A method for improving the accuracy of numerical simulation of the atmospheric stable boundary layer

Abstract

The present invention provides a method for improving the numerical simulation accuracy of the atmospheric stable boundary layer, including: data optimization and method optimization; reconstructing a data processing module and a data reading module; the reconstructed data processing module automatically performs data quality control, converts wind speed and wind direction into UV, and performs vertical pressure and altitude conversion according to the saturation vapor pressure; the reconstructed data reading module directly reads high spatio-temporal resolution observation data, and directly reads and processes the surface meteorological data and the high spatio-temporal resolution observation data of plain stations to obtain an OBSDOMAIN file. The present invention simplifies the data processing process by reconstructing an Improved-OBS module for processing high spatio-temporal resolution data, enabling faster and more efficient initial data processing in data assimilation; improving the simulation accuracy of the stable boundary layer: assimilating more stations and higher-resolution observation data under atmospheric stable conditions, and significantly improving the simulation accuracy of the stable boundary layer.

Description

Technical Field

[0001] The present invention provides a method for improving the numerical simulation accuracy of the atmospheric stable boundary layer, specifically in the technical field of meteorological analysis models. Background Art

[0002] The atmospheric stable boundary layer is usually accompanied by temperature inversion, calm wind, high temperature and high humidity, and weak turbulence, which is one of the most important factors leading to severe air pollution. The stable boundary layer can occur in various situations, and the very weak turbulence and its intermittency in the stable boundary layer can interact with other small-scale processes. Currently, there is a lack of long-term statistical studies on the characteristics of the stable boundary layer during heavy pollution, and the simulation accuracy of the stable boundary layer during heavy pollution needs to be improved.

[0003] In many existing boundary layer and meteorological models, the Monin-Obukhov Similarity Theory (MOST) is often used to obtain the mean meteorological variables in the lower boundary layer. Under unstable and neutral atmospheric conditions, these models can generally reproduce boundary turbulence and its characteristics. However, under stable atmospheric conditions, due to the sensitivity to frequently observed sub-mesoscale phenomena (such as gravity waves, meandering motions, radiative divergence, intermittency), the application of the Monin-Obukhov Similarity Theory cannot successfully characterize turbulence, stability functions, and other boundary layer characteristics. The WRF (Weather Research and Forecasting) model based on the Monin-Obukhov Similarity Theory has been widely used in the numerical simulation community for weather forecasting and air quality research. However, due to the above reasons, its boundary characteristics under stable conditions show certain limitations. For example, the treatment of turbulence inside the Planetary Boundary Layer (PBL) is insufficient, resulting in the model underestimating pollutant concentrations when pollutant concentrations increase explosively; when reproducing the trend of daily average concentrations through models (such as WRF-Chem, WRF-CMAQ), the pollutant concentrations in the Stable Boundary Layer (SBL) are underestimated, especially at night. When pollutant concentrations are wrongly underestimated, this will affect radiation, further affecting the PBL structure, climate, and even health. Therefore, during the stable boundary period, it is necessary to improve the model simulation accuracy through other means.

[0004] The nudging method is a commonly used method to improve model performance. Observational nudging (OBS-nudging) is an option in WRF's four-dimensional data assimilation system. It is an assimilation method that gradually approximates the simulation results to the observational results by attaching a forcing term to the control equation and is used in many studies to improve model performance.

[0005] However, due to the very coarse resolution of meteorological observational data in the current benchmark OBS-nudging module (usually the temporal resolution of surface observational data is 6 hours, and there is only data from conventional meteorological observation stations; similarly, radiosonde observational data is only available at 00 and 12 UTC every day and the vertical resolution is dozens or hundreds of meters). Therefore, even if the OBS-nudging module is selected, the WRF model cannot reproduce the exact boundary layer characteristics under stable conditions. The current OBS-nudging still has limitations in the simulation studies of meteorological elements and turbulent exchange, which also affects related studies using the WRF model, including subsequent simulation studies such as WRF-Chem and WRF-CMAQ.

[0006] With the progress of atmospheric monitoring methods and equipment, meteorological observations have higher resolutions in both space and time. The improvement in data availability provides an opportunity to provide high-quality nudging, which in turn helps to reduce model biases and better reproduce observational results. Therefore, constructing an OBS-nudging module that directly processes high spatio-temporal resolution data and applying it to the WRF model study of the stable boundary layer can show more precise characteristics of the stable boundary layer boundary, which has important theoretical value and practical significance.

