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A precipitation detection algorithm for 118GHz channels based on FY-3C MWHS II and FY-3C MWTS II
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The MicroWave Humidity Sounder II (MWHS II) is a cross-track microwave sounder flying on FengYun (FY)-3C satellite. It has 15 channels ranging from 89.0 to 191.0 GHz, eight (channels 2-9) of which are located near 118.75 GHz along an oxygen absorption line, five (channels 11-15) of which are located near 183.31 GHz water vapor absorption line and the remaining two channels 1 and 10 are two window channels centered at 89.0 and 150.0 GHz. A new precipitation detection algorithm for 118GHz channels was developed based on the radiation characters of the double O2 absorption bands (118 and 50-60 GHz). Since both of the 118 GHz and 50-60 GHz oxygen absorption bands are sensitive to atmospheric temperature, the radiation observed in the two bands has a specific inherent constraint relationship under the clear-sky conditions. However, the frequencies of 118 GHz channels are approximately twice that of the 50-60 GHz channels, and the two bands have different absorption and scattering characteristics for atmospheric hydrometeors. The radiance transfer mode VDISORT was used to simulate the sensitivity of the 118 GHz and 50-60 GHz channels to five kinds of hydrometeors (cloud water, rainwater, ice, snow, and graupel) in the cloud atmosphere. The results show that the 50-60 GHz channels are more sensitive to rainwater, and the 118 GHz channels are more sensitive to the other four types of hydrometeors. Therefore, the inherent constraint of the observational radiance between 118 GHz and 50-60 GHz channels under clear-sky condition is no longer valid for a cloudy scenario. In this paper, the machine learning system TensorFlow was used to construct a model for predicting the brightness of 118 GHz channels using 50-60 GHz observations under clear-sky conditions, and the accuracy of the prediction model was validated using independent samples. Then this neural network-based predictive model was used for 118 GHz channel precipitation detection. When the difference between actual observed and predicted bright temperature for 118 GHz channel is more massive than three times of the standard deviation of the prediction model, it is thought that the MWHS II observation is contaminated by precipitation or cloud. At last, this new precipitation detection algorithm for 118 GHz was validated by simulated measurements. The results show that both the precipitation detection POD (test probability) and PC (correct rate) for 118 GHz channels are above 90%.
Title: A precipitation detection algorithm for 118GHz channels based on FY-3C MWHS II and FY-3C MWTS II
Description:
The MicroWave Humidity Sounder II (MWHS II) is a cross-track microwave sounder flying on FengYun (FY)-3C satellite.
It has 15 channels ranging from 89.
0 to 191.
0 GHz, eight (channels 2-9) of which are located near 118.
75 GHz along an oxygen absorption line, five (channels 11-15) of which are located near 183.
31 GHz water vapor absorption line and the remaining two channels 1 and 10 are two window channels centered at 89.
0 and 150.
0 GHz.
A new precipitation detection algorithm for 118GHz channels was developed based on the radiation characters of the double O2 absorption bands (118 and 50-60 GHz).
Since both of the 118 GHz and 50-60 GHz oxygen absorption bands are sensitive to atmospheric temperature, the radiation observed in the two bands has a specific inherent constraint relationship under the clear-sky conditions.
However, the frequencies of 118 GHz channels are approximately twice that of the 50-60 GHz channels, and the two bands have different absorption and scattering characteristics for atmospheric hydrometeors.
The radiance transfer mode VDISORT was used to simulate the sensitivity of the 118 GHz and 50-60 GHz channels to five kinds of hydrometeors (cloud water, rainwater, ice, snow, and graupel) in the cloud atmosphere.
The results show that the 50-60 GHz channels are more sensitive to rainwater, and the 118 GHz channels are more sensitive to the other four types of hydrometeors.
Therefore, the inherent constraint of the observational radiance between 118 GHz and 50-60 GHz channels under clear-sky condition is no longer valid for a cloudy scenario.
In this paper, the machine learning system TensorFlow was used to construct a model for predicting the brightness of 118 GHz channels using 50-60 GHz observations under clear-sky conditions, and the accuracy of the prediction model was validated using independent samples.
Then this neural network-based predictive model was used for 118 GHz channel precipitation detection.
When the difference between actual observed and predicted bright temperature for 118 GHz channel is more massive than three times of the standard deviation of the prediction model, it is thought that the MWHS II observation is contaminated by precipitation or cloud.
At last, this new precipitation detection algorithm for 118 GHz was validated by simulated measurements.
The results show that both the precipitation detection POD (test probability) and PC (correct rate) for 118 GHz channels are above 90%.
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