Research on GA-MOBP Neural Network Retrieval Method of Temperature and Humidity Profiles from Ground-based Microwave Radiometer

2021 ◽  
Author(s):  
Hu Taiyang ◽  
Yang Mingyi ◽  
Zhang Lili ◽  
Zhang Jinyu ◽  
Shao Xiaolang
Keyword(s):  
Neural Network ◽  
Microwave Radiometer ◽  
Retrieval Method ◽  
Temperature And Humidity ◽  
Humidity Profiles

scholarly journals A model-based approach to adjust microwave observations for operational applications: results of a campaign at Munich Airport in winter 2011/2012

2013 ◽  
Vol 6 (10) ◽  
pp. 2879-2891 ◽  
Author(s):  
J. Güldner
Keyword(s):  
Neural Network ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Regression Method ◽  
Training Data ◽  
Data Set ◽  
Numerical Weather ◽  
Model Based ◽  
Temperature And Humidity ◽  
Airport Site

Abstract. In the frame of the project "LuFo iPort VIS" which focuses on the implementation of a site-specific visibility forecast, a field campaign was organised to offer detailed information to a numerical fog model. As part of additional observing activities, a 22-channel microwave radiometer profiler (MWRP) was operating at the Munich Airport site in Germany from October 2011 to February 2012 in order to provide vertical temperature and humidity profiles as well as cloud liquid water information. Independently from the model-related aims of the campaign, the MWRP observations were used to study their capabilities to work in operational meteorological networks. Over the past decade a growing quantity of MWRP has been introduced and a user community (MWRnet) was established to encourage activities directed at the set up of an operational network. On that account, the comparability of observations from different network sites plays a fundamental role for any applications in climatology and numerical weather forecast. In practice, however, systematic temperature and humidity differences (bias) between MWRP retrievals and co-located radiosonde profiles were observed and reported by several authors. This bias can be caused by instrumental offsets and by the absorption model used in the retrieval algorithms as well as by applying a non-representative training data set. At the Lindenberg observatory, besides a neural network provided by the manufacturer, a measurement-based regression method was developed to reduce the bias. These regression operators are calculated on the basis of coincident radiosonde observations and MWRP brightness temperature (TB) measurements. However, MWRP applications in a network require comparable results at just any site, even if no radiosondes are available. The motivation of this work is directed to a verification of the suitability of the operational local forecast model COSMO-EU of the Deutscher Wetterdienst (DWD) for the calculation of model-based regression operators in order to provide unbiased vertical profiles during the campaign at Munich Airport. The results of this algorithm and the retrievals of a neural network, specially developed for the site, are compared with radiosondes from Oberschleißheim located about 10 km apart from the MWRP site. Outstanding deviations for the lowest levels between 50 and 100 m are discussed. Analogously to the airport experiment, a model-based regression operator was calculated for Lindenberg and compared with both radiosondes and operational results of observation-based methods. The bias of the retrievals could be considerably reduced and the accuracy, which has been assessed for the airport site, is quite similar to those of the operational radiometer site at Lindenberg above 1 km height. Additional investigations are made to determine the length of the training period necessary for generating best estimates. Thereby three months have proven to be adequate. The results of the study show that on the basis of numerical weather prediction (NWP) model data, available everywhere at any time, the model-based regression method is capable of providing comparable results at a multitude of sites. Furthermore, the approach offers auspicious conditions for automation and continuous updating.

scholarly journals Accuracy of retrieving temperature and humidity profiles by ground-based microwave radiometry in truly complex terrain

2015 ◽  
Vol 8 (8) ◽  
pp. 3355-3367 ◽  
Author(s):  
G. Massaro ◽  
I. Stiperski ◽  
B. Pospichal ◽  
M. W. Rotach
Keyword(s):  
Complex Terrain ◽  
Microwave Radiometer ◽  
Radiosonde Data ◽  
Alpine Valley ◽  
Topographic Slope ◽  
Retrieval Algorithms ◽  
Temperature And Humidity ◽  
Upper Level ◽  
The One ◽  
Humidity Profiles

