scholarly journals The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation

2021 ◽  
Vol 15 (6) ◽  
pp. 2781-2802
Author(s):  
Linlu Mei ◽  
Vladimir Rozanov ◽  
Evelyn Jäkel ◽  
Xiao Cheng ◽  
Marco Vountas ◽  
...  
Keyword(s):  
Grain Size ◽  
Land Surface ◽  
Model Simulation ◽  
Atmospheric Correction ◽  
Aircraft Measurements ◽  
Airborne Measurements ◽  
Snow Properties ◽  
Snow Particle ◽  
The Difference ◽  
Mean Square Errors

Abstract. To evaluate the performance of the eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in the Part 1 companion paper to this paper, we apply the XBAER algorithm to the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board Sentinel-3. Snow properties – snow grain size (SGS), snow particle shape (SPS) and specific surface area (SSA) – are derived under cloud-free conditions. XBAER-derived snow properties are compared to other existing satellite products and validated by ground-based and aircraft measurements. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) aerosol optical thickness (AOT) and the aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a look-up-table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN-simulated surface directional reflectances at 0.55 and 1.6 µm. The SSA is derived for a retrieved SGS and SPS pair. XBAER-derived SGS, SPS and SSA have been validated using in situ measurements from the recent campaign SnowEx17 during February 2017. The comparison shows a relative difference between the XBAER-derived SGS and SnowEx17-measured SGS of less than 4 %. The difference between the XBAER-derived SSA and SnowEx17-measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonably explained by the SnowEx17-observed snow particle shapes. Intensive validation shows that (1) for SGS and SSA, XBAER-derived results show high correlation with field-based measurements, with correlation coefficients higher than 0.85. The root mean square errors (RMSEs) of SGS and SSA are around 12 µm and 6 m2/kg. (2) For SPS, aggregate SPS retrieved by XBAER algorithm is likely to be matched with rounded grains while single SPS in XBAER is possibly linked to faceted crystals. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R=0.82 and R=0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability in the aircraft track of the PAMARCMiP campaign. The comparison between XBAER-derived SGS results and the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonably explained by campaign-based and laboratory investigations, indicating a reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities in XBAER-derived SGS and SSA both over Greenland and Arctic-wide agree with the snow metamorphism process.

scholarly journals The retreival of snow properties from SLSTR/Sentinel-3 – part 2: results and validation

2020 ◽  
Author(s):  
Linlu Mei ◽  
Vladimir Rozanov ◽  
Evelyn Jäkel ◽  
Xiao Cheng ◽  
Marco Vountas ◽  
...  
Keyword(s):  
Grain Size ◽  
Land Surface ◽  
Model Simulation ◽  
Atmospheric Correction ◽  
Aircraft Measurements ◽  
Airborne Measurements ◽  
Snow Properties ◽  
Snow Particle ◽  
The Difference

Abstract. To evaluate the performance of eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in part 1 of the companion paper, this manuscript applies the XBAER algorithm on the Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI) instruments onboard Sentinel-3. Snow properties: Snow Grain Size (SGS), Snow Particle Shape (SPS), and Specific Surface Area (SSA) are derived under cloud-free conditions. XBAER derived snow properties are compared to other existing satellite products and validated by ground-based/aircraft measurements. Cloud screening is performed by standard XBAER algorithm synergistically using OLCI and SLSTR instruments both onboard Sentinel-3. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) Aerosol Optical Thickness (AOT) and aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a Look-Up-Table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN simulated surface directional reflectances at 0.55 and 1.6 μm. The SSA is derived for a given SGS and SPS pair. XBAER derived SGS, SPS and SSA have been validated using in-situ measurements from the recent campaign SnowEx17 during February 2017. The comparison of the retrieved SGS with the in-situ data shows a relative difference between XBAER-derived SGS and SnowEx17 measured SGS of less than 4 %. The difference between XBAER-derived SSA and SnowEx17 measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonable-explained by the SnowEx17 observed snow particle shapes. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R = 0.82 and R = 0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability of the aircraft track of PAMARCMiP campaign. The comparison between XBAER-derived SGS results and MODIS Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonable-explained by campaign-based and laboratory investigations, indicating reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities of XBAER-derived SGS and SSA over both Greenland and Arctic-wide agree with the snow metamorphism process.

scholarly journals Determination of land surface reflectance using the AATSR dual-view capability

2014 ◽  
Vol 7 (7) ◽  
pp. 7451-7494
Author(s):  
L. Sogacheva ◽  
P. Kolmonen ◽  
T. H. Virtanen ◽  
E. Rodriguez ◽  
A.-M. Sundström ◽  
...  
Keyword(s):  
Land Surface ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Surface Reflectance ◽  
Imaging Spectrometer ◽  
Comparison Results ◽  
Moderate Resolution ◽  
The Difference ◽  
Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995–2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite.

