Vertical three-dimensional quantitative tracking and tracing method for heat of heat wave event

Abstract

The invention relates to the field of climate change, and discloses a vertical three-dimensional quantitative tracking and tracing method for heat of a heat wave event, and the method comprises the steps: collecting data; identifying the space-time range of the high-temperature heat wave event; heat sources with abnormal temperature during the high-temperature heat wave event are tracked; contribution quantification of a heat source driving factor during a high-temperature heat wave event; analyzing vertical characteristics of atmospheric heat transfer; according to the method, the heat abnormality of the heat wave event is traced, and the thermodynamic structure characteristics of the atmospheric temperature abnormality of the whole troposphere and the multi-scale driving mechanism of the heat abnormality are disclosed in combination with the vertical section analysis, so that the understanding of the extreme high-temperature event is deepened; and a scientific basis is provided for formulating a more accurate disaster prevention and reduction strategy for regional climate abnormity caused by global climate change.

Description

Technical Field

[0001] This invention relates to the field of climate change, and in particular to a method for vertical three-dimensional quantitative tracking and source tracing of heat waves. Background Technology

[0002] Against the backdrop of global warming, extreme heat waves are becoming more frequent and intensified worldwide. These events are typically accompanied by significant non-adiabatic heating processes, leading to increased near-surface sensible heat flux, sustained increases in near-surface temperatures, and further creating a vicious cycle of land-atmosphere interactions. Climate warming not only exacerbates the intensity of heat waves but also prolongs their duration and increases their frequency, causing a significant shift in the probability distribution of extreme heat events.

[0003] High-temperature heat waves pose multidimensional challenges to human society, the economy, and natural ecosystems. Although existing research has extensively explored heat anomalies, key scientific questions remain regarding the physical driving mechanisms of heat accumulation: traditional methods struggle to dynamically track and quantify the spatiotemporal contributions of different thermodynamic processes, failing to accurately elucidate the formation mechanisms of high-temperature events. Particularly in the context of climate change, the thermodynamic evolution along air mass transport paths involves complex synergies of advection, adiabatic changes, and non-adiabatic heating processes, and the dominant mechanisms of these processes often exhibit significant spatiotemporal heterogeneity. Therefore, there is an urgent need to develop a dynamic heat tracking method based on the Lagrange framework, combined with temperature anomaly decomposition equations, to quantitatively assess the contribution of different physical processes to extreme high temperatures. This research approach will help improve the predictive capabilities of heat wave events and provide theoretical basis and scientific support for the risk prevention and control of high-temperature disasters. Summary of the Invention

[0004] To address the lack of detailed research on the physical mechanisms of anomalous heat accumulation during heat waves, this method provides a vertical, three-dimensional quantitative tracking and source tracing approach for heat waves. By identifying the spatiotemporal extent of heat waves, dynamically tracking the heat sources of temperature anomalies, and quantifying the contribution rates of different driving factors, it systematically analyzes the formation mechanism of heat anomalies. The method focuses on exploring the vertical structural characteristics of atmospheric heat transport, the thermodynamic driving processes of heat anomalies during heat waves, and the spatial differentiation of the thermodynamic structure and physical driving mechanisms of tropospheric temperature anomalies. The innovation of this method lies in its first-ever dynamic quantitative separation of the sources of heat accumulation during heat waves, overcoming the limitations of traditional methods in analyzing the synergistic effects of physical processes. This framework can provide theoretical and technical support for the refined prediction of regional heat waves under the background of global climate change and provide a scientific basis for risk assessment and optimization of disaster prevention and mitigation strategies for high-temperature disasters.

[0005] This invention provides a method for vertical three-dimensional quantitative tracking and source tracing of heat in heat wave events, specifically including the following steps: Step S1: Data Acquisition; Collect data for the study area, including: daily average temperature observation data, HYSPLIT driven data from the hybrid single-particle Lagrange integral trajectory model, and diurnal scale data; Step S2: Identification of the spatiotemporal range of the high temperature heat wave event; Combining the daily average temperature observation data obtained in Step S1, calculate the anomaly value of the daily temperature relative to its sliding climatological temperature, identify the maximum positive temperature anomaly, and define its high temperature peak date, high temperature core area, and heat wave main area; Step S3: Tracking the heat source of temperature anomalies during high-temperature heat wave events; Combining the HYSPLIT hybrid single-particle Lagrange integral trajectory model obtained in Step S1 with the data-driven model, the heat source and transport process of heat changes during heat wave events are tracked by the backward trajectory of air masses, thus providing technical support for heat tracking and constructing a vertical three-dimensional heat tracking model. Step S4: Quantify the contribution of heat source driving factors during high-temperature heat wave events; Combine the heat source tracked by the vertical three-dimensional heat model obtained in Step S3, use the Lagrange temperature anomaly equation to quantify the contribution of advection transport, adiabatic and non-adiabatic processes of heat accumulation to the heat source of high-temperature heat wave events along the air mass movement trajectory, and analyze the main physical processes that lead to extreme high temperatures. Step S5: Vertical characteristic analysis of atmospheric heat transport; Combine the reanalysis data in the diurnal data obtained in Step S1 to calculate atmospheric wet hydrostatic energy and saturated wet hydrostatic energy, explore the vertical characteristics of atmospheric thermodynamics of high temperature heat wave events, and analyze the physical driving structure of the temperature anomaly of the entire troposphere during the high temperature heat wave from the perspective of vertical profile.

