A multi-scale coupled heavy pollution weather forecasting and early warning method and system
By employing a multi-scale coupled method for forecasting and early warning of heavy pollution weather, and utilizing multi-source heterogeneous data to reconstruct a three-dimensional analysis field and a WM-μ Chain conceptual model, the problem of vertical observation and multi-scale coupling in heavy pollution weather monitoring was solved, achieving high-precision early warning results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 河北省气象灾害防御和环境气象中心(河北省预警信息发布中心)
- Filing Date
- 2026-02-26
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies for monitoring heavy pollution weather suffer from insufficient vertical observation capabilities, unclear multi-scale coupling mechanisms, weak physical support for early warning models, and inadequate integration of observations and models, resulting in insufficient accuracy and precision in early warning.
A multi-scale coupled heavy pollution weather forecasting and early warning method is adopted. By receiving and processing multi-source heterogeneous data, a three-dimensional analysis field is reconstructed using a data assimilation algorithm. Combined with the WM-μ Chain conceptual model, key indicators at each scale are quantified to generate a series of early warning products, and a forecast-observation closed-loop optimization mechanism is established.
It has achieved precise and seamless monitoring of heavy pollution processes, improved the lead time, accuracy and interpretability of early warnings, broken through the bottleneck of vertical observation and multi-scale coupling, constructed an early warning model with clear mechanism, and improved the fusion accuracy of observation and model.
Smart Images
Figure CN122153786A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of weather forecasting and early warning technology, and in particular to a multi-scale coupled method and system for forecasting and early warning of heavy pollution weather. Background Technology
[0002] In the fields of meteorology and environment, the monitoring and early warning of regional heavy pollution weather (such as high concentrations of PM2.5 and O3 pollution) is a current research focus. Existing technologies mainly rely on ground-based air quality monitoring networks, twice-daily routine meteorological soundings, and satellite remote sensing data for monitoring and analysis. Forecasts and early warnings are mostly based on statistical models, empirical thresholds, or single numerical model outputs, focusing on the analysis of synoptic-scale circulation background or near-surface diffusion conditions. Existing technologies have the following problems:
[0003] (1) Insufficient vertical observation capability: The existing operational observation network has a weak ability to capture the vertical continuous distribution and fine structure of pollutants and meteorological elements in the atmospheric boundary layer (especially from the ground to hundreds of meters above the ground). It lacks high temporal resolution "three-dimensional" data and it is difficult to depict the three-dimensional dynamic process of vertical transport, accumulation and outbreak of pollutants.
[0004] (2) The multi-scale coupling mechanism is not well understood: Existing methods lack effective quantitative observation and diagnosis of the cross-scale coupling and nonlinear interaction between "weather-scale forcing, mesoscale topographic response and microscale turbulence triggering" during heavy pollution weather, which leads to a vague understanding of the key physical mechanisms of the triggering timing and intensity change of the pollution process.
[0005] (3) The physical mechanism support of the early warning model is weak: Most of the current operational early warning models are "black box" or empirical statistical models. The forecast results lack clear physical and chemical mechanism support. The accuracy and precision of the forecast of heavy pollution processes (especially the outbreak and turning point stages) need to be improved. The lead time and accuracy of the early warning are insufficient.
[0006] (4) Insufficient fusion of observation and model: In traditional methods, the assimilation and fusion technology between multi-source heterogeneous observation data (such as tower base, remote sensing, and mobile platform data) and high-resolution numerical models is not applied in depth enough, making it difficult to reconstruct the three-dimensional thermodynamic field of polluted weather with high accuracy, which limits the improvement of mechanism research and forecast models.
[0007] Therefore, there is an urgent need for a multi-scale coupled method and system for forecasting and early warning of heavy pollution weather, so as to achieve accurate and seamless monitoring and early warning of heavy pollution processes. Summary of the Invention
[0008] This invention provides a multi-scale coupled method and system for forecasting and early warning of heavy pollution weather, so as to achieve accurate and seamless monitoring and early warning of heavy pollution processes.
[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0010] This invention provides a multi-scale coupled method for forecasting and early warning of heavy pollution weather, comprising:
[0011] S1: Receive and standardize multi-source heterogeneous data from various observation platforms, and use data assimilation algorithms to fuse the multi-source heterogeneous data to reconstruct a three-dimensional analysis field of thermal-dynamic-pollution field;
[0012] S2: Based on the fused data and the analysis field, run the coupled diagnostic algorithm of "weather-scale forcing W-mesoscale response M-microscale triggering μ" to quantify key indicators at each scale and establish an early warning indicator library;
[0013] S3: Based on the output of the WM-μ Chain conceptual model, automatically generate a series of progressive early warning products ranging from weather-scale potential forecasts to micro-scale imminent warnings.