An Atmospheric Correction Method for Medium Resolution Spectral Imager Thermal IR Soundings

2012 ◽  
Vol 500 ◽  
pp. 397-402 ◽  
Author(s):  
Hai Lei Liu ◽  
Li Sheng Xu ◽  
Ji Lie Ding ◽  
Ba Sang ◽  
Xiao Bo Deng
Keyword(s):  
Water Vapor ◽  
Land Surface ◽  
Atmospheric Correction ◽  
Correction Method ◽  
Qinghai Lake ◽  
Medium Resolution ◽  
Interpolation Procedure ◽  
The Difference ◽  
Spectral Imager

Based on the thermal radiative transfer equation (RTE), a new atmospheric correction method named Single Band Water Vapor Dependent (SBWVD) method is developed for land surface temperature (LST) retrieval for the FY-3A Medium Resolution Spectral Imager (MERSI) with only one thermal infrared (TIR) channel. Assuming that the surface emissivity is known, water vapor content (WVC) is the only one parameter for input to the SBWVD algorithm to retrieve LST from MERSI TIR observations. FY-3A MERSI Level 2 water vapor product is employed to evaluate the performance of the proposed method, and a 2-D data interpolation procedure is applied in order to match the MERSI L1B data in spatial resolution. Some tests, including numerical simulation for MERSI sensor and the synchronous measurements of MERSI and the radiosondes for the radiative calibration of the FY-3A tests in Qinghai Lake, have been carried out for the proposed algorithm, respectively. The results show that the difference between the retrieved LST and the in-situ measurements is less than 0.6 K for most situations. The comparison with the MODIS LST products (V5) shows that the root mean square error (RMSE) is under 0.72 K. Thus, our proposed new algorithm is applicable for the atmospheric correction and LST retrieval using MERSI TIR channel observations.

scholarly journals Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

2019 ◽  
Vol 11 (22) ◽  
pp. 2655 ◽  
Author(s):  
He ◽  
Zhang ◽  
Liang ◽  
Yu ◽  
Wang
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Satellite Observation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Operational Environment ◽  
Mean Square Errors ◽  
Directional Reflectance

The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.

scholarly journals Evaluation of a new middle-lower tropospheric CO<sub>2</sub> product using data assimilation

2013 ◽  
Vol 13 (9) ◽  
pp. 4487-4500 ◽  
Author(s):  
A. Tangborn ◽  
L. L. Strow ◽  
B. Imbiriba ◽  
L. Ott ◽  
S. Pawson
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Model Simulation ◽  
Lower Troposphere ◽  
Earth Observing System ◽  
Peak Sensitivity ◽  
The Difference ◽  
Using Data ◽  
Mean Differences

Abstract. Atmospheric CO2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the GEOS-5 (Goddard Earth Observing System Model, Version 5) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO2 boundary fluxes was also completed. The analyzed and simulated CO2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the used channels contain useful information on CO2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in situ surface observation locations in more remote areas.

scholarly journals Determination of land surface reflectance using the AATSR dual-view capability

2015 ◽  
Vol 8 (2) ◽  
pp. 891-906 ◽  
Author(s):  
L. Sogacheva ◽  
P. Kolmonen ◽  
T. H. Virtanen ◽  
E. Rodriguez ◽  
A.-M. Sundström ◽  
...  
Keyword(s):  
Land Surface ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Surface Reflectance ◽  
Imaging Spectrometer ◽  
Comparison Results ◽  
Moderate Resolution ◽  
The Difference ◽  
Along Track

Abstract. In this study, a method is presented to retrieve the surface reflectance using the radiances measured at the top of the atmosphere for the two views provided by the Advanced Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual-view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated versus surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (Moderate Resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with somewhat higher values (up to 0.05) obtained by ADV over bright surfaces. The difference between the ADV- and MODIS-retrieved surface reflectances is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation in the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for both the AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover a 17-year period of measurements (1995–2012), as well as a prototype for the Sea and Land Surface Temperature Radiometer (SLSTR) planned to be launched in the fall of 2015 onboard the Sentinel-3 satellite.