[0006] A storage device that stores instructions and data for implementing a method for vertical three-dimensional quantitative tracking and tracing of heat waves.

[0007] A vertical three-dimensional quantitative tracking and tracing device for heat waves includes a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement a vertical three-dimensional quantitative tracking and tracing method for heat waves.

[0008] The beneficial effects provided by this invention are: (1) This invention identifies the spatiotemporal range of high-temperature heat wave events through dynamic thresholds and constructs a vertical three-dimensional heat tracking model based on the Lagrange tracking model to track the heat sources and transport processes of heat changes during heat wave events. It quantifies the contribution of advection transport, adiabatic, and non-adiabatic processes to the heat sources of high-temperature heat wave events, analyzes the main physical processes that lead to extreme high-temperature events, and realizes the accurate quantification of the heat accumulation mechanism of high-temperature heat wave events.

[0009] (2) This invention further combines vertical structure analysis to reveal the physical driving differences of temperature anomalies in the entire troposphere, clarify the vertical structure of tropospheric atmospheric temperature anomalies during heat wave events, and elucidate the differentiated dominant role of adiabatic and non-adiabatic processes in different longitude regions, breaking through the limitations of traditional static models in being unable to analyze the dynamic evolution of heat transport. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the implementation of a vertical three-dimensional quantitative tracking and source tracing method for heat waves. Figure 2 It is the temporal evolution of near-surface temperature anomalies in the high-temperature peak region of the Yangtze River Basin heat wave event; Figure 3 This refers to the heat source and physical factors contributing to the near-surface high temperature anomaly during the 2022 Yangtze River Basin heat wave event. Figure 4 It is the source of heat and physical factors contributing to the near-surface high temperature anomaly during the 2006 Yangtze River Basin heat wave event; Figure 5 It is the source of heat and physical factors contributing to the near-surface high temperature anomaly during the 1978 Yangtze River heat wave event; Figure 6 It is the source of heat and physical factors contributing to the near-surface high temperature anomaly during the 1959 Yangtze River heat wave event; Figure 7 This refers to the peak of the 2022 Yangtze River basin heatwave event, specifically the abnormal 5-day average atmospheric temperature (August 21-25, 2022). The air pressure – longitude vertical profile (average 24.25 °N to 34.25 °N); Figure 8 This refers to the peak of the 2006 Yangtze River basin heatwave event, with an abnormal 5-day average atmospheric temperature (August 30 to September 3, 2022). The air pressure – longitude vertical profile (average 24.25 °N to 34.25 °N); Figure 9 This refers to the peak of the 1978 Yangtze River basin heatwave event, with an abnormal 5-day average atmospheric temperature (July 6th to July 10th, 2022). The air pressure – longitude vertical profile (average from 27.5 °N to 37.5 °N); Figure 10 This refers to the peak of the 1959 Yangtze River basin heatwave event, with an abnormal 5-day average atmospheric temperature (August 15 to September 1, 2022). The air pressure – longitude vertical profile (average 27.75 °N to 37.75 °N); Figure 11 This is a schematic diagram of the hardware device of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0012] This invention provides a method for vertical three-dimensional quantitative tracking and source tracing of heat waves, specifically including the following steps: Step S1: Data Acquisition; Collect data for the study area, including: daily average temperature observation data, HYSPLIT driven data from the hybrid single-particle Lagrange integral trajectory model, and diurnal scale data; It should be noted that step S1 specifically includes: We collected near-surface 2-meter daily average air temperature observation data from the CN05.1 daily gridded dataset for China; we also collected HYSPLIT driven data from the National Center for Environmental Prediction-National Center for Atmospheric Research (NECP / NCAR) provided by the United States, with variables including surface pressure, precipitation, near-surface 2-meter air temperature, near-surface 10-meter zonal and meridional winds, geopotential height, whole-layer air temperature, whole-layer zonal and meridional winds, and vertical velocity; we collected diurnal data from NECP / NCAR, with variables including whole-layer air temperature, geopotential height, near-surface 2-meter daily average air temperature, and specific humidity; and we collected reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Generation 5, with monthly variables including surface pressure, zonal and meridional winds, and specific humidity, and diurnal variables including near-surface 2-meter daily average air temperature and vertical velocity synthesized from 6-hour data.

