A method for identifying enhancement of dry hot wind disaster of winter wheat under constraint of atmospheric circulation anomaly

Abstract

The application discloses a winter wheat dry hot wind disaster enhancement identification method constrained by atmospheric circulation anomaly, and belongs to the technical field of agricultural meteorological disaster monitoring. The atmospheric circulation anomaly capable of representing the formation background of the dry hot wind is calculated, and the strength, persistence and spatial organization features of the circulation anomaly are further extracted, so as to convert the atmospheric circulation anomaly into an atmospheric circulation anomaly prior constraint factor with spatial distribution features and time persistence features. By using the prior constraint factor, a large-scale physical background constraint can be provided for subsequent winter wheat dry hot wind disaster identification, so as to reduce false alarms and misjudgments caused by local anomalies, and improve the stability, regional consistency and process rationality of the winter wheat dry hot wind disaster identification in a key growth period.

Description

Technical Field

[0001] This invention belongs to the field of agricultural meteorological disaster monitoring technology, specifically relating to the field of winter wheat hot and dry wind disaster identification technology, and more specifically to an enhanced identification method for winter wheat hot and dry wind disaster constrained by atmospheric circulation anomalies. Background Technology

[0002] Identifying hot and dry wind disasters in winter wheat requires not only determining whether there are abnormal high temperatures and drought in the target area, but also identifying the background, duration, and spatial impact of the hot and dry wind complex process during key growth stages, in conjunction with the growth process of winter wheat.

[0003] Existing methods for monitoring agricultural drought or high temperatures mainly fall into three technical categories: one is based on ground meteorological elements and soil moisture observations, establishing relevant indicators to assess the risk of agricultural drought or heat damage; another is based on remote sensing indicators to identify abnormal crop growth and moisture status; and the third combines machine learning or deep learning methods to fuse and model multi-source data for crop drought and hot wind disaster risk assessment. These methods already have a certain application foundation in agricultural drought monitoring and crop growth diagnosis. For example, existing systems utilize vegetation indices, surface temperature, and crop planting history information to rapidly identify agricultural drought and water scarcity, and comprehensively determine the extent and intensity of crop drought through temporal and spatial dimensions, demonstrating the strong operational feasibility of multi-source remote sensing and crop history information in agricultural disaster monitoring.

[0004] However, the existing methods still have significant limitations. First, many methods focus on anomaly detection of surface meteorological factors, soil moisture, or remotely sensed vegetation status, essentially reflecting more the surface response or outcome characterization of crops after stress, and are difficult to predict the atmospheric background conditions in the early stages of hot and dry wind disasters. Second, traditional hot and dry wind disaster identification indicators are easily affected by factors such as differences in observation scale in high-coverage crop areas, leading to misjudgments. Although existing multi-source fusion or machine learning methods can improve the fitting ability to complex nonlinear relationships, most still use surface meteorological, remotely sensed vegetation, soil moisture, and crop state variables as the main inputs, usually treating atmospheric circulation background as a general meteorological environmental factor, and rarely constructing it into a physically meaningful prior constraint factor to constrain the spatial consistency and temporal persistence of hot and dry wind disaster identification.

[0005] Studies have shown that atmospheric circulation anomalies have significant a priori constraint value for identifying hot and dry wind disasters. From the formation mechanism of hot and dry wind events in winter wheat, the resulting combined hot and dry process is not caused by isolated local meteorological conditions, but is closely related to large-scale atmospheric circulation anomalies such as anomalous anticyclones, high-pressure ridges or blocking patterns, regional subsidence, mid-to-low-level warm advection, weakened water vapor transport, or enhanced water vapor flux divergence. These circulation anomalies can influence surface warming, cloud cover reduction, enhanced evapotranspiration, precipitation suppression, and soil moisture loss, thus providing background conditions for the simultaneous occurrence, development, and regional expansion of high temperatures and drought during the critical growth period of winter wheat.

[0006] Existing technologies tend to focus on outcome indicators, making it difficult to characterize the atmospheric circulation background preceding the formation of hot and dry wind disasters in winter wheat. Based on these issues, it is necessary to propose a method for constructing prior constraint factors for atmospheric circulation anomalies in the identification of hot and dry wind disasters in winter wheat. Summary of the Invention

[0007] The present invention aims to at least partially solve one of the technical problems existing in the related art.

[0008] The purpose of this invention is to provide an enhanced identification method for hot and dry wind disasters in winter wheat constrained by atmospheric circulation anomalies. The method transforms the intensity, persistence, and spatial continuity of atmospheric circulation anomalies into prior constraint factors that can be used for the identification of hot and dry wind disasters, thereby providing atmospheric circulation physical background constraints for the identification of hot and dry wind disasters in winter wheat during the key growth period and improving the accuracy of the identification of hot and dry wind disasters in winter wheat.

[0009] To achieve the above objectives, the present invention provides a method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints, comprising the following steps:

[0010] S1. Obtain historical atmospheric circulation reanalysis data of winter wheat planting areas during key growth stages, and construct an atmospheric circulation dataset based on the time series window of key growth stages of winter wheat.

[0011] S2. Based on the atmospheric circulation dataset, the various atmospheric circulation variables are standardized to obtain the anomaly fields of each atmospheric circulation variable. Then, the standardized anomaly fields are converted into a circulation anomaly risk field with consistent risk direction through the risk direction function.

[0012] Within the time window of the key growth stages of winter wheat, the anomalous intensity features, anomalous persistence features, and anomalous spatial continuity features are extracted based on the anomalous circulation risk field.

