scholarly journals Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations

2020 ◽  
Vol 12 (8) ◽  
pp. 1291
Author(s):  
Wan Wu ◽  
Xu Liu ◽  
Qiguang Yang ◽  
Daniel K. Zhou ◽  
Allen M. Larar
Keyword(s):  
Water Vapor ◽  
Atmospheric Temperature ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Climate Data ◽  
Fingerprinting Method ◽  
Level 2

We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively.

scholarly journals Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies

2013 ◽  
Vol 13 (14) ◽  
pp. 6687-6711 ◽  
Author(s):  
M. J. Alvarado ◽  
V. H. Payne ◽  
E. J. Mlawer ◽  
G. Uymin ◽  
M. W. Shephard ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Fixed Ratio ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Temperature Profiles ◽  
A Posteriori ◽  
The Impact ◽  
Temperature Water

Abstract. Modern data assimilation algorithms depend on accurate infrared spectroscopy in order to make use of the information related to temperature, water vapor (H2O), and other trace gases provided by satellite observations. Reducing the uncertainties in our knowledge of spectroscopic line parameters and continuum absorption is thus important to improve the application of satellite data to weather forecasting. Here we present the results of a rigorous validation of spectroscopic updates to an advanced radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM), against a global dataset of 120 near-nadir, over-ocean, nighttime spectra from the Infrared Atmospheric Sounding Interferometer (IASI). We compare calculations from the latest version of LBLRTM (v12.1) to those from a previous version (v9.4+) to determine the impact of spectroscopic updates to the model on spectral residuals as well as retrieved temperature and H2O profiles. We show that the spectroscopy in the CO2 ν2 and ν3 bands is significantly improved in LBLRTM v12.1 relative to v9.4+, and that these spectroscopic updates lead to mean changes of ~0.5 K in the retrieved vertical temperature profiles between the surface and 10 hPa, with the sign of the change and the variability among cases depending on altitude. We also find that temperature retrievals using each of these two CO2 bands are remarkably consistent in LBLRTM v12.1, potentially allowing these bands to be used to retrieve atmospheric temperature simultaneously. The updated H2O spectroscopy in LBLRTM v12.1 substantially improves the a posteriori residuals in the P-branch of the H2O ν2 band, while the improvements in the R-branch are more modest. The H2O amounts retrieved with LBLRTM v12.1 are on average 14% lower between 100 and 200 hPa, 42% higher near 562 hPa, and 31% higher near the surface compared to the amounts retrieved with v9.4+ due to a combination of the different retrieved temperature profiles and the updated H2O spectroscopy. We also find that the use of a fixed ratio of HDO to H2O in LBLRTM may be responsible for a significant fraction of the remaining bias in the P-branch relative to the R-branch of the H2O ν2 band. There were no changes to O3 spectroscopy between the two model versions, and so both versions give positive a posteriori residuals of ~ 0.3 K in the R-branch of the O3 ν3 band. While the updates to the H2O self-continuum employed by LBLRTM v12.1 have clearly improved the match with observations near the CO2 ν3 band head, we find that these updates have significantly degraded the match with observations in the fundamental band of CO. Finally, significant systematic a posteriori residuals remain in the ν4 band of CH4, but the magnitude of the positive bias in the retrieved mixing ratios is reduced in LBLRTM v12.1, suggesting that the updated spectroscopy could improve retrievals of CH4 from satellite observations.

scholarly journals Radiance-based Retrieval Bias Mitigation for the MOPITT Instrument: The Version 8 Product

2019 ◽  
Author(s):  
Merritt N. Deeter ◽  
David P. Edwards ◽  
Gene L. Francis ◽  
John C. Gille ◽  
Debbie Mao ◽  
...  
Keyword(s):  
Fossil Fuels ◽  
Atmospheric Transport ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Bias Drift ◽  
Atmospheric Carbon Monoxide ◽  
Nearly Continuous ◽  
Retrieval Bias

Abstract. The MOPITT (Measurements of Pollution in the Troposphere) satellite instrument has been making nearly continuous observations of atmospheric carbon monoxide (CO) since 2000. Satellite observations of CO are routinely used to analyze emissions from fossil fuels and biomass burning, and the atmospheric transport of those emissions. Recent enhancements to the MOPITT retrieval algorithm have resulted in the release of the Version 8 (V8) product. V8 products benefit from updated spectroscopic data for water vapor and nitrogen used to develop the operational radiative transfer model and exploit a new method for minimizing retrieval biases through parameterized radiance bias correction. Validation results illustrate clear improvements with respect to long-term bias drift and geographically variable retrieval bias.