[0013] Further, in step S1, the observation dataset CN05.1 is a high-resolution gridded observation product based on observation data from over 2,400 national-level stations (basic, benchmark, and general stations) of the National Meteorological Information Center, with a spatial resolution of 0.25°×0.25° and a time span of 1961–2023; the HYSPLIT hybrid single-particle Lagrange integral trajectory model driving data consists of surface and barosphere data from the NECP / NCAR reanalysis data of the National Center for Environmental Prediction-National Center for Atmospheric Research, with a temporal resolution of 6 hours and a spatial resolution of 2. The spatial resolution is 2.5° × 2.5°, and the barospheres include 10 hPa–1000 hPa (17 barospheres); daily surface and barosphere data from the National Center for Environmental Prediction-National Center for Atmospheric Research (NECP / NCAR) reanalysis data, with a spatial resolution of 2.5° × 2.5°, and the barospheres include 10 hPa–1000 hPa (17 barospheres); monthly surface and barosphere data from the European Centre for Weather Prediction (ECMWF) Generation 5 reanalysis data, with a spatial resolution of 0.25° × 0.25°, and the barospheres include 1 hPa–1000 hPa (37 barospheres).

[0014] Step S2: Identification of the spatiotemporal range of the high temperature heat wave event; Combining the daily average temperature observation data obtained in Step S1, calculate the anomaly value of the daily temperature relative to its sliding climatological temperature, identify the maximum positive temperature anomaly, and define its high temperature peak date, high temperature core area, and heat wave main area; In step S2, the daily average temperature observation data obtained in step S1 is the daily average temperature at 2 meters above the ground. Using the study period as the center date, the moving average of this data for 5 years before and after the study period, and for 7 days before and after (11 × 15 = 165 days), is calculated as the climatological temperature. Then, the anomaly value of the daily temperature within the study period relative to its moving climatological temperature is calculated, and the maximum positive anomaly value of the daily temperature is identified. The corresponding date and location are defined as the peak date and peak location. The peak period range of the heat wave event is defined as 5 days centered on the peak date, and the spatial range is defined as a 10° × 10° range centered on the peak location, as shown in the following formula:

[0015]

[0016] In the formula, Here, i represents the climatological temperature, i represents the year offset, which ranges from 5 years before and after the current date, for a total of 11 values, and j represents the date offset, which ranges from 7 days before and after the current date, for a total of 15 values. The average daily temperature at 2 meters above the ground. It is the anomaly between the daily average temperature and its sliding climatological temperature.

[0017] Step S3: Tracking the heat source of temperature anomalies during high-temperature heat wave events; Combining the HYSPLIT hybrid single-particle Lagrange integral trajectory model obtained in Step S1 with the data-driven model, the heat source and transport process of heat changes during heat wave events are tracked by the backward trajectory of air masses, thus providing technical support for heat tracking and constructing a vertical three-dimensional heat tracking model. In step S3, a Lagrange temperature anomaly equation is constructed based on the thermodynamic energy equation. A vertical three-dimensional heat tracking model, constructed using the HYSPLIT hybrid single-particle Lagrange integral trajectory model, is used to track the heat source of temperature anomalies during high-temperature heat waves, as shown in the following equation:

[0018] In the formula, Climatic temperature, This is a temperature anomaly, calculated from the difference between the temperature of an air mass at a specific location and time along its trajectory and the corresponding climatological temperature. This indicates the time step of the air mass along its trajectory ( ). Abnormal temperature changes (6 hours). For horizontal wind speed, For gradient operators, For air pressure, For standard reference pressure, Vertical velocity, For potential temperature, These variables are constants; they are all values ​​corresponding to the location of the air mass along its trajectory, where temperature, pressure, and potential temperature are all obtained from the output of the HYSPLIT model. The values ​​were obtained by interpolation from NCEP / NCAR reanalysis data.