[0013] S3. For each atmospheric circulation variable, the anomalous intensity feature is used as the basic contribution, and the anomalous persistence feature is gated using a persistence gate function, and the anomalous spatial continuity feature is gated using a spatial continuity gate function. The gated anomalous persistence feature and spatial continuity feature are then fused with the anomalous intensity feature to obtain the univariate circulation anomalous prior component of the atmospheric circulation variable.

[0014] S4. The obtained multiple univariate circulation anomaly prior components are used as atmospheric circulation anomaly prior constraint factors, and fused with traditional identification features constructed based on surface meteorological elements, soil moisture or crop status to form an enhanced identification feature set. Based on the enhanced identification feature set, the winter wheat hot and dry wind disaster is identified.

[0015] A further preferred technical solution of the present invention is that the historical atmospheric circulation reanalysis data obtained in step S1 includes one or more of the following: 500 hPa geopotential height, 500 hPa vertical velocity, 850 hPa temperature, 850 hPa wind field, and whole-layer water vapor flux divergence.

[0016] The critical growth stage is at least one of the jointing stage, heading and flowering stage, and grain filling stage in the winter wheat growth cycle. The critical growth stage time sequence window is based on the target date of the critical growth stage, and extracts continuous data for a preset number of days before and after it.

[0017] Preferably, in step S2, based on the atmospheric circulation dataset, various atmospheric circulation variables are standardized to obtain the anomaly fields of each atmospheric circulation variable; specifically:

[0018] First, the average background climate field for the corresponding period is constructed based on historical atmospheric circulation data;

[0019] Set up a spatial grid ,time The original circulation variables on are ,in Indicates the type of atmospheric circulation variable;

[0020] Standardize the atmospheric circulation variables to obtain the anomalous fields of each atmospheric circulation variable. , represented as:

[0021] ;

[0022] in, This is a stabilizing term, used to avoid calculation instability caused by excessively small standard deviations in the historical period; The mean of the circulation variable; This represents the standard deviation for the same period in history. For a moment The corresponding climatological time index is used to match the time of the historical climate mean background field.

[0023] Preferably, in step S2, the standardized anomaly field is converted into a circulation anomaly risk field with consistent risk direction using a risk direction function; specifically:

[0024] For the first Anomaly fields of atmospheric circulation variables By setting directional coefficients, a larger processed variable value indicates a greater likelihood of favorable conditions for the formation of hot and dry wind disasters in winter wheat, thus obtaining an abnormal circulation risk field. , represented as:

[0025] ;

[0026] in, For direction coefficients, The rules are set as follows:

[0027] The larger the positive anomaly at 500 hPa, the stronger the control of the high-pressure ridge or anticyclone. =1; the larger the negative anomaly of the 500hPa geopotential height, the weaker the control of the high-pressure ridge or anticyclone. =-1;

[0028] When the vertical velocity at 500 hPa is positive due to the upward motion anomaly, at this time =-1; when the vertical velocity at 500 hPa is positive due to the subsidence anomaly. =1;

[0029] A larger positive temperature anomaly at 850 hPa indicates a stronger positive thermal anomaly in the middle and lower atmosphere. =1; when the 850 hPa temperature negative anomaly is larger, it indicates a stronger negative thermal anomaly in the middle and lower layers. =-1;

[0030] A larger positive anomaly in the 850 hPa wind field along the direction of dry heat transport indicates a stronger transport of warm, dry airflow in the lower atmosphere. =1; the larger the negative anomaly in the 850 hPa wind field along the direction of dry heat transport, the weaker the transport of warm and dry airflow in the lower layer. =-1;

[0031] A larger positive anomaly in the overall water vapor flux divergence indicates stronger water vapor divergence or insufficient water vapor input. =1; When the negative anomaly of the overall water vapor flux divergence is larger, it indicates stronger water vapor convergence or excessive water vapor input. =-1.

[0032] Preferably, in step S2, anomaly intensity features are extracted based on the circulation anomaly risk field; specifically:

[0033] Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting anomaly intensity features to characterize the strength of anomalies and cumulative effects. The anomaly intensity feature consists of window average anomaly, window peak anomaly, and window cumulative positive anomaly, and is represented as:

[0034] ;

[0035] in, This indicates an abnormal window average. This indicates an abnormal window size. This indicates a positive window accumulation error. , , These are the weighting coefficients, and , Take a value of 0.3 to 0.5. Take a value of 0.2 to 0.4. Take a value of 0.2 to 0.4.

[0036] Preferably, in step S2, the anomalous persistence features are extracted based on the anomalous circulation risk field; specifically:

[0037] Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting anomalous persistence features to characterize whether atmospheric circulation anomalies conducive to the formation of hot and dry wind disasters during the target fertility stage have continuous maintenance characteristics. ;

[0038] First, according to the... Individual circulation anomaly risk field Set anomaly detection threshold ,when At that time, it was considered that the grid At any moment There exists an abnormal circulation background conducive to the formation of hot and dry wind disasters; Anomalous persistence characteristics of individual circulation variables It is calculated from the number of days the anomaly lasted and the longest consecutive anomaly days, using the following formula:

[0039] ;

[0040] in, Indicates the number of days the anomaly lasted. Indicates the longest consecutive number of abnormal days; , These are the weighting coefficients. ; This indicates the number of time samples included in the atmospheric circulation data within the historical time series window.

