution of each water vapor source.

[0065] The calculation method of the final relative water vapor contribution of each water vapor source includes the following sub-steps:

[0066] S6-1, all air particles with a relative humidity greater than 80% at the starting point position are screened.

[0067] S6-2, the precipitation at the starting point position is calculated according to the following formula:

[0068]

[0069] wherein, P sfc represents surface precipitation, g is the acceleration of gravity, represents the water vapor reduction amount of the air particle at the initial position, Δ p is the vertical range of the air particle, and is calculated every 1 hour.

[0070] S6-3, reverse tracking along the air particle motion trajectory until the air particle humidity is less than the threshold .

[0071] 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:

[0072]

[0073] wherein, m is n the previous moment of the moment, Δ q m is m the water vapor absorption amount from the moment to n , Δ q n is n the air specific humidity at the moment.

[0074] S6-5, for the precipitation process, the total loss of water vapor is Δ q r , then the reduced water vapor amount corresponding to each moment is:

[0075]

[0076] S6-6, update the water vapor absorption contribution score of each moment according to the calculation result.

[0077] S6-7 records the water vapor variation of each air particle 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 For:

[0078]

[0079] where, Δ q z is the total water vapor absorbed by the air particle in the first z water vapor source.

[0080] 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 to this extreme snowstorm process. 0d in the figure 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) shows the relative water vapor contribution of different regions when the water vapor in the particles is released (0d), which shows the regions with a relative contribution higher than 1%.

[0081] 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 for this extreme snowstorm process, the Sea of Japan (JS) contributes the most, accounting for 39.7%; followed by North China (NC), accounting for 33.3%; the Yellow Sea (YS) contributes 15%; the Bohai Sea (BHS) contributes 8.4%. Although the tracked particles are not from the above-mentioned areas, most of the particles pass through the above-mentioned areas during the movement to the snowfall area. In general, the substantial humidification of the particles along the way, especially through the ocean, is a necessary condition for the occurrence of this heavy snowfall.

Claims

1. A method for tracing the source and quantitatively assessing the contribution of water vapor in a severe rain and snow process, characterized in that, Includes the following steps: S1. For a certain heavy rain and snow process in the target area, based on the hourly precipitation observation data of the national automatic weather stations in the target area covering this heavy rain and snow process, establish a cumulative precipitation area map and an hourly precipitation distribution map during the duration of this heavy rain and snow process, and determine the heavy precipitation area and heavy precipitation period during this period. S2. Based on the determined areas and periods of heavy precipitation, establish a water vapor tracking scheme for this heavy rain and snow process, and determine the tracking area and tracking time of the HYSPLIT tracking mode. S3. Use the fifth-generation atmospheric reanalysis grid data or other refined grid data released by the European Centre for Medium-Range Weather Forecasts as the initial field of the HYSPLIT tracking model to determine the trajectory source of the air particles that caused this heavy rain and snow event. S4. Classify the filtered air particle trajectories to obtain the air particle trajectories that contributed to this heavy rain and snow process. S5. Identify the water vapor source areas of this heavy rain and snow event based on the symbols of dq in the trajectories of air particles that contributed to the event; classify the water vapor source areas of this heavy rain and snow event according to administrative divisions and geographical regions. S6. Using a water vapor source attribution algorithm, quantitatively confirm 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. The calculation of the final relative water vapor contribution of each water vapor source in step S6 includes the following sub-steps: S6-1 screening yielded all air particles with a relative humidity greater than 80% at the starting point. S6-2 calculates the precipitation at the starting point using the following formula: ， in, P sfc Indicates surface precipitation. g It is the acceleration due to gravity. Δ represents the decrease in water vapor content of air particles at the initial position. p The vertical range of air particles is calculated every hour. S6-3 tracks the air particles in reverse along their trajectory until the humidity of the air particles is less than 0.2 g / kg; S6-4 records the contribution fraction of each air particle absorbing water vapor during the water vapor absorption process. f m for: ， in, m for n The moment before time, Δ q m for m Time's up n The amount of water vapor absorbed at any given time. q n for n The air is more humid at that moment; For the precipitation process, the total water vapor loss in S6-5 is Δ q r This corresponds to the decrease in water vapor at each moment. for: ， S6-6 updates the water vapor absorption contribution score at each moment based on the calculation results; S6-7 records the change in water vapor content of air particles at each moment, and then calculates the water vapor contribution according to the division of water vapor source areas to obtain the water vapor contribution of each water vapor source area at the initial moment. for: ， Where, Δ q z For air particles in the first z The total amount of water vapor absorbed by each water vapor source.

2. The method for tracing the source and quantitatively assessing the contribution of water vapor in a severe rain and snow process according to claim 1, characterized in that, The water vapor tracking scheme in step S2 is as follows: Based on the characteristics of water vapor transport, the altitude layers of 700 hPa, 850 hPa, 925 hPa and 950 hPa are determined as the trajectory tracking altitude layers. The heavy precipitation area determined in step S1 is used as the trajectory tracking area. Air particles at different altitude layers within the trajectory tracking area are selected as the starting particles for trajectory tracking. The start time of the heavy precipitation period determined in step S1 is used as the start time of the trajectory tracking area. Backward trajectory tracking is performed for 10 days, and the position of the trajectory point is output once per hour.

3. The method for tracing the source and quantitatively assessing the contribution of water vapor in a severe rain and snow process according to claim 1, characterized in that, The specific operation method of step S4 includes the following sub-steps: S4-1 uses the principle of minimizing the K-means cost function to classify the tracked air particle trajectories; the K-means cost function J for: ， in, S i Representative belongs to the first 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 that cluster; S4-2 randomly selects the center points of several clusters as the initial center points, assigns the trajectory of each air particle to the center point of the nearest cluster, and calculates the average position of all trajectories within each cluster as the new center point of that cluster; repeats this operation until the center point of the cluster no longer changes or the maximum number of iterations is reached; 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 k Value and corresponding cost function J From the relationship diagram, identify the "elbow" points on the curve, which are the cost functions. J The point where the rate of descent slows down significantly, this point corresponds to k The value represents the suggested optimal number of clusters, and also the final number of air particle trajectory categories; S4-4 selects air particles that contribute to heavy precipitation events from the tracking results of the HYSPLIT tracking model based on the following two screening criteria: The initial relative humidity of the air particles is greater than 80%; 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.

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