Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar
Abstract
:1. Introduction
2. Materials and Method
2.1. Materials
2.1.1. Measurement Principle
2.1.2. Forward Problem and Inverse Problem
2.2. Data Sources
2.3. Pre-Treatment of the Experimental Data
2.4. Evaluation Metrics
2.5. MonoRTM Reliability Validation
2.5.1. MonoRTM
2.5.2. Brightness Temperature Correction
2.6. Experimental Methods and Analysis
2.6.1. Retrieval Method
2.6.2. Sample Construction
2.6.3. Sensitivity Experiments of Cloud Information
3. Results
4. Conclusions
- (1)
- The MonoRTM simulated brightness temperature had significantly higher errors under cloudy conditions than clear conditions. This phenomenon was more significant in the water vapor channel and the first five channels of the retrieval temperature, and it can be speculated that these channels may be more sensitive to the water vapor content of the cloud information compared to the other channels.
- (2)
- The effect of different cloud-base heights and cloud thicknesses on the temperature profiles was not significant. However, for the relative humidity profiles, altering the cloud-base height and thickness led to significant changes in the relative humidity and its peak. Altering the thickness led to a significant increase in the relative humidity within the cloud layer. For low cloud conditions, when changing the cloud-base height or cloud thickness, 2 km is the critical height layer for significant differences in RH profiles; for high cloud conditions, the critical height layer is 4 km.
- (3)
- For temperature profile retrieval under clear conditions, both the BPNN and GMR retrievals demonstrated better performance. Overall, the errors of the temperature profiles increased with an increase in altitude, and the error of GMR retrieval was slightly higher than that of BPNN retrieval. However, for RH profile retrieval, the BPNN retrieval of the RH profiles was significantly better than GMR retrieval. This is related to the fact that some channels of the microwave radiometer are more sensitive to water vapor information.
- (4)
- For temperature and RH profile retrieval under cloudy conditions, it can be seen from the typical cases that the temperature profile retrieval was basically stable; in the height ranges of single-layer and double-layer clouds, the sounding relative humidity increased sharply due to the influence of water vapor in the clouds. The comparison experiments revealed that cloud thickness was the main factor affecting the relative humidity profiles. For thick clouds, the GMR and BPNN retrieval method without cloud information demonstrated the largest errors. With the cloud information, the accuracy of the BPNN retrieval was improved above 2 km, especially in thick clouds. The retrieval temperature and relative humidity profiles with the cloud information were better than the retrieval without the cloud information. The retrieval temperature and RH profiles with the cloud information were closer to the sounding data compared with the retrieval without the cloud information. From a quantitative point of view, the errors of the retrieval of temperature and relative humidity profiles slightly improved with the addition of cloud information.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Specifications |
---|---|
Resolution of brightness temperature (K) | ≤0.2 |
Measurement range of brightness temperature (K) | 0–400 |
Brightness temperature accuracy (K) | 0.5 |
Brightness temperature sensitivity | K-band: ≤0.25 K; V-band: ≤0.3 K |
Observation channels on K-band (GHz) | CH1~CH8: 22.235, 22.5, 23.035, 23.835, 25, 26.235, 28, and 30 |
Observation channels on V-band (GHz) | CH9~CH22: 51.25, 51.76, 52.28, 52.8, 53.34, 53.85, 54.4, 54.94, 55.5, 56.02, 56.66, 57.29, 57.96, and 58.8 |
Vertical resolution (m) | 25 (surface-500) 50 (500–2000) 250 (2000–10,000) |
Time resolution (min) | 2 in 2018 |
Average beamwidth (°) | 3.8 for K-band, and 1.9 for K-band |
Radiometer calibration Methods | Liquid nitrogen calibration Tipping calibration |
Parameters | Specifications |
---|---|
Working frequency | Ka-band, 35 GHz ± 200 MHz |
Antenna scanning method | Vertical fixed pointing |
Beamwidth | ≤0.6° |
First secondary valve | ≤−20 dB |
Antenna gain | ≥50 dB |
Transmit peak power | ≥20 W |
Ultimate improvement factor | ≥25 dB |
Receive system linear dynamic range | ≥80 dB |
System minimum measurable signal power | ≤−110 dBm |
Detection height range | Detection ≥ 15 km |
Detection blind area | ≤150 m |
Distance resolution | 30 m |
Reflectivity factor (Z) | ≤1 dBZ |
Radial velocity (V) | ≤0.5 m/s |
Velocity spectrum width (W) | ≤0.5 m/s |
Cloud-top height | cloud height < 1000 m, ±100 m; cloud height ≥ 1000 m, ±10% |
Cloud-base height | cloud height < 1000 m, ±100 m; cloud height ≥ 1000 m, ±10% |
Channel Frequency/GHZ | Coefficient K | Coefficient B |
---|---|---|
22.235 | 0.96 | −3.82 |
22.500 | 0.99 | −4.95 |
23.035 | 0.96 | −2.95 |
23.835 | 0.96 | −3.14 |
25.000 | 0.90 | −1.91 |
26.235 | 0.90 | −0.58 |
28.000 | 0.90 | −0.56 |
30.000 | 0.90 | −0.43 |
51.250 | 0.80 | 0.37 |
51.760 | 0.80 | 16.91 |
52.280 | 0.70 | 33.18 |
52.800 | 0.79 | 31.18 |
53.340 | 0.85 | −28.23 |
53.850 | 0.90 | −1.33 |
54.400 | 1.00 | −18.53 |
54.940 | 1.08 | −23.77 |
55.500 | 1.00 | −9.68 |
56.020 | 1.00 | −8.48 |
56.660 | 1.00 | −25.83 |
57.290 | 1.05 | −16.29 |
57.960 | 1.09 | −26.05 |
58.800 | 1.08 | −22.49 |
Retrieval Algorithm | Advantage | Disadvantage |
---|---|---|
Neural network | Very high precision; Fast computing speed; No modeling required; Algorithmic stability. | Relies on historical data |
Kalman filter | Fast computing speed; Error estimation | Relies on historical data; Relies on precision filtering model; Filter divergence. |
Genetic algorithm | Monitoring of anomalous changes | Long computation time |
Iterative algorithm | Simple to use | Instability |
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Zhang, L.; Ma, Y.; Lei, L.; Wang, Y.; Jin, S.; Gong, W. Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar. Atmosphere 2024, 15, 1064. https://doi.org/10.3390/atmos15091064
Zhang L, Ma Y, Lei L, Wang Y, Jin S, Gong W. Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar. Atmosphere. 2024; 15(9):1064. https://doi.org/10.3390/atmos15091064
Chicago/Turabian StyleZhang, Longwei, Yingying Ma, Lianfa Lei, Yujie Wang, Shikuan Jin, and Wei Gong. 2024. "Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar" Atmosphere 15, no. 9: 1064. https://doi.org/10.3390/atmos15091064