[0007] The surface observational data and radiosonde data required by the benchmark assimilation module are sourced from the National Center for Atmospheric Research in the United States, and the data storage format is the binary Bufr format. After the data is downloaded, it needs to go through a long data decoding process to convert the data into the Obs format, and then the OBSDOMAIN file is calculated by setting relevant parameters in the namelist.oa file and using OBSGRID.EXE, and finally it is used for the WRF model simulation run. Benchmark assimilation website: https: / / www2.mmm.ucar.edu / wrf / users / docs / nudging.html

[0008] This technology has the following disadvantages:

[0009] 1) The original OBS-nudging data from NCAR (National Center for Atmospheric Research) has very coarse temporal and spatial resolutions. There are only a few stations on the North China Plain, and the surface meteorological data is available only four times a day. The sounding data from NCAR is available only twice a day, and the resolution in the vertical direction is very low, ranging from dozens of meters to over a hundred meters.

[0010] 2) The benchmark assimilation module takes a long time and has low efficiency in processing data due to the decoding process.

[0011] 3) There are deficiencies in the accuracy of the WRF model in simulating the stable boundary layer. The WRF model coupled with the original data benchmark assimilation cannot reproduce accurate boundary layer characteristics under stable conditions. Summary of the Invention

[0012] In view of the above technical problems, the present invention provides a method for improving the accuracy of numerical simulation of the atmospheric stable boundary layer, including:

[0013] (1) Data optimization

[0014] Surface meteorological data (from the China Meteorological Administration): Hourly surface meteorological observation data of 460 stations in the North China region of the China Meteorological Administration, including 2-meter temperature (T2), 2-meter relative humidity (RH2), and 10-meter wind direction and wind speed (WS10).

[0015] Upper-air meteorological data (from self-observations): High vertical resolution L-band radiosonde data at plain stations in the North China region (with a height interval of several meters, from 1000 hPa to 700 hPa, usually with more than 400 layers of data), including temperature profiles, relative humidity profiles, and wind profiles at 00, 03, 06, 09, 12, 15, 18, and 21 hours Beijing time every day.

[0016] (2) Method optimization

[0017] Reconstruct a data processing module and a data reading module;

[0018] The reconstructed data processing module (Datadeal.exe) can automatically perform data quality control, convert wind speed and wind direction to UV, and perform vertical pressure and height conversions according to saturated vapor pressure, etc.;

[0019] The reconstructed data reading module (Improved-OBS.exe) can directly read high spatio-temporal resolution observation data, directly read and process the surface meteorological data and high spatio-temporal resolution observation data at plain stations to obtain the OBSDOMAIN file;

[0020] The reconstruction module can speed up the simulation time, improve efficiency and simulation accuracy. Especially when the atmosphere is in a stable state, the accuracy of the numerical simulation of the stable boundary layer can be greatly improved.

[0021] The method optimization specifically includes the following steps:

[0022] Step 1: Remove outliers from the sounding data and run the data quality control module;

[0023] Step 2: Convert the Beijing time corresponding to all data of ground observation data and sounding data into the universal time UTC, and run the time conversion module;

[0024] Step 3: Calculate the corresponding air pressure value according to the saturated steam value, temperature, altitude and other data, and run the air pressure altitude conversion module;

[0025] Step 4: Convert the wind direction and wind volume observed by the radiosonde into the wind components in the longitude and latitude, and convert the Celsius into the corresponding Kelvin, and run the wind temperature processing module;

[0026] Step 5: Use the integrated output module to process all quality-controlled ground observation data and sounding data into OBSDOMAIN files, which are convenient for subsequent real.exe and wrf.exe operations, that is, to couple observation assimilation in the WRF model to simulate the atmospheric stable boundary layer.

[0027] The technical effects of the present invention are as follows:

[0028] 1. The data processing process is simplified by rebuilding an Improved-OBS module that processes high temporal and spatial resolution data, making the initial data processing in data assimilation faster and more efficient.

[0029] 2. Improve the resolution of assimilated data: The improved module can directly read detection data with high temporal and spatial resolution, and assimilate observation data from more stations and with higher resolution.

