An apparatus for automatically generating a variety of data assimilation forecasts suitable for lightning

Abstract

The application discloses a device for automatically generating lightning data assimilation prediction, comprising: a job time automatic extraction module; a data collection module; a pre-processing, data assimilation and model prediction dynamic job flow automatic generation module; a dynamic job script running module; the job time automatic extraction module is used for obtaining computer machine time and converting into model start time, setting prediction time limit parameters; the data collection module is used for automatically collecting global model prediction field, lightning and other detection data according to the model start time and prediction time limit parameters provided by the foregoing module; the pre-processing, data assimilation and model prediction dynamic job flow automatic generation module is composed of a data statistics submodule, a dynamic assimilation strategy and a system flow script automatic generation submodule. The device of the application realizes the practical application of the lightning data assimilation method and the dynamic assimilation strategy automatic generation, thereby effectively reducing the operation failure rate of the numerical prediction system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of numerical weather prediction, and particularly relates to a device and method for automatically generating a lightning and other multi-data assimilation prediction suitable device and method. BACKGROUND

[0002] Numerical weather prediction is the basis of modern weather prediction. The regional mesoscale numerical weather prediction system established according to the weather and climate characteristics of different regions is an important cornerstone for improving the accuracy of weather prediction, and has been widely used in meteorology, oceanography, hydrology, transportation, environment, power, aviation, shipping and other departments. Numerical weather prediction plays an increasingly important role in ensuring social stability, economic development, production and life safety and other aspects. Ensuring the normal operation of the numerical weather prediction system and ensuring the timely completion of numerical prediction products are the top priority of numerical prediction business work. A complete regional mesoscale numerical weather prediction system involves many links, for example, the system is usually composed of collection and decoding of multiple observation data, collection of global model prediction field, preprocessing module, assimilation system, prediction model, post-processing module, product production and release, etc. Any link failure will affect the timely release of numerical weather prediction products, and will have a chain effect on subsequent processing based on numerical prediction products and their application in other fields. In order to better ensure the normal operation of the numerical weather prediction system, more and more numerical prediction systems use monitoring systems such as SMS system (Supervisor Monitor Scheduler), ecFlow, etc. to monitor and manage the numerical weather prediction system business process in real time, so as to quickly locate the process where the fault occurs, and provide a convenient means for subsequent timely handling of faults and restarting of the system. Monitoring the running end of the numerical prediction model system can quickly locate the fault point, but since it takes time to exclude faults and re-run the model system, and there may be cases where the fault cannot be quickly excluded, it cannot ensure that the system can be completed on time, and may even affect the operation of the next time model system. In fact, the essence of monitoring the running end of the numerical prediction model system is to monitor the faults that occur in the running process of the model system, and the monitoring itself does not reduce the fault rate of the model system. We need to avoid or resolve some potential faults as much as possible at the front end of the model system, i.e. the job flow end of the model system, which is an effective way to reduce the running faults of the model system.

[0003] A mature numerical weather prediction system has a fixed job flow written in shell script language, which logically connects the collection and decoding of various observation data, the collection of global model prediction field, the preprocessing module, the assimilation system, the prediction model, the post-processing module, the product production and release, etc. The system runs through automatic extraction of time information. Therefore, the real-time business operation of the numerical weather prediction system uses the pre-set, fixed job flow to complete the operation of the entire system. This is the common method for establishing numerical weather prediction systems at home and abroad. However, the use of pre-set, fixed job flow in actual business operation at least faces the problem of system operation failure caused by global model prediction field or observation data failure, which leads to the failure of numerical weather prediction products. For example: ① The global model prediction field provides the background field and the side boundary condition of the regional mesoscale model, which is the key data for system operation. The absence of any prediction time limit of the global model prediction field will cause the system to interrupt, which not only wastes computing resources, but also causes the numerical prediction product to be missing. In actual business, the phenomenon of job failure caused by the absence of all or part of the time limit data of the global model prediction field occurs from time to time. ② Due to various unexpected reasons, such as observation data upload and download failure, network failure, server address, username, password or path change of observation data storage, data file name change, data format change, etc., the observation data collection and decoding process will be affected, and the system may fail when performing data assimilation according to the fixed job flow, which will cause the job to be interrupted. This kind of failure also occurs from time to time in actual business.