[0041] Preferably, in step S2, the spatial continuity features of the anomaly are extracted based on the anomaly risk field of the circulation; specifically:

[0042] Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting the spatial continuity features of anomalies ;

[0043] The abnormal spatial continuity feature The formula used to characterize whether atmospheric circulation anomalies conducive to hot and dry wind disasters form a continuous control structure in the stable winter wheat planting area and its adjacent affected areas is:

[0044] ;

[0045] in, It represents the contiguousness of the anomaly spatial grid, used to characterize whether the circulation anomaly forms a continuous region with a certain spatial scale, and is determined according to the ratio of the effective anomaly spatial grid area to the area of ​​the target circulation analysis region; It represents the winter wheat area coverage, used to characterize the degree of coverage of continuous anomalous areas over stable winter wheat planting areas, and is determined based on the number of winter wheat planting grids covered by the effective anomalous spatial grid; , These are the weighting coefficients, and .

[0046] As a preferred option, step S3 specifically involves:

[0047] The first Atmospheric circulation variables in the spatial grid Historical years and the target fertility stage The following are the abnormal intensity characteristics Abnormal persistence characteristics and the characteristics of spatial continuity in anomalies The first method is constructed using a gating constraint fusion approach. Univariate circulation anomaly prior components of atmospheric circulation variables , represented as:

[0048] ;

[0049] in, This is a gate function for the persistence of atmospheric circulation anomalies. Both are spatial continuity gate functions for atmospheric circulation anomalies, and their values ​​range from 0 to 1.

[0050] Preferably, the atmospheric circulation anomaly persistence gating function , represented as:

[0051] ;

[0052] in, For persistence threshold, For continuous gating adjustment parameters; when hour, Or approaching 1; when hour, This weakens the contribution of short-term anomalies to prior components; Take a value of 0.3 to 0.5. Take 0.5 to 2;

[0053] Spatial continuity gating function of atmospheric circulation anomalies , represented as:

[0054] ;

[0055] in, For spatial continuity threshold, For spatial continuity gating adjustment parameters; when hour, ;when hour, This weakens the contribution of spatially isolated anomalies or spatially unorganized anomalies to prior components. Take a value of 0.3 to 0.6. Take 0.5 to 2.

[0056] As a preferred option, step S4 specifically involves:

[0057] For any spatial grid ,years Key growth stages of winter wheat Construct an enhanced feature set containing prior components of each univariate circulation anomaly. , represented as:

[0058] ;

[0059] in, Traditional identification features, consisting of surface heat and drought indicators, soil moisture indicators, or crop status indicators, are used in traditional identification algorithms. This represents multiple univariate circulation anomaly prior components; Indicates the number of atmospheric circulation variables involved in the identification;

[0060] Based on the enhanced recognition feature set To identify hot and dry wind disasters affecting winter wheat.

[0061] Beneficial effects: This invention transforms atmospheric circulation anomalies into prior constraint factors, rather than using them as ordinary input variables, enabling large-scale circulation background to directly participate in the constraint process for identifying hot and dry wind disasters in winter wheat.

[0062] This invention takes into account the intensity, persistence, and spatial continuity of circulation anomalies, and can better characterize the large-scale background conditions required for the formation and maintenance of hot and dry wind disasters.

[0063] This invention improves the physical consistency of multivariate fusion by unifying atmospheric circulation variables with different physical meanings into anomalous expressions with consistent risk directions for dry-thermal composite risks through risk direction unification processing.

[0064] This invention combines abnormal circulation processes with key growth stages of winter wheat, which can highlight the background risk of hot and dry wind disasters during sensitive stages such as heading and flowering, and grain filling.

[0065] In summary, this invention calculates atmospheric circulation anomalies that characterize the background of hot and dry wind formation, and further extracts the intensity, persistence, and spatial organization characteristics of these anomalies, transforming them into prior constraint factors for atmospheric circulation anomalies with spatial distribution and temporal persistence characteristics. Using these prior constraint factors, a large-scale physical background constraint is provided for subsequent identification of hot and dry wind disasters in winter wheat, thereby reducing false alarms and misjudgments caused by local anomalies and improving the stability, regional consistency, and process rationality of hot and dry wind disaster identification during critical growth periods. Attached Figure Description

[0066] Figure 1 This is a flowchart of the method for enhancing the identification of hot and dry wind disasters in winter wheat under the constraint of atmospheric circulation anomalies, as described in this invention.

[0067] Figure 2 This is a map showing the risk identification area for hot and dry wind disasters in winter wheat, as described in this embodiment of the invention. Figure 2 (a) shows the risk identification map obtained by the traditional identification method. Figure 2 (b) is the risk identification map obtained by the method of the present invention.

[0068] Figure 3 This is a comparison map of the spatial distribution of winter wheat dry and hot wind disaster risk identification areas obtained by traditional identification methods and the method of the present invention.

[0069] Figure 4This is a time series comparison chart of the winter wheat hot and dry wind disaster risk index obtained by traditional identification methods and the method of the present invention.

[0070] Figure 5 This is a comparison chart showing the quantitative comparison results between the traditional identification method and the method of the present invention. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0072] To address the problem that existing methods for identifying hot and dry wind disasters in winter wheat rely solely on remote sensing information of observed ground soil, meteorological elements, and vegetation, which makes it difficult to monitor the conditions for the formation of hot and dry wind disasters in advance, resulting in limited ability to identify such disasters.

[0073] This embodiment provides a method for enhancing the identification of hot and dry wind disasters in winter wheat under the constraint of atmospheric circulation anomalies, such as... Figure 1 As shown, this method mainly involves extracting atmospheric circulation anomaly process features related to the formation background of hot and dry winds, and transforming the intensity, persistence and spatial continuity of atmospheric circulation anomalies into prior constraint factors that can be used for the identification of hot and dry wind disasters. This provides atmospheric circulation physical background constraints for the identification of hot and dry wind disasters during the key growth period of winter wheat, and improves the accuracy of identifying hot and dry wind disasters in winter wheat.