[0030] 3. The refined high temporal and spatial resolution reconstruction assimilation module can significantly improve the accuracy of stable boundary layer simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Implementation of the benchmark assimilation module and the improved assimilation module calculation process;

[0032] Figure 2 Detailed steps for improving the module for the implementation example;

[0033] Figure 3 The WRF simulation flow chart of the embodiment (observation assimilation is the improved assimilation module);

[0034] Figure 4a Profile comparison of the observed and simulated temperatures at the plain site at 00:00 on January 19, 2018, for Example

[0035] Figure 4b Profile comparison of the observed and simulated relative humidities at the plain site at 00:00 on January 19, 2018, for Example

[0036] Figure 4c Profile comparison of the observed and simulated wind speeds at the plain site at 00:00 on January 19, 2018, for Example

[0037] Figure 4d Mean temperature deviation between the WRF baseline simulation and the coupled high - resolution assimilation simulation at the plain site at 00:00 on January 19, 2018, for Example

[0038] Figure 4e Mean relative humidity deviation between the WRF baseline simulation and the coupled high - resolution assimilation simulation at the plain site at 00:00 on January 19, 2018, for Example

[0039] Figure 4f Mean wind speed deviation between the WRF baseline simulation and the coupled high - resolution assimilation simulation at the plain site at 00:00 on January 19, 2018, for Example Detailed implementation mode

[0040] The specific technical solutions of the present invention will be described in conjunction with the embodiments.

[0041] The surface observation data and sounding data required by the baseline assimilation module are sourced from the National Center for Atmospheric Research in the United States, and the data storage format is the binary Bufr format. After the data is downloaded, it needs to go through a long - time data decoding process to convert the data into the Obs format, and then the OBSDOMAIN file is calculated by setting relevant parameters in the namelist.oa file and using OBSGRID.EXE, and finally it is used for the WRF model simulation run. According to the above - mentioned baseline observation assimilation, taking the North China region as an example, there are only about 100 ground observation data sites during model calculation, and the time resolution of the data is usually 3 hours or 6 hours, while the vertical profile resolution shown by the sounding data is usually dozens of meters or even hundreds of meters, and there are only a few to more than a dozen layers of sounding data. When the OBSDOMAIN file processed by the baseline assimilation module is used for WRF simulation of unstable atmospheric conditions, the relevant meteorological conditions have good effects. However, when used for simulation under stable atmospheric conditions, the deviation between the simulated value and the observed value of the meteorological data will increase. Taking the ground wind speed as an example, the simulated value is usually twice the observed value.

[0042] The present invention improves the assimilation module, including a reconstructed data processing module and a reconstructed data reading module, and reconstructs an Improved-OBS module that directly reads high spatio-temporal resolution observation data. It can directly read high spatio-temporal resolution observed meteorological elements into the improved module, skip the decoding and conversion of data formats, and perform direct processing. At the same time, it skips the OBSGRID.EXE process and directly converts it into an OBSDOMAIN file for subsequent processing, such as Figure 1 as shown

[0043] The detailed data sources and improvement processes are as follows:

[0044] 1. Surface observation data:

[0045] Most Chinese meteorological observation stations use weather phenomenon instruments and platinum resistance temperature sensors to detect temperature, humidity, wind direction, and wind speed.

[0046] Platinum resistance temperature sensor: The resistance value of the platinum resistance changes with temperature, and the temperature of the measured object is calculated by measuring its resistance value.

[0047] Weather phenomenon instrument: An intelligent multi-variable sensor composed of a scattering visibility meter, a precipitation monitoring system sensor, and sensors such as temperature, humidity, wind direction, and wind speed.

[0048] The surface data collection of the implementation case: Use the hourly surface meteorological observation data of 460 stations in North China of the China Meteorological Administration, including 2-meter temperature (T2), 2-meter relative humidity (RH2), and 10-meter wind direction and wind speed (WS10).

[0049] 2. Radiosonde data observation method:

[0050] The meteorological radiosonde observation equipment used mainly includes: a zero-pressure balloon carrier platform, an L-band GTS1-1 type radiosonde, and ground equipment such as a radiosonde base measurement box, a portable hydrogen production instrument, and an L-band secondary wind-finding radar.

[0051] Super-pressure balloon carrier platform: The zero-pressure balloon adopts a water-drop natural shape design and is mostly made of latex. It takes a one-way ascending method to conduct detections at different altitudes.