Drivers of Surface Downwelling Longwave Irradiance Changes from 1984 through 2017

2021 ◽  
Author(s):  
Joseph Clark
Keyword(s):  
Water Vapor ◽  
Greenhouse Gases ◽  
Atmospheric Temperature ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Gases ◽  
Background Temperature ◽  
The Impact

<p>Relatively few studies have taken observationally driven approaches toward understanding the impact that atmospheric gases and temperatures have on surface downwelling longwave irradiance (SDLI) changes. This is despite the fact that changes in SDLI contribute significantly to climate change. Using reanalysis, observations, and the Rapid Radiative Transfer Model Global (RRTMG; Mlawer et al. 1997; Iacono et al. 2008), we linearly separate the contributions to SDLI changes from 1984 through 2017 caused by the following variables: atmospheric temperature, H<sub>2</sub>O, CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O, CFC-11, and CFC-12. The results show that spatial and temporal variations in SDLI are primarily caused by spatial and temporal variations in atmospheric temperatures and water vapor amounts. Specifically, we find that atmospheric temperatures and water vapor amounts contribute about 10 times more to SDLI variations from 1984 through 2017 than the remaining greenhouse gases. Climatologically, spatial variability in atmospheric temperature and water vapor also play a role in determining the impact on SDLI of CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O, CFC-11, and CFC-12. SDLI trends directly attributable to CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O, CFC-11, and CFC-12 are strongest over regions with climatologically high temperatures and low water vapor amounts. In other words, the impact of the greenhouse gases varies in space, with its strength depending on the background temperature and moisture fields, even if the change in gas mixing ratio is spatially uniform. Finally, CO<sub>2 </sub>contributed 10 times more to the SDLI trends of 0.05-0.30 W m<sup>-2</sup> / decade (depending on location) from 1984 through 2017 than any other greenhouse gas.</p><p> </p><p><strong>References</strong></p>

scholarly journals An Innovative Calibration Method for the Inversion of Satellite Observations

2010 ◽  
Vol 49 (12) ◽  
pp. 2458-2473 ◽  
Author(s):  
Filipe Aires ◽  
Frédéric Bernardo ◽  
Héléne Brogniez ◽  
Catherine Prigent
Keyword(s):  
Water Vapor ◽  
Single Channel ◽  
Weather Prediction ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Sensor Calibration ◽  
Transfer Model ◽  
Nwp Model

Abstract Retrieval schemes often use two important components: 1) a radiative transfer model (RTM) inside the retrieval procedure or to construct the learning dataset for the training of the statistical retrieval algorithms and 2) a numerical weather prediction (NWP) model to provide a first guess or, again, to construct a learning dataset. This is particularly true in operational centers. As a consequence, any physical retrieval or similar method is limited by inaccuracies in the RTM and NWP models on which it is based. In this paper, a method for partially compensating for these errors as part of the sensor calibration is presented and evaluated. In general, RTM/NWP errors are minimized as best as possible prior to the training of the retrieval method, and then tolerated. The proposed method reduces these unknown and generally nonlinear residual errors by training a separate preprocessing neural network (NN) to produce calibrated radiances from real satellite data that approximate those radiances produced by the “flawed” NWP and RTM models. The final “compensated/flawed” retrieval assures better internal consistency of the retrieval procedure and then produces more accurate results. To the authors’ knowledge, this type of NN model has not been used yet for this purpose. The calibration approach is illustrated here on one particular application: the retrieval of atmospheric water vapor from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Humidity Sounder for Brazil (HSB) measurements for nonprecipitating scenes, over land and ocean. Before being inverted, the real observations are “projected” into the space of the RTM simulation space from which the retrieval is designed. Validation of results is performed with radiosonde measurements and NWP analysis departures. This study shows that the NN calibration of the AMSR-E/HSB observations improves water vapor inversion, over ocean and land, for both clear and cloudy situations. The NN calibration is efficient and very general, being applicable to a large variety of problems. The nonlinearity of the NN allows for the calibration procedure to be state dependent and adaptable to specific cases (e.g., the same correction will not be applied to medium-range measurement and to extreme conditions). Its multivariate nature allows for a full exploitation of the complex correlation structure among the instrument channels, making the calibration of each single channel more robust. The procedure would make it possible to project the satellite observations in a reference observational space defined by radiosonde measurements, RTM simulations, or other instrument observational space.

scholarly journals Performance of the line-by-line radiative transfer model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies

2013 ◽  
Vol 13 (1) ◽  
pp. 79-144 ◽  
Author(s):  
M. J. Alvarado ◽  
V. H. Payne ◽  
E. J. Mlawer ◽  
G. Uymin ◽  
M. W. Shephard ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Weather Forecasting ◽  
Fixed Ratio ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Temperature Profiles ◽  
The Impact ◽  
Temperature Water

Abstract. Modern data assimilation algorithms depend on accurate infrared spectroscopy in order to make use of the information related to temperature, water vapor (H2O), and other trace gases provided by satellite observations. Reducing the uncertainties in our knowledge of spectroscopic line parameters and continuum absorption is thus critical to improving the application of satellite data to weather forecasting. Here we present the results of a rigorous validation of spectroscopic updates to an advanced radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM), against a global dataset of 120 near-nadir, over-ocean, nighttime spectra from the Infrared Atmospheric Sounding Instrument (IASI). We compare calculations from the latest version of LBLRTM (v12.1) to those from a previous version (v9.4+) to determine the impact of spectroscopic updates to the model on spectral residuals as well as retrieved temperature and H2O profiles. We show that the spectroscopy in the CO2 ν2 and ν3 bands is significantly improved in LBLRTM v12.1 relative to v9.4+, and that these spectroscopic updates lead to changes of ~0.5 K in the retrieved vertical temperature profiles below 10 hPa, with the sign of the change and the variability among cases depending on altitude. We also find that temperature retrievals using each of these two CO2 bands are remarkably consistent in LBLRTM v12.1, potentially allowing these bands to be used to retrieve atmospheric temperature simultaneously. The updated H2O spectroscopy in LBLRTM v12.1 substantially improves the residuals in the P-branch of the H2O ν2 band, while the improvements in the R-branch are more modest. The H2O amounts retrieved with LBLRTM v12.1 are on average 14% lower between 100 and 200 hPa, 42% higher near 562 hPa, and 31% higher near the surface compared to the amounts retrieved with v9.4+ due to a combination of the different retrieved temperature profiles and the updated H2O spectroscopy. We also find that the use of a fixed ratio of HDO to H2O in LBLRTM may be responsible for a significant fraction of the remaining bias in the P-branch of the H2O ν2 band. There were no changes to O3 spectroscopy between the two model versions, and so both versions gives positive residuals of ~0.3 K in the R-branch of the O3 ν3 band. While the updates to the H2O self continuum employed by LBLRTM v12.1 have clearly improved the match with observations near the CO2 ν3 bandhead, we find that these updates have significantly degraded the match with observations in the fundamental band of CO. Finally, significant systematic residuals remain in the ν4 band of CH4, but the magnitude of the positive bias in the retrieved mixing ratios is reduced in LBLRTM v12.1, suggesting that the updated spectroscopy could improve retrievals of CH4 from satellite observations.

scholarly journals Validating TROPOMI aerosol layer height retrievals with CALIOP data

2019 ◽  
Author(s):  
Swadhin Nanda ◽  
Martin de Graaf ◽  
J. Pepijn Veefkind ◽  
Maarten Sneep ◽  
Mark ter Linden ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Ultraviolet Region ◽  
Transfer Model ◽  
Aerosol Layer ◽  
Estimation Technique ◽  
Layer Height ◽  
Level 2 ◽  
Orthogonal Polarisation

Abstract. The Tropospheric Monitoring Instrument's (TROPOMI) level-2 aerosol layer height (ALH) product has now been released to the general public. This product is retrieved using TROPOMI's measurements of the oxygen A-band, radiative transfer model (RTM) calculations augmented by neural networks and an iterative optimal estimation technique. The TROPOMI ALH product will deliver aerosol layer height estimates over cloud-free scenes over the ocean and land that contain aerosols above a certain threshold of the measured UV absorbing index (UVAI) in the ultraviolet region. This paper provides background for the ALH product and explores its quality by comparing ALH estimates to similar quantities derived from spaceborne lidars observing the same scene. The spaceborne lidar chosen for this study is the Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, which flies in formation with NASA's A-train constellation since 2006 and is a proven source of data for studying aerosol layer heights. The influence of the surface and clouds are discussed and the aspects of the TROPOMI ALH algorithm that will require future development efforts are highlighted.

scholarly journals Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG

2020 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Lucie Leonarski ◽  
Laurent C.-Labonnote ◽  
Mathieu Compiègne ◽  
Jérôme Vidot ◽  
Anthony J. Baran ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Signal To Noise Ratio ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water

The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (IWP) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose IWP is between about 1 and 300 g/m2).

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

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