Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Qi, Yajie; Fan, Shuiyong; Li, Bai; Mao, Jiajia; Lin, D.

doi:10.3390/atmos13010074

Cited by 10 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Agri Radiance and Mwr Datamentioning

confidence: 99%

“…Hence, the assimilation of MWR data can enhance weather forecasts of NWP models. For instance, a case study was conducted in Beijing to assess heavy rainfall by assimilating data from seven ground-based MWRs using 3-Dimensional Variational Assimilation (3DVAR) [38]. Nevertheless, MWR's utilization remains insufficient.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…As a new type of atmospheric microwave remote sensing detection equipment, MWR has the advantages of long-term continuous observation, low equipment maintenance cost and high path resolution in the troposphere. It is the most ideal detection equipment for measuring cloud liquid water and water vapor density at this stage [2,3]. MWR inversion of temperature and humidity profile algorithm is mainly divided into physical inversion method and statistical inversion method.…”

Section: Introductionmentioning

confidence: 99%

Inversion of tropospheric temperature and humidity profile based on atmospheric empirical database

Sun,

Li,

Jiao

et al. 2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

View full text Add to dashboard Cite

Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Cited by 10 publications

References 34 publications

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

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

Inversion of tropospheric temperature and humidity profile based on atmospheric empirical database

Contact Info

Product

Resources

About