Ground-Based Temperature and Humidity Profiling Using Spectral Infrared and Microwave Observations. Part I: Simulated Retrieval Performance in Clear-Sky Conditions

2009 ◽  
Vol 48 (5) ◽  
pp. 1017-1032 ◽  
Author(s):  
Ulrich Löhnert ◽  
D. D. Turner ◽  
S. Crewell
Keyword(s):  
Optimal Estimation ◽  
Retrieval Algorithm ◽  
Microwave Measurements ◽  
Tropospheric Temperature ◽  
Clear Sky ◽  
Infrared Measurements ◽  
Temperature And Humidity ◽  
Retrieval Scheme ◽  
Estimation Scheme ◽  
Temperature Profiler

Abstract Two independent ground-based passive remote sensing methods are used to retrieve lower-tropospheric temperature and humidity profiles in clear-sky cases. A simulation study for two distinctly different climatic zones is performed to evaluate the accuracies of a standard microwave profiler [humidity and temperature profiler (HATPRO)] and an infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] by applying a unified optimal estimation scheme to each instrument. Different measurement modes for each instrument are also evaluated in which the retrieval uses different spectral channels and observational view angles. In addition, both instruments have been combined into the same physically consistent retrieval scheme to evaluate the differences between a combined retrieval relative to the single-instrument retrievals. In general, retrievals derived from only infrared measurements yield superior RMS error and bias to retrievals derived only from microwave measurements. The AERI retrievals show high potential, especially for retrieving humidity in the boundary layer, where accuracies are on the order of 0.25–0.5 g m−3 for a central European climate. In the lowest 500 m the retrieval accuracies for temperature from elevation-scanning microwave measurements and spectral infrared measurements are very similar (0.2–0.6 K). Above this level the accuracies of the AERI retrieval are significantly more accurate (<1 K RMSE below 4 km). The inclusion of microwave measurements to the spectral infrared measurements within a unified physical retrieval scheme only results in improvements in the high-humidity tropical climate. However, relative to the HATPRO retrieval, the accuracy of the AERI retrieval is more sensitive to changes in the measurement uncertainty. The discussed results are drawn from a subset of “pristine” clear-sky cases: in the general case in which clouds and aerosols are present, the combined HATPRO–AERI retrieval algorithm is expected to yield much more beneficial results.

scholarly journals The GRAPE aerosol retrieval algorithm

2009 ◽  
Vol 2 (2) ◽  
pp. 679-701 ◽  
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.

scholarly journals Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3 μm reflectance measurements

2017 ◽  
Author(s):  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Philippe Nédélec ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Processing ◽  
Pearson Correlation ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Validation Data ◽  
Data Set ◽  
Airborne Measurements ◽  
Clear Sky ◽  
Infrared Measurements

Abstract. We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311–2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data was processed with the Shortwave Infrared CO Retrieval algorithm SICOR that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA’s Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites. For Ny-Ålesund, Lauder, Mauna Loa, and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. This improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6.0 ppb and a strong correlation between the validation data set and the SCIAMACHY data sets with a mean Pearson correlation coefficient r = 0.7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set, which is less sensitive to the spatial representativeness of the satellite and validation measurement. For example, at the cities Teheran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171.2 ppb and 57.9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. The validation improves significantly for cloudy sky retrievals with b = 52.3 ppb and 5.0 ppb, respectively. This is due to a reduced retrieval sensitivity to CO below the cloud and so to the altitude range, which is mostly affected by strong local surface emissions. At the less urbanized region around the airportWindhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15.5 ppb, but can be even further improved considering cloudy SCIAMACHY observations with a mean CO bias of b = 0.2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short wave infrared measurements present a valuable addition to the clear-sky only data set. Moreover, the study represents the first application of the S5P algorithm for operational CO data processing on cloudy observations prior to the launch of the S5P mission.

scholarly journals A New Algorithm for the Retrieval of Atmospheric Profiles from GNSS Radio Occultation Data in Moist Air and Comparison to 1DVar Retrievals