[0004] In addition, with the gradual development of lightning data assimilation technology research at home and abroad in recent years, more and more studies show that the assimilation of lightning data in numerical weather prediction models is of great benefit to improving the information of convective activity in the initial field of the model and further improving the short-term numerical prediction ability of severe convective weather. However, since lightning observations are not conventional variables in numerical prediction models, lightning data cannot be directly used for model initialization, so currently both at home and abroad use indirect methods to assimilate lightning data. The three-dimensional variational assimilation method of lightning data proposed by Zhang et al. (2017) is one of the assimilation schemes. This scheme uses the empirical relationship between lightning frequency and water vapor and graupel mixing ratio established by Fierro et al. (2012) to invert lightning data into water vapor mixing ratio, and then performs mathematical transformation to obtain relative humidity, which is then assimilated into the WRFDA three-dimensional variational assimilation system. In the process of implementing this assimilation method, many challenges are faced. For example, on the one hand, the global numerical model background field does not contain the physical quantity of graupel mixing ratio, so how to reasonably obtain the graupel mixing ratio and how to specifically implement the lightning data assimilation are one of the key technologies for applying this lightning data assimilation method to practice. On the other hand, unlike conventional meteorological observation data, lightning data has temporal and spatial discontinuity, that is, at a certain assimilation time, the number of lightning data observed in the entire model region may be zero. In the case of lightning data and no lightning data, how to consider different assimilation strategies and operation processes in actual business application to avoid system failure due to lack of lightning data and waste of computer resources is also a key issue for the implementation of real-time business operation of lightning data assimilation.

[0005] Therefore, it is necessary to establish a numerical prediction system suitable for real-time business operation, which can reduce system operation failure as much as possible in the operation process and contains lightning and other data assimilation. It should have the following functions: (1) It can be determined whether the numerical model system has the necessary conditions for operation according to the global model prediction field data collected. If the global model prediction field data is missing at the current time, the global model prediction field data of the previous or even more distant time is used instead, and the subsequent data assimilation and model prediction system process is adjusted accordingly; (2) It can be determined whether to start the lightning data assimilation system according to the lightning data collected, and the system process is adjusted accordingly according to the specific situation of the global model prediction field data, sounding, ground, radar, wind profiler radar, microwave radiometer and other detection data. SUMMARY

[0006] In view of the deficiencies in the prior art, the application provides a device for automatically generating lightning and other data assimilation prediction, which realizes the practical application of lightning data assimilation method, and realizes automatic judgment of reasonable data assimilation strategy and model prediction operation process based on actually obtained lightning data, global model prediction field data, sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field and other detection data in various different situations, and generates corresponding dynamic operation process shell script in real time, thereby effectively reducing the operation failure rate of numerical prediction system.

[0007] In order to achieve the above-mentioned purpose, the technical scheme of the application is as follows:

[0008] A device for automatically generating lightning and other data assimilation prediction, characterized in that it comprises: a job time automatic extraction module; a data collection module; a dynamic operation process automatic generation module of preprocessing, data assimilation and model prediction; and a dynamic operation script running module.

[0009] The job time automatic extraction module is used to obtain computer machine time at the starting time of the system device and convert it into model start time and set prediction time limit parameters.

[0010] The data collection module is used to automatically collect global model prediction field, lightning and other detection data according to the model start time and prediction time limit parameters provided by the aforementioned module.

[0011] The dynamic operation process automatic generation module of preprocessing, data assimilation and model prediction is composed of a data statistics submodule, a dynamic assimilation strategy and a system process script automatic generation submodule.

[0012] The data statistics submodule is composed of a global model background field and side boundary condition data statistics son module, a lightning data statistics son module and an other detection data statistics son module.

[0013] The global model background field and side boundary condition data statistics son module is used to count the collection of global model background field and side boundary condition data, and output global model background field and side boundary condition data collection status indication code.

