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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (26 March 2020) | Viewed by 39988

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Dr. Richard Müller

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German Weather Service, Frankfurter Street 135, 63067 Offenbach, Germany
Interests: remote sensing of surface radiation; clouds and aerosols; sensor calibration; methods for “merging” in situ data with remote sensing data
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Dear Colleagues,

Changing of the atmosphere drives processes in weather, climate, and even oceans. The measurements, detection and predictions in atmospheric areas is of great importance for humans. Remote sensing is a key tool to monitor and analyze the atmosphere. Atmospheric remote sensing uses modern satellite instruments, as well as ground-based LIDAR, ceilometer and radar to study important processes.

We invite you to submit reviews and outstanding articles to this Special Issue in order to improve the current knowledge on remote sensing of the atmosphere. Papers addressing retrieval methods, validation of remote sensing data, calibration and applications based on remote sensing data in atmospheric areas, such as air quality, clouds, aerosols, water vapor, precipitation, wind, as well as chemical components, etc., are welcome.

The applications or technologies in your work should be novel and should bring new information to this area.

Dr. Richard Müller
Guest Editor

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Keywords

Atmosphere remote sensing
Renewable Energy
Air quality
UV index
Solar and wind energy systems
Ground-based LIDAR

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Published Papers (8 papers)

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Research

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30 pages, 23907 KiB

Open AccessFeature PaperArticle

Statistical Analysis of 1996–2017 Ozone Profile Data Obtained by Ground-Based Microwave Radiometry

by Konstantin P. Gaikovich, Elena P. Kropotkina and Sergey B. Rozanov

Remote Sens. 2020, 12(20), 3374; https://doi.org/10.3390/rs12203374 - 15 Oct 2020

Cited by 2 | Viewed by 2598

Abstract

Trends in the ozone layer remain among the major problems of the atmosphere physics; thus, results of measurements of the ozone altitude distribution (profile), carried out in the same place and via the same method, are very important. This paper presents the results of the statistical analysis of ensembles of ozone profiles obtained from ground-based microwave radiometry data acquired at the P.N. Lebedev Physical Institute over a period of two decades (1996–2017). The data collected show the significant difference between monthly mean statistical parameters of ozone profiles of the decades 1996–2006 and 2007–2017. The main and unexpected result is the drastic decrease in monthly root-mean-square (rms) variances of ozone profiles over Moscow above 30 km in cold months of the decade 2007–2017 (if compared to the variances in the decade 1996–2006) with the maximum fall by 46% at 39 km in February monthly mean variances. The decade change of variances obtained by averaging over all nine months in the analysis (from September to May) has the same decrease with maximum fall by 25% at 38 km. Additionally, significant decade changes were revealed in other monthly mean statistical parameters: probability density of ozone profile variances, inter-altitude covariance and correlation functions, and time covariance and correlation—as well as their frequency spectra. The decade change of the ozone profile obtained by averaging over the nine months appeared much less significant: the decrease by 5.7% at the altitude of 19 km (with 1.5% sampling error), minor decrease by 2.6% (with sampling error 1.5%) in the profile maximum at 37 km, and increases of 1.7% at 28 km and 2.5% at 47 km (with sampling errors 1.7%)—lower and higher of this maximum. In addition to that, the corresponding averaged mean total column (integral) ozone content above 20 km remained practically unchanged: 4.61 g/m² for decade 1996–2006 as compared to 4.58 g/m² for 2007–2017. Possible explanations of revealed offsets are proposed and discussed. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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18 pages, 6031 KiB

Open AccessArticle

Denoising Algorithm for the FY-4A GIIRS Based on Principal Component Analysis

by Sihui Fan, Wei Han, Zhiqiu Gao, Ruoying Yin and Yu Zheng

Remote Sens. 2019, 11(22), 2710; https://doi.org/10.3390/rs11222710 - 19 Nov 2019