2019 ◽  
Vol 11 (23) ◽  
pp. 2729 ◽  
Author(s):  
Li ◽  
Kirchengast ◽  
Scherllin-Pirscher ◽  
Schwaerz ◽  
Nielsen ◽  
...  
Keyword(s):  
Radio Occultation ◽  
Optimal Estimation ◽  
Satellite System ◽  
Temporal Variations ◽  
Uncertainty Estimation ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
Moist Air ◽  
Temperature And Humidity ◽  
Humidity Profiles

The Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a key technique for obtaining thermodynamic profiles of temperature, humidity, pressure, and density in the Earth’s troposphere. However, due to refraction effects of both the dry air and water vapor at low altitudes, retrieval of accurate profiles is challenging. Here we introduce a new moist air retrieval algorithm aiming to improve the quality of RO-retrieved profiles in moist air and including uncertainty estimation in a clear sequence of steps. The algorithm first uses RO dry temperature and pressure and background temperature/humidity and their uncertainties to retrieve humidity/temperature and their uncertainties. These temperature and humidity profiles are then combined with their corresponding background profiles by optimal estimation employing inverse-variance weighting. Finally, based on the optimally estimated temperature and humidity profiles, pressure and density profiles are computed using hydrostatic and equation-of-state formulas. The input observation and background uncertainties are dynamically estimated, accounting for spatial and temporal variations. We show results from applying the algorithm on test datasets, deriving insights from both individual profiles and statistical ensembles, and from comparison to independent 1D-Variational (1DVar) algorithm-derived moist air retrieval results from Radio Occultation Meteorology Satellite Application Facility Copenhagen (ROM-SAF) and University Corporation for Atmospheric Research (UCAR) Boulder RO processing centers. We find that the new scheme is comparable in its retrieval performance and features advantages in the integrated uncertainty estimation that includes both estimated random and systematic uncertainties and background bias correction. The new algorithm can therefore be used to obtain high-quality tropospheric climate data records including uncertainty estimation.

scholarly journals Potential of AOD Retrieval Using Atmospheric Emitted Radiance Interferometer (AERI)

2022 ◽  
Vol 14 (2) ◽  
pp. 407
Author(s):  
Jongjin Seo ◽  
Haklim Choi ◽  
Young-Suk Oh
Keyword(s):  
Information Needs ◽  
Optimal Estimation ◽  
Retrieval Algorithm ◽  
Weather Forecasts ◽  
Clear Sky ◽  
Infrared Radiance ◽  
Remote Sensor ◽  
Filter Radiometer ◽  
Potential Possibility ◽  
Performance Of Algorithm

Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, we developed an optimal-estimation-based aerosol optical depth (AOD) retrieval algorithm using the hyperspectral infrared downwelling emitted radiance of the Atmospheric Emitted Radiance Interferometer (AERI). The proposed algorithm is based on the phenomena that the thermal infrared radiance measured by a ground-based remote sensor is sensitive to the thermodynamic profile and degree of the turbid aerosol in the atmosphere. To assess the performance of algorithm, AERI observations, measured throughout the day on 21 October 2010 at Anmyeon, South Korea, were used. The derived thermodynamic profiles and AODs were compared with those of the European center for a reanalysis of medium-range weather forecasts version 5 and global atmosphere watch precision-filter radiometer (GAW-PFR), respectively. The radiances simulated with aerosol information were more suitable for the AERI-observed radiance than those without aerosol (i.e., clear sky). The temporal variation trend of the retrieved AOD matched that of GAW-PFR well, although small discrepancies were present at high aerosol concentrations. This provides a potential possibility for the retrieval of nighttime AOD.

scholarly journals The GRAPE aerosol retrieval algorithm

2009 ◽  
Vol 2 (2) ◽  
pp. 981-1026 ◽  
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.

scholarly journals Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3  µm reflectance measurements

2017 ◽  
Vol 10 (5) ◽  
pp. 1769-1782 ◽  
Author(s):  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Philippe Nédélec ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Processing ◽  
Pearson Correlation ◽  
Short Wave ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Data Set ◽  
Airborne Measurements ◽  
Clear Sky ◽  
Infrared Measurements