[0019] Step S4: Quantify the contribution of heat source driving factors during high-temperature heat wave events; Combine the heat source tracked by the vertical three-dimensional heat model obtained in Step S3, use the Lagrange temperature anomaly equation to quantify the contribution of advection transport, adiabatic and non-adiabatic processes of heat accumulation to the heat source of high-temperature heat wave events along the air mass movement trajectory, and analyze the main physical processes that lead to extreme high temperatures. In step S4, based on the heat source tracking results of the vertical thermal model obtained in step S3, which traces the heat sources of temperature anomalies during high-temperature heat waves, the Lagrange temperature anomaly equation is further calculated and quantified to determine the contributions of advection heat transport, adiabatic heating, and non-adiabatic heating processes to the increase in near-surface temperature in the basin during the heat wave event. From a Lagrange perspective, the air mass is located... and time abnormal temperature at time The source can be broken down into factors from the onset time of the temperature anomaly. Accumulated to The contributions of each physical process during time are given by the following formula:

[0020] In the equation, the four terms on the right-hand side represent temperature anomalies caused by changes in climatological temperature over time, temperature anomalies caused by horizontal advection of the climatological temperature gradient, and temperature anomalies caused by vertical motion (adiabatic). Temperature anomalies caused by non-adiabatic processes along the trajectory.

[0021] Step S5: Vertical characteristics analysis of atmospheric heat transport; Combine the reanalysis data in the diurnal data obtained in Step S1 to calculate atmospheric wet hydrostatic energy and saturated wet hydrostatic energy, explore the vertical characteristics of atmospheric thermodynamics of high temperature heat wave events, and analyze the physical driving structure of the temperature anomaly of the entire troposphere during the high temperature heat wave from the perspective of vertical profile. In step S5, to investigate the role of vertical temperature structure related to convective stability in the development of heat waves, atmospheric hydrostatic energy ( ) and saturated wet hydrostatic energy ( To analyze the atmospheric moisture convection stability, the following formula is used:

[0022]

[0023] In the formula, This represents the specific heat capacity at constant pressure. Indicates height The temperature at that location It is the latent heat of vaporization. It is gravitational acceleration. For height The height of the position, For height The saturated specific humidity at that location.

[0024] The embodiments use extreme high-temperature heat wave events in the Yangtze River Basin in 1959, 1978, 2006, and 2022 as examples to further describe the technical solution of the present invention based on a vertical three-dimensional quantitative tracking and tracing method for heat waves. These embodiments are for illustrative purposes only and are not intended to limit the scope of application of the invention; they are equally applicable to different regions or time periods.

[0025] The method of this invention is a vertical three-dimensional quantitative tracking and source tracing method for heat waves, and the implementation flowchart is as follows: Figure 1 As shown, the specific steps are as follows: (1) Data collection; In this embodiment, near-surface 2-meter daily average air temperature (unit: K) with a spatial resolution of 0.25°×0.25° was collected from the daily gridded dataset CN05.1 for China from 1961 to 2023. Six-hourly data with a spatial resolution of 2.5°×2.5° from 1948 to 2022 were collected from the NECP / NCAR reanalysis data of the National Center for Environmental Prediction and Research at the National Center for Atmospheric Research (NCER) in the United States as driving data for the HYSPLIT mixed single-event Lagrange integral trajectory model. This included surface air pressure (hPa), 6-hour cumulative precipitation (m), near-surface 2-meter air temperature (K), and near-surface 10-meter zonal and meridional winds (ms). -1 ), and geopotential height (in gpm), air temperature (in K), zonal wind and meridional wind (in m / s) at 10 hPa–1000 hPa (17 pressure layers). -1 ) and vertical velocity (unit: ms) -1 )data; We collected diurnal air temperature (K) at 10 hPa–1000 hPa, geopotential height (m) at 500 hPa and 850 hPa, and specific humidity (kg / kg) at a spatial resolution of 2.5° × 2.5° from 1948 to 2022 from the National Center for Environmental Prediction–National Center for Atmospheric Research (NECP / NCAR) reanalysis data. -1 ), surface geopotential height (unit: m), daily average air temperature at 2 meters above the ground (unit: K), and specific humidity at 2 meters above the ground (unit: kg). -1 )data; It also collected monthly surface pressures with a spatial resolution of 0.25°×0.25° from 1950 to 2023, as well as specific humidity (unit: kg kg) from 1 hPa to 1000 hPa (37 pressure layers), from the European Centre for Weather Forecasts' fifth-generation reanalysis data (ERA5). -1 Zonal winds and meridional winds (unit: m / s) -1 Data, diurnal 500 hPa vertical velocity (unit: Pa·s) synthesized from 6-hour data. -1 Table 1 shows the selected data. Table 1 Main Data Information

