Retrieval of atmospheric properties with radiometric measurements using neural network
TL;DR: In this paper, three retrieval techniques have been used to obtain the temperature and relative humidity profiles from brightness temperatures, namely; piecewise linear regression, feed forward neural network and neural back propagation network.
About: This article is published in Atmospheric Research.The article was published on 2016-11-15. It has received 21 citations till now. The article focuses on the topics: Microwave radiometer & Radiometer.
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TL;DR: Artificial neural networks and least squares support vector machines as intelligent methods based on absorption spectra in the range of 230-300nm have been used for determination of antihistamine decongestant contents.
27 citations
TL;DR: In this article, the retrievals of temperature and humidity profiles from a ground-based microwave radiometer (MWR) using radiosonde soundings were verified using a comparison with airport radiosonde data, revealing a good agreement in particular for temperature, with the two measurements generally within 1'k from the surface to 10'k.
23 citations
TL;DR: In this paper, a critical analysis is done on the variability of instability indices and their significant signature to meteorological parameters and atmospheric pollution over Indian region in the warming atmosphere during 2005-2015.
20 citations
TL;DR: In this article, the long-term trends of the parameters related to convection and instability obtained from 27 radiosonde stations across six subdivisions over the Indian region during the period 1980-2016 are presented.
Abstract: . Long-term trends
of the parameters related to convection
and instability obtained from 27 radiosonde stations across six subdivisions
over the Indian region during the period 1980–2016 are presented. A total of
16 parcel and instability parameters along with moisture content, wind shear,
and thunderstorm and rainfall frequencies have been utilized for this
purpose. Robust fit regression analysis is employed on the regional average
time series to calculate the long-term trends on both a seasonal and a yearly
basis. The level of free convection (LFC) and the equilibrium level (EL) height
are found to ascend significantly in all Indian subdivisions. Consequently,
the coastal regions (particularly the western coast) experience increases
in severe thunderstorms (TSS) and severe rainfall (SRF) frequency in the
pre-monsoon period, while the inland regions (especially Central India) experience an
increase in ordinary thunderstorms (TSO) and weak rainfall (WRF) frequency during
the monsoon and post-monsoon periods. The 16–20-year periodicity is found to
dominate the long-term trends significantly compared to other periodicities
and the increase in TSS, and convective available potential energy (CAPE) is
found to be more severe after the year 1999. The enhancement in moisture transport
and associated cooling at 100 hPa along with the dispersion of boundary layer
pollutants are found to be the main causes for the increase in CAPE, which leads
to more convective severity in the coastal regions. However, in inland
regions, moisture-laden winds are absent and the presence of strong capping
effect of pollutants on instability in the lower troposphere has resulted in
more convective inhibition energy (CINE). Hence, TSO and occurrences of WRF have increased particularly in these regions.
19 citations
Cites background from "Retrieval of atmospheric properties..."
...It is mainly because of the higher accuracy and reliability of this in situ measurement technique that these datasets are widely used worldwide nowadays for calibrating other continuous profiler instruments (Chakraborty and Maitra, 2016)....
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TL;DR: A nowcasting technique has been proposed to estimate the impending rain accumulation using ground-based radiometric measurements at Kolkata, a tropical location, and a prediction model is developed and tested on several intense rain events during the period 2014–2015.
Abstract: A nowcasting technique has been proposed to estimate the impending rain accumulation using ground-based radiometric measurements at Kolkata (22.65°N, 88.45°E), a tropical location. It has been observed that the normalized variation of brightness temperature (BT) at 31 GHz along with the standard deviation of BT at 22 GHz and instability indices, namely, lifting index, have shown definite changes before rain events. A combination of these three parameters can be effective in predicting rain events both qualitatively and quantitatively. Accordingly, a prediction model is developed and tested on several intense rain events during the period 2014–2015. The model is found to perform reasonably well in predicting intense rain about 70–75 min in advance with an efficiency of 80%.
14 citations
Cites methods from "Retrieval of atmospheric properties..."
...1 GHz has been used for generating profiles of rain rate in this paper [42]–[44]....
