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

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 648
Author(s):  
Qing Li ◽  
Ming Wei ◽  
Zhenhui Wang ◽  
Sulin Jiang ◽  
Yanli Chu
Keyword(s):  
Water Vapor ◽  
Density Profile ◽  
Lower Troposphere ◽  
Microwave Remote Sensing ◽  
Atmospheric Temperature ◽  
Cloud Base ◽  
Vapor Density ◽  
Brightness Temperatures ◽  
Temperature And Humidity ◽  
Cloud Thickness

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.

scholarly journals Evaluation and Improvement of the Quality of Ground-Based Microwave Radiometer Clear-Sky Data

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 435
Author(s):  
Qing Li ◽  
Ming Wei ◽  
Zhenhui Wang ◽  
Yanli Chu
Keyword(s):  
Water Vapor ◽  
Temperature Inversion ◽  
Atmospheric Temperature ◽  
Radiosonde Data ◽  
Vapor Density ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Temperature And Humidity ◽  
Humidity Profiles

To assess the quality of the retrieved products from ground-based microwave radiometers, the “clear-sky” Level-2 data (LV2) products (profiles of atmospheric temperature and humidity) filtered through a radiometer in Beijing during the 24 months from January 2010 to December 2011 were compared with radiosonde data. Evident differences were revealed. Therefore, this paper investigated an approach to calibrate the observed brightness temperatures by using the model-simulated brightness temperatures as a reference under clear-sky conditions. The simulation was completed with a radiative transfer model and National Centers for Environmental Prediction final analysis (NCEP FNL) data that are independent of the radiometer system. Then, the least-squares method was used to invert the calibrated brightness temperatures to the atmospheric temperature and humidity profiles. A comparison between the retrievals and radiosonde data showed that the calibration of the brightness temperature observations is necessary, and can improve the inversion of temperature and humidity profiles compared with the original LV2 products. Specifically, the consistency with radiosonde was clearly improved: the correlation coefficients are increased, especially, the correlation coefficient for water vapor density increased from 0.2 to 0.9 around the 3 km height; the bias decreased to nearly zero at each height; the RMSE (root of mean squared error) for temperature profile was decreased by more than 1 degree at most heights; the RMSE for water vapor density was decreased from greater than 4 g/m3 to less than 1.5 g/m3 at 1 km height; and the decrease at all other heights were also noticeable. In this paper, the evolution of a temperature inversion process is given as an example, using the high-temporal-resolution brightness temperature after quality control to obtain a temperature and humidity profile every two minutes. Therefore, the characteristics of temperature inversion that cannot be seen by conventional radiosonde data (twice daily) were obtained by radiometer. This greatly compensates for the limited temporal coverage of radiosonde data. The approach presented by this paper is a valuable reference for the reprocessing of the historical observations, which have been accumulated for years by less-calibrated radiometers.

Ground-Based Microwave Radiometer Profiler Observations before a Heavy Rainfall

2011 ◽  
Vol 137 ◽  
pp. 312-315
Author(s):  
Wen Jing Xu ◽  
Hong Yan Liu
Keyword(s):  
Water Vapor ◽  
Temporal Resolution ◽  
Density Profile ◽  
Heavy Rainfall ◽  
Microwave Radiometer ◽  
Cloud Water ◽  
Vapor Density ◽  
Apparent Change

Ground-based 12-channel microwave radiometer profiler TP/WVP-3000 can provide temperature and vapor density profile per minute up to 10 km height. The observations feature apparent change before heavy rainfall obtained by TP/WVP-3000 is presented in this paper. It demonstrates the detailed thermodynamic features that the atmosphere becomes colder and drier above height 3-4 km about 9 hours before the rain, the integrated water vapor gradually increases from 5 cm to 9 cm, the integrated cloud water change from near zero to 15 mm and the vapor density also increases rapidly about half an hour before the rain, which can be concluded that the radiometer profiler is able to improve the understanding of mesoscale weather in this case due to the profiler significantly improves the temporal resolution of atmospheric thermodynamic observations.

scholarly journals A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays

2019 ◽  
Vol 9 (7) ◽  
pp. 1446 ◽  
Author(s):  
Fei Yang ◽  
Jiming Guo ◽  
Junbo Shi ◽  
Yinzhi Zhao ◽  
Lv Zhou ◽  
...  
Keyword(s):  
Water Vapor ◽  
Density Profile ◽  
Temporal Distribution ◽  
Precipitable Water ◽  
Absolute Error ◽  
New Method ◽  
Reference Station ◽  
Full Potential ◽  
Vapor Density ◽  
Unknown Parameters

The spatio-temporal distribution of atmospheric water vapor information can be obtained by global positioning system (GPS) water vapor tomography. GPS signal rays pass through the tomographic area from different boundaries because the scope of the research region (latitude, longitude, and altitude) is designated in the process of tomographic modeling, the influence of the geographic distribution of receivers, and the geometric location of satellite constellations. Traditionally, only signal rays penetrating the entire tomographic area are considered in the computation of water vapor information, whereas those passing through the sides are neglected. Therefore, the accuracy of the tomographic result, especially at the bottom of the area, does not reach its full potential. To solve this problem, this paper proposes a new method that simultaneously considers the discretized tomographic voxels and the troposphere outside the research area as unknown parameters. This method can effectively improve the utilization of existing GPS observations and increase the number of voxels crossed by satellite signals, especially by increasing the proportion of voxels penetrated. A tomographic experiment is implemented using GPS data from the Hong Kong Satellite Positioning Reference Station Network. Compared to the traditional method, the proposed method increases the number of voxels crossed by signal rays and the utilization of the observed data by 15.14% and 19.68% on average, respectively. Numerical results, including comparisons of slant water vapor (SWV), precipitable water vapor (PWV), and water vapor density profile, show that the proposed method is better than traditional methods. In comparison to the water vapor density profile, the root-mean-square error (RMS), mean absolute error (MAE), standard deviation (SD), and bias of the proposed method are 1.39, 1.07, 1.30, and −0.21 gm−3, respectively. For the SWV and PWV comparison, the RMS/MAE of the proposed method are 10.46/8.17 mm and 4.00/3.39 mm, respectively.

