The global tropospheric ammonia distribution as seen in the 13-year AIRS  measurement record

Abstract. Ammonia (NH3) plays an increasingly important role in the global biogeochemical cycle of reactive nitrogen as well as in aerosol formation and climate. We present extensive and nearly continuous global ammonia measurements made by the Atmospheric Infrared Sounder (AIRS) from the Aqua satellite to identify and quantify major persistent and episodic sources as well as to characterize seasonality. We examine the 13-year period from September 2002 through August 2015 with a retrieval algorithm using an optimal estimation technique with a set of three, spatially and temporally uniform a priori profiles. Vertical profiles show good agreement (∼  5–15 %) between AIRS NH3 and the in situ profiles from the winter 2013 DISCOVER-AQ (DISCOVER-Air Quality) field campaign in central California, despite the likely biases due to spatial resolution differences between the two instruments. The AIRS instrument captures the strongest consistent NH3 concentrations due to emissions from the anthropogenic (agricultural) source regions, such as South Asia (India/Pakistan), China, the United States (US), parts of Europe, Southeast (SE) Asia (Thailand/Myanmar/Laos), the central portion of South America, as well as Western and Northern Africa. These correspond primarily to irrigated croplands, as well as regions with heavy precipitation, with extensive animal feeding operations and fertilizer applications where a summer maximum and a secondary spring maximum are reliably observable. In the Southern Hemisphere (SH) regular agricultural fires contribute to a spring maximum. Regions of strong episodic emissions include Russia and Alaska as well as parts of South America, Africa, and Indonesia. Biomass burning, especially wildfires, dominate these episodic NH3 high concentrations.

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The global tropospheric ammonia distribution as seen in the 13 year AIRS measurement record

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-35823-2015 ◽

2015 ◽

Vol 15 (24) ◽

pp. 35823-35856 ◽

Cited By ~ 10

Author(s):

J. X. Warner ◽

Z. Wei ◽

L. L. Strow ◽

R. R. Dickerson ◽

J. B. Nowak

Keyword(s):

South America ◽

A Priori ◽

Optimal Estimation ◽

Retrieval Algorithm ◽

Aerosol Formation ◽

Atmospheric Infrared Sounder ◽

Animal Feeding Operations ◽

Source Regions ◽

Summer Maximum ◽

Spring Maximum

Abstract. Ammonia (NH3) plays an increasingly important role in the global biogeochemical cycle of reactive nitrogen as well as in aerosol formation and climate. We present extensive and nearly continuous global ammonia measurements made by the Atmospheric Infrared Sounder (AIRS) from the Aqua satellite to identify and quantify major persistent and episodic sources as well as to characterize seasonality. We examine the 13 year period from September 2002 through August 2015 with a retrieval algorithm using an optimal estimation technique with a set of three, spatially and temporally uniform a priori profiles. Vertical profiles show good agreement (~5–15 %) between AIRS NH3 and the in situ profiles from the winter 2013 DISCOVER-AQ field campaign in central California, despite the likely biases due to spatial resolution differences between the two instruments. AIRS captures the strongest consistent NH3 emissions from the anthropogenic (agricultural) source regions, such as, South Asia (India/Pakistan), China, the US, parts of Europe, SE Asia (Thailand/Myanmar/Laos), the central portion of South America, as well as Western and Northern Africa. These correspond primarily to croplands with extensive animal feeding operations and fertilizer applications where a summer maximum and secondary spring maximum are reliably observable. In the Southern Hemisphere (SH) regular agricultural fires contribute to a spring maximum. Regions of strong episodic emissions include Russia and Alaska as well as parts of South America, Africa, and Indonesia. Biomass burning, especially wildfires, dominate these episodic NH3 emissions.

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The GRAPE aerosol retrieval algorithm

Atmospheric Measurement Techniques ◽

10.5194/amt-2-679-2009 ◽

2009 ◽

Vol 2 (2) ◽

pp. 679-701 ◽

Cited By ~ 54

Author(s):

G. E. Thomas ◽

C. A. Poulsen ◽

A. M. Sayer ◽

S. H. Marsh ◽

S. M. Dean ◽

...