[0052] L-band GTS1-2 type radiosonde: The L-band GTS-1 radiosonde is widely used in radiosonde stations to provide fine-resolution profiles of meteorological elements such as temperature, pressure, relative humidity, and wind speed.

[0053] Electronic radiosonde base measurement box: A standard device for measuring the base value of the L-band radar sounding system. It is an instrument that provides comprehensive tests for comparing the accuracy of temperature, humidity, and air pressure base points before the radiosonde is released, and can provide temperature, relative humidity, air pressure standard devices and a stable temperature and humidity comparison environment for the base point comparison.

[0054] Portable hydrogen generation instrument: To meet the hydrogen demand for upper-air meteorological sounding.

[0055] L-band secondary wind-finding radar: The L-band secondary wind-finding radar is a ground receiving device of the upper-air sounding system currently in use in China's operations. It is mainly used to amplify and demodulate the reply signals and sounding signals sent back by the radiosonde.

[0056] Observation of sounding data in the implementation case: We conducted a one-month sounding observation (December 25, 2017 - January 24, 2018) at a plain site in North China. The altitude ranged from 1000 hPa to 700 hPa, the vertical resolution was 5 - 10 meters, and there were approximately 400 layers of data in the vertical direction. The time resolution was 3 hours, including data such as temperature profiles, relative humidity profiles, and wind profiles at 00, 03, 06, 09, 12, 15, 18, and 21 Beijing time every day. The subsequent results take the plain site in North China as an example.

[0057] 3. Detailed steps of the improvement module (including data preprocessing and processing into OBSDOMAIN files), as Figure 2 shown;

[0058] First step: Since there may be outliers in the data in the vertical direction transmitted back by the sounding data (the data detected by the sounding data is transmitted back by temperature sensors, altitude sensors, humidity sensors, etc., but due to the complex environment of the upper-air atmosphere, for altitude data, there may be a situation where the transmitted data occasionally becomes smaller as the altitude increases), first remove the outliers in the sounding data and run the data quality control module.

[0059] Second step: Since the corresponding time during the subsequent WRF simulation is Coordinated Universal Time (UTC), it is necessary to convert all the data of the surface observation data and sounding data corresponding to Beijing time to UTC, and run the time conversion module.

[0060] Third step: Calculate the corresponding air pressure value based on data such as the saturated vapor value, temperature, and altitude, and run the air pressure - altitude conversion module.

[0061] Fourth step: Convert the wind direction and wind volume observed by the radiosonde into wind components in the meridional and zonal directions, and at the same time convert Celsius to the corresponding Kelvin, and run the wind - temperature processing module.

[0062] Step 5: Use the integrated output module to process all quality-controlled surface observation data and sounding data into OBSDOMAIN files, which are convenient for subsequent real.exe and wrf.exe runs, that is, to simulate the atmospheric stable boundary layer by coupling observation assimilation in the WRF model.

[0063] Results of implementation cases:

[0064] Simulation process:

[0065] Prepare the corresponding meteorological field data, terrain data, and land cover type data for the WRF model to generate the basic initial meteorological field and terrain; select appropriate atmospheric boundary layer parameterization schemes (including microphysical processes, cumulus convection schemes, radiation transfer schemes, surface layer schemes, land surface process schemes, etc.), and at the same time add the processed high-resolution data and benchmark data for subsequent WRF simulations. Finally, obtain the corresponding simulation results, such as Figure 3 shown.

[0066] Result comparison:

[0067] Since the results under unstable conditions simulated based on the benchmark assimilation module can meet the usage conditions, after high-resolution data assimilation simulation in this case (this time period includes the periods when the atmosphere is in a stable state and an unstable state), the time when the atmosphere is stable (i.e., from January 14th to 20th) is selected for comparison between the linear assimilation simulation values and the observed values.

