Observation-based numerical model temperature bias correction method and system

Abstract

The present application relates to the technical fields of weather forecast and power grid operation and maintenance, and discloses a temperature deviation correction method and system based on observation of a numerical model, to solve the technical problem of large temperature deviation in the prior art under the condition of rapid transit of cold air and cold wave. The system comprises: a data acquisition module for collecting meteorological station observation data of a selected area and required numerical weather prediction data; a data preprocessing module for grid division and basic data interpolation; an initial field assimilation correction module for performing fusion assimilation based on observation and numerical weather prediction data, obtaining an optimized initial field, and then performing temperature prediction numerical calculation; a temperature prediction calculation module for carrying out temperature numerical prediction calculation in the future; and a data storage module for storing temperature numerical prediction results.

Description

Technical Field

[0001] This invention relates to the fields of meteorological forecasting and power grid operation and maintenance technology, and in particular to a method and system for correcting temperature deviations in numerical models based on observations. Background Technology

[0002] The four precipitation phases in winter—rain, sleet, snow, and sleet—are highly correlated with atmospheric temperature stratification. Snow that forms in the upper atmosphere may melt completely to form rain when it encounters a strong warm layer, or partially melt to form sleet when it encounters a moderately warm layer. Snowfall only occurs when the warm layer is very weak or absent. Differences in temperature stratification can lead to drastically different precipitation phases at the ground, making snowfall prediction very difficult.

[0003] Existing snowfall prediction models primarily consider the temperature characteristics of the lower atmosphere; therefore, the accuracy of atmospheric temperature predictions in numerical models has a significant impact on the accuracy of snowfall forecasts. To ensure computational stability, numerical models tend to be conservative in their calculations of the rapid passage of cold air masses, resulting in calculated temperature drops being smaller than actual. There is an urgent need to establish an observation-based numerical model temperature bias correction method to improve the accuracy of temperature predictions during the rapid impact of cold air masses. Summary of the Invention

[0004] The purpose of this invention is to disclose an observation-based numerical model temperature deviation correction method and system to solve the technical problem of large temperature deviation in the case of rapid passage of cold air during cold waves in the prior art.

[0005] To achieve the above objectives, the observation-based numerical model temperature deviation correction method of the present invention includes:

[0006] (1) Based on the snow cover prediction area required, select the area to be expanded westward and northward by more than 0.5 times to construct the simulation area of ​​the numerical model.

[0007] (2) Select a suitable numerical weather prediction model and divide the numerical model calculation grid according to the simulation area selected in (1).

[0008] (3) Collect high-resolution topographic elevation data and land use data of the area, and discretize them according to the grid divided in (2) to obtain the topographic elevation and land use type of each grid point.

[0009] (4) Select a numerical calculation scheme suitable for winter precipitation prediction and constrain the radiation, convection, boundary layer, and microphysical processes of the numerical model.

[0010] (5) Collect temperature observation data from all ground meteorological stations in the region (1) for a period of time before the time when temperature forecasting is required.

[0011] (6) Select an assimilation scheme to combine the meteorological station data collected in (5) with the initial field data in the numerical model. The optimal fusion of the observational data and initial field information is achieved by minimizing a given objective function, thus achieving the goal of assimilating the upstream observational data. The objective function can be defined as:

[0012]

[0013] Among them, J b The degree of fit between the analysis field and the background field is defined, J. o The degree of fit between the analysis field and the observation field is defined, where x is the analysis field to be determined. b As the background field, y o Let H be the observation field, H be the observation operator, B be the background error covariance matrix, and R be the observation error covariance matrix.

[0014] (7) Using the settings of (1)-(5) and the assimilated initial values ​​formed by (6), numerical calculations are carried out to obtain the temperature prediction values ​​for a future period of time.

[0015] To achieve the above objectives, the present invention also discloses an observation-based numerical model temperature deviation correction system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-described method when executing the computer program.

[0016] The present invention has the following beneficial effects:

[0017] Based on the assimilation scheme, the collected meteorological station data and the initial field data in the numerical model are combined by solving the minimization of a given objective function to achieve the optimal fusion of observation data and initial field information, thereby achieving the goal of assimilating upstream observation data; thus solving the technical problem of large temperature deviation under the rapid passage of cold waves and cold air in the existing technology.

[0018] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0020] Figure 1 This is a schematic diagram of the observed temperature at 03:00 on February 7, 2022, as disclosed in an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the temperature deviation calculated after temperature assimilation correction at 03:00 on February 7, 2022, as disclosed in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of the temperature deviation at 03:00 on February 7, 2022, without the use of temperature assimilation calculation, as disclosed in an embodiment of the present invention.

[0023] Figure 4 This is a structural block diagram of the functional modules in the observation-based numerical model temperature deviation correction system program disclosed in the embodiments of the present invention. Detailed Implementation

[0024] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0025] Example 1

[0026] This embodiment forecasts the snowfall event in the Hunan power grid from February 6th to 7th, 2022. Specifically, the observation-based numerical model temperature bias correction method includes the following steps:

[0027] Step 1: Select the snow cover prediction area as including Hunan, Hubei, Chongqing, Shaanxi, Sichuan and other regions.

[0028] Step 2: Select The Weather Research and Forecasting Model (WRF) and divide the numerical model computation grid according to the simulation area selected in (1).

[0029] Step 3: Collect topographic elevation data and land use data of the area with a horizontal resolution of 30m×30m. Discretize the data according to the grid divided in (2) to obtain the topographic elevation and land use type of each grid point.

