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Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over l...
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... consists of two bands, C-band in inland regions and S-band in coastal regions. In order to classify O−B biases with different precipitation intensities, eight S-band weather radars were selected: GRFY (Ground radar in Fuyang, Anhui Province); GRHF (Ground radar in Hefei, Anhui Province); GRXZ (Ground radar in Xuzhou, Jiangsu Province); GRNJ (Ground radar in Nanjing, Jiangsu Province); GRLYG (Ground radar in Lianyungang, Jiangsu Province); GRYC (Ground radar in Yancheng, Jiangsu Province); GRNT (Ground radar in Nantong, Jiangsu Province); and GRHZ (Ground radar in Hangzhou, Zhejiang Province) in East China (Figure 1). These radars were all China new-generation weather radar S-band A-type (CINRAD-SA) with a wavelength of ~10 cm and effective precipitation detection distance of ~230 km. ...
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... PDF distribution of O−B under different intensities of precipitation is shown in Figures 11 and 12 for channels 11-15 and channels 2-9, respectively. It shows that the mean values of O−B generally decreased within 0-5, 5-20, 20-35, and >35 dBZ in sequence for each channel. ...
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... line and 118.75 GHz line respectively, biases in the moderate and intense precipitation (>20 dBZ) categories exhibited a larger standard deviation than those under precipitation-free and light precipitation (<20 dBZ) conditions. Figure 13 indicates O−B biases versus radar reflectivity for channels 11-15 and 2-9. Generally, the response of O−B biases to radar reflectivity was gradually enhanced with the detection height decreases from channels 11-15 and 2-9. ...
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... consists of two bands, C-band in inland regions and S-band in coastal regions. In order to classify O−B biases with different precipitation intensities, eight S-band weather radars were selected: GRFY (Ground radar in Fuyang, Anhui Province); GRHF (Ground radar in Hefei, Anhui Province); GRXZ (Ground radar in Xuzhou, Jiangsu Province); GRNJ (Ground radar in Nanjing, Jiangsu Province); GRLYG (Ground radar in Lianyungang, Jiangsu Province); GRYC (Ground radar in Yancheng, Jiangsu Province); GRNT (Ground radar in Nantong, Jiangsu Province); and GRHZ (Ground radar in Hangzhou, Zhejiang Province) in East China (Figure 1). These radars were all China new-generation weather radar S-band A-type (CINRAD-SA) with a wavelength of ~10 cm and effective precipitation detection distance of ~230 km. ...
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... positive biases were also found in the southeast of the study area around 30°N for channel 14, implying that observed brightness temperatures are affected by latitude and season, thus having higher values closer to the tropical region in the summer. The PDF distribution of O−B under different intensities of precipitation is shown in Figure 11 and Figure 12 for channels 11-15 and channels 2-9, respectively. It shows that the mean values of O−B generally decreased within 0-5, 5-20, 20-35, and >35 dBZ in sequence for each channel. ...
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... positive biases were also found in the southeast of the study area around 30°N for channel 14, implying that observed brightness temperatures are affected by latitude and season, thus having higher values closer to the tropical region in the summer. The PDF distribution of O−B under different intensities of precipitation is shown in Figure 11 and Figure 12 for channels 11-15 and channels 2-9, respectively. It shows that the mean values of O−B generally decreased within 0-5, 5-20, 20-35, and >35 dBZ in sequence for each channel. ...
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... line and 118.75 GHz line respectively, biases in the moderate and intense precipitation (>20 dBZ) categories exhibited a larger standard deviation than those under precipitation-free and light precipitation (<20 dBZ) conditions. Figure 13 indicates O−B biases versus radar reflectivity for channels 11-15 and 2-9. Generally, the response of O−B biases to radar reflectivity was gradually enhanced with the detection height decreases from channels 11-15 and 2-9. ...
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... O−B biases for channels 7-9 whose detection heights close to the surface decreased greatly under precipitating conditions, and there was a nearly linear correlation between the O−B biases and radar reflectivity. Figure 12. The same as Figure 11, but for channels 2-9 (a-h). ...