[0026] (2) Identification of the spatiotemporal range of high-temperature heat wave events; In this embodiment, based on the near-surface 2-meter daily average temperature data obtained in embodiment (1), the anomaly value of the daily temperature relative to its sliding climatological temperature is calculated, the maximum positive temperature anomaly is identified, and the peak date and location of the high temperature are defined. A 10°×10° range is delineated as the spatial range of the heat wave event, centered on the identified high temperature peak location. The 1959 and 1978 heat wave events were concentrated in the middle and lower reaches of the river, the 2006 heat wave event was concentrated in the Sichuan-Chongqing region, and the 2022 heat wave event exhibited record-breaking high temperatures across the entire basin. This example uses ERA5 data to identify heat wave events and further verifies the identification results using CN05.1 data. Table 2 shows the peak periods and ranges of the four heat wave events determined based on the ERA5 results, as follows:

[0027]

[0028] In the formula, Here, i represents the climatological temperature, i represents the year offset, which ranges from 5 years before and after the current date, for a total of 11 values, and j represents the date offset, which ranges from 7 days before and after the current date, for a total of 15 values. The average daily temperature at 2 meters above the ground. It is the anomaly between the daily average temperature and its sliding climatological temperature.

[0029] Table 2. Definition of study areas for high-temperature heat waves in the Yangtze River Basin in 1959, 1978, 2006, and 2022.

[0030] (3) Tracking the heat sources of abnormal temperatures during heat waves; In this embodiment, the HYSPLIT data-driven model driven by the hybrid single-particle Lagrange integral trajectory model obtained in embodiment (1) is used to track the heat changes during the heat wave event by tracking the backward trajectory of the air mass. A vertical three-dimensional heat tracking model is constructed to further quantify the heat source and track the air mass movement trajectory at a height of 100 m–10000 m (100 m intervals). Meteorological element information such as temperature and potential temperature of the air mass along the trajectory is obtained.

[0031] Based on the thermodynamic energy equation, a Lagrange temperature anomaly equation is constructed. Combined with the HYSPLIT mixed single-particle Lagrange integral trajectory model, the heat source of temperature anomalies during high-temperature heat waves is traced, as shown in the following equation:

[0032] In the formula, Climatic temperature, This is a temperature anomaly, calculated from the difference between the temperature of an air mass at a specific location and time along its trajectory and the corresponding climatological temperature. This indicates the time step of the air mass along its trajectory ( ). Abnormal temperature changes (6 hours). For horizontal wind speed, For gradient operators, For air pressure, Standard reference pressure ( =1000 hPa), Vertical velocity, For potential temperature, A constant ( These variables represent the corresponding values ​​of the air mass along its trajectory, with temperature, pressure, and potential temperature all derived from the HYSPLIT model output. The values ​​were obtained by interpolation from NCEP / NCAR reanalysis data.

[0033] (4) Quantification of the contribution of heat source driving factors during high-temperature heat wave events; In this embodiment, based on the heat source tracking results of the vertical thermal model obtained in embodiment (3) for tracking the heat source of temperature anomalies during high-temperature heat waves, further calculations of the Lagrange temperature anomaly equation can quantify the contributions of advection heat transport, adiabatic heating, and non-adiabatic heating processes to the increase in near-surface temperature in the Yangtze River basin during heat wave events. From a Lagrange perspective, the air mass is located... and time abnormal temperature at time The source can be broken down into factors from the onset time of the temperature anomaly ( ) accumulated to The contributions of each physical process during time are given by the following formula:

[0034] In the equation, the four terms on the right-hand side represent the temperature anomalies caused by the change of climatological temperature over time, the temperature anomalies caused by the horizontal advection of the climatological temperature gradient, the temperature anomalies caused by vertical motion, and the temperature anomalies caused by non-adiabatic processes along the trajectory.

[0035] Figure 2 This study illustrates the temporal evolution of near-surface temperature anomalies in the peak temperature regions during four heatwave events in the Yangtze River basin. In the 1959 event, the temperature anomaly gradually increased before August 21st and decreased after the peak, but rebounded after September 2nd due to atmospheric stability inhibiting convection. The 1978 event showed a continuous upward trend, reaching its maximum on July 8th. The 2006 event saw an overall increase in temperature anomalies, with high temperatures concentrated between August 30th and September 3rd. The 2022 event peaked on August 22nd and then decreased due to cold advection from Siberia. Figure 3 As shown, the non-adiabatic heating that dominated the 2022 heat wave event was mainly driven by non-adiabatic processes, resulting in the accumulation of near-surface high-temperature heat. The westward extension of the Western Pacific subtropical high was manifested as enhanced subsidence, leading to adiabatic warming to compensate for the earlier cooling. The advection of cold air from Siberia was the main reason for the end of the high temperature event.