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References
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TL;DR: In this article, the feasibility of nowcasting convective activity is examined by using thermodynamic indices derived from the ground-based microwave radiometer (MWR) observations located at a tropical station, Gadanki (13.5°N, 79.2°E).
Abstract: [1] In the present study, the feasibility of nowcasting convective activity is examined by using thermodynamic indices derived from the ground-based microwave radiometer (MWR) observations located at a tropical station, Gadanki (13.5°N, 79.2°E). There is a good comparison between thermodynamic parameters derived from MWR and colocated GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. Using MWR observations, a nowcasting technique was developed with the data of 26 thunderstorm cases observed at Gadanki. The analysis showed that there are sharp changes in some thermodynamic indices, such as the K index, the humidity index, precipitable water content, the stability index, and equivalent potential temperature lapse rates, about 2–4 h before the occurrence of thunderstorm. A superepoch analysis was made to examine the composite temporal variations of the thermodynamic indices associated with the occurrence of thunderstorms. The superepoch analysis revealed that 2–4 h prior to the storm occurrence, appreciable variations in many parameters are observed, suggesting thermodynamic evolution of the boundary layer convective instability. It is further demonstrated that by monitoring these variations it is possible to predict the ensuing thunderstorm activity over the region at least 2 h in advance. The association between the temporal evolution of thermodynamic indices and convective activity has been tested for the independent case of nine thunderstorms. The present results suggest that ground-based MWR observations can be used effectively to predict the occurrence of thunderstorms at least 2 h in advance.
313 citations
TL;DR: In this paper, the mean accuracy and variability of the radiosonde water vapor measurements relative to simultaneous measurements from the University of Colorado (CU) Cryogenic Frostpoint Hygrometer (CFH), a reference-quality standard of known absolute accuracy, were determined.
Abstract: [1] A detailed assessment of radiosonde water vapor measurement accuracy throughout the tropospheric column is needed for assessing the impact of observational error on applications that use the radiosonde data as input, such as forecast modeling, radiative transfer calculations, remote sensor retrieval validation, climate trend studies, and development of climatologies and cloud and radiation parameterizations. Six operational radiosonde types were flown together in various combinations with a reference-quality hygrometer during the Atmospheric Infrared Sounder (AIRS) Water Vapor Experiment-Ground (AWEX-G), while simultaneous measurements were acquired from Raman lidar and microwave radiometers. This study determines the mean accuracy and variability of the radiosonde water vapor measurements relative to simultaneous measurements from the University of Colorado (CU) Cryogenic Frostpoint Hygrometer (CFH), a reference-quality standard of known absolute accuracy. The accuracy and performance characteristics of the following radiosonde types are evaluated: Vaisala RS80-H, RS90, and RS92; Sippican Mark IIa; Modem GL98; and the Meteolabor Snow White hygrometer. A validated correction for sensor time lag error is found to improve the accuracy and reduce the variability of upper tropospheric water vapor measurements from the Vaisala radiosondes. The AWEX data set is also used to derive and validate a new empirical correction that improves the mean calibration accuracy of Vaisala measurements by an amount that depends on the temperature, relative humidity, and sensor type. Fully corrected Vaisala radiosonde measurements are found to be suitably accurate for AIRS validation throughout the troposphere, whereas the other radiosonde types are suitably accurate under only a subset of tropospheric conditions. Although this study focuses on the accuracy of nighttime radiosonde measurements, comparison of Vaisala RS90 measurements to water vapor retrievals from a microwave radiometer reveals a 6–8% dry bias in daytime RS90 measurements that is caused by solar heating of the sensor. An AWEX-like data set of daytime measurements is highly desirable to complete the accuracy assessment, ideally from a tropical location where the full range of tropospheric temperatures can be sampled.
265 citations
TL;DR: In this article, an empirical method for correcting the radiosonde humidity profiles is developed based on a constant scaling factor, which is consistent with interpretations of Vaisala RS80 radiosonde data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE).
