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KR-102962051-B1 - RADAR REFLECTIVITY-BASED RELATIVE HUMIDITY CALCULATION AND DATA ASSIMILATION DEVICE AND METHOD

KR102962051B1KR 102962051 B1KR102962051 B1KR 102962051B1KR-102962051-B1

Abstract

The present disclosure relates to an apparatus and method for calculating relative humidity based on radar reflectance and for data assimilation. An apparatus for radar reflectance-based data assimilation according to one embodiment of the present disclosure may include a relationship derivation unit that derives a statistical regression relationship between reflectance and relative humidity using radar reflectance observation data and radiosonde observation data; a calculation unit that calculates a target relative humidity by inputting a target radar reflectance into the statistical regression relationship and calculates a water vapor mixing ratio using the target relative humidity; and a data assimilation input unit that inputs the water vapor mixing ratio into a data assimilation process of a pre-prepared numerical weather prediction model.

Inventors

  • 민기홍
  • 양유곤
  • 김정훈

Assignees

  • 경북대학교 산학협력단
  • 서울대학교 산학협력단

Dates

Publication Date
20260507
Application Date
20251209

Claims (10)

  1. A relationship derivation unit that derives a statistical regression relationship between reflectance and relative humidity using radar reflectance observation data and radiosonde observation data; A calculation unit that calculates the target relative humidity by inputting the target radar reflectance into the statistical regression equation and calculates the water vapor mixing ratio using the target relative humidity; and It includes a data assimilation input unit that inputs the above-mentioned water vapor mixing ratio into the data assimilation process of a pre-prepared numerical weather prediction model; and The above relationship derivation unit is, The above radar reflectance observation data is converted into a CAPPI (Constant Altitude Plan Position Indicator) grid, and A radar reflectance-based data assimilation device characterized by matching the above CAPPI grid and the above radiosonde observation data according to altitude to derive reflectance-relative humidity samples, and deriving the above statistical regression relationship equation based on the above reflectance-relative humidity samples.
  2. delete
  3. In paragraph 1, The above relationship derivation unit is, A radar reflectance-based data assimilation device characterized by classifying the above reflectance-relative humidity samples into intervals of predetermined reflectance unit intervals according to reflectance values, calculating the average of the relative humidity values included in each interval to generate a representative relative humidity for each interval, and deriving the above statistical regression relationship equation based on the above representative relative humidity values.
  4. In paragraph 1, The above calculation unit is, When the above target radar reflectance is greater than or equal to a predefined saturation threshold, the above target relative humidity is set to the saturated relative humidity to calculate the above water vapor mixing ratio, and A radar reflectance-based data assimilation device characterized by calculating the target relative humidity using the statistical regression equation when the target radar reflectance exceeds a predefined precipitation threshold and is less than the saturation threshold.
  5. In paragraph 4, The above data assimilation input unit is, A radar reflectance-based data assimilation device characterized by inputting the water vapor mixing ratio into the data assimilation process when the above target radar reflectance exceeds the above precipitation threshold.
  6. A relationship derivation step for deriving a statistical regression relationship between reflectance and relative humidity using radar reflectance observation data and radiosonde observation data; A calculation step of inputting the target radar reflectance into the statistical regression equation to calculate the target relative humidity and calculating the water vapor mixing ratio using the target relative humidity; and The above-mentioned water vapor mixing ratio is input into the data assimilation process of a pre-prepared numerical weather prediction model; including a data assimilation input step. The above relationship derivation step is, The above radar reflectance observation data is converted into a CAPPI (Constant Altitude Plan Position Indicator) grid, and A radar reflectance-based data assimilation method characterized by matching the above CAPPI grid and the above radiosonde observation data according to altitude to derive reflectance-relative humidity samples, and deriving the above statistical regression relationship equation based on the above reflectance-relative humidity samples.
  7. delete
  8. In paragraph 6, The above relationship derivation step is, A radar reflectance-based data assimilation method characterized by classifying the above reflectance-relative humidity samples into intervals of predetermined reflectance unit intervals according to reflectance values, calculating the average of the relative humidity values included in each interval to generate a representative relative humidity for each interval, and deriving the above statistical regression relationship equation based on the above representative relative humidity values.
  9. In paragraph 6, The above calculation step is, When the above target radar reflectance is greater than or equal to a predefined saturation threshold, the above target relative humidity is set to the saturated relative humidity to calculate the above water vapor mixing ratio, and A radar reflectance-based data assimilation method characterized by calculating the target relative humidity using the statistical regression equation when the target radar reflectance exceeds a predefined precipitation threshold and is less than the saturation threshold.
  10. In Paragraph 9, The above data assimilation input step is, A radar reflectance-based data assimilation method characterized by inputting the water vapor mixing ratio into the data assimilation process when the above target radar reflectance exceeds the above precipitation threshold.