Keyword(s):

A Priori ◽

Optimal Estimation ◽

Effective Radius ◽

Retrieval Algorithm ◽

Surface Reflectance ◽

Data Set ◽

Cloud Parameters ◽

Aerosol Retrieval ◽

Retrieval Scheme ◽

Theoretical Performance

Abstract. The aerosol component of the Oxford-Rutherford Aerosol and Cloud (ORAC) combined cloud and aerosol retrieval scheme is described and the theoretical performance of the algorithm is analysed. ORAC is an optimal estimation retrieval scheme for deriving cloud and aerosol properties from measurements made by imaging satellite radiometers and, when applied to cloud free radiances, provides estimates of aerosol optical depth at a wavelength of 550 nm, aerosol effective radius and surface reflectance at 550 nm. The aerosol retrieval component of ORAC has several incarnations – this paper addresses the version which operates in conjunction with the cloud retrieval component of ORAC (described by Watts et al., 1998), as applied in producing the Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) data-set. The algorithm is described in detail and its performance examined. This includes a discussion of errors resulting from the formulation of the forward model, sensitivity of the retrieval to the measurements and a priori constraints, and errors resulting from assumptions made about the atmospheric/surface state.

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Five years of CO, HCN, C2H6, C2H2, CH3OH, HCOOH and H2CO total columns measured in the Canadian high Arctic

Atmospheric Measurement Techniques ◽

10.5194/amt-7-1547-2014 ◽

2014 ◽

Vol 7 (6) ◽

pp. 1547-1570 ◽

Cited By ~ 22

Author(s):

C. Viatte ◽

K. Strong ◽

K. A. Walker ◽

J. R. Drummond

Keyword(s):

Time Series ◽

Fourier Transform ◽

Biomass Burning ◽

Atmospheric Chemistry ◽

A Priori ◽

Estimation Method ◽

Optimal Estimation ◽

High Arctic ◽

Retrieval Algorithm ◽

Data Set

Abstract. We present a five-year time series of seven tropospheric species measured using a ground-based Fourier transform infrared (FTIR) spectrometer at the Polar Environment Atmospheric Research Laboratory (PEARL; Eureka, Nunavut, Canada; 80°05' N, 86°42' W) from 2007 to 2011. Total columns and temporal variabilities of carbon monoxide (CO), hydrogen cyanide (HCN) and ethane (C2H6) as well as the first derived total columns at Eureka of acetylene (C2H2), methanol (CH3OH), formic acid (HCOOH) and formaldehyde (H2CO) are investigated, providing a new data set in the sparsely sampled high latitudes. Total columns are obtained using the SFIT2 retrieval algorithm based on the optimal estimation method. The microwindows as well as the a priori profiles and variabilities are selected to optimize the information content of the retrievals, and error analyses are performed for all seven species. Our retrievals show good sensitivities in the troposphere. The seasonal amplitudes of the time series, ranging from 34 to 104%, are captured while using a single a priori profile for each species. The time series of the CO, C2H6 and C2H2 total columns at PEARL exhibit strong seasonal cycles with maxima in winter and minima in summer, in opposite phase to the HCN, CH3OH, HCOOH and H2CO time series. These cycles result from the relative contributions of the photochemistry, oxidation and transport as well as biogenic and biomass burning emissions. Comparisons of the FTIR partial columns with coincident satellite measurements by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) show good agreement. The correlation coefficients and the slopes range from 0.56 to 0.97 and 0.50 to 3.35, respectively, for the seven target species. Our new data set is compared to previous measurements found in the literature to assess atmospheric budgets of these tropospheric species in the high Arctic. The CO and C2H6concentrations are consistent with negative trends observed over the Northern Hemisphere, attributed to fossil fuel emission decrease. The importance of poleward transport for the atmospheric budgets of HCN and C2H2 is highlighted. Columns and variabilities of CH3OH and HCOOH at PEARL are comparable to previous measurements performed at other remote sites. However, the small columns of H2CO in early May might reflect its large atmospheric variability and/or the effect of the updated spectroscopic parameters used in our retrievals. Overall, emissions from biomass burning contribute to the day-to-day variabilities of the seven tropospheric species observed at Eureka.