[0014] Furthermore, S1 includes:
[0015] Acquire multi-source heterogeneous data from each of the aforementioned observation platforms;
[0016] The multi-source heterogeneous data is subjected to quality control and standardization processing, including outlier removal, time alignment, unit unification, and coordinate transformation;
[0017] The processed multi-source heterogeneous data is used as an observation factor and input into the data assimilation system;
[0018] Set the numerical mode, run the data assimilation loop, and generate a three-dimensional analysis field that integrates the multi-source heterogeneous data, including wind field, temperature field, humidity field, various pollutant concentration fields, as well as boundary layer height and turbulent kinetic energy derived field.
[0019] Furthermore, S2 includes:
[0020] Weather-scale W-scale diagnostics: Calculate and track key circulation indices and transregional transport fluxes from the analyzed field and reanalysis data;
[0021] Mesoscale M-response analysis: Outputs quantitative topographic parameters, including valley wind circulation intensity index, leeward slope convergence line location and intensity, and temperature stratification stability;
[0022] Microscale μ-triggered signal identification: Based on gradient tower observations and assimilated analysis fields, key near-surface microphysical parameters are calculated: turbulent kinetic energy, sensible heat flux, latent heat flux, inversion layer height and thickness, and time evolution sequences are analyzed;
[0023] By analyzing historical and real-time cases, we establish statistical and physical correlation models between key parameters at various scales and sudden increases in pollutant concentrations, and determine the critical thresholds or combinations of criteria that trigger heavy pollution.
[0024] Furthermore, S3 includes:
[0025] The critical thresholds and criteria determined in S2 are combined and integrated into the index library and core algorithm of the "WM-μ Chain" conceptual model.
[0026] Furthermore, it also includes:
[0027] Establish a dual-drive verification mechanism of "forecast-observation": After each pollution process, use real-time observation data to verify the accuracy of the early warning products;
[0028] The test results are fed back to the WM-μ Chain conceptual model, and the indicator thresholds and algorithm parameters in the model are continuously optimized and iteratively updated to form a closed loop of "observation → model → early warning → test → optimization".
[0029] This invention also provides a multi-scale coupled heavy pollution weather forecasting and early warning system, comprising:
[0030] Analysis field reconstruction module: used to receive and standardize multi-source heterogeneous data from various observation platforms, and use data assimilation algorithms to fuse the multi-source heterogeneous data to reconstruct a three-dimensional analysis field of thermodynamic-dynamic-pollution field;
[0031] Quantization module: Based on the fused data and the analysis field, it runs the "weather-scale forcing W-mesoscale response M-microscale triggering μ" coupled diagnostic algorithm to quantify key indicators at each scale and establish an early warning indicator library;
[0032] Output module: Used to automatically generate a series of progressive early warning products, ranging from weather-scale potential forecasts to microscale imminent alerts, based on the output of the WM-μChain conceptual model.
[0033] Compared with the prior art, the technical solution disclosed in this invention has the following beneficial effects:
[0034] This invention constructs a three-dimensional integrated observation system capable of vertically continuous, high-resolution, and dynamic tracking of atmospheric boundary layer pollutants and meteorological elements; based on multi-source observation data assimilation technology, it accurately reconstructs the three-dimensional structure of heavy pollution weather and quantitatively analyzes its multi-scale (synoptic scale, mesoscale, and microscale) coupling mechanism; and establishes a multi-scale coupled heavy pollution weather conceptual model and early warning method with a clear physical mechanism, driven by observation data, and capable of operational application, thereby achieving accurate and seamless monitoring and early warning of heavy pollution processes. Attached Figure Description
[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0036] Figure 1 A schematic diagram of the multi-scale coupled heavy pollution weather forecasting and early warning method provided in the embodiments of the present invention. Detailed Implementation
[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0039] This invention provides a multi-scale coupled method and system for forecasting and warning of heavy pollution weather, so as to achieve accurate and seamless monitoring and warning of heavy pollution processes.
[0040] like Figure 1 As shown, this embodiment of the invention provides a multi-scale coupled method for forecasting and early warning of heavy pollution weather, including:
[0041] S1: Receive and standardize multi-source heterogeneous data from various observation platforms, and use data assimilation algorithms, such as three-dimensional variational and ensemble Kalman filtering, to fuse the multi-source heterogeneous data into a high-resolution numerical model to reconstruct the three-dimensional analysis field of thermodynamic-dynamic-pollution field.
