Remote Sensing

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6 pages, 215 KiB

Open AccessEditorial

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

by Victoria E. Cachorro and Manuel Antón

Remote Sens. 2020, 12(13), 2074; https://doi.org/10.3390/rs12132074 - 28 Jun 2020

Viewed by 1835

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

Research

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21 pages, 2716 KiB

Open AccessFeature PaperArticle

Column Integrated Water Vapor and Aerosol Load Characterization with the New ZEN-R52 Radiometer

by Antonio Fernando Almansa, Emilio Cuevas, África Barreto, Benjamín Torres, Omaira Elena García, Rosa Delia García, Cristian Velasco-Merino, Victoria Eugenia Cachorro, Alberto Berjón, Manuel Mallorquín, César López, Ramón Ramos, Carmen Guirado-Fuentes, Ramón Negrillo and Ángel Máximo de Frutos

Remote Sens. 2020, 12(9), 1424; https://doi.org/10.3390/rs12091424 - 30 Apr 2020

Cited by 10 | Viewed by 3807

Abstract

The study shows the first results of the column-integrated water vapor retrieved by the new ZEN-R52 radiometer. This new radiometer has been specifically designed to monitor aerosols and atmospheric water vapor with a high degree of autonomy and robustness in order to allow [...] Read more.

The study shows the first results of the column-integrated water vapor retrieved by the new ZEN-R52 radiometer. This new radiometer has been specifically designed to monitor aerosols and atmospheric water vapor with a high degree of autonomy and robustness in order to allow the expansion of the observations of these parameters to remote desert areas from ground-based platforms. The ZEN-R52 device shows substantial improvements compared to the previous ZEN-R41 prototype: a smaller field of view, an increased signal-to-noise ratio, better stray light rejection, and an additional channel (940 nm) for precipitable water vapor (PWV) retrieval. PWV is inferred from the ZEN-R52 Zenith Sky Radiance (ZSR) measurements using a lookup table (LUT) methodology. The improvement of the new ZEN-R52 in terms of ZSR was verified by means of a comparison with the ZEN-R41, and with the Aerosol Robotic Network (AERONET) Cimel CE318 (CE318-AERONET) at Izaña Observatory, a Global Atmosphere Watch (GAW) high mountain station (Tenerife, Canary Islands, Spain), over a 10-month period (August 2017 to June 2018). ZEN-R52 aerosol optical depth (AOD) was extracted by means of the ZEN–AOD–LUT method with an uncertainty of ±0.01 ± 0.13*AOD. ZEN-R52 PWV extracted using a new LUT technique was compared with quasi-simultaneous (±30 s) Fourier Transform Infrared (FTIR) spectrometer measurements as reference. A good agreement was found between the two instruments (PWV means a relative difference of 9.1% and an uncertainty of ±0.089 cm or ±0.036 + 0.061*PWV for PWV <1 cm). This comparison analysis was extended using two PWV datasets from the same CE318 reference instrument at Izaña Observatory: one obtained from AERONET (CE318-AERONET), and another one using a specific calibration of the 940-nm channel performed in this work at Izaña Atmospheric Research Center Observatory (CE318-IARC), which improves the PWV product. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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19 pages, 13590 KiB

Open AccessArticle

Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

by Cintia Carbajal Henken, Lisa Dirks, Sandra Steinke, Hannes Diedrich, Thomas August and Susanne Crewell

Remote Sens. 2020, 12(7), 1170; https://doi.org/10.3390/rs12071170 - 6 Apr 2020

Cited by 9 | Viewed by 3007

Abstract

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high [...] Read more.

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m

^{2}

) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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Figure 1