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Citations
... To make better use of microwave radiance observations for data assimilation, the removal of data contaminated by hydrometeor particles is important. The most common cloud detection method allows for the detection of precipitation based on the deviation between observation and simulation brightness temperature (O-B) of satellite channels [22]. A scattering index [linear regression model of channel 15 and channels 1-3 of AMSU-A, used by the Advanced TIROS Operational Vertical Sounder (ATOVS) and Advanced Very High Resolution Radiometer (AVHRR) Pre-processing Package (AAPP)] has also been employed by English et al. [23]. ...
... Then the value of composite reflectivity whose measure time is closest to the time when MWHS-2 passes the East China and measure range is in the area of MWHS-2's filed-of-view (FOV), is selected to calculate the average value in each FOV. Radar reflectivity factor is often used for cloud detection [21]- [25]. In this study, the scenes are flagged as cloudy when the radar reflectivity exceeds 5 dBZ. ...
This paper presents a stand-alone cloud detection algorithm over land (CDL) for Microwave Humidity Sounder -2 (MWHS-2), which is characterized by the first operational satellite sensor measuring 118.75 GHz. The CDL is based on the advanced machine learning (ML) algorithm Gradient Boosting Decision Tree (GBDT), which achieves the state-of-the-art performance on tabular data, with high accuracy, fast training speed, great generalization ability, and weight factor ranking of predictors (or features). Given that the new generation weather radar of China (CINRAD) provides improved cloud information with extensive temporal-spatial coverage, the observations from CINRAD are used to train the algorithm in this study. There are four groups of radiometric information employed to evaluate the CDL: all frequency ranges from MWHS-2 (all-algorithm), the humidity channels near 183.31 GHz (hum-algorithm), the temperature channels near 118.75 GHz (tem-algorithm), and the window channels at 89 and 150 GHz (win-algorithm). It is revealed that the tem-algorithm (around 118.75 GHz) has a superior performance for CDL along with the optimal values of most evaluation metrics. Although the all-algorithm uses all available frequencies, it shows inferior ability for CDL. Followed are the win-algorithm and hum-algorithm, and the win-algorithm performs better. The analysis also indicates that the latitude, zenith angle, and the azimuth are the top ranking features for all four algorithms. The presented algorithm CDL can be applied in the quality control processes of assimilating microwave (MW) radiances or in the retrieval of atmospheric and surface parameters for cloud filtering.
... To make better use of microwave radiance observations for data assimilation, the removal of data contaminated by hydrometeor particles is important. The most common cloud detection method allows for the detection of precipitation based on the deviation between observation and simulation brightness temperature (O-B) of satellite channels [22]. A scattering index [linear regression model of channel 15 and channels 1-3 of AMSU-A, used by the Advanced TIROS Operational Vertical Sounder (ATOVS) and Advanced Very High Resolution Radiometer (AVHRR) Pre-processing Package (AAPP)] has also been employed by English et al. [23]. ...
To make better use of microwave radiance observations for data assimilation, removal of radiances contaminated by hydrometeor particles is one of the most important steps. Generally, all observations below the middle troposphere are eliminated before the analysis when precipitation is present. However, the altitude of the cloud top varies; when the weighting function peak height of a channel is higher than the altitude of the cloud top, observations are not affected by the absorption or scattering of cloud particles. Thus, the radiative transfer calculation can be performed under a clear sky scenario. In this paper, a dynamic channel selection (DCS) method was developed to determine the radiance observations unaffected by clouds under cloudy conditions in assimilation. First, the sensitivity of cloud liquid water (CLW) profiles to radiance from the microwave temperature sounding frequencies was analyzed. It was found that the impact of CLW on transmittance can be neglected where the cloud top height is below the weighting function peak height. Second, three lookup tables were devised through analysis of the impact of cloud fraction and cloud top height on radiance, which is the basis of the DCS method. The unified cloud top height of the Microwave Temperature Sounder (MWTS)-2 fields of view (FOVs) can be calculated by remapping the cloud mask and cloud top height data from the Medium Resolution Spectral Imager-2 (MERSI-2). Observations from various channels may be removed or retained based on real-time dynamic unified cloud top height data. Twelve-hour and long-term time-series brightness temperature simulation experiments both showed that an increase in the amount of observations used for data assimilation of more than 300% can be achieved by application of DCS, but this had no effect on the amount of error. Through DCS, areas of strong precipitation can be accurately identified and removed, and more observations above cloud top height can be included in the data assimilation. The application of DCS to data assimilation will greatly improve the data utilization rate, and therefore allow for more accurate characterization of upper atmospheric circulation.
