scholarly journals Polar night retrievals of trace gases in the Arctic using the Extended-range Atmospheric Emitted Radiance Interferometer

2013 ◽  
Vol 6 (1) ◽  
pp. 547-586
Author(s):  
Z. Mariani ◽  
K. Strong ◽  
M. Palm ◽  
R. Lindenmaier ◽  
C. Adams ◽  
...  
Keyword(s):  
Sea Level ◽  
Trace Gases ◽  
The Other ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Polar Night ◽  
Lower Altitude ◽  
Extended Range ◽  
Ch4 And N2o ◽  
Total Column

Abstract. The Extended-range Atmospheric Emitted Radiance Interferometer (E-AERI) was installed at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Nunavut, Canada in October 2008. Spectra from the E-AERI provide information about the radiative balance and budgets of trace gases in the Canadian high Arctic. Measurements are taken every seven minutes year-round, including polar night when the solar-viewing spectrometers at PEARL are not operated. This allows E-AERI measurements to fill the gap in the PEARL dataset during the four months of polar night. Measurements were taken year-round in 2008–2009 at the PEARL Ridge Lab, which is 610 m above sea-level, and from 2011-onwards at the Zero-Altitude PEARL Auxiliary Lab (0PAL), which is 15 km from the Ridge Lab at sea level. Total columns of O3, CO, CH4, and N2O have been retrieved using a modified version of the SFIT2 retrieval algorithm adapted for emission spectra. This provides the first nighttime measurements of these species at Eureka. Changes in the total columns driven by photochemistry and dynamics are observed. Analyses of E-AERI retrievals indicate accurate spectral fits (root-mean-square residuals < 1.5%) and a 10–15% uncertainty in the total column, depending on the trace gas. O3 comparisons between the E-AERI and a Bruker IFS 125HR Fourier transform infrared (FTIR) spectrometer, three Brewer spectrophotometers, two UV-visible ground-based spectrometers, and a System D'Analyse par Observations Zenithales (SAOZ) at PEARL are made from 2008–2009 and for 2011. 125HR CO, CH4, and N2O columns are also compared with the E-AERI measurements. Mean relative differences between the E-AERI and the other spectrometers are 1–14% (depending on the gas), which are less than the E-AERI's total column uncertainties. The E-AERI O3 and CO measurements are well correlated with the other spectrometers; the best correlation is with the 125HR (r > 0.92). The 24-h diurnal cycle and 365-day seasonal cycle of CO are observed and their amplitudes are quantified by the E-AERI (6–12% and 46%, respectively). The seasonal variability of H2O has an impact on the retrievals, leading to larger uncertainties in the summer months. Despite increased water vapour at the lower-altitude site 0PAL, measurements at 0PAL are consistent with measurements at PEARL.

scholarly journals Year-round retrievals of trace gases in the Arctic using the Extended-range Atmospheric Emitted Radiance Interferometer

2013 ◽  
Vol 6 (6) ◽  
pp. 1549-1565 ◽  
Author(s):  
Z. Mariani ◽  
K. Strong ◽  
M. Palm ◽  
R. Lindenmaier ◽  
C. Adams ◽  
...  
Keyword(s):  
Sea Level ◽  
Trace Gases ◽  
The Other ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Polar Night ◽  
Lower Altitude ◽  
Extended Range ◽  
Ch4 And N2o ◽  
Total Column