Figure 1
Examples of spatial sampling for IASI, MIRS and MODIS for 24 April 2013 over a German domain, at overpass times of about 19:34 UTC, 20:30 UTC and 12:30 UTC, respectively. On the right are the GPS stations (circles) from the German GPS network used in this study and corresponding IWV values at the MODIS overpass time. For this day IWV products from 236 GPS stations were available. Areas in grey indicate that no (successful) IWV retrievals were performed or obtained. Full article ">Figure 2
Temporal sampling of the IASI, MIRS and MODIS instruments at all GPS stations combined for one year, assuming for each satellite overpass a maximum distance of 20 km and a maximum time difference of 7.5 min between satellite pixel and GPS station. Full article ">Figure 3
2D histograms and probability density functions for IWV GPS vs. IWV from IASI, MIRS, MODIS and MODIS-FUB. The dashed black-white diagonal line is the 1:1 line, while the dashed red-blue line presents a linear fit through the data. Corresponding statistical quantities can be found in <a href="#remotesensing-12-01170-t002" class="html-table">Table 2</a>. Full article ">Figure 4
Bias (inner circle colors ranging from blue to red) and standard deviation (std; outer circle colors in greyish colors) at each GPS station for all matched IWV observations within one year. Only the GPS stations are included with at least 20 valid satellite-GPS data pairs. Full article ">Figure 5
Comparison of monthly mean of IWV from GPS to IASI, MIRS, MODIS and MODIS-FUB for all months from June 2012 to May 2013. The error bars show the standard deviation. Bias and root mean square error (RMSE) are given in kg/m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>. Full article ">Figure 6
Averaging time period vs. error (using as reference GPS climatologies computed from all 15 min IWV values within an averaging time period) obtained from bootstrap using a sample size of 10,000 for each averaging time period. The dots show the median and the bars indicate the 5% and 95% confidence levels. Left: for GPS IWV values for all matched satellite pixels. Middle: for GPS IWV values for all matched satellite pixels with successfully retrieved satellite IWV values. Right: satellite IWV values for all matched satellite pixels and successfully retrieved satellite IWV values Full article ">Figure 7
IWV distributions for all GPS IWV observations, for all GPS IWV observations during daytime as well as for certain clear-sky and certain cloudy cases. Full article ">Figure 8
Hourly deviations from the IWV daily mean (zero black dashed line) computed using all 15 min GPS IWV values from the one-year time period (black line) and using all 15 min GPS IWV values from the years 2007–2013 (grey line). Overlaid are the deviations of the satellite daily means, computed using the GPS IWV values for matched GPS-satellite cases, to the GPS daily mean from the one-year time period (colored horizontal lines). The relative frequency of occurrence of satellite IWV values at corresponding hourly time intervals is shown in the lower parts of the panels. Results are presented for local time (LT) for four seasons: June–July–August (JJA), September–October–November (SON), December–January–February (DJF), March–April–May (MAM). Full article ">Figure 9
IWV distributions for GPS IWV values for all GPS time steps (black), for GPS IWV values for all time steps matched with satellite observations (green), for GPS IWV values for all timesteps matched with satellite observations with valid IWV retrieval (blue), and for all matched valid satellite IWV values (red). Full article ">

26 pages, 5897 KiB

Open AccessArticle

Satellite-Observed Variations and Trends in Carbon Monoxide over Asia and Their Sensitivities to Biomass Burning

by Xun Zhang, Jane Liu, Han Han, Yongguang Zhang, Zhe Jiang, Haikun Wang, Lingyun Meng, Yi Chen Li and Yi Liu

Remote Sens. 2020, 12(5), 830; https://doi.org/10.3390/rs12050830 - 4 Mar 2020

Cited by 35 | Viewed by 6873

Abstract

As the carbon monoxide (CO) total column over Asia is among the highest in the world, it is important to characterize its variations in space and time. Using Measurements of Pollution in the Troposphere (MOPITT) and Atmospheric InfraRed Sounder (AIRS) satellite data, the [...] Read more.