[0014] The lightning data statistics son module is used to count lightning data collection and data quantity, and output lightning data collection status indication code.

[0015] The other detection data statistics son module is used to count the collection of sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field and other detection data, and output other detection data collection status indication code.

[0016] The dynamic assimilation strategy and system process script automatic generation submodule is composed of 17 processes from process A1 to process E; the dynamic assimilation strategy and system process automatic generation submodule is used for automatically judging and generating a dynamic operation shell script containing pre-processing, data assimilation and model prediction according to different conditions of global model background field and side boundary condition data, lightning data and other detection data collection status indication code; wherein processes A1, B1, C1, D1, A2, B2, C2 and D2 contain lightning data assimilation modules; the lightning data assimilation module is used for three-dimensional variational assimilation of lightning data.

[0017] The dynamic operation script running module is used for executing the dynamic script command generated automatically.

[0018] Further, the dynamic assimilation strategy and system process automatic generation submodule is based on data including global model background field and side boundary condition data, lightning data and other detection data.

[0019] Further, the other detection data includes sounding, ground, wind profile radar, microwave radiometer and radar inverted wind field.

[0020] Beneficial effects: 1) The present application uses the global model prediction field as the model background field from 6 hours before the assimilation time to the assimilation time, and uses the mesoscale model prediction field to replace the global model prediction field, which contains physical quantities such as graupel mixing ratio, water vapor mixing ratio and temperature necessary for lightning data assimilation, so that the three-dimensional variational assimilation scheme of lightning data can be realized, and the short-term and nowcasting numerical prediction effect of severe convective weather is improved.

[0021] 2) The method and device provided by the present application can improve the operability and efficiency of lightning data assimilation operation in business application.

[0022] 3) Compared with the current main numerical prediction system, the present application has the characteristics of simplicity, convenience, high efficiency and effective reduction of system operation failure rate, and is easy to popularize and apply to various regional numerical prediction model systems. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The principle block diagram of the lightning data assimilation prediction device automatically generated by the present application is shown.

[0024] Figure 2 The running process diagram of the global model background field and side boundary condition data statistical module is shown.

[0025] Figure 3 The dynamic assimilation strategy and system process automatic generation module runs a flow chart;

[0026] Figure 4 The assimilation flow chart of the lightning data assimilation module. DETAILED DESCRIPTION

[0027] The present application will be described below with reference to specific examples. Those skilled in the art will understand that the examples are only used to illustrate the present application, and do not limit the scope of the present application in any way.

[0028] As shown in Figures 1-4 , an apparatus for automatically generating a device suitable for lightning and other data assimilation prediction, comprising:

[0029] Job time automatic extraction module; data collection module; dynamic job flow chart automatic generation module of pre-processing, data assimilation and model prediction; dynamic job script running module;

[0030] The job time automatic extraction module is used to obtain the computer machine time at the starting time of the system device and convert it into the model start time, and set the prediction time limit parameter;

[0031] The data collection module is used to automatically collect global model prediction field, lightning, and other detection data according to the model start time and prediction time limit parameter provided by the foregoing module;

[0032] The dynamic job flow chart automatic generation module of pre-processing, data assimilation and model prediction is composed of a data statistics submodule, a dynamic assimilation strategy and system process script automatic generation submodule;

[0033] The data statistics submodule is composed of a global model background field and side boundary condition data statistics son module, a lightning data statistics son module, and other detection data statistics son module;

[0034] The global model background field and side boundary condition data statistics son module is used to count the collection of global model background field and side boundary condition data (hereinafter referred to as bgb data) and output the global model background field and side boundary condition data collection status indicator code; when the latest time bgb data is missing, further count the collection of the previous time bgb data, and within a maximum of 4 times, count whether there is bgb data available for normal operation of the model, and output the bgb data collection status indicator code.