Abstract. The Extended-range Atmospheric Emitted Radiance Interferometer (E-AERI) was installed at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Nunavut, Canada in October 2008. Spectra from the E-AERI provide information about the radiative balance and budgets of trace gases in the Canadian high Arctic. Measurements are taken every 7 min year-round, including polar night when the solar-viewing spectrometers at PEARL are not operated. This allows E-AERI measurements to fill the gap in the PEARL dataset during the four months of polar night. Measurements were taken year-round in 2008–2009 at the PEARL Ridge Lab, which is 610 m a.s.l. (above sea-level), and from 2011 onwards at the Zero-Altitude PEARL Auxiliary Lab (0PAL), which is at sea level 15 km from the Ridge Lab. Total columns of O3, CO, CH4, and N2O have been retrieved using a modified version of the SFIT2 retrieval algorithm adapted for emission spectra. This provides the first ground-based nighttime measurements of these species at Eureka. Changes in the total columns driven by photochemistry and dynamics are observed. Analyses of E-AERI retrievals indicate accurate spectral fits (root-mean-square residuals consistent with noise) and a 10–15% uncertainty in the total column, depending on the trace gas. O3 comparisons between the E-AERI and a Bruker IFS 125HR Fourier transform infrared (FTIR) spectrometer, three Brewer spectrophotometers, two UV-visible ground-based spectrometers, and a System D'Analyse par Observations Zenithales (SAOZ) at PEARL are made from 2008–2009 and for 2011. 125HR CO, CH4, and N2O columns are also compared with the E-AERI measurements. Mean relative differences between the E-AERI and the other spectrometers are 1–10% (14% is for the un-smoothed profiles), which are less than the E-AERI's total column uncertainties. The E-AERI O3 and CO measurements are well correlated with the other spectrometers (r > 0.92 with the 125HR). The 24 h diurnal cycle and 365-day seasonal cycle of CO are observed and their amplitudes are quantified by the E-AERI (6–12 and 46%, respectively). The seasonal variability of H2O has an impact on the retrievals, leading to larger uncertainties in the summer months. Despite increased water vapour at the lower-altitude site 0PAL, measurements at 0PAL are consistent with measurements at PEARL.

A Remarkable Array: Species Diversity in the United States

2000 ◽  
Author(s):  
Bruce A. Stein ◽  
Larry E. Morse
Keyword(s):  
United States ◽  
North America ◽  
Sea Level ◽  
Eastern China ◽  
The United States ◽  
The Other ◽  
Death Valley ◽  
The Arctic ◽  
Blue Ridge Mountains ◽  
Little Tennessee River

The Carolina hemlock (Tsuga caroliniana) survives in just a few rocky streambeds along the lower slopes of the Blue Ridge Mountains. Other species of hemlock abound across the United States, but none bear a close resemblance to this particular tree. The closest relatives of the Carolina hemlock, in fact, survive in only one other forest on Earth, some 7,000 miles away in Hubei province of eastern China. The forests of eastern Asia and eastern North America are so similar that if you were suddenly transported from one to the other, you would be hard-pressed to tell them apart. In the swift mountain streams rushing past these seemingly displaced hemlocks live a number of small, colorful fish known as darters. Darters are found only in North America and have evolved into a prolific variety of fishes. Up to 175 species inhabit U.S. waters, including the famous snail darter (Percina tanasi), which brought endangered species issues to the fore when it held up construction of the Tellico Dam on the Little Tennessee River. How is it that these two organisms, hemlock and darter, one with its closest relatives on the other side of the globe and the other found nowhere else in the world, came to be living side by side? Just how many plants and animals share the piece of Earth that we know as the United States of America? Why these and not others? These are central questions for understanding the diversity of the nation’s living resources. The United States encompasses an enormous piece of geography. With more than 3.5 million square miles of land and 12,000 miles of coastline, it is the fourth largest country on Earth, surpassed only by Russia, Canada, and China. The nation spans nearly a third of the globe, extending more than 120 degrees of longitude from eastern Maine to the tip of the Aleutian chain, and 50 degrees in latitude from Point Barrow above the Arctic Circle to the southern tip of Hawaii below the tropic of Cancer. This expanse of terrain includes an exceptional variety of topographic features, from Death Valley at 282 feet below sea level to Mt. McKinley at 20,320 feet above sea level.

scholarly journals Comparison of total column ozone obtained by the IASI-MetOp satellite with ground-based and OMI satellite observations in the southern tropics and subtropics

2015 ◽  
Vol 33 (9) ◽  
pp. 1135-1146 ◽  
Author(s):  
A. M. Toihir ◽  
H. Bencherif ◽  
V. Sivakumar ◽  
L. El Amraoui ◽  
T. Portafaix ◽  
...  
Keyword(s):  
The Other ◽  
Retrieval Algorithm ◽  
Bias Error ◽  
Ozone Monitoring Instrument ◽  
Total Column Ozone ◽  
European Organization ◽  
Comparison Results ◽  
Column Ozone ◽  
Meteorological Satellite ◽  
Total Column