Cited by 9 | Viewed by 5598

Abstract

The Geostationary Interferometric Infrared Sounder (GIIRS) is the first high-spectral resolution advanced infrared (IR) sounder onboard the new-generation Chinese geostationary meteorological satellite FengYun-4A (FY-4A). The GIIRS has 1650 channels, and its spectrum ranges from 700 to 2250 cm⁻¹ with an unapodized spectral resolution of 0.625 cm⁻¹. It represents a significant breakthrough for measurements with high temporal, spatial and spectral resolutions worldwide. Many GIIRS channels have quite similar spectral signal characteristics that are highly correlated with each other in content and have a high degree of information redundancy. Therefore, this paper applies a principal component analysis (PCA)-based denoising algorithm (PDA) to study simulation data with different noise levels and observation data to reduce noise. The results show that the channel reconstruction using inter-channel spatial dependency and spectral similarity can reduce the noise in the observation brightness temperature (BT). A comparison of the BT observed by the GIIRS (O) with the BT simulated by the radiative transfer model (B) shows that a deviation occurs in the observation channel depending on the observation array. The results show that the array features of the reconstructed observation BT (rrO) depending on the observation array are weakened and the effect of the array position on the observations in the sub-center of the field of regard (FOR) are partially eliminated after the PDA procedure is applied. The high observation and simulation differences (O-B) in the sub-center of the FOR array notably reduced after the PDA procedure is implemented. The improvement of the high O-B is more distinct, and the low O-B becomes smoother. In each scan line, the standard deviation of the reconstructed background departures (rrO-B) is lower than that of the background departures (O-B). The observation error calculated by posterior estimation based on variational assimilation also verifies the efficiency of the PDA. The typhoon experiment also shows that among the 29 selected assimilation channels, the observation error of 65% of the channels was reduced as calculated by the triangle method. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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14 pages, 6975 KiB

Open AccessFeature PaperArticle

Improved Empirical Coefficients for Estimating Water Vapor Weighted Mean Temperature over Europe for GNSS Applications

by Zofia Baldysz and Grzegorz Nykiel

Remote Sens. 2019, 11(17), 1995; https://doi.org/10.3390/rs11171995 - 23 Aug 2019

Cited by 8 | Viewed by 2870

Abstract

Development of the so-called global navigation satellite system (GNSS) meteorology is based on the possibility of determining a precipitable water vapor (PWV) from a GNSS zenith wet delay (ZWD). Conversion of ZWD to the PWV requires application of water vapor weighted mean temperature [...] Read more.

T_{m}

) measurements, which can be done using a surface temperature (

T_{s}

) and its linear dependency to the

T_{m}

. In this study we analyzed up to 24 years (1994–2018) of data from 49 radio-sounding (RS) stations over Europe to determine reliable coefficients of the

T_{m} - T_{s}

relationship. Their accuracy was verified using 109 RS stations. The analysis showed that for most of the stations, there are visible differences between coefficients estimated for the time of day and night. Consequently, the ETm4 model containing coefficients determined four times a day is presented. For hours other than the primary synoptic hours, linear interpolation was used. However, since this approach was not enough in some cases, we applied the dependence of

T_{m} - T_{s}

coefficients on the time of day using a polynomial (ETmPoly model). This resulted in accuracy at the level of 2.8 ± 0.3 K. We also conducted an analysis of the impact of this model on the PWV GNSS. Analysis showed that differences in PWV reached 0.8 mm compared to other commonly used models. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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21 pages, 9631 KiB

Open AccessArticle

Comparison of Tropospheric Path Delay Estimates from GNSS and Space-Borne SAR Interferometry in Alpine Conditions

by Karina Wilgan, Muhammad Adnan Siddique, Tazio Strozzi, Alain Geiger and Othmar Frey

Remote Sens. 2019, 11(15), 1789; https://doi.org/10.3390/rs11151789 - 31 Jul 2019

Cited by 6 | Viewed by 4149

Abstract

We compare tropospheric delays from Global Navigation Satellite Systems (GNSS) and Synthetic Aperture Radar (SAR) Interferometry (InSAR) in a challenging mountainous environment in the Swiss Alps, where strong spatial variations of the local tropospheric conditions are often observed. Tropospheric delays are usually considered to be an error for both GNSS and InSAR, and are typically removed. However, recently these delays are also recognized as a signal of interest, for example for assimilation into numerical weather models or climate studies. The GNSS and InSAR are techniques of complementary nature, as one has sparse spatial but high temporal resolution, and the other very dense spatial coverage but repeat pass of only a few days. This raises expectations for a combination of these techniques. For this purpose, a comprehensive comparison between the techniques must be first performed. Due to the relative nature of InSAR estimates, we compare the difference slant tropospheric delays (