[0035] As shown in Figure 2 , a global model background field and side boundary condition data statistics module schematic diagram, the specific process is:

[0036] (1)Assume that the mode start time is t0, the forecast validity is ftime0 (unit: h), and the time 6 h before t0 is t6, i.e. t6 = t0-6h. The bgbfile6 represents the folder where the bgb data at t6 is located, the bgb.${t6}.f${count} is the bgb file at t6 output at an interval of 3 h, wherein ${t6} represents the string corresponding to t6, ${count} represents the 3-h interval string from 00, 03, 06 to (ftime0+6), and bgb6 is the statistical result code of the bgb data at t6, which is used to count the bgb data:

[0037] If the bgbfile6 folder does not exist, bgb6 = 0;

[0038] If the bgbfile6 folder exists, further count whether the number of bgb data bgb.${t6}.f${count} files under the folder meets the demand of the side boundary condition of the forecast validity ftime0, if yes, bgb6 = 1, otherwise bgb6 = 0.

[0039] (2) If bgb6 = 1, the bgb data counting ends.

[0040] (3) If bgb6 = 0, further count the bgb data collection at t12, wherein t12 is the time 12 h before t0, i.e. t12 = t0-12h. The bgbfile12 represents the folder where the bgb data at t12 is located, the bgb.${t12}.f${count} is the bgb file at t12 output at an interval of 3 h, wherein ${t12} represents the string corresponding to t12, ${count} represents the 3-h interval string from 06, 09, 12 to (ftime0+12), and bgb12 is the statistical result code of the bgb data at t12, which is used to count the bgb data:

[0041] If the bgbfile12 folder does not exist, bgb12 = 0;

[0042] If the bgbfile12 folder exists, further count whether the bgb data bgb.${t12}.f${count} file under the folder meets the demand of the side boundary condition of the forecast validity ftime0, if yes, bgb12 = 1, otherwise bgb12 = 0.

[0043] (4) If bgb12 = 1, the bgb data counting ends.

[0044] (5) If bgbl2 = 0, further statistics of the bgb data at t18 is collected, where t18 is a time point 18 hours before t0, i.e. t18 = t0 - 18h. bgbfilel8 represents a folder where the bgb data at t18 is located, bgb.${t18}.f${count} is a bgb file at t18 outputted at an interval of 3h, where ${t18} represents a string corresponding to t18, ${count} represents a string of 3h intervals from 12, 15, 18 to (ftime0 + 18), and bgbl8 is a statistical result code of the bgb data at t18, and the bgb data is counted:

[0045] If the bgbfilel8 folder does not exist, bgbl8 = 0;

[0046] If the bgbfilel8 folder exists, further statistics of whether the bgb data bgb.${t18}.f${count} under the folder meets the demand of the side boundary condition of the forecast validity ftime0 is collected. If yes, bgbl8 = 1, otherwise bgbl8 = 0.

[0047] (6) If bgbl8 = 1, the bgb data statistics ends.

[0048] (7) If bgbl8 = 0, further statistics of the bgb data at t24 is collected, where t24 is a time point 24 hours before t0, i.e. t24 = t0 - 24h. bgbfile24 represents a folder where the bgb data at t24 is located, bgb.${t24}.f${count} is a bgb file at t24 outputted at an interval of 3h, where ${t24} represents a string corresponding to t24, ${count} represents a string of 3h intervals from 18, 21, 24 to (ftime0 + 24), and bgb24 is a statistical result code of the bgb data at t24, and the bgb data is counted:

[0049] If the bgbfile24 folder does not exist, bgb24 = 0, and the bgb data statistics ends;

[0050] If the bgbfile24 folder exists, further statistics of whether the bgb data bgb.${t24}.f${count} under the folder meets the demand of the side boundary condition of the forecast validity ftime0 is collected. If yes, bgb24 = 1, and the bgb data statistics ends; otherwise bgb24 = 0, and the bgb data statistics ends.