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A Rain-Rate Retrieval Algorithm for Attenuated Radar Measurements

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2279.1 ◽

2010 ◽

Vol 49 (3) ◽

pp. 381-393 ◽

Cited By ~ 12

Author(s):

Prabhat K. Koner ◽

Alessandro Battaglia ◽

Clemens Simmer

Keyword(s):

Rain Rate ◽

Least Squares Method ◽

A Priori ◽

Estimation Method ◽

Optimal Estimation ◽

Retrieval Algorithm ◽

W Band ◽

Regularization Techniques ◽

Radar Measurements ◽

Regularized Total Least Squares

Abstract A dynamic regularization scheme for rain-rate retrievals from attenuated radar measurements is presented. Most regularization techniques, including the optimal estimation method, use the state-space parameters to regularize the problem, which will always lead to a bias in the solution. To avoid this problem the authors introduce an evolutionary regularization technique, which is based on the spatial derivative of the measured reflectivity profile and allows for a bias-free global solution. The regularization strength is determined by the quadratic eigenvalue solution using the regularized total least squares method. With the new method, the authors perform a retrieval of rain-rate profiles from simulated measurements of a nadir-pointing W-band (94 GHz) radar, in a configuration similar to the cloud radar employed on CloudSat. The simulations assume that multiple scattering is negligible and only liquid hydrometeors are taken into account. The authors compare the results of this method with the outcome of an optimal estimation method and demonstrate that their method is superior in terms of reliability, correlation coefficient, and dispersion to the optimal estimation method for layers experiencing high values of attenuation; therefore, the a priori bias typical for optimal estimation solutions is avoided.

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Retrieval of macrophysical cloud parameters from MIPAS: algorithm description

Atmospheric Measurement Techniques ◽

10.5194/amt-4-683-2011 ◽

2011 ◽

Vol 4 (4) ◽

pp. 683-704 ◽

Cited By ~ 17

Author(s):

J. Hurley ◽

A. Dudhia ◽

R. G. Grainger

Keyword(s):

A Priori ◽

Michelson Interferometer ◽

Optimal Estimation ◽

Single Scattering ◽

Stratospheric Aerosol ◽

Retrieval Algorithm ◽

Cloud Detection ◽

Retrieval Process ◽

Error Sources ◽

Cloud Parameters

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT has the potential to be particularly useful for studying high, thin clouds, which have been difficult to observe in the past. This paper details the development, implementation and testing of an optimal-estimation-type retrieval for three macrophysical cloud parameters (cloud top height, cloud top temperature and cloud extinction coefficient) from infrared spectra measured by MIPAS. A preliminary estimation of a parameterisation of the optical and geometrical filling of the measurement field-of-view by cloud is employed as the first step of the retrieval process to improve the choice of a priori for the macrophysical parameters themselves. Preliminary application to single-scattering simulations indicates that the retrieval error stemming from uncertainties introduced by noise and by a priori variances in the retrieval process itself is small – although it should be noted that these retrieval errors do not include the significant errors stemming from the assumption of homogeneity and the non-scattering nature of the forward model. Such errors are preliminarily and qualitatively assessed here, and are likely to be the dominant error sources. The retrieval converges for 99% of input cases, although sometimes fails to converge for vetically-thin (<1 km) clouds. The retrieval algorithm is applied to MIPAS data; the results of which are qualitatively compared with CALIPSO cloud top heights and PARASOL cloud opacities. From comparison with CALIPSO cloud products, it must be noted that the cloud detection method used in this algorithm appears to potentially misdetect stratospheric aerosol layers as cloud. This algorithm has been adopted by the European Space Agency's "MIPclouds" project.

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Technical Note: Temporal change in averaging kernels as a source of uncertainty in trend estimates of carbon monoxide retrieved from MOPITT

Atmospheric Chemistry and Physics ◽

10.5194/acp-13-11307-2013 ◽

2013 ◽

Vol 13 (22) ◽

pp. 11307-11316 ◽

Cited By ~ 12

Author(s):

J. Yoon ◽

A. Pozzer ◽

P. Hoor ◽

D. Y. Chang ◽

S. Beirle ◽

...