Abstract. Within the Innsbruck Box project, a ground-based microwave radiometer (RPG-HATPRO) was operated in the Inn Valley (Austria), in very complex terrain, between September 2012 and May 2013 to obtain temperature and humidity vertical profiles of the full troposphere with a specific focus on the valley boundary layer. In order to assess its performance in a deep alpine valley, the profiles obtained by the radiometer with different retrieval algorithms based on different climatologies are compared to local radiosonde data. A retrieval that is improved with respect to the one provided by the manufacturer, based on better resolved data, shows a significantly smaller root mean square error (RMSE), both for the temperature and humidity profiles. The improvement is particularly substantial at the heights close to the mountaintop level and in the upper troposphere. Lower-level inversions, common in an alpine valley, are resolved to a satisfactory degree. On the other hand, upper-level inversions (above 1200 m) still pose a significant challenge for retrieval. For this purpose, specialized retrieval algorithms were developed by classifying the radiosonde climatologies into specialized categories according to different criteria (seasons, daytime, nighttime) and using additional regressors (e.g., measurements from mountain stations). The training and testing on the radiosonde data for these specialized categories suggests that a classification of profiles that reproduces meaningful physical characteristics can yield improved targeted specialized retrievals. A novel and very promising method of improving the profile retrieval in a mountainous region is adding further information in the retrieval, such as the surface temperature at fixed levels along a topographic slope or from nearby mountaintops.

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.

scholarly journals Research on Retrieval of Atmospheric Temperature and Humidity Profiles from combined Ground-based Microwave Radiometer and Cloud Radar Observations

2016 ◽  
Author(s):  
Yunfei Che ◽  
Shuqing Ma ◽  
Fenghua Xing ◽  
Siteng Li ◽  
Yaru Dai
Keyword(s):  
Neural Network ◽  
Microwave Radiometer ◽  
Radiosonde Data ◽  
Training Dataset ◽  
Cloud Base ◽  
Optimal Method ◽  
Cloud Radar ◽  
Cloud Thickness ◽  
Humidity Profiles ◽  
Cloud Base Height

Abstract. This paper focuses on the retrieval of temperature and relative humidity profiles through combining ground-based microwave radiometer observations with those of millimeter-wavelength cloud radar. The cloud-base height and cloud thickness from the cloud radar were added into the atmospheric profile retrieval process, and a back propagation neural network method was used as the retrieval tool. Because substantial data are required to train a neural network, and microwave radiometer data are insufficient for this purpose, eight years of radiosonde data from Beijing were used as a database. The model MonoRTM was used to calculate the brightness temperature of the same channel as the microwave radiometer. Part of the cloud-base height and cloud thickness in the training dataset was also estimated using the radiosonde data. The accuracy of the results was analyzed by comparing with L-band sounding radar data, and quantified using the mean bias, root-mean-square error and correlation coefficient. The statistical results showed that inversion with cloud information was the optimal method. Compared with the inversion profiles without cloud information, the RMSE values after adding the cloud information were to a varying degree reduced for the vast majority of height layers. These reductions were particularly clear in layers with cloud present. The maximum reduction of RMSE for temperature was 2.2 K, and for the humidity profile was 16 %.

scholarly journals Forecast indices from ground-based microwave radiometer for operational meteorology

2014 ◽  
Vol 7 (7) ◽  
pp. 6971-7011 ◽  
Author(s):  
D. Cimini ◽  
M. Nelson ◽  
J. Güldner ◽  
R. Ware
Keyword(s):  
Microwave Radiometer ◽  
Correlation Coefficients ◽  
Weather Conditions ◽  
60 Ghz ◽  
Additional Advantage ◽  
Temperature And Humidity ◽  
Vapour Density ◽  
Atmospheric Observations ◽  
Nearly Continuous ◽  
Humidity Profiles