scholarly journals Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4673
Author(s):  
Qiurui He ◽  
Zhenzhan Wang ◽  
Jiaoyang Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Bias Correction ◽  
Deep Neural Network ◽  
Microwave Remote Sensing ◽  
Atmospheric Temperature ◽  
Superior Performance ◽  
Atmospheric Parameters ◽  
Temperature And Humidity ◽  
Humidity Profiles

The shallow neural network (SNN) is a popular algorithm in atmospheric parameters retrieval from microwave remote sensing. However, the deep neural network (DNN) has a stronger nonlinear mapping capability compared to SNN and has great potential for applications in microwave remote sensing. The Microwave Humidity and Temperature Sounder (Beijing, China, MWHTS) onboard the Fengyun-3 (FY-3) satellite has the ability to independently retrieve atmospheric temperature and humidity profiles. A study on the application of DNN in retrieving atmospheric temperature and humidity profiles from MWHTS was carried out. Three retrieval schemes of atmospheric parameters in microwave remote sensing based on DNN were performed in the study of bias correction of MWHTS observation and the retrieval of the atmospheric temperature and humidity profiles using MWHTS observations. The experimental results show that, compared with SNN, DNN can obtain better bias-correction results when applied to MWHTS observation, and can obtain higher retrieval accuracy of temperature and humidity profiles in all three retrieval schemes. Meanwhile, DNN shows higher stability than SNN when applied to the retrieval of temperature and humidity profiles. The comparative study of DNN and SNN applied in different atmospheric parameter retrieval schemes shows that DNN has a more superior performance.

MicroPulse DIAL (MPD) ground-based network for Thermodynamic Profiling in the Lower Troposphere

2020 ◽  
Author(s):  
Scott Spuler ◽  
Robert Stillwell ◽  
Matt Hayman ◽  
Tammy Weckwerth ◽  
Kevin Repasky
Keyword(s):  
Water Vapor ◽  
Weather Forecasting ◽  
Low Cost ◽  
Lower Troposphere ◽  
Atmospheric Temperature ◽  
Lower Atmosphere ◽  
High Spectral Resolution ◽  
Montana State University ◽  
Aerosol Scattering ◽  
Thermodynamic Variables

&lt;p&gt;The National Center for Atmospheric Research and Montana State University have developed a 5-unit ground-based test network of MicroPulse Differential Absorption Lidar (MPD) instruments to continuously measure high-vertical-resolution water vapor profiles in the lower atmosphere. These diode-laser-based instruments are accurate, low-cost, operate unattended, do not require external calibration, and eye-safe &amp;#8211; all key features to enable larger 'national-scale' networks needed to characterize atmospheric moisture variability, which influences important processes related to weather and climate.&amp;#160; Enhancements to the water vapor MPD architecture have been recently developed that enable quantitative aerosol measurements and atmospheric temperature profiling by simultaneously measuring O2 absorption and aerosol backscatter ratio. This combination of measurements allows for the first DIAL measurements of atmospheric temperature with useful accuracy. The MPD has been demonstrated to provide continuous, range-resolved measurements of atmospheric thermodynamic variables, water vapor and temperature, and quantitative measurements of aerosol scattering from a high spectral resolution (HSRL) channel.&amp;#160; Thus, a network of these instruments shows promise to provide atmospheric profiling capabilities needed by both the climate and weather forecasting research communities.&lt;/p&gt;

A Study of a Retrieval Method for Temperature and Humidity Profiles from Microwave Radiometer Observations Based on Principal Component Analysis and Stepwise Regression

2011 ◽  
Vol 28 (3) ◽  
pp. 378-389 ◽  
Author(s):  
Haobo Tan ◽  
Jietai Mao ◽  
Huanhuan Chen ◽  
P. W. Chan ◽  
Dui Wu ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Water Vapor ◽  
Stepwise Regression ◽  
Microwave Radiometer ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Vapor Density ◽  
Retrieval Method ◽  
Brightness Temperatures

Abstract This paper discusses the application of principal component analysis and stepwise regression in the retrieval of vertical profiles of temperature and humidity based on the measurements of a 35-channel microwave radiometer. It uses the radiosonde data of 6 yr from Hong Kong, China, and the monochromatic radiative transfer model (MonoRTM) to calculate the brightness temperatures of the 35 channels of the radiometer. The retrieval of the atmospheric profile is then established based on principal component analysis and stepwise regression. The accuracy of the retrieval method is also analyzed. Using an independent sample, the root-mean-square error of the retrieved temperature is less than 1.5 K, on average, with better retrieval results in summer than in winter. Likewise, the root-mean-square error of the retrieved water vapor density reaches a maximum value of 1.4 g m−3 between 0.5 and 2 km, and is less than 1 g m−3 for all other heights. The retrieval method is then applied to the actual measured brightness temperatures by the 35-channel microwave radiometer at a station in Nansha, China. It is shown that the statistical model as developed in this paper has better retrieval results than the profiles obtained from the neural network as supplied with the radiometer. From numerical analysis, the error with the water vapor density retrieval is found to arise from the treatment of cloud liquid water. Finally, the retrieved profiles from the radiometer are studied for two typical weather phenomena during the observation period, and the retrieved profiles using the method discussed in the present paper is found to capture the evolution of the atmospheric condition very well.

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