Abstract. We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311–2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data were processed with the Shortwave Infrared CO Retrieval algorithm (SICOR) that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA's Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites. For Ny-Ålesund, Lauder, Mauna Loa and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. The situation improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6. 0 ppb and a strong correlation between the validation and the SCIAMACHY results with a mean Pearson correlation coefficient r = 0. 7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set. For example, at the cities Tehran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171. 2 ppb and 57.9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. For cloudy-sky retrievals, the validation improves significantly. Here the retrieved column is mainly sensitive to CO above the cloud and so not affected by the strong local surface emissions. Adjusting the MOZAIC/IAGOS measurements to the vertical sensitivity of the retrieval, the mean bias adds up to b = 52. 3 ppb and 5.0 ppb for Tehran and Beijing. At the less urbanised region around the airport Windhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15. 5 ppb, but can be even further improved for cloudy SCIAMACHY observations with a mean bias of b = 0. 2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short-wave infrared measurements present a major extension of the clear-sky-only data set, which more than triples the amount of data and adds unique observations over the oceans. Moreover, the study represents the first application of the S5P algorithm for operational CO data processing on cloudy observations prior to the launch of the S5P mission.

scholarly journals A Fast Retrieval of Cloud Parameters Using a Triplet of Wavelengths of Oxygen Dimer Band around 477 nm

2021 ◽  
Vol 13 (1) ◽  
pp. 152
Author(s):  
Haklim Choi ◽  
Xiong Liu ◽  
Gonzalo Gonzalez Abad ◽  
Jongjin Seo ◽  
Kwang-Mog Lee ◽  
...  
Keyword(s):  
Scattered Light ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
The Novel ◽  
Cloud Parameters ◽  
Retrieval Scheme ◽  
The Difference ◽  
The Look ◽  
Time Required ◽  
Cloud Top Pressure

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

Retrievals of Atmospheric Carbonyl Sulfide from IASI

2021 ◽  
Author(s):  
Michael P. Cartwright ◽  
Jeremy J. Harrison ◽  
David P. Moore
Keyword(s):  
Gross Primary Production ◽  
Carbonyl Sulfide ◽  
Optimal Estimation ◽  
Stratospheric Aerosol ◽  
Data Set ◽  
Spatial Coverage ◽  
Retrieval Scheme ◽  
The University ◽  
Fresh Insight

<p>Carbonyl sulfide (OCS) is the most abundant sulfur containing gas in the atmosphere and is an important source of stratospheric aerosol. Furthermore, it has been shown that OCS can be used as a proxy for photosynthesis, which is a powerful tool in quantifying global gross primary production. While considerable improvements have been made in our understanding of the location and magnitude of OCS fluxes over the past few decades, recent studies highlight the need for a new satellite dataset to help reduce the uncertainties in current estimations. The Infrared Atmospheric Sounding Interferometer (IASI) instruments on-board the MetOp satellites offer over 14 years of nadir viewing radiance measurements with excellent spatial coverage. Given that there are currently three IASI instruments in operation, there is the potential for a significantly larger OCS dataset than is currently available elsewhere. Retrievals of OCS from these IASI radiances have been made using an adapted version of the University of Leicester IASI Retrieval Scheme (ULIRS). OCS total column amounts are calculated from profiles retrieved on a 31-layer equidistant pressure grid, using an optimal estimation approach for microwindows in the range 2000 – 2100 cm<sup>-1</sup> wavenumbers. Sensitivity of the measurements peak in the mid-troposphere, between 5 – 10 km.</p><p>The outlook of this work is to produce a long-term OCS satellite observational data set that provides fresh insight to the spatial distribution and trend of atmospheric OCS. Here, we present subsets of data in the form of case studies for different geographic regions and time periods.</p>

scholarly journals On variability of total ozone derived from TOVS data over peninsular Indian sub-continent and adjoining oceanic area

MAUSAM ◽  
2022 ◽  
Vol 53 (4) ◽  
pp. 503-514
Author(s):  
R. SURESH
Keyword(s):  
Total Ozone ◽  
Monsoon Season ◽  
Lower Troposphere ◽  
Southwest Monsoon ◽  
Retrieval Algorithm ◽  
Tropospheric Temperature ◽  
Minimum Concentration ◽  
Upper Troposphere ◽  
Positive Correlation Coefficient ◽  
Longitudinal Variability