[0074] The following is combined Figures 1-5 This invention describes an enhanced identification method for hot and dry wind disasters in winter wheat constrained by atmospheric circulation anomalies, provided by the present invention.

[0075] S1. Construct an atmospheric circulation dataset based on the time series windows of key growth stages of winter wheat.

[0076] First, historical atmospheric circulation reanalysis data for winter wheat growing areas during key growth stages are obtained. The historical period can be 5 years or more, or 20 years or more, with a minimum of 10 years of continuous historical data being the most preferred.

[0077] Atmospheric circulation reanalysis data includes one or more of the following: 500 hPa geopotential height, 500 hPa vertical velocity, 850 hPa temperature, 850 hPa wind field, and overall water vapor flux divergence. Atmospheric circulation field data are used to subsequently calculate the intensity, persistence, and spatial continuity of atmospheric circulation anomalies, and to construct prior constraint factors for atmospheric circulation anomalies. Specifically, the 500 hPa geopotential height characterizes the background of enhanced anomalous high-pressure ridges or anticyclone control; the 500 hPa vertical velocity characterizes the background of enhanced regional subsidence; the 850 hPa temperature characterizes the accumulation process of mid-to-low-level thermal anomalies; the 850 hPa wind field characterizes the characteristics of warm-dry advection transport and changes in ventilation conditions; and the overall water vapor flux divergence characterizes the background of insufficient regional water vapor input or enhanced water vapor divergence.

[0078] The critical growth stage is at least one of the jointing, heading and flowering, and grain-filling stages in the winter wheat growth cycle. The critical growth stage time series window is centered on the target date of the critical growth stage, extracting continuous data for a preset number of days before and after it. The preset number of days is determined based on the formation and cumulative impact characteristics of hot and dry wind disasters, specifically extracting 10-15 days of continuous data before and 10-15 days of continuous data after the disaster to retain the circulation background information before the disaster formation, the heat and drought synergy information during the disaster, and the crop response information after the disaster.

[0079] Assuming the first year in history Year, spatial grid The baseline time corresponding to the target fertility stage is ,in, Indicates the reproductive stage category, then the historical time series window corresponding to this grid for that year. Represented as:

[0080] ;

[0081] in, Indicated in spatial grid ,time The set of characteristic variables on; Indicates the length of time for forward retrieval during the target reproductive stage. This indicates the length of time required for backward retrieval during the target reproductive stage.

[0082] After the above processing, a historical time series atmospheric circulation dataset is formed, which takes the stable winter wheat planting area as the spatial constraint, the unified time series window as the benchmark, and the key growth stage as the time alignment reference. This dataset provides a data foundation for the subsequent extraction of historical circulation anomaly features and heat and drought process features.

[0083] S2. Calculate the anomaly characteristics of atmospheric circulation variables.

[0084] After obtaining the atmospheric circulation dataset for the key growth stages of winter wheat, in order to identify atmospheric circulation anomalies closely related to the formation of hot and dry wind disasters in winter wheat, this embodiment extracts atmospheric circulation anomaly variables based on atmospheric circulation field data within the historical time series window, and extracts process features from three aspects: atmospheric circulation anomaly intensity, atmospheric circulation anomaly persistence, and atmospheric circulation anomaly spatial continuity, providing input for the subsequent construction of atmospheric circulation anomaly prior constraint factors.

[0085] The atmospheric circulation anomaly process characteristics are based on multiple atmospheric circulation variables related to the formation mechanism of the dry-heat complex process, and consist of the following three steps: 1. Standardized calculation of historical atmospheric circulation variable anomalies; 2. Calculation of the atmospheric circulation anomaly risk direction function; 3. Extraction of atmospheric circulation anomaly features. Through this processing, univariate atmospheric circulation anomalies can be transformed into prior features with temporal process attributes and spatial organization attributes, which can be used to characterize the degree of favorable large-scale circulation background for the occurrence of dry-heat wind disasters in the target area during the key growth stages of winter wheat.

[0086] The specific steps are as follows:

[0087] S21. Calculate the anomalies of standardized atmospheric circulation variables.

[0088] To eliminate the influence of climatological differences between different years and regions on the results of circulation anomaly feature extraction, the historical atmospheric circulation variable anomaly standardization calculation first constructs the corresponding period's climatological mean background field based on historical upper-level circulation field data.

[0089] Set up a spatial grid ,time The original circulation variables on are ,in This indicates the type of atmospheric circulation variable. Since different circulation anomaly variables have different dimensions and numerical ranges, they are standardized to enhance comparability. When the circulation variables are 500 hPa geopotential height, 500 hPa vertical velocity, 850 hPa temperature, 850 hPa wind speed, and water vapor flux divergence, the standardized geopotential height anomaly field, vertical velocity anomaly field, temperature anomaly field, wind field anomaly field, and water vapor flux divergence anomaly field can be obtained respectively. :

[0090] ;

[0091] in, This is a stabilizing term, used to avoid calculation instability caused by excessively small standard deviations in the historical period; The mean of the circulation variable; This represents the standard deviation for the same period in history. For a moment The corresponding climatological time index is used to match the time of the historical climate mean background field.

[0092] S22. The standardized anomaly field is transformed into a circulation anomaly risk field with consistent risk direction through the risk direction function.

[0093] Since different atmospheric circulation variables have different physical directions of influence on the formation of hot and dry wind disasters, meaning that different variables have positive and negative correlations with hot and dry wind disasters, this invention constructs an anomalous risk direction function of the atmospheric circulation based on the anomalous field of standardized atmospheric circulation variables. The larger the processed variable value, the more favorable it is for the formation of hot and dry wind disasters in winter wheat, facilitating subsequent calculation of prior constraint factors.