[0042] In this embodiment of the invention, the method further includes the following steps before step S1:
[0043] Materials and Equipment: Gradient observation towers of 100 meters and above; sensors for meteorological elements such as temperature, humidity, wind direction, and wind speed (installed at different heights on the gradient towers, such as 1m, 2m, 4m, 8m, 16m, 32m, 48m, 64m, 80m, 96m, etc.); PM2.5, O3, NO... xPollutant concentration analyzers (installed at different heights in the gradient tower, such as 8m, 48m, 96m, etc.); ultrasonic anemometers (for observing three-dimensional wind); tethered airship platforms and their onboard portable mass spectrometers (PTR-MS), particulate matter size spectrometers, and miniature weather stations; multi-rotor UAVs and their onboard miniature gas sensors; and ground-based remote sensing equipment such as microwave radiometers, wind profiler radars, and lidar.
[0044] Installation and Deployment: Establish observation stations in the target area (e.g., in front of the Taihang Mountains). Gradient tower sensors are installed and calibrated at the designed height; ground-based remote sensing equipment is deployed around the gradient towers; tethered boats and UAVs are on standby at designated take-off and landing sites during intensive observation periods.
[0045] Observation operation:
[0046] Routine continuous observation: Gradient tower and ground-based remote sensing network conduct uninterrupted automatic observation throughout the year. The data sampling frequency is: 1-minute average of meteorological elements and pollutant concentrations, 30-minute turbulent flux calculation, and remote sensing data is collected at the highest time resolution of the equipment.
[0047] Enhanced targeted monitoring: Initiated during the prediction or occurrence of severe pollution weather.
[0048] Tethered airship observation: Control the airship to conduct 0-1000m elevation and descent detection with a vertical resolution of ≤20m. The single profile time is about 60-90 minutes. During critical periods such as pollution accumulation, outbreak, and dissipation, the frequency is increased to once every 3-4 hours.
[0049] Drone observation: In key areas where pollution plumes may converge (such as the piedmont convergence zone), plan three-dimensional flight paths for drones (0-500m altitude), carry sensors to perform gridded or tracking sampling, and fly at intervals of 2-3 hours.
[0050] Specific observation methods include:
[0051] (1) Gradient Tower Observation Subsystem: Multi-parameter sensors are deployed at different heights of the tower (e.g., 8m, 48m, 96m) to simultaneously collect PM2.5, O3, and NO. x Concentrations of pollutants such as SO2, CO, and NH3, as well as meteorological elements such as wind, temperature, humidity, and pressure, and micrometeorological parameters such as sensible heat, latent heat, and turbulent kinetic energy.
[0052] (2) Ground-based remote sensing network: integrates equipment such as microwave radiometer, wind profiler radar, and lidar to provide vertical profiles of temperature, humidity, wind field, and aerosols.
[0053] (3) Mobile observation platform: tethered airships equipped with portable monitoring equipment to achieve vertical profile observation of pollutants and meteorological elements from 0 to 1000m; UAVs equipped with micro sensors to achieve three-dimensional spatial tracking of pollution plumes.
[0054] S2: Based on the fused data and the analysis field, run the coupled diagnostic algorithm of "weather-scale forcing W-mesoscale response M-microscale triggering μ" to quantify key indicators at each scale and establish an early warning indicator library;
[0055] S3: Based on the output of the WM-μ Chain conceptual model, automatically generate a series of progressive early warning products ranging from weather-scale potential forecasts to micro-scale imminent warnings.
[0056] Furthermore, S1 includes:
[0057] Acquire multi-source heterogeneous data from each of the aforementioned observation platforms;
[0058] The multi-source heterogeneous data is subjected to quality control and standardization processing, including outlier removal, time alignment, unit unification, and coordinate transformation;
[0059] The processed multi-source heterogeneous data (gradient tower vertical point data, remote sensing vertical profile, satellite inversion products, and mobile platform path data) are used as observation factors and input into a data assimilation system, such as the DA module of WRF-Chem.
[0060] Set a high-resolution numerical model, such as a 1km nested grid, run a data assimilation loop, and generate a three-dimensional analysis field that integrates the multi-source heterogeneous data, including wind field, temperature field, humidity field, various pollutant concentration fields, as well as boundary layer height and turbulent kinetic energy derived fields.