Abstract. This paper presents comparison results of the total column ozone (TCO) data product over 13 southern tropical and subtropical sites recorded from the Infrared Atmospheric Sounder Interferometer (IASI) onboard the EUMETSAT (European organization for the exploitation of METeorological SATellite) MetOp (Meteorological Operational satellite program) satellite. TCO monthly averages obtained from IASI between June 2008 and December 2012 are compared with collocated TCO measurements from the Ozone Monitoring Instrument (OMI) on the OMI/Aura satellite and the Dobson and SAOZ (Système d'Analyse par Observation Zénithale) ground-based instruments. The results show that IASI displays a positive bias with an average less than 2 % with respect to OMI and Dobson observations, but exhibits a negative bias compared to SAOZ over Bauru with a bias around 2.63 %. There is a good agreement between IASI and the other instruments, especially from 15° S southward where a correlation coefficient higher than 0.87 is found. IASI exhibits a seasonal dependence, with an upward trend in autumn and a downward trend during spring, especially before September 2010. After September 2010, the autumn seasonal bias is considerably reduced due to changes made to the retrieval algorithm of the IASI level 2 (L2) product. The L2 product released after August (L2 O3 version 5 (v5)) matches TCO from the other instruments better compared to version 4 (v4), which was released between June 2008 and August 2010. IASI bias error recorded from September 2010 is estimated to be at 1.5 % with respect to OMI and less than ±1 % with respect to the other ground-based instruments. Thus, the improvement made by O3 L2 version 5 (v5) product compared with version 4 (v4), allows IASI TCO products to be used with confidence to study the distribution and interannual variability of total ozone in the southern tropics and subtropics.

Note on Selection of Coordinate Systems for Geodynamics

1975 ◽  
Vol 26 ◽  
pp. 395-407
Author(s):  
S. Henriksen
Keyword(s):  
Sea Level ◽  
The Other ◽  
Coordinate Systems ◽  
Mean Sea Level ◽  
The Earth ◽  
The One ◽  
Static Systems ◽  
Selection Of

The first question to be answered, in seeking coordinate systems for geodynamics, is: what is geodynamics? The answer is, of course, that geodynamics is that part of geophysics which is concerned with movements of the Earth, as opposed to geostatics which is the physics of the stationary Earth. But as far as we know, there is no stationary Earth – epur sic monere. So geodynamics is actually coextensive with geophysics, and coordinate systems suitable for the one should be suitable for the other. At the present time, there are not many coordinate systems, if any, that can be identified with a static Earth. Certainly the only coordinate of aeronomic (atmospheric) interest is the height, and this is usually either as geodynamic height or as pressure. In oceanology, the most important coordinate is depth, and this, like heights in the atmosphere, is expressed as metric depth from mean sea level, as geodynamic depth, or as pressure. Only for the earth do we find “static” systems in use, ana even here there is real question as to whether the systems are dynamic or static. So it would seem that our answer to the question, of what kind, of coordinate systems are we seeking, must be that we are looking for the same systems as are used in geophysics, and these systems are dynamic in nature already – that is, their definition involvestime.

scholarly journals A Proof-of-Concept Algorithm for the Retrieval of Total Column Amount of Trace Gases in a Multi-Dimensional Atmosphere

2021 ◽  
Vol 13 (2) ◽  
pp. 270
Author(s):  
Adrian Doicu ◽  
Dmitry S. Efremenko ◽  
Thomas Trautmann
Keyword(s):  
Trace Gases ◽  
Three Dimensional ◽  
Principal Component ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Proof Of Concept ◽  
Discrete Ordinate ◽  
Distribution Method ◽  
Total Column