[0094] Let the direction be consistent after the first The circulation anomaly risk field is ,but:

[0095] ;

[0096] in, For direction coefficients, .

[0097] Directional coefficient The setting rules are as follows:

[0098] The larger the positive anomaly at 500 hPa, the stronger the control of the high-pressure ridge or anticyclone. =1; the larger the negative anomaly of the 500hPa geopotential height, the weaker the control of the high-pressure ridge or anticyclone. =-1;

[0099] When the vertical velocity at 500 hPa is positive due to the upward motion anomaly, at this time =-1; when the vertical velocity at 500 hPa is positive due to the subsidence anomaly. =1;

[0100] A larger positive temperature anomaly at 850 hPa indicates a stronger positive thermal anomaly in the middle and lower atmosphere. =1; when the 850 hPa temperature negative anomaly is larger, it indicates a stronger negative thermal anomaly in the middle and lower layers. =-1;

[0101] A larger positive anomaly in the 850 hPa wind field along the direction of dry heat transport indicates a stronger transport of warm, dry airflow in the lower atmosphere. =1; the larger the negative anomaly in the 850 hPa wind field along the direction of dry heat transport, the weaker the transport of warm and dry airflow in the lower layer. =-1;

[0102] A larger positive anomaly in the overall water vapor flux divergence indicates stronger water vapor divergence or insufficient water vapor input. =1; When the negative anomaly of the overall water vapor flux divergence is larger, it indicates stronger water vapor convergence or excessive water vapor input. =-1.

[0103] S23. Calculate the intensity characteristics of atmospheric circulation anomalies.

[0104] Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting anomaly intensity features to characterize the strength of anomalies and cumulative effects. The anomaly intensity feature consists of window average anomaly, window peak anomaly, and window cumulative positive anomaly, and is represented as:

[0105] ;

[0106] in, This indicates an abnormal window average. This indicates an abnormal window size. This indicates a positive window accumulation error. , , These are the weighting coefficients, and , Take a value of 0.3 to 0.5. Take a value of 0.2 to 0.4. Take a value of 0.2 to 0.4.

[0107] S24. Calculate the characteristics of the persistence of atmospheric circulation anomalies.

[0108] Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting anomalous persistence features to characterize whether atmospheric circulation anomalies conducive to the formation of hot and dry wind disasters during the target fertility stage have continuous maintenance characteristics. ;

[0109] First, according to the... Individual circulation anomaly risk field Set anomaly detection threshold ,when At that time, it was considered that the grid At any moment There exists an abnormal circulation background conducive to the formation of hot and dry wind disasters; Anomalous persistence characteristics of individual circulation variables It is calculated from the number of days the anomaly lasted and the longest consecutive anomaly days, using the following formula:

[0110] ;

[0111] in, Indicates the number of days the anomaly lasted. Indicates the longest consecutive number of abnormal days; , These are the weighting coefficients. ; This indicates the number of time samples included in the atmospheric circulation data within the historical time series window.

[0112] S25. Extract the spatial continuity characteristics of atmospheric circulation anomalies.

[0113] Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting the spatial continuity features of anomalies ;

[0114] The abnormal spatial continuity feature The formula used to characterize whether atmospheric circulation anomalies conducive to hot and dry wind disasters form a continuous control structure in the stable winter wheat planting area and its adjacent affected areas is:

[0115] ;

[0116] in, It represents the contiguousness of the anomaly spatial grid, used to characterize whether the circulation anomaly forms a continuous region with a certain spatial scale, and is determined according to the ratio of the effective anomaly spatial grid area to the area of ​​the target circulation analysis region; It represents the winter wheat area coverage, used to characterize the degree of coverage of continuous anomalous areas over stable winter wheat planting areas, and is determined based on the number of winter wheat planting grids covered by the effective anomalous spatial grid; , These are the weighting coefficients, and .

[0117] Through the processing in step S2, it can be determined whether atmospheric circulation anomalies form a spatially organized continuous control area, and further, whether this continuous control area affects the stable winter wheat planting area. This allows for the differentiation between locally isolated anomalies and the atmospheric circulation background formed by regional hot and dry wind disasters, providing spatial constraint information for the prior constraint factors of atmospheric circulation anomalies.

[0118] S3. Construct prior constraint factors for atmospheric circulation anomalies.

[0119] In this invention, the prior constraint factor of atmospheric circulation anomaly refers to the degree of favorable conditions for the formation of hot and dry wind disasters given in advance by the large-scale atmospheric circulation anomaly background before identifying hot and dry wind disasters using information on the influence of ground meteorological elements, soil moisture, crop status or yield.

[0120] After processing in step S2, multiple atmospheric circulation variables can be obtained in the spatial grid. Historical years and the target fertility stage The following are the abnormal intensity characteristics Abnormal persistence characteristics and the characteristics of spatial continuity in anomalies This step is used to convert the aforementioned anomaly intensity characteristics, anomaly persistence characteristics, and anomaly spatial continuity characteristics into prior constraint factors of atmospheric circulation anomalies that can characterize the background of the formation of hot and dry wind disasters in winter wheat.

[0121] For each atmospheric circulation variable, the anomalous intensity feature is used as the basic contribution, and the anomalous persistence feature is gated using a persistence gate function, and the anomalous spatial continuity feature is gated using a spatial continuity gate function. The gated anomalous persistence feature and spatial continuity feature are then fused with the anomalous intensity feature to obtain the univariate circulation anomalous prior component of the atmospheric circulation variable.