[0036] Figure 4 This study reveals the heat source and physical driving mechanism of near-surface high temperature anomalies during the 2006 Yangtze River Basin heat wave (August 25–September 4): Near-surface heat accumulation in the high-temperature peak area (101°E–111°E, 24.25°N–34.25°N) was mainly dominated by non-adiabatic heating processes. The air mass originated from the local Yangtze River Basin and the East Asian–South Asian continental margin sea (South China Sea / West Pacific Ocean), and continuously absorbed surface sensible heat as it rose from east to west under the influence of easterly winds. The high-temperature anomaly fluctuations were regulated by adiabatic processes. From August 30 to September 1, the westward extension of the high-pressure cell triggered enhanced subsidence, and the air mass compression and warming led to a temporary increase in the adiabatic contribution. On September 4, the advection of cold air from Siberia turned the advection process into a negative contribution, and the near-surface temperature dropped abruptly, marking the end of the heat wave.

[0037] Figure 5 This study demonstrates the characteristics of near-surface heat transport during the 1978 Yangtze River Basin heat wave (June 22–July 15): In the early stage of the heat wave (June 25–July 5), the air mass originated from the western Pacific Ocean, and heat accumulation was dominated by advection transport and non-adiabatic heating; in the middle stage (July 5–July 8), the Tibetan Plateau became the core heat source, and the air mass descended from the plateau to the Yangtze River Basin, significantly enhancing the contribution of adiabatic compression and warming; during the peak of the heat wave, the subtropical high merged with the Tibetan high, and the violent descent of the air mass led to the maximum contribution of adiabatic heating, with advection heat transport simultaneously increasing; in the later stage (July 12), the subtropical high retreated eastward, and advection heat transport dominated heat accumulation, alleviating the heat wave.

[0038] Figure 6 The study elucidates the near-surface heat transport mechanism during the 1959 Yangtze River Basin heat wave (August 15–September 5): In the early to mid-stages of the heat wave (August 17–August 25), the air mass originated from the Bay of Bengal and the Indochina Peninsula, and was lifted and transported to the Yangtze River Basin under the drive of the southwest monsoon, with non-adiabatic heating dominating the heat accumulation; after the peak of the heat wave (August 25–August 31), the western ridge of the subtropical high controlled the Yangtze River Basin, and the subsidence of local air masses led to an enhanced contribution of adiabatic warming. At the same time, the advection of the European continental air mass and the combined effect of the western Pacific airflow made advection and non-adiabatic processes the main causes of heat accumulation in the later stage.

[0039] (5) Vertical characteristics analysis of atmospheric heat transport; In this embodiment, based on the reanalysis data obtained in embodiment (1), the atmospheric hydrostatic energy is calculated. ) and saturated wet hydrostatic energy ( The vertical atmospheric thermodynamic characteristics of high-temperature heat wave events in the Yangtze River Basin are investigated, as shown in the following formula:

[0040]

[0041] In the formula, Indicates the specific heat capacity at constant pressure ( =1004.7090 J kg -1 K -1 ), Indicates height The temperature at that location It is the latent heat of vaporization ( =2.5008×10 6 J kg -1 ), It is the acceleration due to gravity ( =9.8 ms 2 ), For height The height of the position, For height The saturated specific humidity at that location.

[0042] like Figure 7 As shown, the vertical differentiation mechanism is dominated by adiabatic heating in the lower eastern layer (sinking heating) and non-adiabatic heating in the entire western layer (>+4K); the advection effect is weak and its contribution is negligible; the heat source in the upper layer is dominated by non-adiabatic heating (>500 hPa). Figure 8 This study reveals the vertical thermodynamic structure of atmospheric temperature anomalies during the peak of the 2006 heatwave (August 30–September 3): the high-temperature anomalies were concentrated in the lower troposphere around 700 hPa, with an intensity of +3 K to +4 K; the heat accumulation in the lower troposphere between 600 and 850 hPa was mainly driven by non-adiabatic heating, which was dominated by surface sensible heat flux. The mechanism was that the air mass, under the influence of easterly winds, moved from the low-temperature zone to the high-temperature zone and continuously absorbed sensible heat as it rose from east to west; the adiabatic heating contribution in the eastern part of the study area was significant, especially around 500 hPa, where the adiabatic warming contribution caused by subsidence exceeded +3 K; advection heat transport showed a weak positive contribution at 500 hPa, but its overall impact was limited; the air mass in the high-temperature anomaly zone had long-distance transport characteristics, with a Lagrange formation distance greater than 1400 km and a Lagrange age greater than 4 days, confirming that the heat anomalies in the lower troposphere depended on the synergistic effect of long-distance non-adiabatic heating and mid-level adiabatic warming.