Abstract. Today, commercial microwave radiometers profilers (MWRP) are robust and unattended instruments providing real time accurate atmospheric observations at ~ 1 min temporal resolution under nearly all-weather conditions. Common commercial units operate in the 20–60 GHz frequency range and are able to retrieve profiles of temperature, vapour density, and relative humidity. Temperature and humidity profiles retrieved from MWRP data are used here to feed tools developed for processing radiosonde observations to obtain values of forecast indices (FI) commonly used in operational meteorology. The FI considered here include K index, Total Totals, KO index, Showalter index, T1 Gust, Fog Threat, Lifted Index, S Index (STT), Jefferson Index, MDPI, Thompson Index, TQ Index, and CAPE. Values of FI computed from radiosonde and MWRP-retrieved temperature and humidity profiles are compared in order to quantitatively demonstrate the level of agreement and the value of continuous FI updates. This analysis is repeated for two sites at midlatitude, the first one located at low altitude in Central Europe (Lindenberg, Germany), while the second one located at high altitude in North America (Whistler, Canada). It is demonstrated that FI computed from MWRP well correlate with those computed from radiosondes, with the additional advantage of nearly continuous update. The accuracy of MWRP-derived FI is tested against radiosondes, taken as a reference, showing different performances depending upon index and environmental situation. Overall, FI computed from MWRP retrievals agree well with radiosonde values, with correlation coefficients usually above 0.8 (with few exceptions). We conclude that MWRP retrievals can be used to produce meaningful FI, with the advantage (with respect to radiosondes) of nearly continuous update.

scholarly journals A model-based approach to adjust microwave observations for operational applications: results of a campaign at Munich Airport in winter 2011/2012

2013 ◽  
Vol 6 (2) ◽  
pp. 2935-2954 ◽  
Author(s):  
J. Güldner
Keyword(s):  
Neural Network ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Weather Forecast ◽  
Forecast Model ◽  
Regression Method ◽  
Numerical Weather ◽  
Model Based ◽  
Temperature And Humidity ◽  
Airport Site

Abstract. In the frame of the project "LuFo iPort VIS" which focuses on the implementation of a site specific visibility forecast, a field campaign was organised to offer detailed information to a numerical fog model. As part of additional observing activities a 22-channel microwave radiometer profiler (MWRP) was operating at the Munich Airport site in Germany from October 2011 to February 2012 in order to provide vertical temperature and humidity profiles as well as cloud liquid water information. Independently from the model-related aims of the campaign, the MWRP observations were used to study their capabilities to work in operational meteorological networks. Over the past decade a growing quantity of MWRP has been introduced and a user community (MWRnet) was established to encourage activities directed at the set up of an operational network. On that account, the comparability of observations from different network sites plays a fundamental role for any applications in climatology and numerical weather forecast. In practice, however, systematic temperature and humidity differences (bias) between MWRP retrievals and co-located radiosonde profiles were observed and reported by several authors. This bias can be caused by instrumental offsets as well as by the absorption model used in the retrieval algorithms. At the Lindenberg observatory besides a neural network provided by the manufacturer, a measurement-based regression method was developed to reduce the bias. These regression operators are calculated on the basis of coincident radiosonde observations and MWRP brightness temperature (TB) measurements. However, MWRP applications in a network require comparable results at just any site, even if no radiosondes are available. The motivation of this work is directed to a verification of the suitability of the operational local forecast model COSMO-EU of the Deutscher Wetterdienst (DWD) for the calculation of model-based regression operators in order to provide unbiased vertical profiles during the campaign at Munich Airport. The results of this algorithm and the retrievals of a neural network, specially developed for the site, are compared with radiosondes from Oberschleißheim located about 10 km apart from the MWRP site. The bias of the retrievals could be considerably reduced and the accuracy, which has been assessed for the airport site, is quite similar to those of the operational radiometer site at Lindenberg above 1 km height. Additional investigations are made to determine the length of the training period necessary for generating best estimates. Thereby three months have proven to be adequate. The results of the study show that on the basis of numerical weather prediction (NWP) model data, available everywhere at any time, the model-based regression method is capable to provide comparable results at a multitude of sites. Furthermore, the approach offers auspicious conditions for automation and continuous updating.

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