As the carbon monoxide (CO) total column over Asia is among the highest in the world, it is important to characterize its variations in space and time. Using Measurements of Pollution in the Troposphere (MOPITT) and Atmospheric InfraRed Sounder (AIRS) satellite data, the variations and trends in CO total column over Asia and its seven subregions during 2003–2017 are investigated in this study. The CO total column in Asia is higher in spring and winter than in summer and autumn. The seasonal maximum and minimum are in spring and summer respectively in the regional mean over Asia, varying between land and oceans, as well as among the subregions. The CO total column in Asia shows strong interannual variation, with a regional mean coefficient of variation of 5.8% in MOPITT data. From 2003 to 2017, the annual mean of CO total column over Asia decreased significantly at a rate of (0.58 ± 0.15)% per year (or −(0.11 ± 0.03) × 10¹⁷ molecules cm⁻² per year) in MOPITT data, resulting from significant CO decreases in winter, summer, and spring. In most of the subregions, significant decreasing trends in CO total column are also observed, more obviously over areas with high CO total column, including eastern regions of China and the Sichuan Basin. The regional decreasing trends in these areas are over 1% per year. Over the entire Asia, and in fire-prone subregions including South Siberia, Indo-China Peninsula, and Indonesia, we found significant correlations between the MOPITT CO total column and the fire counts from the Moderate Resolution Imaging Spectroradiometer (MODIS). The variations in MODIS fire counts may explain 58%, 60%, 36%, and 71% of the interannual variation in CO total column in Asia and these three subregions, respectively. Over different land cover types, the variations in biomass burning may explain 62%, 52%, and 31% of the interannual variation in CO total column, respectively, over the forest, grassland, and shrubland in Asia. Extremes in CO total column in Asia can be largely explained by the extreme fire events, such as the fires over Siberia in 2003 and 2012 and over Indonesia in 2006 and 2015. The significant decreasing trends in MODIS fire counts inside and outside Asia suggest that global biomass burning may be a driver for the decreasing trend in CO total column in Asia, especially in spring. In general, the variations and trends in CO total column over Asia detected by AIRS are similar to but smaller than those by MOPITT. The two datasets show similar spatial and temporal variations in CO total column over Asia, with correlation coefficients of 0.86–0.98 in the annual means. This study shows that the interannual variation in atmospheric CO in Asia is sensitive to biomass burning, while the decreasing trend in atmospheric CO over Asia coincides with the decreasing trend in MODIS fire counts from 2003 to 2017. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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18 pages, 9278 KiB

Open AccessArticle

Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager—A Case Study

by Xiaowei Jiang, Jun Li, Zhenglong Li, Yunheng Xue, Di Di, Pei Wang and Jinlong Li

Remote Sens. 2020, 12(4), 670; https://doi.org/10.3390/rs12040670 - 18 Feb 2020

Cited by 9 | Viewed by 3132

Abstract

The distribution of tropospheric moisture in the environment is highly associated with storm development. Therefore, it is important to evaluate the uncertainty of moisture fields from numerical weather prediction (NWP) models for better understanding and enhancing storm prediction. With water vapor absorption band [...] Read more.

The distribution of tropospheric moisture in the environment is highly associated with storm development. Therefore, it is important to evaluate the uncertainty of moisture fields from numerical weather prediction (NWP) models for better understanding and enhancing storm prediction. With water vapor absorption band radiance measurements from the advanced imagers onboard the new generation of geostationary weather satellites, it is possible to quantitatively evaluate the environmental moisture fields from NWP models. Three NWP models—Global Forecast System (GFS), Unified Model (UM), Weather Research and Forecasting (WRF)—are evaluated with brightness temperature (BT) measurements from the three moisture channels of Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite for Typhoon Linfa (2015) case. It is found that the three NWP models have similar performance for lower tropospheric moisture, and GFS has a smaller bias for middle tropospheric moisture. Besides, there is a close relationship between moisture forecasts in the environment and the tropical cyclone (TC) track forecasts in GFS, while regional WRF does not show this pattern. When the infrared and microwave sounder radiance measurements from polar orbit satellite are assimilated in regional WRF, it is clearly shown that the environment moisture fields are improved compared with that with only conventional data are assimilated. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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21 pages, 15213 KiB

Open AccessArticle

Preliminary Evaluation of the Error Budgets in the TALIS Measurements and Their Impact on the Retrievals

by Wenyu Wang, Zhenzhan Wang and Yongqiang Duan

Remote Sens. 2020, 12(3), 468; https://doi.org/10.3390/rs12030468 - 2 Feb 2020

Cited by 6 | Viewed by 2331

Abstract

The THz Atmospheric Limb Sounder (TALIS) is a Chinese sub-millimeter limb sounder being designed by National Space Science Center of the Chinese Academy of Sciences to measure the temperature and chemical constituents vertically in the middle and upper atmosphere, with good precision and [...] Read more.