[0061] Furthermore, S2 includes:
[0062] Weather-scale W-diagnostics: Calculate and track key circulation indices (such as blocking high intensity, low-level jet location) and transregional transport fluxes from the analyzed field and reanalysis data;
[0063] Mesoscale M-response analysis: Outputs quantitative topographic parameters, including valley wind circulation intensity index, leeward slope convergence line location and intensity, and temperature stratification stability (such as Richardson number).
[0064] Microscale μ-trigger signal identification: Based on gradient tower observations and assimilated analysis fields, key microphysical parameters of the near-surface layer are calculated: turbulent kinetic energy, sensible heat flux, latent heat flux, inversion layer bottom height and thickness, and the time evolution series of the above parameters are analyzed.
[0065] By analyzing historical and real-time cases, we establish statistical and physical correlation models between key parameters at various scales and sudden increases (outbreaks) in pollutant concentrations, and determine the critical thresholds or combinations of criteria that trigger heavy pollution.
[0066] Furthermore, S3 includes:
[0067] The critical thresholds and criteria determined in S2 are combined and integrated into the index library and core algorithm of the "WM-μ Chain" conceptual model.
[0068] In this embodiment of the invention, the model operates according to a preset logic chain:
[0069] If synoptic indicators show that there are circulation patterns conducive to pollution accumulation within the next 72 hours (such as Rossby wave energy transmission paths, East Asian troughs, or subtropical high anomalies), then a "heavy pollution potential warning" will be triggered.
[0070] Based on the potential warning, if the mesoscale response indicators (such as valley wind convergence intensity, local wind shear, static stability index, etc.) reach the monitoring threshold within the next 24 hours, it will be upgraded to a "heavy pollution process warning" and the potentially high-impact areas will be identified.
[0071] Within the process warning area, if microscale triggering indicators (such as a sharp drop in boundary layer height, a sudden decrease in TKE, and the establishment of surface temperature inversion) meet the outbreak conditions within the next 6 hours, the highest level "imminent heavy pollution outbreak warning" will be issued.
[0072] The early warning product generation system automatically converts the model output into the graphic and text products required by the business and publishes them through the business platform.
[0073] The method also includes:
[0074] Establish a dual-drive verification mechanism of "forecast-observation": After each pollution process, use real-time observation data to verify the accuracy of the early warning products;
[0075] The test results are fed back to the WM-μ Chain conceptual model, and the indicator thresholds and algorithm parameters in the model are continuously optimized and iteratively updated to form a closed loop of "observation → model → early warning → test → optimization".
[0076] Based on the same idea, this invention also provides a multi-scale coupled heavy pollution weather forecasting and early warning system, including:
[0077] Analysis field reconstruction module: used to receive and standardize multi-source heterogeneous data from various observation platforms, and use data assimilation algorithms to fuse the multi-source heterogeneous data to reconstruct a three-dimensional analysis field of thermodynamic-dynamic-pollution field;
[0078] Quantization module: Based on the fused data and the analysis field, it runs the "weather-scale forcing W-mesoscale response M-microscale triggering μ" coupled diagnostic algorithm to quantify key indicators at each scale and establish an early warning indicator library;
[0079] Output module: Used to automatically generate a series of progressive early warning products, ranging from weather-scale potential forecasts to microscale imminent alerts, based on the output of the WM-μChain conceptual model.
[0080] In view of the aforementioned deficiencies of the prior art, the advantages of the technical solution provided by the embodiments of the present invention are as follows:
[0081] (1) Achieved three-dimensional, high spatiotemporal resolution comprehensive observation: Through multi-platform collaborative networking of "fixed vertical layer + mobile tracking layer", the bottleneck of traditional observation in vertical dimension and dynamic tracking has been broken through. It can capture the three-dimensional structure and evolution details of polluted weather in an all-round and continuous manner, providing unprecedented data support for mechanism research.
[0082] (2) Revealed the multi-scale coupling physical mechanism of heavy pollution weather: The innovative "WM-μChain" multi-scale coupling diagnostic method was proposed and implemented. Through targeted observation experiments and data assimilation technology, the dynamic driving effect of pollution process from large-scale circulation forcing to micro-scale turbulence triggering was systematically quantified for the first time, enabling the analysis of pollution causes to move from qualitative to quantitative.
[0083] (3) A clear and operational early warning model was constructed: Based on observation data, a multi-scale coupled conceptual model was constructed, which transformed the complex physical mechanism into an operable early warning indicator system, promoting the transformation of early warning technology from "empirical statistics" to "mechanism-driven". The "three-layer progressive" early warning information output by the model (72h potential → 24h enhancement zone → 6h outbreak alarm) significantly improved the lead time, accuracy and interpretability of the early warning.