Keyword(s):

Carbon Monoxide ◽

Atmospheric Chemistry ◽

Temporal Change ◽

A Priori ◽

Optimal Estimation ◽

Technical Note ◽

Retrieval Algorithm ◽

Near Surface ◽

Surface Level ◽

Surface Carbon

Abstract. It has become possible to retrieve the global, long-term trends of trace gases that are important to atmospheric chemistry, climate, and air quality from satellite data records that span more than a decade. However, many of the satellite remote sensing techniques produce measurements that have variable sensitivity to the vertical profiles of atmospheric gases. In the case of constrained retrievals like optimal estimation, this leads to a varying amount of a priori information in the retrieval and is represented by an averaging kernel (AK). In this study, we investigate to what extent the estimation of trends from retrieved data can be biased by temporal changes of averaging kernels used in the retrieval algorithm. In particular, the surface carbon monoxide data retrieved from the Measurements Of Pollution In The Troposphere (MOPITT) instrument from 2001 to 2010 were analyzed. As a practical example based on the MOPITT data, we show that if the true atmospheric mixing ratio is continuously 50% higher or lower than the a priori state, the temporal change of the averaging kernel at the surface level gives rise to an artificial trend in retrieved surface carbon monoxide, ranging from −10.71 to +13.21 ppbv yr−1 (−5.68 to +8.84 % yr−1) depending on location. Therefore, in the case of surface (or near-surface level) CO derived from MOPITT, the AKs trends multiplied by the difference between true and a priori states must be quantified in order to estimate trend biases.

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The GRAPE aerosol retrieval algorithm

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-2-981-2009 ◽

2009 ◽

Vol 2 (2) ◽

pp. 981-1026 ◽

Cited By ~ 5

Author(s):

G. E. Thomas ◽

C. A. Poulsen ◽

A. M. Sayer ◽

S. H. Marsh ◽

S. M. Dean ◽

...

Keyword(s):

A Priori ◽

Optimal Estimation ◽

Effective Radius ◽

Retrieval Algorithm ◽

Surface Reflectance ◽

Data Set ◽

Cloud Parameters ◽

Aerosol Retrieval ◽

Retrieval Scheme ◽

Theoretical Performance

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Five years of CO, HCN, C2H6, C2H2, CH3OH, HCOOH, and H2CO total columns measured in the Canadian High Arctic

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-11345-2013 ◽

2013 ◽

Vol 6 (6) ◽

pp. 11345-11403

Author(s):

C. Viatte ◽

K. Strong ◽

K. A. Walker ◽

J. R. Drummond

Keyword(s):

Fourier Transform ◽

Biomass Burning ◽

Atmospheric Chemistry ◽

A Priori ◽

Estimation Method ◽

Correlation Coefficients ◽

Optimal Estimation ◽

High Arctic ◽

Retrieval Algorithm ◽

Atmospheric Variability

Abstract. We present a five-year timeseries of seven tropospheric species measured using a ground-based Fourier Transform InfraRed (FTIR) spectrometer at the Polar Environment Atmospheric Research Laboratory (PEARL, Eureka, Nunavut, Canada, 80°05' N, 86°42' W) from 2007 to 2011. Total columns and temporal variabilities of carbon monoxide (CO), hydrogen cyanide (HCN), and ethane (C2H6), as well as the first derived total columns at Eureka of acetylene (C2H2), methanol (CH3OH), formic acid (HCOOH), and formaldehyde (H2CO) are investigated, providing a new dataset in the sparsely sampled high latitudes. Total columns are obtained using the SFIT2 retrieval algorithm based on the Optimal Estimation Method. The microwindows, as well as the a priori profiles and variabilities are selected to optimize the information content of the retrievals, and error analyses are performed for all seven species. Our retrievals show good sensitivities in the troposphere. The seasonal amplitudes of the timeseries, ranging from 34 to 104%, are captured while using a single a priori profile for each species. The timeseries of the CO, C2H6 and C2H2 total columns at PEARL exhibit strong seasonal cycles with maxima in winter and minima in summer, in opposite phase to the HCN, CH3OH, HCOOH and H2CO timeseries. These cycles result from the relative contributions of the photochemistry, oxidation, and transport, as well as biogenic and biomass burning emissions. Comparisons of the FTIR partial columns with coincident satellite measurements by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) show good agreement. The correlation coefficients and the slopes range from 0.56 to 0.97, and 0.50 to 3.35, respectively, for the seven target species. Our new dataset is compared with previous measurements found in the literature to assess atmospheric budgets of these tropospheric species in the high Arctic. The CO and C2H6 concentrations are consistent with negative trends observed over the Northern Hemisphere, attributed to fossil fuel emission decrease. The importance of poleward transport on the atmospheric budgets of HCN and C2H2 is highlighted. Columns and variabilities of CH3OH, and HCOOH at PEARL are comparable to previous measurements performed at other remote sites. However, the small columns of H2CO in early May might reflect its large atmospheric variability, and/or the effect of the updated spectroscopic parameters used in our retrievals. Overall, emissions from biomass burning contribute to the day-to-day variabilities of the seven tropospheric species observed at Eureka.