The THz Atmospheric Limb Sounder (TALIS) is a Chinese sub-millimeter limb sounder being designed by National Space Science Center of the Chinese Academy of Sciences to measure the temperature and chemical constituents vertically in the middle and upper atmosphere, with good precision and vertical resolution. This paper presents a simulation study that assesses the measurement errors and their impacts on the retrievals. Three error sources, including instrument uncertainties, calibration errors and a priori errors, are considered. The sideband weight uncertainty, the local oscillator, the pointing angle offsets and the measurement noise (NEDT), are considered as instrument uncertainties. Calibration errors consist of the hot target offset, the nonlinearity residual of the two-point calibration, use of the Rayleigh–Jeans (R–J) approximation and the choice of the antenna pattern. A priori profile errors of temperature, pressure and species are also considered. The results suggest that the antenna pattern mainly affects the retrievals in the troposphere. The NEDT is a major error source affecting all of the retrievals. The R–J approximation has a great impact upon the retrievals at 643 GHz, and should not be used. The local oscillator offset leads to an obvious error above 50 km. The effect of nonlinearity residuals cannot be neglected above 70 km. The impact of the sideband weight uncertainty and the hot target offset are relatively small. The pointing and the a priori errors can be neglected in most observation regions. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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18 pages, 4546 KiB

Open AccessArticle

Intercomparison of Integrated Water Vapor Measurements at High Latitudes from Co-Located and Near-Located Instruments

by Ermanno Fionda, Maria Cadeddu, Vinia Mattioli and Rosa Pacione

Remote Sens. 2019, 11(18), 2130; https://doi.org/10.3390/rs11182130 - 13 Sep 2019

Cited by 7 | Viewed by 2827

Abstract

Data from global positioning system (GPS) ground-based receivers, ground-based microwave radiometers (MWRs), and radiosondes (RS) at two high-latitude sites were compared. At one site, the North Slope of Alaska (NSA), Barrow, Alaska (USA), the instruments were co-located, while at the other site, the [...] Read more.

Data from global positioning system (GPS) ground-based receivers, ground-based microwave radiometers (MWRs), and radiosondes (RS) at two high-latitude sites were compared. At one site, the North Slope of Alaska (NSA), Barrow, Alaska (USA), the instruments were co-located, while at the other site, the second ARM Mobile Facility (AMF2), Hyytiälä, Finland, the GPS receiver was located about 20 km away from the MWRs and RS. Differences between the GPS-derived integrated water vapor (IWV) and the other three instruments were analyzed in terms of mean differences and standard deviation. A comparison of co-located and near-located independently calibrated instruments allowed us to isolate issues that may be specific to a single system and, to some extent, to isolate the effects of the distance between the GPS receiver and the remaining instruments. The results showed that at these two high-latitude sites, when the IWV was less than 15 kg/m², the GPS agreed with other instruments within 0.5–0.7 kg/m². When the variability of water vapor was higher, mostly in the summer months, the GPS agreed with other instruments within 0.8–1 kg/m². The total random uncertainty between the GPS and the other systems was of the order of 0.6–1 kg/m² and was the dominant effect when the IWV was higher than 15 kg/m². Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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14 pages, 794 KiB

Open AccessEditor’s ChoiceArticle

Water Vapor Calibration: Using a Raman Lidar and Radiosoundings to Obtain Highly Resolved Water Vapor Profiles

by Birte Solveig Kulla and Christoph Ritter

Remote Sens. 2019, 11(6), 616; https://doi.org/10.3390/rs11060616 - 13 Mar 2019

Cited by 9 | Viewed by 4705

Abstract

We revised the calibration of a water vapor Raman lidar by co-located radiosoundings for a site in the high European Arctic. For this purpose, we defined robust criteria for a valid calibration. One of these criteria is the logarithm of the water vapor [...] Read more.

