A Dual-Frequency Method of Eliminating Liquid Water Radiation to Remotely Sense Cloudy Atmosphere by Ground-Based Microwave Radiometer

Li, Jiangman; Guo, Lixin; Lin, Leke; Zhao, Yiyang; Zhao, Zhenwei; Shu, Tingting; Han, Hengmin

doi:10.2528/pier13010201

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…3> The BP neural network was trained using the historical radiosonde data too [24][25][26], and the inversion profiles were retrieved using the real radiosonde data obtained in the experiment. 4> Concerning the 1D-VAR, choosing the mid-latitude reference atmosphere as the initialize state vector (according to ITU-R P835-5, 2012), the background data is the average temperature profile calculated by the radiosonde data.…”

Section: Resultsmentioning

confidence: 99%

Profiling Boundary Layer Temperature Using Microwave Radiometer in East Coast of China

Wang¹,

Zhao²,

Lin³

et al. 2014

PIER M

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Abstract-The boundary layer temperature profile is very essential for modeling atmospheric processes, whose information can be obtained using radiosonde data generally. Beside this, ground-based multichannel microwave radiometer (GMR) offers a new opportunity to automate atmospheric observations by providing temperature, humidity and liquid water content with high time resolution, such as MP-3000A ground-based multi-channel radiometer. An experiment in east coast of China for profiling boundary layer temperature was performed at Qingdao Meteorological Station from 1 March to 23 April in 2014 using an MP-3000A radiometer. Three techniques have been applied to retrieve the boundary layer temperature profile by using the experimental data, namely the linear regression method, the back propagation (BP) neural network method and the 1-D Variational (1D-VAR) method. Elevation scanning is introduced to help improve the accuracy and resolution of the retrievals for each technique. These results are compared with the radiosonde data at the same time. The preliminary results achieved by each method show that the average day root-mean-square (rms) error for temperature is within 1.0 K up to 2 km in height. The 1D-VAR technique seems to be the most effective one to improve the precision of the boundary layer temperature profile.

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Section: Resultsmentioning

confidence: 99%

Profiling Boundary Layer Temperature Using Microwave Radiometer in East Coast of China

Wang¹,

Zhao²,

Lin³

et al. 2014

PIER M

Self Cite

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“…Brightness temperature data (T BM ), measured by microwave radiometers, i.e., Level 1 (LV1) data, represent the electromagnetic wave intensity received by the radiometer at specific frequencies. Since the LV1 data require inversion calculations to obtain Level 2 (LV2) products, such as atmospheric temperature and humidity profiles [10,11], the work to accurate evaluation and quality control on LV1 is very important and is even more important than on LV2 [12][13][14][15][16][17]. It has been shown that LV1 data after conventional liquid nitrogen and tipping curve calibrations and correction for systematic deviation have high reliability under clear-sky conditions [18][19][20][21], and the consistency between atmospheric temperature and humidity profile retrievals obtained by inversion and those from RAOB was obviously improved [21].…”

Section: Introductionmentioning

confidence: 99%

Improving the Retrieval of Cloudy Atmospheric Profiles from Brightness Temperatures Observed with a Ground-Based Microwave Radiometer

Wei

Wang

et al. 2021

Atmosphere

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Atmospheric temperature and humidity retrievals from ground-based microwave remote sensing are useful in a variety of meteorological and environmental applications. Though the influence of clouds is usually considered in current retrieval algorithms, the resulting temperature and humidity estimates are still biased high in overcast conditions compared to radiosonde observations. Therefore, there is a need to improve the quality of retrievals in cloudy conditions. This paper presents an approach to make brightness temperature (TB) correction for cloud influence before the data can be used in the inversion of vertical profiles of atmospheric temperature and humidity. A three-channel method is proposed to make cloud parameter estimation, i.e., of the total 22 channels of the ground-based radiometer, three are adopted to set up a relationship between cloud parameters and brightness temperatures, so that the observations from the three channels can be used to estimate cloud thickness and water content and complete the cloud correction for the rest of the channels used in the retrieval. Based on two years of data from the atmosphere in Beijing, a comparison of the retrievals with radiosonde observations (RAOB) shows: (1) the temperature retrievals from this study have a higher correlation with RAOB and are notably better than in the vendor-provided LV2. The bias of the temperature retrievals from this study is close to zero at all heights, and the RMSE is greatly reduced from >5 °C to <2 °C in the layer, from about 1.5 km up to 5 km. The temperature retrievals from this study have higher correlation with RAOB data compared to the vendor-provided LV2, especially at and above a 2 km height. (2) The bias of the water vapor density profile from this study is near to zero, while the LV2 has a positive bias as large as 4 g/m3. The RMSE of the water vapor density profile from this study is <2 g/m3, while the RMSE for LV2 is as large as 10 g/m3. That is, both the bias and RMSE from this study are evidently less than the LV2, with a greater improvement in the lower troposphere below 5 km. Correlation with RAOB is improved even more for the water vapor density. The correlation of the retrievals from this study increases to one within the boundary layer, but the correlation of LV2 with RAOB is only 0.8 at 0.5 km height, 0.7 at 1 km, and even less than 0.5 at 2 km. (3) A parameter named the Cloud Impact index, determined by cloud water concentration and cloud thickness, together with the cloud base height, has been defined to show that both BIAS and RMSE of “high-CI subsample” are larger than those of the “low-CI subsample”, indicating that high-CI cloud has a higher impact on the retrievals and the correction for cloud influence is more necessary.

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A New Method of Tipping Calibration for Ground-Based Microwave Radiometer in Cloudy Atmosphere

Guo

Lin

et al. 2014

IEEE Trans. Geosci. Remote Sensing

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A Dual-Frequency Method of Eliminating Liquid Water Radiation to Remotely Sense Cloudy Atmosphere by Ground-Based Microwave Radiometer

Cited by 4 publications

References 39 publications

Profiling Boundary Layer Temperature Using Microwave Radiometer in East Coast of China

Profiling Boundary Layer Temperature Using Microwave Radiometer in East Coast of China

Improving the Retrieval of Cloudy Atmospheric Profiles from Brightness Temperatures Observed with a Ground-Based Microwave Radiometer

A New Method of Tipping Calibration for Ground-Based Microwave Radiometer in Cloudy Atmosphere

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