[0043] Figure 9The thermodynamic characteristics of the entire troposphere during the peak of the 1978 heat wave (July 6–July 10) were analyzed: the high temperature anomaly in the middle and lower layers (600–850 hPa) (+3K to +4K) was almost entirely dominated by adiabatic heating (>+4K), which was directly related to the air mass compression and warming caused by strong subsidence under the control of the subtropical high; advection heat transport made a positive contribution in the lower layers (+1K to +2K), enhancing the near-surface high temperature; non-adiabatic processes showed a cooling effect in most pressure layers, reflecting the suppression of sensible heat flux; the short distance (<1000 km) and short duration (<3 days) of air mass movement confirmed the high efficiency of adiabatic warming during rapid subsidence.

[0044] Figure 10 This study reveals the vertical differentiation of atmospheric temperature anomalies during the peak of the 1959 heatwave (August 18–August 22): the high temperature anomalies (+3K to +4K) in the middle and lower troposphere (700–950 hPa) were mainly dominated by non-adiabatic heating, with the air mass continuously absorbing sensible heat during its long-distance transport (>1400 km) from the Bay of Bengal; advection heat transport made a weak positive contribution (<+1K) to the entire troposphere, enhancing the high temperature anomalies; the adiabatic process was mainly cooling in the lower troposphere, while it manifested as warming in the middle troposphere; the Lagrange age of the air mass (>4 days) and its long-distance characteristics highlight the key role of non-local heat transport in the high temperatures of the middle and lower troposphere.

[0045] Please refer to Figure 11 , Figure 11 This is a schematic diagram of the hardware device of the present invention.

[0046] The hardware device specifically includes: a vertical three-dimensional quantitative tracking and tracing device 401 for heat waves, a processor 402, and a storage device 403.

[0047] A vertical three-dimensional quantitative tracking and tracing device 401 for heat waves: The vertical three-dimensional quantitative tracking and tracing device 401 for heat waves implements the vertical three-dimensional quantitative tracking and tracing method for heat waves.

[0048] Processor 402: The processor 402 loads and executes the instructions and data in the storage device 403 to implement the vertical three-dimensional quantitative tracking and tracing method for heat waves.

[0049] Storage device 403: The storage device 403 stores instructions and data; the storage device 403 is used to implement the vertical three-dimensional quantitative tracking and tracing method for heat waves.

[0050] Although specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art can make various modifications or additions to the described specific embodiments or use similar methods to replace them, without departing from the direction of the invention or exceeding the scope defined by the appended claims. Those skilled in the art should understand that any modifications, equivalent substitutions, improvements, etc., made to the above embodiments based on the technical essence of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for vertical three-dimensional quantitative tracking and source tracing of heat waves, characterized in that: Includes the following steps: Step S1: Data Acquisition; Collect data for the study area, including: daily average temperature observation data, HYSPLIT driven data of the hybrid single-particle Lagrange integral trajectory model, and diurnal scale data; Step S2: Identification of the spatiotemporal range of the high temperature heat wave event; Combining the daily average temperature observation data obtained in Step S1, calculate the anomaly value of the daily temperature relative to its sliding climatological temperature, identify the maximum positive temperature anomaly, and define its high temperature peak date, high temperature core area, and heat wave main area; Step S3: Tracking the heat source of temperature anomalies during high-temperature heat wave events; Combining the HYSPLIT hybrid single-particle Lagrange integral trajectory model obtained in Step S1 with the data-driven model, the heat source and transport process of heat changes during heat wave events are tracked by the backward trajectory of air masses, thus providing technical support for heat tracking and constructing a vertical three-dimensional heat tracking model. Step S4: Quantify the contribution of heat source driving factors during high-temperature heat wave events; Combine the heat source tracked by the vertical three-dimensional heat model obtained in Step S3, use the Lagrange temperature anomaly equation to quantify the contribution of advection transport, adiabatic and non-adiabatic processes of heat accumulation to the heat source of high-temperature heat wave events along the air mass movement trajectory, and analyze the main physical processes that lead to extreme high temperatures. Step S5: Vertical characteristic analysis of atmospheric heat transport; Combine the reanalysis data in the diurnal data obtained in Step S1 to calculate atmospheric wet hydrostatic energy and saturated wet hydrostatic energy, explore the vertical characteristics of atmospheric thermodynamics of high temperature heat wave events, and analyze the physical driving structure of the temperature anomaly of the entire troposphere during the high temperature heat wave from the perspective of vertical profile.