[0051] The lightning data statistics module is used for counting lightning data collection and data quantity, and outputting a lightning data collection status indication code;

[0052] Assume that the mode reporting time is t0, the time interval is dtime (unit: min), the lightning quantity threshold is threshold, the starting longitude, starting latitude, ending longitude and ending latitude of the mode range are slon, slat, elon and elat respectively, the lightning data file at t0 is lgtn, the lightning data condition result indication code at t0 is lightn0, and the lightning data condition is counted as follows:

[0053] If the lgtn file does not exist, lightn0=0;

[0054] If the lgtn file exists, further count the total quantity num of lightning data in the file which satisfies the following three conditions: (1) slon≤longitude≤elon; (2) slat≤latitude≤elat; (3) t0-dtime≤time≤t0+dtime; when num<threshold, lightn0=0; when num≥threshold, lightn0=1.

[0055] The setting range of dtime is generally between 5-15 min, and the setting of threshold can be about 5, which can be adjusted according to the specific situation.

[0056] The other detection data statistics module is used for counting sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field and other detection data collection, and outputting an other detection data collection status indication code;

[0057] Assume that the mode reporting time is t0, the sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field data files at t0 are high0, surf0, wpr0, mwr0 and radar0 respectively; t6 represents a time point 6h before t0, the sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field data files at t6 are high6, surf6, wpr6, mwr6 and radar6 respectively, and the sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field and other detection data condition result indication codes at t0 and t6 are obs0 and obs6 respectively; the sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field and other detection data conditions are counted as follows:

[0058] If the high0, surf0, wpr0, mwr0 and radar0 files all do not exist, obs0=0; otherwise, obs0=1;

[0059] If high6, surf6, wpr6, mwr6, radar6 files all do not exist, obs6 = 0; otherwise obs6 = 1.

[0060] The dynamic assimilation strategy and system process script automatic generation submodule is composed of 17 processes from process A1 to process E; the dynamic assimilation strategy and system process automatic generation submodule is used to automatically research and generate a dynamic operation shell script containing pre-processing, data assimilation, and model prediction according to different situations of global model background field and side boundary condition data, lightning data, other detection data (sounding, ground, wind profile radar, microwave radiometer, radar inverted wind field), and other types of data collection status indication code, containing one of the 17 possibilities from process A1 to process E; the processes A1, B1, C1, D1, A2, B2, C2, and D2 contain lightning data assimilation modules;

[0061] The schematic diagram is shown in Figure 3

[0062] (1) If bgb6 = 1, lightn0 = 1, obs6 = 1, the main body content is automatically generated as a shell script of operation process A1, and the A1 process is: bgb6 data exists, bgb6 data is used as the global model background field and side boundary condition, and the model has running conditions; there is lightning data at t0, the lightning data assimilation module is started, and there is other detection data at t6, therefore the A1 process is: at t6, the bgb6 data is used as the background field to assimilate the optimal analysis field obtained by assimilating the other detection data at t6 as the initial value to drive the model integration for 6h to t0, and then the lightning data assimilation preprocessing module is started, and the optimal analysis field obtained by assimilating the lightning and other detection data at t0 is used as the initial value to drive the model integration for ftime0 hours.

[0063] (2) If bgb6 = 1, lightn0 = 1, obs6 = 0, the main body content is automatically generated as a shell script of operation process B1, and the B1 process is: bgb6 data exists, bgb6 data is used as the global model background field and side boundary condition, and the model has running conditions; there is lightning data at t0, the lightning data assimilation module is started, but there is no other detection data at t6, therefore the B1 process is: at t6, the bgb6 data is used as the initial value to directly drive the model integration for 6h to t0, and then the lightning data assimilation preprocessing module is started, and the optimal analysis field obtained by assimilating the lightning and other detection data at t0 is used as the initial value to drive the model integration for ftime0 hours.

[0064] ​(3) If bgb6 = 1, lightn0 = 0, obs0 = 1, automatically generate the shell script whose main content is job flow C1, and the C1 flow is: the bgb6 data exists, the bgb6 data is used as the global model background field and side boundary condition, and the model has running conditions; there is no lightning data at the t0 moment, so the lightning data assimilation module is not started, but there is other detection data, so the C1 flow is: the bgb6 data is used as the background field at the t0 moment to assimilate other detection data to obtain the optimal analysis field as the initial value to drive the model to integrate for ftime0 hours.