d S T D

) retrieved from GNSS with the

d S T D

s estimated using Persistent Scatterer Interferometry (PSI) of 32 COSMO-SkyMed SAR images taken in a snow-free period from June to October between 2008 and 2013. The GNSS estimates calculated at permanent geodetic stations are interpolated to the locations of persistent scatterers using an in-house developed least-squares collocation software COMEDIE. The Pearson’s correlation coefficient between InSAR and GNSS estimates averaged over all acquisitions is equal to 0.64 and larger than 0.8 for approximately half of the layers. Better agreement is obtained mainly for days with high variability of the troposphere (relative to the tropospheric conditions at the time of the reference acquisition), expressed as standard deviations of the GNSS-based

d S T D

s. On the other hand, the most common feature for the days with poor agreement is represented by very stable, almost constant GNSS estimates. In addition, there is a weak correlation between the agreement and the water vapor values in the area, as well as with the number of stations in the closest vicinity of the study area. Adding low-cost L-1 only GPS stations located within the area of the study increases the biases for most of the dates, but the standard deviations between InSAR and GNSS decrease for the limited area with low-cost stations. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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23 pages, 8460 KiB

Open AccessArticle

Infrared Hyperspectral and Ultraviolet Remote Measurements of Volcanic Gas Plume at MT Etna during IMAGETNA Campaign

by Nathalie Huret, Charlotte Segonne, Sébastien Payan, Giuseppe Salerno, Valéry Catoire, Yann Ferrec, Tjarda Roberts, Armande Pola Fossi, Delphy Rodriguez, Laurence Croizé, Stéphane Chevrier, Stéphane Langlois, Alessandro La Spina and Tommaso Caltabiano

Remote Sens. 2019, 11(10), 1175; https://doi.org/10.3390/rs11101175 - 17 May 2019

Cited by 5 | Viewed by 5056

Abstract

Quantification of gaseous emission fluxes from volcanoes can yield valuable insights on processes occurring in the Earth’s interior as part of hazard monitoring. It is also an important task in the framework of climate change, in order to refine estimates of natural emissions. Passive open-path UltraViolet (UV) scattered observation by UV camera allows the imaging of volcanic plumes and evaluation of sulfur dioxide (SO₂) fluxes at high temporal resolution during daytime. Another technique of imaging is now available in the InfraRed (IR) spectral domain. Infrared hyperspectral imagers have the potential to overcome the boundary of daytime sampling of the UV, providing measurements also during the night and giving access simultaneously to additional relevant gas species. In this context the IMAGETNA campaign of measurements took place at Mt Etna (Italy) in June 2015. Three different IR imagers (commercial and under developments) were deployed, together with a Fourier Transform InfraRed spectrometer (FTIR) instrument, a UV camera, a Long Wavelength InfraRed (LWIR) camera and a radiometer. We present preliminary results obtained by the two IR cameras under development, and then the IR hyperspectral imager results, coming from full physics retrieval, are compared to those of the UV camera. The comparison points out an underestimation of the SO₂ Slant Column Densities (SCD) of the UV camera by a factor of 3.6. The detailed study of the retrieved SO₂ SCD highlights the promising application of IR imaging in volcanology for remotely volcanic plume gas measurements. It also provides a way to investigate uncertainties in the SO₂ SCD imaging in the UV and the IR. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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16 pages, 4058 KiB

Open AccessArticle

Satellite-based Cloudiness and Solar Energy Potential in Texas and Surrounding Regions

