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.

The Analysis of Atmospheric Water Vapor Based on Ground-Based Microwave Radiometer

2012 ◽  
Vol 500 ◽  
pp. 335-340
Author(s):  
Jie Ying He ◽  
Feng Lin Sun ◽  
Sheng Wei Zhang ◽  
Yu Zhang
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Microwave Radiometer ◽  
Mean Square ◽  
Vapor Density ◽  
China Meteorological Administration ◽  
Density Profiles

The paper introduces a widely used atmospheric absorption models: MPM by Liebe in 1989. Using this absorption model, the paper simulates the temperature and humidity weighting functions and brightness temperature according to the different frequencies and bandwidth of the multi-channel ground-based microwave radiometer. The results show that simulated brightness temperatures are very well agreement with the observation values with an acceptable root mean square error. This paper uses widely used retrieval method of artificial neural network to obtain the water vapor density profiles and calculates the root mean square error of each dataset. Also, to improve the accuracy of retrievals, this paper adopts multi-layers neural network which has two hidden layers. The results show that the retrievals of water vapor density profiles based on ground-based microwave radiometer are agreement with the water vapor density profile which is observed by radiosonde. Grant Nos. GYHY200906035 China Meteorological Administration nonprofit sector (meteorology) special research

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.

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.

scholarly journals Extreme Floods in the Eastern Part of Europe: Large-Scale Drivers and Associated Impacts

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1122
Author(s):  
Monica Ionita ◽  
Viorica Nagavciuc
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Flood Events ◽  
Rainfall Events ◽  
Catchment Areas ◽  
Large Scale Atmospheric Circulation ◽  
Heavy Rainfall Events

The role of the large-scale atmospheric circulation in producing heavy rainfall events and floods in the eastern part of Europe, with a special focus on the Siret and Prut catchment areas (Romania), is analyzed in this study. Moreover, a detailed analysis of the socio-economic impacts of the most extreme flood events (e.g., July 2008, June–July 2010, and June 2020) is given. Analysis of the largest flood events indicates that the flood peaks have been preceded up to 6 days in advance by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent cut-off lows over the analyzed region, and increased water vapor transport over the catchment areas of Siret and Prut Rivers. The vertically integrated water vapor transport prior to the flood peak exceeds 300 kg m−1 s−1, leading to heavy rainfall events. We also show that the implementation of the Flood Management Plan in Romania had positive results during the 2020 flood event compared with the other flood events, when the authorities took several precaution measurements that mitigated in a better way the socio-economic impact and risks of the flood event. The results presented in this study offer new insights regarding the importance of large-scale atmospheric circulation and water vapor transport as drivers of extreme flooding in the eastern part of Europe and could lead to a better flood forecast and flood risk management.

scholarly journals Estimating Water Vapor Using Signals from Microwave Links below 25 GHz

2021 ◽  
Vol 13 (8) ◽  
pp. 1409
Author(s):  
Kun Song ◽  
Xichuan Liu ◽  
Taichang Gao ◽  
Peng Zhang
Keyword(s):  
Water Vapor ◽  
Weather Forecasting ◽  
Water Cycle ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Vapor Density ◽  
Measurement Resolution ◽  
Sensing Model ◽  
Numerical Weather Forecasting ◽  
Mean Square Errors

Water vapor is a key element in both the greenhouse effect and the water cycle. However, water vapor has not been well studied due to the limitations of conventional monitoring instruments. Recently, estimating rain rate by the rain-induced attenuation of commercial microwave links (MLs) has been proven to be a feasible method. Similar to rainfall, water vapor also attenuates the energy of MLs. Thus, MLs also have the potential of estimating water vapor. This study proposes a method to estimate water vapor density by using the received signal level (RSL) of MLs at 15, 18, and 23 GHz, which is the first attempt to estimate water vapor by MLs below 20 GHz. This method trains a sensing model with prior RSL data and water vapor density by the support vector machine, and the model can directly estimate the water vapor density from the RSLs without preprocessing. The results show that the measurement resolution of the proposed method is less than 1 g/m3. The correlation coefficients between automatic weather stations and MLs range from 0.72 to 0.81, and the root mean square errors range from 1.57 to 2.31 g/m3. With the large availability of signal measurements from communications operators, this method has the potential of providing refined data on water vapor density, which can contribute to research on the atmospheric boundary layer and numerical weather forecasting.

scholarly journals New Gridded Product for the Total Columnar Atmospheric Water Vapor over Ocean Surface Constructed from Microwave Radiometer Satellite Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2402
Author(s):  
Weifu Sun ◽  
Jin Wang ◽  
Yuheng Li ◽  
Junmin Meng ◽  
Yujia Zhao ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Atmospheric Water Vapor ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Fusion Product ◽  
Mean Deviation ◽  
Atmospheric Water ◽  
Absolute Deviation ◽  
Better Than

Based on the optimal interpolation (OI) algorithm, a daily fusion product of high-resolution global ocean columnar atmospheric water vapor with a resolution of 0.25° was generated in this study from multisource remote sensing observations. The product covers the period from 2003 to 2018, and the data represent a fusion of microwave radiometer observations, including those from the Special Sensor Microwave Imager Sounder (SSMIS), WindSat, Advanced Microwave Scanning Radiometer for Earth Observing System sensor (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), and HY-2A microwave radiometer (MR). The accuracy of this water vapor fusion product was validated using radiosonde water vapor observations. The comparative results show that the overall mean deviation (Bias) is smaller than 0.6 mm; the root mean square error (RMSE) and standard deviation (SD) are better than 3 mm, and the mean absolute deviation (MAD) and correlation coefficient (R) are better than 2 mm and 0.98, respectively.

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