[0030] Step 4: Select a numerical calculation scheme suitable for winter precipitation prediction. Specifically, select the RRTM scheme for longwave radiation, the Dudhia scheme for shortwave radiation, the Kain-Fritsch scheme for convection processes, and the Kessler scheme for microphysical processes. Constrain the radiation, convection, boundary layer, and microphysical processes of the numerical model.

[0031] Step 5: Collect temperature observation data from ground stations in the selected area in Step 1 from 12:00 on February 5, 2022 to 00:00 on February 6, 2022.

[0032] Step 6: Select a three-dimensional variational assimilation scheme. Combine the meteorological station data collected in Step 5 with the initial field data from the numerical model. Minimize the given objective function to achieve optimal fusion of the observational data and initial field information, thus assimilating the upstream observational data. The objective function is defined as follows:

[0033]

[0034] Among them, J b The degree of fit between the analysis field and the background field is defined, J. o The degree of fit between the analysis field and the observation field is defined, where x is the analysis field to be determined. b As the background field, y o Let H be the observation field, H be the observation operator, B be the background error covariance matrix, and R be the observation error covariance matrix.

[0035] Step 7: Using the settings from Steps 1-5 and the assimilated initial values ​​from Step 6, perform numerical calculations to obtain the predicted temperature values ​​for a future period.

[0036] Calculation results refer to Figures 1 to 3 As shown, after temperature correction calculations, the absolute error of the calculated ground temperature in Yueyang area (within the box) compared to the observed value is reduced to about 1℃. Figure 2 ), while in models that do not assimilate ground observation data ( Figure 3 The calculated temperature in Yueyang was about 3°C ​​higher than expected. Therefore, after assimilating the temperature observation data, the temperature simulation of Yueyang was significantly improved.

[0037] Example 2

[0038] This embodiment discloses an observation-based numerical model temperature deviation correction system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method corresponding to the above embodiment.

[0039] Optionally, such as Figure 4 As shown, the computer program of the system in this embodiment includes modules for implementing the following functions:

[0040] Data acquisition module 1: Used to collect meteorological station observation data for the selected area, as well as the required numerical weather forecast data.

[0041] Data preprocessing module 2: used for grid division and basic data interpolation.

[0042] Initial field assimilation and correction module 3: It is used to perform fusion and assimilation based on observation and numerical weather prediction data, and then perform numerical calculations for temperature prediction after obtaining the optimized initial field.

[0043] Temperature Prediction Calculation Module 4: Used to perform numerical temperature prediction calculations for a future period of time.

[0044] Data storage module 5: Stores temperature numerical prediction results.

[0045] In summary, based on the methods and systems disclosed in the above embodiments, it can be seen that the present invention has at least the following beneficial effects:

[0046] Based on the assimilation scheme, the collected meteorological station data and the initial field data in the numerical model are combined by solving the minimization of a given objective function to achieve the optimal fusion of observation data and initial field information, thereby achieving the goal of assimilating upstream observation data; thus solving the technical problem of large temperature deviation under the rapid passage of cold waves and cold air in the existing technology.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for correcting temperature bias in a numerical model based on observations, characterized in that, include: (1) Based on the snow cover prediction area required, select to expand the area to the west and north by more than 0.5 times to construct the simulation area of ​​the numerical model; (2) Select the WRF weather study and forecast numerical weather prediction model and divide the numerical model calculation grid. The divided grid includes the simulation area selected in step (1). (3) Collect high-resolution topographic elevation data and land use data with a horizontal resolution of 30m×30m for the simulated area, and discretize them according to the grid divided in step (2) to obtain the topographic elevation and land use type at each grid point; (4) Select the numerical calculation scheme for winter precipitation prediction, namely, select the RRTM scheme for longwave radiation, select the Dudhia scheme for shortwave radiation, select the Kain-Fritsch scheme for convection processes, and select the Kessler scheme for microphysical processes, and constrain the radiation, convection, boundary layer and microphysical processes of the numerical model. (5) Collect temperature observation data from all ground meteorological stations in the simulated area in step (1) for a period of time before the temperature prediction is carried out; (6) Select a three-dimensional variational assimilation scheme, and use the meteorological station data collected in step (5) and the initial field data in the numerical model to achieve the best fusion of observation data and initial field information by solving the minimization of the given objective function, so as to achieve the goal of assimilating upstream observation data. (7) Using the settings of steps (1)-(5) and the assimilated initial values ​​formed in step (6), numerical calculations are carried out to obtain the temperature prediction values ​​for a future period of time.

2. The method according to claim 1, characterized in that, Step (6) The objective function is: in, The degree of fit between the analysis field and the background field is defined. The degree of fit between the analytical field and the observed field is defined. For the desired analysis field, As background scene, For the observation field, B is the observation operator, and B is the background error covariance matrix. It is the observation error covariance matrix.

3. An observation-based numerical model temperature deviation correction system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in claim 1 or 2 above.

4. The system according to claim 3, characterized in that, The computer program includes modules for implementing the following functions: Data acquisition module: used to collect observation data from meteorological stations in the selected area, as well as the required numerical weather forecast data; Data preprocessing module: used for grid generation and basic data interpolation; Initial field assimilation and correction module: used to fuse and assimilate observational and numerical weather prediction data to obtain an optimized initial field for temperature prediction numerical calculations. Temperature prediction calculation module: used to perform numerical temperature prediction calculations for a future period of time; Data storage module: Stores the predicted temperature values.

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