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Technical Note: Temporal change in averaging kernels as a source of uncertainty in trend estimates of carbon monoxide retrieved from MOPITT

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-20319-2013 ◽

2013 ◽

Vol 13 (8) ◽

pp. 20319-20340

Author(s):

J. Yoon ◽

A. Pozzer ◽

P. Hoor ◽

D. Y. Chang ◽

S. Beirle ◽

...

Keyword(s):

Carbon Monoxide ◽

Atmospheric Chemistry ◽

Temporal Change ◽

A Priori ◽

Optimal Estimation ◽

Technical Note ◽

Retrieval Algorithm ◽

Near Surface ◽

Surface Level ◽

Surface Carbon

Abstract. It is now possible to monitor the global and long-term trends of trace gases that are important to atmospheric chemistry, climate, and air quality with satellite data records that span more than a decade. However, many of the remote sensing techniques used by satellite instruments produce measurements that have variable sensitivity to the vertical profiles of atmospheric gases. In the case of constrained retrievals like optimal estimation, this leads to a varying amount of a priori information in the retrieval and is represented by an averaging kernel. In this study, we investigate to what extent such trends can be biased by temporal changes of averaging kernels used in the retrieval algorithm. In particular, the surface carbon monoxide data retrieved from the Measurements Of Pollution In The Troposphere (MOPITT) instrument from 2001 to 2010 were analysed. As a practical example based on the MOPITT data, we show that if the true atmospheric mixing ratio is continuously 50% higher or lower than the a priori state, the temporal change of the averaging kernel at the surface level gives rise to an artificial trend in retrieved surface carbon monoxide, ranging from −10.71 to +13.21 ppbv yr−1 (−5.68 to +8.84% yr−1) depending on location. Therefore, in the case of surface (or near-surface level) CO derived from MOPITT, the AKs trends multiplied by the difference between true and a priori states must be quantified in order to estimate trend biases.

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HCOOH measurements from space: TES retrieval algorithm and observed global distribution

Atmospheric Measurement Techniques ◽

10.5194/amt-7-2297-2014 ◽

2014 ◽

Vol 7 (7) ◽

pp. 2297-2311 ◽

Cited By ~ 21

Author(s):

K. E. Cady-Pereira ◽

S. Chaliyakunnel ◽

M. W. Shephard ◽

D. B. Millet ◽

M. Luo ◽

...

Keyword(s):

Degrees Of Freedom ◽

Transport Model ◽

A Priori ◽

Optimal Estimation ◽

Initial Guess ◽

Global Distribution ◽

Retrieval Algorithm ◽

Vertical Resolution ◽

Chemical Transport Model ◽

Thermal Contrast

Abstract. Presented is a detailed description of the TES (Tropospheric Emission Spectrometer)-Aura satellite formic acid (HCOOH) retrieval algorithm and initial results quantifying the global distribution of tropospheric HCOOH. The retrieval strategy, including the optimal estimation methodology, spectral microwindows, a priori constraints, and initial guess information, are provided. A comprehensive error and sensitivity analysis is performed in order to characterize the retrieval performance, degrees of freedom for signal, vertical resolution, and limits of detection. These results show that the TES HCOOH retrievals (i) typically provide at best 1.0 pieces of information; (ii) have the most vertical sensitivity in the range from 900 to 600 hPa with ~ 2 km vertical resolution; (iii) require at least 0.5 ppbv (parts per billion by volume) of HCOOH for detection if thermal contrast is greater than 5 K, and higher concentrations as thermal contrast decreases; and (iv) based on an ensemble of simulated retrievals, are unbiased with a standard deviation of ±0.4 ppbv. The relative spatial distribution of tropospheric HCOOH derived from TES and its associated seasonality are broadly correlated with predictions from a state-of-the-science chemical transport model (GEOS-Chem CTM). However, TES HCOOH is generally higher than is predicted by GEOS-Chem, and this is in agreement with recent work pointing to a large missing source of atmospheric HCOOH. The model bias is especially pronounced in summertime and over biomass burning regions, implicating biogenic emissions and fires as key sources of the missing atmospheric HCOOH in the model.

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The global tropospheric ammonia distribution as seen in the 13-year AIRS measurement record