Description

Device and Method for Radar Reflectivity-Based Relative Humidity Calculation and Data Assimilation The present disclosure relates to an apparatus and method for calculating relative humidity based on radar reflectance and performing data assimilation, and more specifically, to a technology that combines radar reflectance and radiosonde-based altitude-specific atmospheric data to derive a statistical relationship between reflectance and relative humidity, uses this to continuously calculate relative humidity and water vapor mixing ratios, and solves the problem of discontinuity in data assimilation. Conventional meteorological data assimilation techniques primarily utilize radar reflectance to analyze precipitation areas and calculate water vapor mixing ratios by assuming the atmosphere is saturated above a certain critical reflectance level. Radar is highly useful for providing information related to precipitation and offers the advantage of observing the distribution of water bodies. Additionally, radiosondes are used to analyze atmospheric conditions by obtaining temperature and humidity data at different altitudes. In particular, radar technology has advanced to the point where it can provide altitude-specific reflectance structures in the form of CAPPI, enabling its use in the estimation of various meteorological variables. These technologies play a crucial role in meteorological observation and analysis. However, existing technologies have had limitations in quantitatively and precisely linking the relationship between reflectance and humidity. Currently, the widely used method involves fixing the relative humidity at 100% and calculating the water vapor mixing ratio when reflectance exceeds a specific threshold (e.g., 30 dBZ). However, this simplified approach fails to adequately reflect the diverse humidity conditions observed in actual data. In particular, the reflectance range below the threshold is not utilized at all during the data assimilation process, leading to an incomplete reflection of the atmospheric humidity structure. This acts as a significant constraint in more accurately understanding and predicting atmospheric conditions. Furthermore, discontinuous phenomena involving sudden fluctuations in values occur around the threshold, and the threshold itself often does not match actual weather conditions. This leads to problems where precipitation or water vapor mixing ratios are overestimated or underestimated, which negatively impacts the performance of numerical weather prediction models. Consequently, these limitations make it difficult to accurately reproduce the occurrence, development, and duration of precipitation, thereby reducing the reliability and accuracy of weather forecasts. FIG. 1 is a diagram illustrating the schematic configuration of a radar reflectance-based data assimilation device according to one embodiment of the present invention. FIG. 2 is an exemplary diagram of the operation process of a radar reflectance-based data assimilation device according to one embodiment of the present invention. Figure 3 is an example diagram showing a scatter plot and a regression line by analyzing radar reflectance and radiosonde relative humidity data from the summer of 2021 to 2023 at 1 dBZ intervals. FIG. 4 is a flowchart illustrating a radar reflectance-based data assimilation method according to an embodiment of the present invention. FIGS. 5 to 7 are experimental results for demonstrating the performance of a radar reflectance-based data assimilation device according to an embodiment of the present invention. The following detailed description of the invention refers to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that various embodiments of the invention are different but need not be mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in relation to one embodiment. It should also be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the following detailed description is not intended to be limiting, and the scope of the invention is limited only by the appended claims, including all equivalents to those claimed therein, provided appropriately described. Similar reference numerals in the drawings refer to the same or similar functions across various aspects. The components according to the present invention are defined by functional distinction rather than physical distinction, and can be defined by the functions each performs. Each component may be implemented as hardware or as program code and proce