[0122] The first Atmospheric circulation variables in the spatial grid Historical years and the target fertility stage The following are the abnormal intensity characteristics Abnormal persistence characteristics and the characteristics of spatial continuity in anomalies The first method is constructed using a gating constraint fusion approach. Univariate circulation anomaly prior components of atmospheric circulation variables , represented as:

[0123] ;

[0124] in, This is a gate function for the persistence of atmospheric circulation anomalies. Both are spatial continuity gate functions for atmospheric circulation anomalies, and their values ​​range from 0 to 1.

[0125] Atmospheric circulation anomaly persistence gating function , represented as:

[0126] ;

[0127] in, For persistence threshold, For continuous gating adjustment parameters; when hour, Or approaching 1; when hour, This weakens the contribution of short-term anomalies to prior components; Take a value of 0.3 to 0.5. Take 0.5 to 2;

[0128] Spatial continuity gating function of atmospheric circulation anomalies , represented as:

[0129] ;

[0130] in, For spatial continuity threshold, For spatial continuity gating adjustment parameters; when hour, ;when hour, This weakens the contribution of spatially isolated anomalies or spatially unorganized anomalies to prior components. Take a value of 0.3 to 0.6. Take 0.5 to 2.

[0131] Through the aforementioned gating constraint fusion method, the univariate circulation anomaly prior component only exhibits high values ​​when a certain atmospheric circulation variable simultaneously possesses strong anomaly, sustained characteristics, and spatial continuous control characteristics; when the anomaly only manifests as a short-duration strong anomaly or a locally isolated anomaly, its prior contribution is weakened. This can improve the characterization ability of subsequent prior constraint factors on the regional and persistent dry-heat complex formation background.

[0132] S4. Enhanced identification based on prior constraints of atmospheric circulation anomalies.

[0133] This will obtain multiple univariate circulation anomaly prior components. It is defined as a priori constraint factor for atmospheric circulation anomalies and embedded into the winter wheat hot and dry wind disaster identification algorithm.

[0134] For any spatial grid ,years Key growth stages of winter wheat Construct an enhanced feature set containing prior components of each univariate circulation anomaly. , represented as:

[0135] ;

[0136] in, Traditional identification features, consisting of surface heat and drought indicators, soil moisture indicators, or crop status indicators, are used in traditional identification algorithms. This represents multiple univariate circulation anomaly prior components; This indicates the number of atmospheric circulation variables involved in the identification.

[0137] Enhanced recognition feature set It is used to enhance and expand the traditional recognition input, so that the winter wheat hot and dry wind disaster recognition process not only includes surface heat and drought indicators, soil moisture indicators or crop status indicators, but also further introduces large-scale atmospheric circulation anomaly prior constraint information.

[0138] For example, in a random forest recognition model, the recognition feature set will be enhanced. As input sample features for the random forest, the model learns the correspondence between prior constraints related to surface heat and drought anomalies, crop responses, and atmospheric circulation anomalies during training. Therefore, the model not only identifies conditions based on whether the surface experiences high temperatures and low rainfall, soil moisture loss, or abnormal crop conditions, but also utilizes... Determine whether the anomaly exhibits characteristics of a large-scale circulation anomaly.

[0139] Through the above methods This invention incorporates prior constraint factors for atmospheric circulation anomalies into the input structure of the identification algorithm. Compared to traditional identification methods that only use surface heat and drought indicators, soil moisture indicators, or crop status indicators, this invention can introduce large-scale circulation background information such as high-pressure control, subsidence warming, mid-to-low-level thermal anomalies, warm-dry transport, and water vapor deficit into the identification process, thereby improving the physical consistency and regional reliability of winter wheat hot-dry wind disaster identification.

[0140] To verify the feasibility of the method of this invention and its improvement over traditional methods, this embodiment takes a major winter wheat producing area as the research object, selecting May 10, 2019 to June 8, 2019 as the process analysis period, with the hot and dry wind disaster identification date window used for verification being May 16 to June 8, 2019. Atmospheric circulation data were obtained from NCEP / DOEReanalysis II diurnal reanalysis data, including 500 hPa geopotential height, 850 hPa air temperature, and 500 hPa vertical velocity; surface meteorological data were obtained from NOAA PSL / CPC global daily maximum temperature data and global daily precipitation data. The hot and dry wind disaster event was a hot and dry wind process that affected this major winter wheat producing area from late May to early June 2019.

[0141] Traditional methods primarily reflect the statistical characteristics of the land surface under conditions of high temperatures and low rainfall. In terms of index calculation, traditional methods only utilize surface heat and drought information to construct a risk index. Then, the Sigmoid function is used to convert the two into heat risk and drought risk between 0 and 1, and the product of the two represents the traditional combined heat and dry risk.

[0142] This invention introduces a priori constraints from atmospheric circulation anomalies to the traditional surface heat and drought risk assessment method. Specifically, it calculates the standardized intensities of the 500 hPa geopotential height anomaly, the 850 hPa air temperature anomaly, and the 500 hPa subsidence anomaly, and then calculates a priori factors for circulation anomalies. These factors characterize whether hot and dry wind disasters have a stable large-scale circulation background. The final risk index is jointly determined by the surface heat and drought risk and the circulation prior factors, and a 3-day moving average is used for the regional average time series to reduce the impact of single-day noise. The warning threshold is set at 0.50; a hot and dry wind disaster risk is identified when the risk index reaches or exceeds this threshold.

[0143] Figure 2 (a) shows the results of the traditional method. It can be seen that the traditional method identified a large risk area in the main winter wheat producing area, with a total of 293 grid points. However, since it only relies on surface high temperature and precipitation deficit, some areas lack atmospheric circulation anomaly support, which can easily lead to problems such as the spatial range being too wide or potential false alarms. Figure 2 (b) shows the identification results of the method of the present invention. After adding circulation prior constraints to the traditional thermal drought signal, the method of the present invention retains only 83 identification grid points with strong physical background support, and the identification range is compressed by about 71.7%, which shows that the present invention can weaken the weak correlation region without circulation background.