[0084] (4) Improved the level of deep integration between observation and model: Through advanced data assimilation technology, observation data from multiple platforms and multiple elements are effectively integrated into the numerical model, which greatly improves the accuracy of the initial field and boundary conditions of the model, thereby improving the accuracy of three-dimensional field reconstruction and the reliability of subsequent mechanism diagnosis, forecasting and early warning.
[0085] (5) It has good scalability and application prospects: The observation system architecture, data assimilation process and multi-scale coupling model framework constructed by this invention can provide a technical paradigm for the study of heavy pollution in complex terrain (such as piedmont, basin and valley), and has broad application and promotion value.
[0086] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.
[0087] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.
[0088] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.
[0089] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.
[0090] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.
[0091] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.
Claims
1. A multi-scale coupled method for forecasting and early warning of heavy pollution weather, characterized in that, include: S1: Receive and standardize multi-source heterogeneous data from various observation platforms, and use data assimilation algorithms to fuse the multi-source heterogeneous data to reconstruct a three-dimensional analysis field of thermal-dynamic-pollution field; S2: Based on the fused data and the analysis field, run the "weather-scale forcing W-mesoscale response M-microscale triggering μ" coupled diagnostic algorithm to quantify key indicators at each scale and establish an early warning indicator library; S3: Based on the output of the WM-μ Chain conceptual model, automatically generate a series of progressive early warning products ranging from weather-scale potential forecasts to micro-scale imminent warnings.
2. The multi-scale coupled heavy pollution weather forecasting and early warning method according to claim 1, characterized in that, S1 includes: Acquire multi-source heterogeneous data from each of the aforementioned observation platforms; The multi-source heterogeneous data is subjected to quality control and standardization processing, including outlier removal, time alignment, unit unification, and coordinate transformation; The processed multi-source heterogeneous data is used as an observation factor and input into the data assimilation system; Set the numerical mode, run the data assimilation loop, and generate a three-dimensional analysis field that integrates the multi-source heterogeneous data, including wind field, temperature field, humidity field, various pollutant concentration fields, as well as boundary layer height and turbulent kinetic energy derived field.
3. The multi-scale coupled heavy pollution weather forecasting and early warning method according to claim 1, characterized in that, S2 includes: Weather-scale W-scale diagnostics: Calculate and track key circulation indices and transregional transport fluxes from the analyzed field and reanalysis data; Mesoscale M-response analysis: Outputs quantitative topographic parameters, including valley wind circulation intensity index, leeward slope convergence line location and intensity, and temperature stratification stability; Microscale μ-triggered signal identification: Based on gradient tower observations and assimilated analysis fields, key near-surface microphysical parameters are calculated: turbulent kinetic energy, sensible heat flux, latent heat flux, inversion layer height and thickness, and time evolution sequences are analyzed; By analyzing historical and real-time cases, we establish statistical and physical correlation models between key parameters at various scales and sudden increases in pollutant concentrations, and determine the critical thresholds or combinations of criteria that trigger heavy pollution.
4. The multi-scale coupled heavy pollution weather forecasting and early warning method according to claim 3, characterized in that, S3 includes: The critical thresholds and criteria determined in S2 are combined and integrated into the index library and core algorithm of the "WM-μ Chain" conceptual model.
5. The multi-scale coupled heavy pollution weather forecasting and early warning method according to claim 1, characterized in that, Also includes: Establish a dual-drive verification mechanism of "forecast-observation": After each pollution process, use real-time observation data to verify the accuracy of the early warning products; The test results are fed back to the WM-μ Chain conceptual model, and the indicator thresholds and algorithm parameters in the model are continuously optimized and iteratively updated to form a closed loop of "observation → model → early warning → test → optimization".
6. A multi-scale coupled heavy pollution weather forecasting and early warning system, characterized in that: include: Analysis field reconstruction module: used to receive and standardize multi-source heterogeneous data from various observation platforms, and use data assimilation algorithms to fuse the multi-source heterogeneous data to reconstruct a three-dimensional analysis field of thermodynamic-dynamic-pollution field; Quantization module: Based on the fused data and the analysis field, it runs the "weather-scale forcing W-mesoscale response M-microscale triggering μ" coupled diagnostic algorithm to quantify key indicators at each scale and establish an early warning indicator library; Output module: Used to automatically generate a series of progressive early warning products, ranging from weather-scale potential forecasts to microscale imminent alerts, based on the output of the WM-μChain conceptual model.