We revised the calibration of a water vapor Raman lidar by co-located radiosoundings for a site in the high European Arctic. For this purpose, we defined robust criteria for a valid calibration. One of these criteria is the logarithm of the water vapor mixing ratio between the sonde and the lidar. With an error analysis, we showed that for our site correlations smaller than 0.95 could be explained neither by noise in the lidar nor by wrong assumptions concerning the aerosol or Rayleigh extinction. However, highly variable correlation coefficients between sonde and consecutive lidar profiles were found, suggesting that small scale variability of the humidity was our largest source of error. Therefore, not all co-located radiosoundings are useful for lidar calibration. As we assumed these changes to be non-systematic, averaging over several independent measurements increased the calibration’s quality. The calibration of the water vapor measurements from the lidar for individual profiles varied by less than ±5%. The seasonal median, used for calibration in this study, was stable and reliable (confidence ±1% for the season with most calibration profiles). Thus, the water vapor mixing ratio profiles from the Koldewey Aerosol Raman Lidar (KARL) are very accurate. They show high temporal variability up to 4 km altitude and, therefore, provide additional, independent information to the radiosonde. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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Full article ">Figure 1
Exemplary lidar profile used for calibration (20 February 2018). Bins used for calibration (<math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>) are highlighted with green points. Conditions under which heights are excluded from calibration as in <a href="#remotesensing-11-00616-t001" class="html-table">Table 1</a> due to deficiencies in lidar (green) or radiosonde data (blue) are marked. Full article ">Figure 2
Pearson correlation coefficient between log<math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <msub> <mi>w</mi> <mrow> <mi>R</mi> <mi>S</mi> </mrow> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math> and log<math display="inline"><semantics> <mrow> <mo stretchy="false">(</mo> <msub> <mi>S</mi> <mi>lidar</mi> </msub> <mo stretchy="false">)</mo> </mrow> </semantics></math> at heights where data would fulfill all other criteria to be selected for calibration (<math display="inline"><semantics> <msub> <mi>z</mi> <mi>i</mi> </msub> </semantics></math>) during the nights from 3 March 2018 to 4 March 2018, 6 March 2018 to 7 March 2018 and 11 March 2018 to 12 March 2018. Colors indicate the temporal difference between the start of the radiosonde and the lidar measurement. Red line shows the threshold for minimal correlation applied here. Start of radiosounding measurement indicated by black, dashed line. Full article ">Figure 3
Calibration factor for winter 2017/2018. Light green I: median of all <math display="inline"><semantics> <msub> <mi>C</mi> <mi>i</mi> </msub> </semantics></math>; dark green I: median of <math display="inline"><semantics> <msub> <mi>C</mi> <mi>i</mi> </msub> </semantics></math>, when using only one profile at a time; grey: seasonal median of <math display="inline"><semantics> <msub> <mi>C</mi> <mi>i</mi> </msub> </semantics></math> ±3%; black: seasonal median of <math display="inline"><semantics> <msub> <mi>C</mi> <mi>i</mi> </msub> </semantics></math> ±5%. Full article ">Figure 4
Probability density function of <math display="inline"><semantics> <msub> <mi>C</mi> <mi>i</mi> </msub> </semantics></math> for different years and different radiosonde products as reference datasets. Respective median C indicated by an I. Full article ">Figure 5
(a) Exemplary lidar profile used for calibration (20 February 2018); (b) ratio between the calibrated lidar signal and radiosounding measurement, with (green) and without (orange) overlap correction for all profiles with parallel measurements in 2018; and (c) probability density function (PDF) of ratio between radiosounding and the uncorrected (orange) and corrected (green) lidar signal in the lowermost 400 m. The ratio between the signals in the altitudes chosen for calibration is shown as a reference (dark green). Full article ">Figure 6
(a) PDF of Pearson correlation coefficient how different realizations of noise in the lidar degrade a perfect r = 1 correlation; (b) PDF of the error due to different Rayleigh atmospheres; (c) median (dotted) and 95% percentile (dashed) of the error due to aerosol extinction as a function of altitude; (d) PDF of the aerosol induced error from AOD and Ångström exponent; (e) PDF of Pearson correlation coefficient of the combined error due to noise and aerosol; and (f) accuracy for the determination of the lidar calibration constant. Full article ">Figure 7
Water vapor mixing ratio dynamics over Ny-Ålesund during the nights from 3 March 2018 to 4 March 2018, 6 March 2018 to 7 March 2018 and 11 March 2018 to 12 March 2018. Times of radiosounding measurements indicated by black, dashed line. Full article ">

25 pages, 3867 KiB

Open AccessArticle

Evaluation of Bias Correction Methods for GOSAT SWIR XH₂O Using TCCON data

by Tran Thi Ngoc Trieu, Isamu Morino, Hirofumi Ohyama, Osamu Uchino, Ralf Sussmann, Thorsten Warneke, Christof Petri, Rigel Kivi, Frank Hase, David F. Pollard, Nicholas M. Deutscher, Voltaire A. Velazco, Laura T. Iraci, James R. Podolske and Manvendra K. Dubey

Remote Sens. 2019, 11(3), 290; https://doi.org/10.3390/rs11030290 - 1 Feb 2019

Cited by 2 | Viewed by 4877

Abstract

This study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH₂O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing [...] Read more.

