Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: A Case Study in Beijing

Qi, Yajie; Fan, Shuiyong; Mao, Jiajia; Bai, Li; Guo, Chunwei; Zhang, Shuting

doi:10.3390/atmos12050551

Cited by 11 publications

(6 citation statements)

References 39 publications

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“…There has been considerable effort in developing retrieval techniques for ground-based MWRs, which, when applied to Numerical Weather Prediction (NWP), have shown accuracy levels comparable to those of radiosonde measurements [46]. Research incorporating MWR-derived profiles has underscored their utility in improving local weather forecasts [38,[47][48][49][50]. The RPG-HATPRO MWR system features 14 channels: 7 K-band channels dedicated to water vapor profile retrieval and 7 V-band channels (within the oxygen band) for temperature profile retrieval.…”

Section: Quality Controlmentioning

confidence: 99%

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

Zhang,

Gao,

et al. 2024

Remote Sensing

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The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

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Section: Quality Controlmentioning

confidence: 99%

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

Zhang,

Gao,

et al. 2024

Remote Sensing

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“…When utilized for Numerical Weather Prediction (NWP), MWR soundings have been found to exhibit accuracy on par with radiosonde soundings [49]. Several studies incorporating MWR retrieval profiles have demonstrated their potential in enhancing the forecast of local weather phenomena [50][51][52][53][54]. The RPG-HATPRO MWR system consists of 14 channels, which are divided into seven K-band channels for water vapor profile retrieval and seven V-band channels (oxygen band) for temperature profile retrieval.…”

Section: Agri Radiance and Mwr Datamentioning

confidence: 99%

“…The inversion software, provided by the MWR manufacturers, is utilized to generate level-2 products. These products include temperature and relative humidity profiles [54,55].…”

Section: Agri Radiance and Mwr Datamentioning

confidence: 99%

Effects of Joint Assimilation of FY-4A AGRI and Ground-based Microwave Radiometer on a Heavy Rainfall in Kaifeng Area of Central China in the Summer of 2022

Zhang,

Gao,

et al. 2023

Preprint

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This study aims to evaluate the combined assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data for predicting short-duration heavy rainfall. The WRFDA 4.3 framework is utilized to establish the data assimilation interface. The structural characteristics of this joint assimilation approach are analyzed using a typical heavy rainfall event in the Kaifeng region of Central China. The findings indicate the following: (1) The joint assimilation of FY-4A AGRI and ground-based MWR data effectively corrects the initial moisture content in model simulations. Data assimilation significantly improves the simulation of 24-hour accumulated rainfall. Furthermore, the joint assimilation of AGRI radiance and MWR data outperforms assimilating either of them individually; (2) In the heavy rainfall stage, from the low level to the high level, strong upward movement occurs in the center of the rainstorm. The water vapor in the process of this rainstorm mainly comes from the westward air transport in front of the high trough. The water vapor is continuously transported to the heavy rainfall belt through this channel, providing sufficient water vapor for the maintenance of the rainstorm; (3) The combination of the interaction between high and low level vorticity and divergence in the atmosphere and the vertical upward movement, the emergence of low level jet and the generation of unstable energy in the atmospheric environment jointly lead to the occurrence of the rainstorm process; and (4) The terrain-sensitive experiment results show that, the existence of the topography of Taihang Mountain changes the precipitation in Kaifeng area, and reducing the terrain height will lead to a 50%-60% decrease in the overall precipitation of the precipitation center, and the precipitation range will also be greatly reduced. An increase of more than 50% in terrain height will lead to an increase in precipitation center, precipitation range and precipitation intensity, and an increase of 10%-20% in overall precipitation; An increase of more than 75% in the terrain height will cause the rain belt to shift eastward by about 0.5°E, and the precipitation center will shift significantly eastward. When the terrain height increased by more than 100%, the precipitation did not continue to increase, which was basically the same as the control test. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance and ground-based microwave radiometer in numerical weather models.

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“…Meanwhile, when the sun is in the observation direction of the MWR, the BT data will be abnormally increased due to the influence of solar radiation, especially those used in low latitudes [14]. Therefore, it is essential to control the quality of the MWR observation data for a better forecast [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer

Luo

Gu³

et al. 2023

Remote Sensing

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The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.

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Impact of Assimilating Ground-Based Microwave Radiometer Data on the Precipitation Bifurcation Forecast: A Case Study in Beijing

Cited by 11 publications

References 39 publications

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

Effects of Joint Assimilation of FY-4A AGRI and Ground-based Microwave Radiometer on a Heavy Rainfall in Kaifeng Area of Central China in the Summer of 2022

Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer

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