The total ozone derived from TOVS data from NOAA 12 satellite through one step physical retrieval algorithm of  International TOVS Processing Package (ITPP) version 5.0 has been used to identify  its diurnal, monthly, latitudinal and longitudinal variability during 1998 over the domain Equator to 26° N / 60-100° E. The linkage of  maximum total ozone with warmer tropopause and lower stratosphere has been re-established. The colder upper tropospheric temperature which is normally associated with maximum ozone concentration throughout the year elsewhere in the world  has also been identified in this study but the relationship gets reversed during southwest  monsoon months(June-September) over the domain considered. The moisture  available in abundance in the lower troposphere gets precipitated due to the convective instability prevailing in the atmosphere during monsoon season and very little moisture is only available for vertical transport into the upper troposphere atop 500 hPa. The latent heat released by the  precipitation processes warms up the middle and upper atmosphere. The warm and dry upper troposphere could be the reason for less depletion of ozone in the upper troposphere during monsoonal  months and this is supported by the positive correlation coefficient prevailing in monsoon season between  total ozone and upper tropospheric (aloft 300 hPa) temperature. The warmness in middle and upper troposphere which is associated with less depletion and/or production of more  ozone in the upper troposphere may  perhaps contribute  for the  higher total ozone during monsoon months than in other seasons over peninsular Indian region.  The minimum concentration is observed during January (226 DU) over 6° N and the maximum (283DU) over 18° N during August. Longitudinal variability is less pronounced than the latitudinal variability.

scholarly journals Measurement Characteristics of an airborne Microwave Temperature Profiler (MTP)

2020 ◽  
Author(s):  
Mareike Kenntner ◽  
Andreas Fix ◽  
Matthias Jirousek ◽  
Franz Schreier ◽  
Jian Xu ◽  
...  
Keyword(s):  
The State ◽  
Retrieval Algorithm ◽  
Oxygen Absorption ◽  
Transfer Model ◽  
Flight Altitude ◽  
Standard Measurement ◽  
Noise Characteristics ◽  
Measurement Strategy ◽  
Temperature Profiler ◽  
Calibration Parameters

Abstract. The Microwave Temperature Profiler (MTP), an airborne passive microwave radiometer, records radiances in order to estimate temperature profiles around flight altitude. From these data the state of the atmosphere can be derived and important dynamical processes (e.g. gravity waves) assessed. DLR has acquired a copy of the MTP from NASA-JPL, which was designed as a wing-canister instrument and is deployed on the German research aircraft HALO. For this instrument a thorough analysis of instrument characteristics has been made. This is necessary to correctly determine the accuracy and precision of MTP measurements, and crucial for a retrieval algorithm to derive vertical profiles of absolute atmospheric temperatures. Using a laboratory set-up, the frequency response function and antenna diagram of the instrument was carefully characterised. A cold-chamber was used to simulate the changing in-flight conditions and to derive noise characteristics as well as reliable calibration parameters for brightness temperature calculations, which are compared to those calculated from campaign data. Furthermore, using the radiative transfer model Py4CAtS, the sensitivity to the atmospheric layers around flight altitude was investigated. It was found that using the standard measurement settings, the DLR-MTP’s vertical range of sensitivity is limited to 3 km around flight altitude, but can be significantly increased by adjusting the standard measurement strategy, including slightly weaker oxygen absorption lines and a different set of viewing angles. Calibration parameters do clearly depend on the state of the instrument; using a built-in heated target for calibration may yield large errors in brightness temperatures, due to a misinterpretation of the measured absolute temperature. With here presented corrections to the calibration parameter calculations, the measurement noise becomes the dominant source of uncertainty and it is possible to measure the atmospheric temperature around flight level with a precision of 0.38 K. This is the first time such a thorough instrument characterisation of a MTP instrument is published. With the presented results, it is now possible to identify significant temperature fluctuation signals in MTP data and choose the best possible measurement strategy fitting the purpose of the measurement campaign.

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