Abstract: Thousands of comparisons between total precipitable water vapor (PWV) obtained from radiosonde (Vaisala RS80-H) profiles and PWV retrieved from a collocated microwave radiometer (MWR) were made at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains Cloud and Radiation Testbed (SGP CART) site in northern Oklahoma from 1994 to 2000. These comparisons show that the RS80-H radiosonde has an approximate 5% dry bias compared to the MWR. This observation is consistent with interpretations of Vaisala RS80 radiosonde data obtained during the Tropical Ocean Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA COARE). In addition to the dry bias, analysis of the PWV comparisons as well as of data obtained from dual-sonde soundings done at the SGP show that the calibration of the radiosonde humidity measurements varies considerably both when the radiosondes come from different calibration batches and when the radiosondes come from the same calibration batch. This variability can result in peak-to-peak differences between radiosondes of greater than 25% in PWV. Because accurate representation of the vertical profile of water vapor is critical for ARM’s science objectives, an empirical method for correcting the radiosonde humidity profiles is developed based on a constant scaling factor. By using an independent set of observations and radiative transfer models to test the correction, it is shown that the constant humidity scaling method appears both to improve the accuracy and reduce the uncertainty of the radiosonde data. The ARM data are also used to examine a different, physically based, correction scheme that was developed recently by scientists from Vaisala and the National Center for Atmospheric Research (NCAR). This scheme, which addresses the dry bias problem as well as other calibration-related problems with the RS80-H sensor, results in excellent agreement between the PWV retrieved from the MWR and integrated from the corrected radiosonde. However, because the physically based correction scheme does not address the apparently random calibration variations observed, it does not reduce the variability either between radiosonde calibration batches or within individual calibration batches.
257 citations
TL;DR: In this article, the mean bias error of Vaisala RS92 radiosondes is characterized as a function of its known dependences on height, relative humidity, and time of day (or solar altitude angle).
Abstract: Relative humidity (RH) measurements from Vaisala RS92 radiosondes are widely used in both research and operational applications, although the measurement accuracy is not well characterized as a function of its known dependences on height, RH, and time of day (or solar altitude angle). This study characterizes RS92 mean bias error as a function of its dependences by comparing simultaneous measurements from RS92 radiosondes and from three reference instruments of known accuracy. The cryogenic frostpoint hygrometer (CFH) gives the RS92 accuracy above the 700 mb level; the ARM microwave radiometer gives the RS92 accuracy in the lower troposphere; and the ARM SurTHref system gives the RS92 accuracy at the surface using 6 RH probes with NIST-traceable calibrations. These RS92 assessments are combined using the principle of Consensus Referencing to yield a detailed estimate of RS92 accuracy from the surface to the lowermost stratosphere. An empirical bias correction is derived to remove the mean bias error, yielding corrected RS92 measurements whose mean accuracy is estimated to be +/-3% of the measured RH value for nighttime soundings and +/-4% for daytime soundings, plus an RH offset uncertainty of +/-0.5%RH that is significant for dry conditions. The accuracy of individual RS92 soundings is further characterized by the 1-sigma "production variability," estimated to be +/-1.5% of the measured RH value. The daytime bias correction should not be applied to cloudy daytime soundings, because clouds affect the solar radiation error in a complicated and uncharacterized way.
252 citations
TL;DR: In this article, a comparison of simultaneous humidity measurements by the Vaisala RS92 radiosonde and by the Cryogenic Frostpoint Hygrometer (CFH) launched at Alajuela, Costa Rica, during July 2005 reveals a large solar radiation dry bias of the RS92 humidity sensor and a minor temperaturedependent calibration error.
Abstract: The comparison of simultaneous humidity measurements by the Vaisala RS92 radiosonde and by the Cryogenic Frostpoint Hygrometer (CFH) launched at Alajuela, Costa Rica, during July 2005 reveals a large solar radiation dry bias of the Vaisala RS92 humidity sensor and a minor temperature-dependent calibration error. For soundings launched at solar zenith angles between 10° and 30°, the average dry bias is on the order of 9% at the surface and increases to 50% at 15 km. A simple pressure- and temperature-dependent correction based on the comparison with the CFH can reduce this error to less than 7% at all altitudes up to 15.2 km, which is 700 m below the tropical tropopause. The correction does not depend on relative humidity, but is able to reproduce the relative humidity distribution observed by the CFH.
244 citations