Ground-Based Microwave Radiometry and Remote Sensing

Karmakar, P. K.

doi:10.1201/b15494

Cited by 4 publications

(8 citation statements)

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“…Satellite measurements were found to overcome these limitations to some extent, but their horizontal and temporal resolution is coarse near to the earth's surface, and so the measured data are not helpful for present-day high-resolution NWP models. Additionally, laser radars and Fourier-transform infrared spectrometers can profile the atmospheric states, but they do not work in the presence of clouds [6].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of the Atmospheric Temperature and Humidity Profiles Using a Feed-Forward Neural Network

Pathak

2021

The 4th International Electronic Conference on Atmospheric Sciences

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Using a feed-forward neural network, an inverse algorithm was developed to profile the vertical structure of temperature and specific humidity. The inverse algorithm (inverse model) was used to calculate temperature and humidity profiles, which were then compared with other existing methods. The inverse model is found efficient in profiling the vertical structure of temperature and humidity as compared to other existing methods. For example, the statistical methods notorious for their high computational cost, altitude-dependent error, and inability to accurately retrieve the vertical temperature and humidity profiles, are enhanced with an inverse model. The inverse model’s diurnal and seasonal cycle profiles are also found superior to those of other existing methods, which could be useful for assimilation in numerical weather forecast models. We suggest that incorporating such an inverse model into the ground-based microwave radiometer (GMWR) will enhance the accuracy of the vertical structure of temperature and humidity profiles, and so the improvement in weather forecasting. The developed inverse model has a resolution of 50 m between the surface to 500 m and 100 m between 500–2000 m, and 500 m beyond 2000 m.

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Section: Introductionmentioning

confidence: 99%

Retrieval of the Atmospheric Temperature and Humidity Profiles Using a Feed-Forward Neural Network

Pathak

2021

The 4th International Electronic Conference on Atmospheric Sciences

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“…The most sophisticated algorithms are necessary when solar or atmospheric radiances are measured, allowing researchers to determine the water vapor profile (AIRS; IASI, ANSU, HIRS sensors) or those of other pollutant gases such as CO or NO 2 . Microwave radiometry/spectroradiometry has particular features but it also has an enormous potential for probing the atmosphere as solar/infrared techniques, since it is not perturbed by clouds or weather [6].…”

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confidence: 99%

Editorial for the Special Issue “Remote Sensing of Atmospheric Components and Water Vapor”

Cachorro

Antón

2020

Remote Sensing

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The observation/monitoring of atmospheric components and water vapor in the atmosphere is today open to very different remote sensing techniques, most of them based on the radiation-matter interaction covering the full electromagnetic spectrum. This SI collects some papers regarding the retrieval, calibration, validation, analysis of data and uncertainties, as well as comparative studies on atmospheric gases and water vapor by remote sensing techniques, where different types of sensors, instruments, and algorithms are used or developed.

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“…Remote sensing is the science or technology for acquiring information, such as identifying, classifying, and determining objects on the earth's surface and the environment from air or space by means of electromagnetic waves Karmakar (2019).…”

Section: Conceptual Stagementioning

confidence: 99%

“…Water vapor is perhaps the most influential atmospheric gas from the perspective of climate and climatic processes due to its high temporal and spatial variability in the lower troposphere and its role in energy transfer Karmakar (2019); Blackwell and Chen (2009).…”

Section: Introductionmentioning

confidence: 99%

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A 22 GHz Pseudo-correlation Water Vapor Radiometer.

Reeves

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The radiation that comes from celestial objects is affected by different atmospheric components, mainly by water vapor, which is a highly variable component. This molecule can scatter, absorb and re-emit radiation and, therefore, attenuate it, affecting astronomical observations. Water vapor radiometry is an accurate method to measure the water vapor content in the atmosphere and, at altitudes 4000 masl, the 22 GHz emission line is chosen to estimate the amount of water vapor present in the at the time of the astronomical observation. This instrument, which is under development at CePIA UdeC laboratory, will provide a notable improvement in the measurement of the line profile of water vapor and will allow atmospheric characterizations that can be validated with other instruments in places where this quantity is relevant. The architecture designed for this project corresponds to a self-calibrated instrument, heterodine and is based on the pseudo-correlation principle. It consists of three parts: frontend, where incoming the RF signal is at a frequency range of 20 - 26 GHz; analog backend, where the RF signal is converted to a manageable frequency range of 0 - 6 GHz, called Intermediate Frequency (IF); and finally, the digital backend where the IF signal is processed in real-time through FFT, with a high spectral resolution of 62.5 kHz; the spectrum is divided intro three bands of 2 GHz each, in different Nyquist zones. Each output of the spectrometer is directly proportional to the brightness temperature of the load or the input, that provides a stable output measurement over time.

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Ground-Based Microwave Radiometry and Remote Sensing

Cited by 4 publications

References 0 publications

Retrieval of the Atmospheric Temperature and Humidity Profiles Using a Feed-Forward Neural Network

Retrieval of the Atmospheric Temperature and Humidity Profiles Using a Feed-Forward Neural Network

Editorial for the Special Issue “Remote Sensing of Atmospheric Components and Water Vapor”

A 22 GHz Pseudo-correlation Water Vapor Radiometer.

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