[0065] (4) If bgb6 = 1, lightn0 = 0, obs0 = 0, automatically generate the shell script whose main content is job flow D1, and the D1 flow is: the bgb6 data exists, the bgb6 data is used as the global model background field and side boundary condition, and the model has running conditions; there is no lightning data and other detection data at the t0 moment, so no data assimilation module is started, so the D1 flow is: the bgb6 data is used as the initial value to directly drive the model to integrate for ftime0 hours.

[0066] (5) If bgb6 = 0, bgb12 = 1, lightn0 = 1, obs6 = 1, automatically generate the shell script whose main content is job flow A2, and the A2 flow is: the bgb6 data is missing, the bgb12 data exists, the bgb12 data is used as the global model background field and side boundary condition, and the model can run; there is lightning data at the t0 moment, so the lightning data assimilation module is started, and there is other detection data at the t6 moment, so the A2 flow is: the bgb12 data is used as the background field at the t6 moment to assimilate the other detection data at the t6 moment to obtain the optimal analysis field as the initial value to drive the model to integrate for 6 hours to the t0 moment, then the lightning data assimilation preprocessing module is started, and the optimal analysis field obtained by assimilating the lightning and other detection data at the t0 moment is used as the initial value to drive the model to integrate for ftime0 hours.

[0067] (6) If bgb6 = 0, bgb12 = 1, lightn0 = 1, obs6 = 0, automatically generate the shell script whose main content is job flow B2, and the B2 flow is: the bgb6 data is missing, the bgb12 data exists, the bgb12 data is used as the global model background field and side boundary condition, and the model can run; there is lightning data at the t0 moment, so the lightning data assimilation module is started, but there is no other detection data at the t6 moment, so the B2 flow is: the bgb12 data is used as the initial value to directly drive the model to integrate for 6 hours to the t0 moment, then the lightning data assimilation preprocessing module is started, and the optimal analysis field obtained by assimilating the lightning and other detection data at the t0 moment is used as the initial value to drive the model to integrate for ftime0 hours.

[0068] (7) If bgb6=0, bgb12=1, lightn0=0, obs0=1, automatically generate the shell script with the main content of job flow C2, the C2 flow is: bgb6 data is missing, bgb12 data exists, use bgb12 data as the global model background field and side boundary condition, the model can run; there is no lightning data at t0 time, so the lightning data assimilation module is not started, but there is other detection data, so the C2 flow is: use bgb12 data as the background field to assimilate other detection data at t0 time to obtain the optimal analysis field as the initial value to drive the model integration for ftime0 hours.

[0069] (8) If bgb6=0, bgb12=1, lightn0=0, obs0=0, automatically generate the shell script with the main content of job flow D2, the D2 flow is: bgb6 data is missing, bgb12 data exists, use bgb12 data as the global model background field and side boundary condition, the model can run; lightning and other detection data are missing at t0 time, so no data assimilation module is started, so the D2 flow is: use bgb12 data as the initial value to directly drive the model integration for ftime0 hours.

[0070] (9) If bgb6=0, bgb12=0, bgb18=1, lightn0=1, obs6=1, automatically generate the shell script with the main content of job flow A3, the A3 flow is similar to the A2 flow, but because bgb6 and bgb12 data are missing and bgb18 data exists, use bgb18 data as the global model background field and side boundary condition.

[0071] (10) If bgb6=0, bgb12=0, bgb18=1, lightn0=1, obs6=0, automatically generate the shell script with the main content of job flow B3, the B3 flow is similar to the B2 flow, but because bgb6 and bgb12 data are missing and bgb18 data exists, use bgb18 data as the global model background field and side boundary condition.

[0072] (11) If bgb6=0, bgb12=0, bgb18=1, lightn0=0, obs0=1, automatically generate the shell script with the main content of job flow C3, the C3 flow is similar to the C2 flow, but because bgb6 and bgb12 data are missing and bgb18 data exists, use bgb18 data as the global model background field and side boundary condition.