This study evaluated three bias correction methods of systematic biases in column-averaged dry-air mole fraction of water vapor (XH₂O) data retrieved from Greenhouse Gases Observing Satellite (GOSAT) Short-Wavelength Infrared (SWIR) observations compared with ground-based data from the Total Carbon Column Observing Network (TCCON). They included an empirically multilinear regression method, altitude bias correction method, and combination of altitude and empirical correction for three cases defined by the temporal and spatial collocation around TCCON site. The results showed that large altitude differences between GOSAT observation points and TCCON instruments are the main cause of bias, and the altitude bias correction method is the most effective bias correction method. The lowest biases result from GOSAT SWIR XH₂O data within a 0.5° × 0.5° latitude × longitude box centered at each TCCON site matched with TCCON XH₂O data averaged over ±15 min of the GOSAT overpass time. Considering land data, the global bias changed from −1.3 ± 9.3% to −2.2 ± 8.5%, and station bias from −2.3 ± 9.0% to −1.7 ± 8.4%. In mixed land and ocean data, global bias and station bias changed from −0.3 ± 7.6% and −1.9 ± 7.1% to −0.8 ± 7.2% and −2.3 ± 6.8%, respectively, after bias correction. The results also confirmed that the fine spatial and temporal collocation criteria are necessary in bias correction methods. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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19 pages, 7435 KiB

Open AccessArticle

A Retrieval of Glyoxal from OMI over China: Investigation of the Effects of Tropospheric NO₂

by Yapeng Wang, Jinhua Tao, Liangxiao Cheng, Chao Yu, Zifeng Wang and Liangfu Chen

Remote Sens. 2019, 11(2), 137; https://doi.org/10.3390/rs11020137 - 11 Jan 2019

Cited by 7 | Viewed by 3523

Abstract

East China is the ‘hotspot’ of glyoxal (CHOCHO), especially over the Pearl River Delta (PRD) region, where glyoxal is yielded from the oxidation of aromatics. To better understand the glyoxal spatial-temporal characteristics over China and evaluate the effectiveness of atmospheric prevention efforts on [...] Read more.

East China is the ‘hotspot’ of glyoxal (CHOCHO), especially over the Pearl River Delta (PRD) region, where glyoxal is yielded from the oxidation of aromatics. To better understand the glyoxal spatial-temporal characteristics over China and evaluate the effectiveness of atmospheric prevention efforts on the reduction of volatile organic compound (VOC) emissions, we present an algorithm for glyoxal retrieval using the Ozone Monitoring instrument (OMI) over China. The algorithm is based on the differential optical absorption spectroscopy (DOAS) and accounts for the interference of the tropospheric nitrogen dioxide (NO₂) spatial-temporal distribution on glyoxal retrieval. We conduct a sensitively test based on a synthetic spectrum to optimize the fitting parameters set. It shows that the fitting interval of 430–458 nm and a 4th order polynomial are optimal for glyoxal retrieval when using the daily mean value of the earthshine spectrum in the Pacific region as a reference. In addition, tropospheric NO₂ pre-fitted during glyoxal retrieval is first proposed and tested, which shows a ±10% variation compared with the reference scene. The interference of NO₂ on glyoxal was further investigated based on the OMI observations, and the spatial distribution showed that changes in the NO₂ concentration can affect the glyoxal result depending on the NO₂ spatial distribution. A method to prefix NO₂ during glyoxal retrieval is proposed in this study and is referred to as OMI-CAS. We perform an intercomparison of the glyoxal from the OMI-CAS with the seasonal datasets provided by different institutions for North China (NC), South China (SC), the Yangtze River Delta (YRD) and the ChuanYu (CY) region in southwestern China in the year 2005. The results show that our algorithm can obtain the glyoxal spatial and temporal variations in different regions over China. OMI-CAS has the best correlations with other datasets in summer, with the correlations between OMI-CAS and OMI-Harvard, OMI-CAS and OMI-IUP, and OMI-CAS and Sciamachy-IUP being 0.63, 0.67 and 0.67, respectively. Autumn results followed, with the correlations of 0.58, 0.36 and 0.48, respectively, over China. However, the correlations are less or even negative for spring and winter. From the regional perspective, SC has the best correlation compared with other regions, with R reaching 0.80 for OMI-CAS and OMI-IUP in summer. The discrepancies between different glyoxal datasets can be attributed to the fitting parameters and larger glyoxal retrieval uncertainties. Finally, useful recommendations are given based on the results comparison according to region and season. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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12 pages, 462 KiB