2. The method for vertical three-dimensional quantitative tracking and tracing of heat waves as described in claim 1, characterized in that: Step S1 is as follows: Near-surface 2-meter daily average temperature observation data were collected from the daily gridded dataset CN05.1 for the Chinese region. Data were collected from the HYSPLIT hybrid single-particle Lagrange integral trajectory model provided by the National Center for Environmental Prediction – National Center for Atmospheric Research (NECP / NCAR). Variables included surface pressure, precipitation, near-surface 2-meter air temperature, near-surface 10-meter zonal and meridional winds, geopotential height, overall air temperature, overall zonal and meridional winds, and vertical velocity. The study collected diurnal data from the National Center for Environmental Prediction – National Center for Atmospheric Research (NECP / NCAR) in the United States, including variables such as whole-layer temperature, geopotential height, daily average temperature at 2 meters above the ground, and specific humidity; and reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Generation 5, including monthly variables such as surface pressure, zonal and meridional winds, and specific humidity, and diurnal variables such as daily average temperature at 2 meters above the ground and vertical velocity synthesized from 6-hour data.

3. The method for vertical three-dimensional quantitative tracking and tracing of heat waves as described in claim 1, characterized in that: In step S2, the daily average temperature observation data obtained in step S1 is the daily average temperature at 2 meters above the ground. Using the study period as the center date, the sliding average of this data for 5 years before and after the study period, and for 7 days before and after, is calculated as the climatological temperature. Then, the anomaly value of the daily temperature within the study period relative to its sliding climatological temperature is calculated, and the maximum positive anomaly value of the daily temperature is identified. The corresponding date and location are defined as the peak date and peak location. The peak period range of the heat wave event is defined as 5 days centered on the peak date, and the spatial range is defined as a 10° × 10° range centered on the peak location, as shown in the following formula: In the formula, Here, i represents the climatological temperature, i is the year offset, with a value range of 5 years before and after the current date, totaling 11 values, and j is the date offset, with a value range of 7 days before and after the current date, totaling 15 values. The average daily temperature at 2 meters above the ground. It is the anomaly between the daily average temperature and its sliding climatological temperature.

4. The method for vertical three-dimensional quantitative tracking and tracing of heat waves as described in claim 1, characterized in that: In step S3, a Lagrange temperature anomaly equation is constructed based on the thermodynamic energy equation. A vertical three-dimensional heat tracking model, constructed using the HYSPLIT hybrid single-particle Lagrange integral trajectory model, is used to track the heat source of temperature anomalies during high-temperature heat waves, as shown in the following equation: In the formula, Climatic temperature, This is a temperature anomaly, calculated from the difference between the temperature of an air mass at a specific location and time along its trajectory and the corresponding climatological temperature. This indicates the time step of the air mass along its trajectory. Abnormal temperature changes; For horizontal wind speed, For gradient operators, For air pressure, For standard reference pressure, Vertical velocity, For potential temperature, These variables are constants; they are all values ​​corresponding to the location of the air mass along its trajectory, where temperature, pressure, and potential temperature are all obtained from the output of the HYSPLIT model. The values ​​were obtained by interpolation from NCEP / NCAR reanalysis data.

5. The method for vertical three-dimensional quantitative tracking and tracing of heat waves as described in claim 1, characterized in that: In step S4, based on the heat source tracking results of the vertical thermal model obtained in step S3, which tracks the heat source of the temperature anomaly during the high-temperature heat wave event, the Lagrange temperature anomaly equation is further calculated and quantified to determine the contribution of the advection heat transport process, adiabatic heating process and non-adiabatic heating process to the increase in near-surface temperature in the basin during the heat wave event. From a Lagrange perspective, the air mass is located... and time abnormal temperature at time The source can be broken down into the time from the onset of the temperature anomaly. Accumulated to The contributions of each physical process during time are given by the following formula: In the equation, the four terms on the right-hand side represent the temperature anomalies caused by the change of climatological temperature over time, the temperature anomalies caused by the horizontal advection of the climatological temperature gradient, the temperature anomalies caused by vertical motion, and the temperature anomalies caused by non-adiabatic processes along the trajectory.

6. The method for vertical three-dimensional quantitative tracking and tracing of heat waves as described in claim 1, characterized in that: In step S5, to investigate the role of vertical temperature structure related to convective stability in the development of heat waves, atmospheric hydrostatic energy is utilized. and saturated wet static energy To analyze the stability of atmospheric moist convection, the following formula is used: In the formula, This represents the specific heat capacity at constant pressure. Indicates height The temperature at that location It is the latent heat of vaporization. It is gravitational acceleration. For height The height of the position, For height The saturated specific humidity at that location.

7. A storage device, characterized in that: The storage device stores instructions and data to implement the vertical three-dimensional quantitative tracking and tracing method for heat waves as described in any one of claims 1 to 6.

8. A vertical three-dimensional quantitative tracking and tracing device for heat waves, characterized in that: include: A processor and a storage device; the processor loads and executes instructions and data in the storage device to implement the vertical three-dimensional quantitative tracking and tracing method for heat waves as described in any one of claims 1 to 6.

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