[0073] (12) If bgb6=0, bgb12=0, bgb18=1, lightn0=0, obs0=0, the shell script of the main body is automatically generated as the job flow D3, which is similar to the flow D2, but because the data of bgb6 and bgb12 are missing and the data of bgb18 exists, the data of bgb18 is used as the global mode background field and the side boundary condition.

[0074] (13) If bgb6=0, bgb12=0, bgb18=0, bgb24=1, lightn0=1, obs6=1, the shell script of the main body is automatically generated as the job flow A4, which is similar to the flow A2, but because the data of bgb6, bgb12 and bgb18 are missing and the data of bgb24 exists, the data of bgb24 is used as the global mode background field and the side boundary condition.

[0075] (14) If bgb6=0, bgb12=0, bgb18=0, bgb24=1, lightn0=1, obs6=0, the shell script of the main body is automatically generated as the job flow B4, which is similar to the flow B2, but because the data of bgb6, bgb12 and bgb18 are missing and the data of bgb24 exists, the data of bgb24 is used as the global mode background field and the side boundary condition.

[0076] (15) If bgb6=0, bgb12=0, bgb18=0, bgb24=1, lightn0=0, obs0=1, the shell script of the main body is automatically generated as the job flow C4, which is similar to the flow C2, but because the data of bgb6, bgb12 and bgb18 are missing and the data of bgb24 exists, the data of bgb24 is used as the global mode background field and the side boundary condition.

[0077] (16) If bgb6=0, bgb12=0, bgb18=0, bgb24=1, lightn0=0, obs0=0, the shell script of the main body is automatically generated as the job flow D4, which is similar to the flow D2, but because the data of bgb6, bgb12 and bgb18 are missing and the data of bgb24 exists, the data of bgb24 is used as the global mode background field and the side boundary condition.

[0078] (17) If bgb6=0, bgb12=0, bgb18=0, bgb24=0, the shell script of the main body is automatically generated as the job flow E, which is that the data of bgb at the time of t6, t12, t18 and t24 are all missing, the system is not started, and the job task is finished.

[0079] The lightning data assimilation module is used for three-dimensional variation assimilation of lightning data. Figure 4 As shown in the foregoing 17 system flow modules, the flow A1, B1, A2, B2, A3, B3, A4, B4 starts the lightning data assimilation module, and the specific implementation and flow of the lightning data assimilation module are as shown in the following steps. Figure 4 As shown in the following steps:

[0080] (1) Starting mode integration at t6 time to t0 time (in the flow A1, A2, A3, A4 and the flow B1, B2, B3, B4, the difference here is whether there is other detection data for assimilation at t6 time, the flow A1, A2, A3, A4 has assimilation, and the flow B1, B2, B3, B4 has no assimilation), outputting the prediction field of the mode at t0 time, which is used as the background field of the subsequent hot start mode into the data assimilation system, and is used as the physical quantity values such as the graupel mixing ratio, relative humidity / bulk humidity, temperature on the mode grid for lightning data assimilation;

[0081] (2) Extracting the physical quantity values such as the graupel mixing ratio, relative humidity / bulk humidity, temperature from the prediction field file at t0 time obtained from (1);

[0082] (3) Reading in the lightning data, and pre-processing the lightning data according to the longitude and latitude data of the mode grid obtained from the prediction field file at t0 time, and converting the lightning data into lightning frequency on the mode grid according to a certain rule;

[0083] (4) Reading in the lightning frequency, the graupel mixing ratio, the relative humidity / bulk humidity, the temperature and other physical quantity values on the mode grid, and inversing the lightning frequency into the relative humidity / bulk humidity;

[0084] (5) The prediction field file at t0 time is used as the background field, and is used together with the adjusted relative humidity / bulk humidity, temperature, pressure physical quantity values on the mode grid obtained from (4) into the data assimilation system.

[0085] The dynamic operation script running module is used for execution of the foregoing automatically generated dynamic script command.

