Effects of Clear-Sky Assimilation of GPM Microwave Imager on the Analysis and Forecast of Typhoon “Chan-Hom”
Abstract
:1. Introduction
2. Observation Data and Assimilation System
2.1. GMI Microwave Imager Data
2.2. The WRFDA Assimilation System
2.3. The Build of GMI Assimilation Module
- (1)
- (2)
- Considering the complex underlying surface of the terrain, only the observations over sea are assimilated, and the observations on the land and the observations with comparatively complex types of sea surface are excluded. Those complex types refer to the mixed predominately sea, mixed predominately sea ice, mixed predominately land, or mixed predominately snow marked by the WRF forecast model.
- (3)
- According to the differences between the observations and the background simulated brightness temperature, the observations beyond a specific threshold (Table 2) are eliminated.
- (4)
- The observations which are larger than 3 after the bias corrections are eliminated. is the standard deviation of observation (Table 2).
- (5)
- (6)
- Channel 8 and channel 9 (89.0 GHz) are sensitive to convective precipitation because channel 1 and channel 2 (10 GHz) are greatly affected by surface emissivity. The study by Yang et al. (2016) [24] in terms of selecting channels of AMSR2 is used as a reference. Thus, only the observation data of channels 5, 6 and 7 with similar wavelengths with AMSR2 are chosen in this study.
3. Case Introduction and Experimental Design
3.1. Typhoon Introduction
3.2. Model Configuration and Experimental Design
4. Results
4.1. Simulation of the GMI Observation
4.2. Bias Correction
4.3. The Impacts on the Analysis
4.3.1. Temperature Increment
4.3.2. Geopotential Height Increment
4.3.3. Low-Layer Flow
4.3.4. Steering Current
4.4. Typhoon Track
5. Conclusions
- (1)
- The clear-sky assimilation of the new GMI radiance data can capture the shape and structure of Typhoon “Chan-Hom” well. The deviation correction coefficient and quality control obtained by the off-line model statistics of VarBC of WRFDA can improve the assimilation effect of the experiment, reduce the standard deviation of observation residual after 3DVar, and ensure the positive effect of GMI DA on typhoon analysis and forecast.
- (2)
- Compared with GTS_DA, it is found that GMI_DA experiment can effectively increase the warm core structure of the typhoon and modify the geopotential height field in the background field of the model. The circulation of the typhoon is also intensified with GMI_DA, which can contribute to the northwest twist of the typhoon.
- (3)
- In the 48-h deterministic forecast, the track error of the typhoon predicted by the GMI_DA experiment is the smallest, with a maximum value below 160 km, when compared with GTS_DA and CTNL.
Author Contributions
Funding
Conflicts of Interest
References
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Channel | Frequency (GHz) | Polarization Mode | Scan Point (km) |
---|---|---|---|
1, 2 | 10.65 | V, H | 19.4 × 32.2 |
3, 4 | 18.7 | V, H | 11.2 × 18.3 |
5 | 23.8 | V | 9.2 × 15.0 |
6, 7 | 36.5 | V, H | 8.6 × 15.0 |
8, 9 | 89.0 | V, H | 4.4 × 7.3 |
10, 11 | 166 | V, H | 4.4 × 7.3 |
12 | 183 ± 3 | V | 4.4 × 7.3 |
13 | 183 ± 7 | V | 4.4 × 7.3 |
Channel | Observation Error | Threshold of CLWP | Threshold of |
---|---|---|---|
5 | 1.60 | 0.25 | 8 |
6 | 1.18 | 0.10 | 6 |
7 | 2.67 | 0.10 | 6 |
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Xu, D.; Shu, A.; Shen, F. Effects of Clear-Sky Assimilation of GPM Microwave Imager on the Analysis and Forecast of Typhoon “Chan-Hom”. Sensors 2020, 20, 2674. https://doi.org/10.3390/s20092674
Xu D, Shu A, Shen F. Effects of Clear-Sky Assimilation of GPM Microwave Imager on the Analysis and Forecast of Typhoon “Chan-Hom”. Sensors. 2020; 20(9):2674. https://doi.org/10.3390/s20092674
Chicago/Turabian StyleXu, Dongmei, Aiqing Shu, and Feifei Shen. 2020. "Effects of Clear-Sky Assimilation of GPM Microwave Imager on the Analysis and Forecast of Typhoon “Chan-Hom”" Sensors 20, no. 9: 2674. https://doi.org/10.3390/s20092674
APA StyleXu, D., Shu, A., & Shen, F. (2020). Effects of Clear-Sky Assimilation of GPM Microwave Imager on the Analysis and Forecast of Typhoon “Chan-Hom”. Sensors, 20(9), 2674. https://doi.org/10.3390/s20092674