An algorithm for the retrieval of total column amount of trace gases in a multi-dimensional atmosphere is designed. The algorithm uses (i) certain differential radiance models with internal and external closures as inversion models, (ii) the iteratively regularized Gauss–Newton method as a regularization tool, and (iii) the spherical harmonics discrete ordinate method (SHDOM) as linearized radiative transfer model. For efficiency reasons, SHDOM is equipped with a spectral acceleration approach that combines the correlated k-distribution method with the principal component analysis. The algorithm is used to retrieve the total column amount of nitrogen for two- and three-dimensional cloudy scenes. Although for three-dimensional geometries, the computational time is high, the main concepts of the algorithm are correct and the retrieval results are accurate.

scholarly journals The other side of sea level change

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Matthias Prange ◽  
Thomas Wilke ◽  
Frank P. Wesselingh
Keyword(s):  
Sea Level ◽  
Sea Level Change ◽  
The Other ◽  
Level Change

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

2014 ◽  
Vol 7 (3) ◽  
pp. 3021-3073 ◽  
Author(s):  
M. Grossi ◽  
P. Valks ◽  
D. Loyola ◽  
B. Aberle ◽  
S. Slijkhuis ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Vertical Column Density ◽  
Data Processor ◽  
Climate Analysis ◽  
Total Column

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

scholarly journals Chemical ozone loss and ozone mini-hole event during the Arctic winter 2010/2011 as observed by SCIAMACHY and GOME-2

2014 ◽  
Vol 14 (7) ◽  
pp. 3247-3276 ◽  
Author(s):  
R. Hommel ◽  
K.-U. Eichmann ◽  
J. Aschmann ◽  
K. Bramstedt ◽  
M. Weber ◽  
...  
Keyword(s):  
Transport Model ◽  
Polar Vortex ◽  
The Arctic ◽  
Optical Absorption Spectroscopy ◽  
Late Winter ◽  
Total Column Ozone ◽  
Bromine Oxide ◽  
Ozone Loss ◽  
Catalytic Cycles ◽  
Total Column

Abstract. Record breaking loss of ozone (O3) in the Arctic stratosphere has been reported in winter–spring 2010/2011. We examine in detail the composition and transformations occurring in the Arctic polar vortex using total column and vertical profile data products for O3, bromine oxide (BrO), nitrogen dioxide (NO2), chlorine dioxide (OClO), and polar stratospheric clouds (PSC) retrieved from measurements made by SCIAMACHY (Scanning Imaging Absorption SpectroMeter for Atmospheric CHartography) on-board Envisat (Environmental Satellite), as well as total column ozone amount, retrieved from the measurements of GOME-2 (Global Ozone Monitoring Experiment) on MetOp-A (Meteorological Experimental Satellite). Similarly we use the retrieved data from DOAS (Differential Optical Absorption Spectroscopy) measurements made in Ny-Ålesund (78.55° N, 11.55° E). A chemical transport model (CTM) has been used to relate and compare Arctic winter–spring conditions in 2011 with those in the previous year. In late winter–spring 2010/2011 the chemical ozone loss in the polar vortex derived from SCIAMACHY observations confirms findings reported elsewhere. More than 70% of O3 was depleted by halogen catalytic cycles between the 425 and 525 K isentropic surfaces, i.e. in the altitude range ~16–20 km. In contrast, during the same period in the previous winter 2009/2010, a typical warm Arctic winter, only slightly more than 20% depletion occurred below 20 km, while 40% of O3 was removed above the 575 K isentrope (~23 km). This loss above 575 K is explained by the catalytic destruction by NOx descending from the mesosphere. In both Arctic winters 2009/2010 and 2010/2011, calculated O3 losses from the CTM are in good agreement to our observations and other model studies. The mid-winter 2011 conditions, prior to the catalytic cycles being fully effective, are also investigated. Surprisingly, a significant loss of O3 around 60%, previously not discussed in detail, is observed in mid-January 2011 below 500 K (~19 km) and sustained for approximately 1 week. The low O3 region had an exceptionally large spatial extent. The situation was caused by two independently evolving tropopause elevations over the Asian continent. Induced adiabatic cooling of the stratosphere favoured the formation of PSC, increased the amount of active chlorine for a short time, and potentially contributed to higher polar ozone loss later in spring.

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