[0086] The design of the present application can automatically analyze and generate the shell script of the automatic operation flow of the 17 different automatic operation flows including pre-processing, data assimilation and mode prediction from the flow A1 to the flow E according to the specific conditions of the global mode background field and side boundary condition data, lightning data, sounding, ground, wind profile radar, microwave radiometer, radar inversion wind field and other detection data collected by the system at the current time, so that different data assimilation strategies under different collection conditions of various data are comprehensively considered, so as to avoid operation failure as much as possible.

[0087] The global pattern background field and side boundary condition data of the application can be replaced by the predicted field data of a mesoscale regional numerical prediction model system at a previous time.

[0088] The physical quantity fields of sleet mixing ratio, relative humidity and temperature extracted from the predicted field of the numerical model after 6 hours of prediction are used to inverse the lightning frequency to relative humidity / specific humidity, which can be replaced by 1 hour, 2 hours or any other hour of prediction; the physical quantity fields of sleet mixing ratio, relative humidity and temperature are used to inverse the lightning frequency to relative humidity / specific humidity, which can be replaced by other physical quantities to inverse the lightning frequency to relative humidity / specific humidity.

[0089] The application is a device for automatically generating a method and device suitable for lightning and other data assimilation prediction, which can be replaced by a method and device for automatically generating a numerical prediction system dynamic operation process suitable for radar reflectivity, satellite radiation, precipitation and other data assimilation.

[0090] Obviously, those skilled in the art can make various modifications and variations to the application without departing from the spirit and scope of the application. Thus, if these modifications and variations of the application fall within the scope of the claims of the application and their equivalent technologies, the application also intends to include these modifications and variations.

Claims

1. A device for automatically generating a multi-data assimilation forecast suitable for lightning, characterized by: include: Automatic operation time extraction module; data collection module; dynamic operation process automatic generation module for preprocessing, data assimilation and model forecasting; dynamic operation script execution module; The automatic operation time extraction module is used to obtain the computer machine time when the system device is started and convert it into the mode start time and set the forecast time parameters; The data collection module is used to automatically collect global model forecast fields, lightning and other detection data according to the model onset time and forecast time parameters provided by the operation time automatic extraction module; The dynamic operation process automatic generation module of the preprocessing, data assimilation and model forecast is composed of a data statistics submodule, a dynamic assimilation strategy and a system process script automatic generation submodule; The data statistics submodule is composed of a global model background field and lateral boundary condition data statistics submodule, a lightning data statistics submodule, and other detection data statistics submodules; The global model background field and lateral boundary condition data statistics module is used to count the global model background field and lateral boundary condition data collection status and output the global model background field and lateral boundary condition data collection status indicator code; The lightning data statistics module is used to count the lightning data collected and the amount of data, and output a lightning data collection status indicator code; The other detection data statistics module is used to count the collection of sounding and ground and wind profiler radar and microwave radiometer and radar inversion wind field data, and output other detection data collection status indication code; The dynamic assimilation strategy and system process script automatic generation submodule consists of 17 processes from processes A1 to E. The dynamic assimilation strategy and system process script automatic generation submodule is used to automatically judge and generate a dynamic operation shell script including one of the 17 possibilities from processes A1 to E, including preprocessing, data assimilation, and model forecasting, based on different situations of the global model background field and lateral boundary condition data, lightning data, and other detection data collection status indicator codes. Among them, processes A1, B1, C1, D1, A2, B2, C2, and D2 include a lightning data assimilation module; the lightning data assimilation module is used to perform three-dimensional variational assimilation of lightning data. The dynamic job script running module is used to execute the aforementioned automatically generated dynamic script commands.

2. The device for automatically generating multiple data assimilation forecasts applicable to lightning according to claim 1, characterized in that: The dynamic assimilation strategy and the system process automatically generate submodules based on data including global model background field and lateral boundary condition data, lightning data and other detection data.

3. The device for automatically generating a variety of data assimilation forecasts applicable to lightning according to claim 1 or 2, characterized in that: The other detection data mentioned include sounding and ground and wind profiler radar, microwave radiometer and radar inverted wind field.

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