A combined natural orthogonal functions/neural network technique for the radiometric estimation of atmospheric profiles

Frate, Fabio Del; Schiavon, G.

doi:10.1029/97rs02219

Cited by 51 publications

(30 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These include the artificial neural network (ANN), Newtonian iteration of statistically retrieved profiles and Bayesian most probable retrieval. ANNs are widely used for different types of infrared and microwave-sounding instruments (Frate and Schiavon, 1998;Binco et al, 2005). Frate and Schiavon (1998) presented an inversion technique to retrieve profiles of temperature and water vapour using MWR.…”

Section: K Ramesh Et Al: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

et al. 2015

View full text Add to dashboard Cite

Abstract. The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5 • N, 79.2 • E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (AN-FIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the AN-FIS(RD + NRD) are less compared to other retrieval methods.The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92 % for temperature profiles for all techniques and more than 99 % for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

show abstract

Section: K Ramesh Et Al: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The algorithm of linear regression (LR) is used to profile the water vapor density [31,42]. 90% of the radiosonde data in June of the years from 1986 to 1995 in Nanjing China station are chosen to calculate the regression coefficients, and the remainder is used to evaluate the retrieval precision in cloudy atmosphere.…”

Section: Linear Regressionmentioning

confidence: 99%

“…A neural network algorithm for the radiometer data to retrieve atmospheric profiles has been developed for data feature extraction and dimensionality reduction [28]. The neural network combined with the natural orthogonal functions shows a good capability of exploiting information provided by other instruments, such as a laser ceilometer [31]. An algorithm that incorporates output from two retrieval techniques, namely, a physical-iterative approach and a computationally efficient statistical method, has been developed to retrieve atmospheric parameters [33].…”

Section: Introductionmentioning

confidence: 99%

“…The method of artificial neural network is used in various fields [20][21][22][23][24]. Artificial neural network is increasingly used for its more accurate estimation of the atmospheric parameters in the case of strong nonlinearities [25][26][27][28][29][30][31][32]. A neural network is used to obtain vertical profiles of temperature from microwave radiometer data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Li¹,

Guo²,

Lin³

et al. 2013

PIER

View full text Add to dashboard Cite

Abstract-Ground-based microwave radiometer is the main device to remotely sense atmosphere passively which can detect the water vapor density, temperature, integral water vapor, etc. Because of the influence of cloud liquid water on the brightness temperature measured by microwave radiometer, the cloud needs to be modeled to retrieve the parameters of cloudy atmosphere. However, the difference between the cloud model and actual cloud may bring on some error in retrieval. Based on the relation between absorption coefficient of liquid water and frequency, a dual-frequency method of eliminating liquid water radiation which is not based on modeling cloud is put forward to retrieve the parameters of cloudy atmosphere. Historical radiosonde data are employed in the calculation of retrieval coefficients to profile the water vapor of cloudy atmosphere. The simulation and experiment results show that the dual-frequency method can eliminate the radiation of liquid water effectively and has a higher precision than conventional method. The integral water vapor in cloudy atmosphere is also retrieved by the dual-frequency method, and the precision is comparable with the method of modeling cloud.

show abstract

“…The developed applications include, e.g., forward and inverse radiative transfer problems (Krasnopolsky, 2008), the prediction of atmospheric parameters (Grivas and Chaloulakou, 2006), the inversion and post processing of remotely sensed data (Mas and Flores, 2008;Del Frate and Schiavon, 1998), ozone retrievals (Di Noia et al, 2012;Sellitto et al, 2011Sellitto et al, , 2012, cloud classification (Christodoulou et al, 2003), land cover classification (Aitkenhead and Aalders, 2008), and feature extraction (Del Frate et al, 2005). Below, we describe the design for the cloud detection algorithm applied to OMI cloud fraction determination.…”

Section: Introductionmentioning

confidence: 99%

A neural network algorithm for cloud fraction estimation using NASA-Aura OMI VIS radiance measurements

et al. 2013

View full text Add to dashboard Cite

The discrimination of cloudy from cloud-free pixels is required in almost any estimate of a parameter retrieved from satellite data in the ultraviolet (UV), visible (VIS) or infrared (IR) parts of the electromagnetic spectrum. In this paper we report on the development of a neural network (NN) algorithm to estimate cloud fractions using radiances measured at the top of the atmosphere with the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained from the application of two different types of neural networks, i.e., extreme learning machine (ELM) and back propagation (BP). The NNs were trained with an OMI data sets existing of six orbits, tested with three other orbits and validated with another two orbits. The results were evaluated by comparison with cloud fractions available from the MODerate Resolution Imaging Spectrometer (MODIS) flying on Aqua in the same constellation as OMI, i.e., with minimal time difference between the OMI and MODIS observations. The results from the ELM and BP NNs are compared. They both deliver cloud fraction estimates in a fast and automated way, and they both performs generally well in the validation. However, over highly reflective surfaces, such as desert, or in the presence of dust layers in the atmosphere, the cloud fractions are not well predicted by the neural network. Over ocean the two NNs work equally well, but over land ELM performs better

show abstract

A combined natural orthogonal functions/neural network technique for the radiometric estimation of atmospheric profiles

Cited by 51 publications

References 10 publications

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

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

A neural network algorithm for cloud fraction estimation using NASA-Aura OMI VIS radiance measurements

Contact Info

Product

Resources

About