[0068] Figures 4a to 4f Figure showing the comparison between the simulated values and the observed values of the temperature profile, relative humidity profile, and wind speed profile at 00:00 on January 19th. Obs is the result of the observed values, no-nuding is the result of the benchmark assimilation simulation, and Vtq-nuding is the result of the coupled high-resolution observation assimilation simulation. It can be seen that compared with the simulation of the coupled high-resolution assimilation, the simulated result of the coupled high-resolution assimilation is in good agreement with the observed temperature profile. However, the temperature profile of the WRF benchmark simulation overestimates the observed values to a certain extent from 1000 hPa to 900 hPa and is lower than the actual observed values from 900 hPa to 850 hPa, as Figure 4a ; among them, the maximum overestimation of the temperature by the WRF benchmark simulation at 1000 hPa reaches 2.3 °C; at the same time, the temperature deviation gradually decreases with the increase in height and reaches the minimum at 850 hPa, which is 0.2 °C, as Figure 4d . For the humidity profile, the result of the WRF benchmark simulation is also worse than that of the coupled high-resolution assimilation simulation; below 930 hPa, the relative humidity profile of the WRF benchmark simulation differs significantly from the observed values, and the relative humidity is significantly underestimated, with a maximum difference of about 60%, as Figure 4b; Comparing the means of the WRF baseline simulation and the coupled high-resolution assimilation simulation, as the altitude increases, the deviation gradually decreases, and the observed value can be better reflected only above 930 hPa, such as Figure 4e ; The result of the coupled high-resolution assimilation simulation is lower than the observed value only between 1000 hPa and 975 hPa, and it fits well with the observed value at other altitudes, such as Figure 4b . The performance of the coupled high-resolution assimilation simulation in the wind profile is still significantly better than that of the baseline simulation, such as Figure 4c , and the degree of agreement with the observed value is much higher than that of the baseline simulation and the observed value; the result of the coupled high-resolution assimilation simulation only overestimates the wind speed at about 980 hPa and 900 hPa (about 1 m / s and 0.9 m / s respectively), and basically coincides with the observed value at other altitudes; while the baseline simulation significantly underestimates the wind speed from 1000 hPa to 860 hPa and significantly overestimates the wind speed above 850 hPa. Below 940 hPa, the wind speed of the WRF baseline simulation is higher than that of the coupled high-resolution assimilation simulation. At 1000 hPa, the WRF baseline simulation is 2 m / s higher; above 890 hPa, the wind speed of the coupled high-resolution assimilation simulation is higher than that of the WRF baseline simulation, and the deviation is about 1.5 m / s, such as Figure 4f .

[0069] In summary, the simulation of the coupled high-resolution observational assimilation is significantly better than the baseline assimilation simulation in reproducing the observational characteristics. Compared with the WRF baseline simulation, the vertical profile deviation of the coupled high-resolution assimilation simulation is smaller and closer to the observed value, that is, the simulation of the coupled high-resolution observational assimilation can better reproduce the vertical profile characteristics of the atmosphere in a stable temperature, wind speed and humidity than the baseline assimilation.

Claims

1. A method for improving the accuracy of numerical simulation of the atmospheric stable boundary layer, characterized in that include:

1. Data Optimization Ground meteorological data: Hourly ground meteorological observation data from 460 stations in North China of the China Meteorological Administration, including 2-meter temperature, 2-meter relative humidity, and 10-meter wind direction and speed; High-altitude meteorological data: high vertical resolution L-band radiosonde data from plain stations in North China, including temperature profiles, relative humidity profiles, and wind profiles at 00:00, 03:00, 06:00, 09:00, 12:00, 15:00, 18:00, and 21:00 Beijing time every day; 2. Method Optimization Reconstruct the data processing module and data reading module; Reconstruct the data processing module to automatically perform data quality control, convert wind speed and direction into UV, and convert vertical pressure and altitude based on saturated vapor pressure; Reconstruct the data reading module to directly read high-temporal and spatial resolution observation data, directly read the ground meteorological data and high-temporal and spatial resolution observation data of plain sites and process them to obtain OBSDOMAIN files; The method optimization specifically includes the following steps: Step 1: Remove outliers from the sounding data and run the data quality control module; Step 2: Convert the Beijing time corresponding to all data of ground observation data and sounding data into the universal time UTC, and run the time conversion module; Step 3: Calculate the corresponding air pressure value according to the saturated steam value, temperature and altitude data, and run the air pressure altitude conversion module; Step 4: Convert the wind direction and wind volume observed by the radiosonde into the wind components in the longitude and latitude, and convert the Celsius into the corresponding Kelvin, and run the wind temperature processing module; Step 5: Use the integrated output module to process all quality-controlled ground observation data and sounding data into OBSDOMAIN files, which are convenient for subsequent real.exe and wrf.exe operations, that is, to couple observation assimilation in the WRF model to simulate the atmospheric stable boundary layer.

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