by Shuang Xia, Alberto M. Mestas-Nuñez, Hongjie Xie and Rolando Vega

Remote Sens. 2019, 11(9), 1130; https://doi.org/10.3390/rs11091130 - 11 May 2019

Cited by 2 | Viewed by 3537

Abstract

Global horizontal irradiance (i.e., shortwave downward solar radiation received by a horizontal surface on the ground) is an important geophysical variable for climate and energy research. Since solar radiation is attenuated by clouds, its variability is intimately associated with the variability of cloud properties. The spatial distribution of clouds and the daily, monthly, seasonal, and annual solar energy potential (i.e., the solar energy available to be converted into electricity) derived from satellite estimates of global horizontal irradiance are explored over the state of Texas, USA and surrounding regions, including northern Mexico and the western Gulf of Mexico. The maximum (minimum) monthly solar energy potential in the study area is 151–247 kWhm⁻² (43–145 kWhm⁻²) in July (December). The maximum (minimum) seasonal solar energy potential is 457–706 kWhm⁻² (167–481 kWhm⁻²) in summer (winter). The available annual solar energy in 2015 was 1295–2324 kWhm⁻². The solar energy potential is significantly higher over the Gulf of Mexico than over land despite the ocean waters having typically more cloudy skies. Cirrus is the dominant cloud type over the Gulf which attenuates less solar irradiance compared to other cloud types. As expected from our previous work, there is good agreement between satellite and ground estimates of solar energy potential in San Antonio, Texas, and we assume this agreement applies to the surrounding larger region discussed in this paper. The study underscores the relevance of geostationary satellites for cloud/solar energy mapping and provides useful estimates on solar energy in Texas and surrounding regions that could potentially be harnessed and incorporated into the electrical grid. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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19 pages, 7950 KiB

Open AccessArticle

A Framework for Estimating Clear-Sky Atmospheric Total Precipitable Water (TPW) from VIIRS/S-NPP

by Shugui Zhou and Jie Cheng

Remote Sens. 2019, 11(8), 916; https://doi.org/10.3390/rs11080916 - 15 Apr 2019

Cited by 35 | Viewed by 3635

Abstract

Atmospheric water vapor content or total precipitable water (TPW) is a highly variable atmospheric constituent, yet it remains one of the meteorological parameters that is most difficult to characterize accurately. We develop a framework for estimating atmospheric TPW from Visible Infrared Imaging Radiometer Suite (VIIRS) data in this study. First, TPW is retrieved from VIIRS top-of-atmosphere (TOA) radiance of channels 15 and 16 using the refined split-window covariance-variance ratio (SWCVR) method. Then, the VIIRS TPW is blended with the microwave integrated retrieval system (MIRS) derived TPW via Bayesian model averaging (BMA) to improve the accuracy of VIIRS TPW. Three years (2014–2017) of ground measurements collected from SuomiNet sites over North America are used to validate the VIIRS TPW and blended TPW. The mean bias error (MBE) and root mean square error (RMSE) of the VIIRS TPW are 0.21 g/cm² and 0.73 g/cm², respectively, and the accuracy of the VIIRS TPW in daytime is much better than at night time. The MBE and RMSE of BMA integrated TPW are 0.06 g/cm² and 0.35 g/cm², and the accuracy difference between daytime and nighttime is also removed. The global radiosonde measurements are also collected to validate the BMA integrated VIIRS TPW. The MBE and RMSE of the BMA integrated TPW are 0.09 g/cm² and 0.44 g/cm² compared to the radiosonde measurements. This accuracy is also superior to the VIIRS TPW. Therefore, it is concluded that the developed framework can be used to derive accurate clear-sky TPW for VIIRS. This is the first time that we can obtain high accuracy TPW from VIIRS. This study will certainly benefit the study of atmospheric processes and climate change. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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Review

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1786 KiB

Open AccessReview

Measurement of the Earth Radiation Budget at the Top of the Atmosphere—A Review

by Steven Dewitte and Nicolas Clerbaux

Remote Sens. 2017, 9(11), 1143; https://doi.org/10.3390/rs9111143 - 7 Nov 2017

Cited by 48 | Viewed by 11154

Abstract

The Earth Radiation Budget at the top of the atmosphere quantifies how the Earth gains energy from the Sun and loses energy to space. It is of fundamental importance for climate and climate change. In this paper, the current state-of-the-art of the satellite measurements of the Earth Radiation Budget is reviewed. Combining all available measurements, the most likely value of the Total Solar Irradiance at a solar minimum is 1362 W/m

^{2}

, the most likely Earth albedo is 29.8%, and the most likely annual mean Outgoing Longwave Radiation is 238 W/m

^{2}

. We highlight the link between long-term changes of the Outgoing Longwave Radiation, the strengthening of El Nino in the period 1985–1997 and the strengthening of La Nina in the period 2000–2009. Full article

(This article belongs to the Special Issue Feature Papers for Section Atmosphere Remote Sensing)

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