[0144] Figure 3 The spatial differences between the identification area of ​​the traditional method and the identification area of ​​the present invention are further shown, where gray represents the identification area of ​​the traditional method and red represents the identification area of ​​the present invention. The results show that the present invention mainly retains the core risk area consistent with the background of dry and hot circulation, improves the physical consistency of the identification results, makes the identification range more precise, and has a higher consistency with the occurrence area of ​​high temperature and drought.

[0145] Figure 4 For the time series comparison of risk indices, using the observed period of hot and dry wind disasters from May 22 to May 25, 2019 as the target process, the method of this invention first reached the warning threshold on May 17, 2019, while the traditional method first reached the warning threshold on May 23, 2019. This invention detected the risk signal 6 days earlier than the traditional method. Furthermore, within another observation risk window from June 2 to June 4, 2019, the risk index of this invention also provided a response, indicating its good process tracking capability for continuous or phased hot and dry wind risks.

[0146] Figure 5 To quantify the comparison results, the average circulation prior support is used to measure whether the identified area is supported by atmospheric circulation anomalies conducive to the formation of hot and dry wind disasters. In this embodiment, the circulation prior factor is calculated based on the positive anomaly of 500 hPa geopotential height, the positive anomaly of 850 hPa air temperature, and the anomaly of 500 hPa sinking motion, and the average value of the grid points identified by each method is taken. The average circulation prior support of the area identified by the traditional method is 0.143, while the method of this invention improves it to 0.231, indicating that the area selected by this invention has stronger large-scale circulation background support. The physical consistency score further comprehensively considers the circulation prior support, the spatial continuity of the identified area, and the proportion of small patches. That is, the more continuous the identified area, the less scattered noise, and the stronger the circulation prior support, the higher the physical consistency score. The results show that the traditional method is about 0.14, while the method of this invention is about 0.23. Based on statistics from two observation periods totaling 7 days, a risk index reaching a threshold of 0.50 is considered a hit. The hit rate of the traditional method is 0.43, while the hit rate of the method of this invention is 0.57. These results demonstrate that this invention can not only identify the risk of hot and dry wind disasters in advance, but also reduce the potential false alarms of the traditional surface threshold method through prior constraints of atmospheric circulation anomalies, thereby improving the physical interpretability and spatiotemporal consistency of the identification results.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for enhancing the identification of hot and dry wind disasters in winter wheat under the constraint of atmospheric circulation anomalies, characterized in that, Includes the following steps: S1. Obtain historical atmospheric circulation reanalysis data of winter wheat planting areas during key growth stages, and construct an atmospheric circulation dataset based on the time series window of key growth stages of winter wheat. S2. Based on the atmospheric circulation dataset, the various atmospheric circulation variables are standardized to obtain the anomaly fields of each atmospheric circulation variable. Then, the standardized anomaly fields are converted into a circulation anomaly risk field with consistent risk direction through the risk direction function. Within the time window of the key growth stages of winter wheat, the anomalous intensity features, anomalous persistence features, and anomalous spatial continuity features are extracted based on the anomalous circulation risk field. S3. For each atmospheric circulation variable, the anomalous intensity feature is used as the basic contribution, and the anomalous persistence feature is gated using a persistence gate function, and the anomalous spatial continuity feature is gated using a spatial continuity gate function. The gated anomalous persistence feature and spatial continuity feature are then fused with the anomalous intensity feature to obtain the univariate circulation anomalous prior component of the atmospheric circulation variable. S4. The obtained multiple univariate circulation anomaly prior components are used as atmospheric circulation anomaly prior constraint factors, and fused with traditional identification features constructed based on surface meteorological elements, soil moisture or crop status to form an enhanced identification feature set. Based on the enhanced identification feature set, the winter wheat hot and dry wind disaster is identified.

2. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 1, characterized in that, The historical atmospheric circulation reanalysis data obtained in step S1 includes one or more of the following: 500 hPa geopotential height, 500 hPa vertical velocity, 850 hPa temperature, 850 hPa wind field, and whole-layer water vapor flux divergence. The critical growth stage is at least one of the jointing stage, heading and flowering stage, and grain filling stage in the winter wheat growth cycle. The critical growth stage time sequence window is based on the target date of the critical growth stage, and extracts continuous data for a preset number of days before and after it.

3. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 1, characterized in that, In step S2, based on the atmospheric circulation dataset, various atmospheric circulation variables are standardized to obtain the anomaly fields of each atmospheric circulation variable; specifically: First, the average background climate field for the corresponding period is constructed based on historical atmospheric circulation data; Set up a spatial grid ,time The original circulation variables on are ,in Indicates the type of atmospheric circulation variable; Standardize the atmospheric circulation variables to obtain the anomalous fields of each atmospheric circulation variable. , is represented as: ； in, This is a stabilizing term used to avoid calculation instability caused by excessively small standard deviations in historical periods; The mean of the circulation variable; This represents the standard deviation for the same period in history. For a moment The corresponding climatological time index is used to match the time of the historical climate mean background field.

4. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 3, characterized in that, In step S2, the standardized anomaly field is transformed into a circulation anomaly risk field with consistent risk direction using a risk direction function; specifically: For the first Anomaly fields of atmospheric circulation variables By setting directional coefficients, a larger processed variable value indicates a greater likelihood of favorable conditions for the formation of hot and dry wind disasters in winter wheat, thus obtaining an abnormal circulation risk field. , is represented as: ； in, For direction coefficients, The rules are set as follows: The larger the positive anomaly at 500 hPa, the stronger the control of the high-pressure ridge or anticyclone. =1; the larger the negative anomaly of the 500 hPa geopotential height, the weaker the control of the high-pressure ridge or anticyclone. =-1; When the vertical velocity at 500 hPa is positive due to the upward motion anomaly, at this time =-1; when the vertical velocity at 500 hPa is positive due to the subsidence anomaly. =1; A larger positive temperature anomaly at 850 hPa indicates a stronger positive thermal anomaly in the middle and lower atmosphere. =1; when the 850 hPa temperature negative anomaly is larger, it indicates a stronger negative thermal anomaly in the middle and lower layers. =-1; A larger positive anomaly in the 850 hPa wind field along the direction of dry heat transport indicates a stronger transport of warm, dry airflow in the lower atmosphere. =1; the larger the negative anomaly in the 850 hPa wind field along the direction of dry heat transport, the weaker the transport of warm and dry airflow in the lower layer. =-1; A larger positive anomaly in the overall water vapor flux divergence indicates stronger water vapor divergence or insufficient water vapor input. =1; When the negative anomaly of the overall water vapor flux divergence is larger, it indicates stronger water vapor convergence or excessive water vapor input. =-1.

5. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 4, characterized in that, In step S2, anomaly intensity features are extracted based on the anomaly risk field of the circulation. Specifically: Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting anomaly intensity features to characterize the strength of anomalies and cumulative effects. The anomaly intensity feature consists of window average anomaly, window peak anomaly, and window cumulative positive anomaly, and is represented as: ； in, This indicates an abnormal window average. This indicates an abnormal window size. This indicates a positive window accumulation error. , , These are the weighting coefficients, and , Take a value of 0.3 to 0.

5. Take a value of 0.2 to 0.

4. Take a value of 0.2 to 0.

4.

6. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 4, characterized in that, In step S2, the anomalous persistence features are extracted based on the anomalous circulation risk field. Specifically: Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting anomalous persistence features to characterize whether atmospheric circulation anomalies conducive to the formation of hot and dry wind disasters during the target fertility stage have continuous maintenance characteristics. ; First, according to the... Individual circulation anomaly risk field Set anomaly detection threshold ,when At that time, it was considered that the grid At any moment There exists an abnormal circulation background conducive to the formation of hot and dry wind disasters; Anomalous persistence characteristics of individual circulation variables It is calculated from the number of days the anomaly lasted and the longest consecutive anomaly days, using the following formula: ； in, Indicates the number of days the anomaly lasted. Indicates the longest consecutive number of abnormal days; , These are the weighting coefficients. ; This indicates the number of time samples included in the atmospheric circulation data within the historical time series window.

7. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 4, characterized in that, In step S2, the spatial continuity features of the anomaly are extracted based on the anomaly risk field of the circulation. Specifically: Atmospheric circulation data based on key reproductive stage time windows Regarding the first Individual circulation anomaly risk field Extracting the spatial continuity features of anomalies ; The abnormal spatial continuity feature The formula used to characterize whether atmospheric circulation anomalies conducive to hot and dry wind disasters form a continuous control structure in the stable winter wheat planting area and its adjacent affected areas is: ； in, It represents the contiguousness of the anomaly spatial grid, used to characterize whether the circulation anomaly forms a continuous region with a certain spatial scale, and is determined according to the ratio of the effective anomaly spatial grid area to the area of ​​the target circulation analysis region; It represents the winter wheat area coverage, used to characterize the degree of coverage of continuous anomalous areas over stable winter wheat planting areas, and is determined based on the number of winter wheat planting grids covered by the effective anomalous spatial grid; , These are the weighting coefficients, and .

8. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 1, characterized in that, Step S3 is as follows: The first Atmospheric circulation variables in the spatial grid Historical years and the target fertility stage The following are the abnormal intensity characteristics Abnormal persistence characteristics and the characteristics of spatial continuity in anomalies The first method is constructed using a gating constraint fusion approach. Univariate circulation anomaly prior components of atmospheric circulation variables , is represented as: ； in, This is a gate function for the persistence of atmospheric circulation anomalies. Both are spatial continuity gate functions for atmospheric circulation anomalies, and their values ​​range from 0 to 1.

9. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 8, characterized in that, The atmospheric circulation anomaly persistence gating function , is represented as: ； in, For persistence threshold, For continuous gating adjustment parameters; when hour, Or approaching 1; when hour, This weakens the contribution of short-term anomalies to prior components; Take a value of 0.3 to 0.

5. Take 0.5 to 2; Spatial continuity gating function of atmospheric circulation anomalies , is represented as: ； in, For spatial continuity threshold, For spatial continuity gating adjustment parameters; when hour, ;when hour, This weakens the contribution of spatially isolated anomalies or spatially unorganized anomalies to prior components. Take a value of 0.3 to 0.

6. Take 0.5 to 2.

10. The method for enhancing the identification of hot and dry wind disasters in winter wheat under atmospheric circulation anomaly constraints according to claim 8, characterized in that, Step S4 is as follows: For any spatial grid ,years Key growth stages of winter wheat Construct an enhanced feature set containing prior components of each univariate circulation anomaly. , is represented as: ； in, Traditional identification features, consisting of surface heat and drought indicators, soil moisture indicators, or crop status indicators, are used in traditional identification algorithms. This represents multiple univariate circulation anomaly prior components; Indicates the number of atmospheric circulation variables involved in the identification; Based on the enhanced recognition feature set To identify hot and dry wind disasters affecting winter wheat.

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