Open AccessFeature PaperLetter

Evaluation of Water Vapor Radiative Effects Using GPS Data Series over Southwestern Europe

by Javier Vaquero-Martínez, Manuel Antón, Arturo Sanchez-Lorenzo and Victoria E. Cachorro

Remote Sens. 2020, 12(8), 1307; https://doi.org/10.3390/rs12081307 - 21 Apr 2020

Cited by 9 | Viewed by 2520

Abstract

Water vapor radiative effects (WVRE) at surface in the long-wave (LW) and short-wave (SW) spectral ranges under cloud and aerosol free conditions are analyzed for seven stations in Spain over the 2007–2015 period. WVRE is calculated as the difference between the net flux [...] Read more.

Water vapor radiative effects (WVRE) at surface in the long-wave (LW) and short-wave (SW) spectral ranges under cloud and aerosol free conditions are analyzed for seven stations in Spain over the 2007–2015 period. WVRE is calculated as the difference between the net flux obtained by two radiative transfer simulations; one with water vapor from Global Positioning System (GPS) measurements and the other one without any water vapor (dry atmosphere). The WVRE in the LW ranges from

107.9 {Wm}^{2}

to

296.7 {Wm}^{- 2}

, while in the SW it goes from

- 64.9 {Wm}^{- 2}

to

- 6.0 {Wm}^{- 2}

. The results show a clear seasonal cycle, which allows the classification of stations in three sub-regions. In general, for total (SW + LW) and LW WVRE, winter (DJF) and spring (MAM) values are lower than summer (JJA) and autumn (SON). However, in the case of SW WVRE, the weaker values are in winter and autumn, and the stronger ones in summer and spring. Positive trends for LW (and total) WVRE may partially explain the well-known increase of surface air temperatures in the study region. Additionally, negative trends for SW WVRE are especially remarkable, since they represent about a quarter of the contribution of aerosols to the strong brightening effect (increase of the SW radiation flux at surface associated with a reduction of the cloud cover and aerosol load) observed since the 2000s in the Iberian Peninsula, but with opposite sign, so it is suggested that water vapor could be partially masking the full magnitude of this brightening. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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Open AccessLetter

Worldwide Evaluation of Ozone Radiative Forcing in the UV-B Range between 1979 and 2014

by David Mateos and Manuel Antón

Remote Sens. 2020, 12(3), 436; https://doi.org/10.3390/rs12030436 - 29 Jan 2020

Cited by 4 | Viewed by 2388

Abstract

Ultraviolet (UV) radiation plays a key role in different planetary mechanisms, thus necessitating a worldwide analysis of this solar spectrum interval. This study offers a worldwide and long-term analysis of ozone radiative forcing (ORF) in the UV-B range between 1979 and 2014. The [...] Read more.

Ultraviolet (UV) radiation plays a key role in different planetary mechanisms, thus necessitating a worldwide analysis of this solar spectrum interval. This study offers a worldwide and long-term analysis of ozone radiative forcing (ORF) in the UV-B range between 1979 and 2014. The method uses monthly total ozone column (TOC) values obtained from the ERA-Interim reanalysis data collection and radiative transfer simulations. A global mean ORF of 0.011 Wm⁻² is obtained, with marked differences between mid-latitude and tropical areas. The mid-latitude belts in the Northern and Southern Hemispheres exhibit the following statistically significant ORF trends between 1982 and 2014 with respect to pre-1980 values: 0.007 Wm⁻² per decade in the 60–45°S belt and around 0.004 Wm⁻² per decade in the 45–30°S and 45–60°N belts. The increase observed in the net UV-B radiation levels at the troposphere might have relevant photochemical effects that impact climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Components and Water Vapor)

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