scholarly journals The IMPROVE-1 Storm of 1–2 February 2001. Part III: Sensitivity of a Mesoscale Model Simulation to the Representation of Snow Particle Types and Testing of a Bulk Microphysical Scheme with Snow Habit Prediction

2007 ◽  
Vol 64 (11) ◽  
pp. 3927-3948 ◽  
Author(s):  
Christopher P. Woods ◽  
Mark T. Stoelinga ◽  
John D. Locatelli
Keyword(s):  
Pacific Northwest ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Velocity Versus ◽  
Constant Density ◽  
Snow Properties ◽  
The Pacific ◽  
Snow Particle ◽  
Microphysical Parameterization ◽  
Prediction Approach

Abstract A mesoscale model simulation of a wide cold-frontal rainband observed in the Pacific Northwest during the Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-1) field study was used to test the sensitivity of the model-produced precipitation to varied representations of snow particles in a bulk microphysical scheme. Tests of sensitivity to snow habit type, by using empirical relationships for mass and velocity versus diameter, demonstrated the defectiveness of the conventional assumption of snow particles as constant density spheres. More realistic empirical mass–diameter relationships result in increased numbers of particles and shift the snow size distribution toward larger particles, leading to increased depositional growth of snow and decreased cloud water production. Use of realistic empirical mass–diameter relationships generally increased precipitation at the surface as the rainband interacted with the orography, with more limited increases occurring offshore. Changes in both the mass–diameter and velocity–diameter relationships significantly redistributed precipitation either windward or leeward when the rainband interacted with the mountain barrier. A method of predicting snow particle habit in a bulk microphysical scheme, and using predicted habit to dynamically determine snow properties in the scheme, was developed and tested. The scheme performed well at predicting the habits present (or not present) in aircraft observations of the rainband. Use of the scheme resulted in little change in the precipitation rate at the ground for the rainband offshore, but significantly increased precipitation when the rainband interacted with the windward slope of the Olympic Mountains. The study demonstrates the promise of the habit prediction approach to treating snow in bulk microphysical schemes.

scholarly journals Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument

2020 ◽  
Vol 12 (15) ◽  
pp. 2500
Author(s):  
Kyeong-Sang Lee ◽  
Sung-Rae Chung ◽  
Changsuk Lee ◽  
Minji Seo ◽  
Sungwon Choi ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Monsoon Season ◽  
Atmospheric Correction ◽  
Optimization Method ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Temporal Consistency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mean Square Errors

The Korea Meteorological Administration successfully launched Korea’s next-generation meteorological satellite, Geo-KOMPSAT-2A (GK-2A), on 5 December 2018. It belongs to the new generation of GEO (Geostationary Elevation Orbit) satellite which offers capabilities to disseminate high spatial- (0.5–2 km) and high temporal-resolution (10 min) observations over a broad area, herein a geographic disk encompassing the Asia–Oceania region. The targeted objective is to enhance our understanding of climate change, owing to a bulk of coherent observations. For such, we developed an algorithm to map the land surface albedo (LSA), which is a major Essential Climate Variable (ECV). The retrieval algorithm devoted to GK-2A/Advanced Meteorological Imager (AMI) data considered Japan’s Himawari-8/Advanced Himawari Imager (AHI) data for prototyping, as this latter owns similar specifications to AMI. Our proposed algorithm is decomposed in three major steps: atmospheric correction, bidirectional reflectance distribution function (BRDF) modeling and angular integration, and narrow-to-broadband conversion. To perform BRDF modeling, the optimization method using normalized reflectance was applied, which improved the quality of BRDF modeling results, particularly when the number of observations was less than 15. A quality assessment was performed to compare our results to those of Moderate Resolution Imaging Spectroradiometer (MODIS) LSA products and ground measurement from Aerosol Robotic Network (AERONET) sites, Australian and New Zealand flux tower network (OzFlux) site and the Korea Flux Network (KoFlux) site from throughout 2017. Our results show dependable spatial and temporal consistency with MODIS broadband LSA data, and rapid changes in LSA due to snowfall and snow melting were well expressed in the temporal profile of our results. Our outcomes also show good agreement with the ground measurements from AERONET, OzFlux and KoFlux ground-based network with root mean square errors (RMSE) of 0.0223 and 0.0306, respectively, which is close to the accuracy of MODIS broadband LSA. Moreover, our results reveal still more reliable LSA products even when clouds are frequently present, such as during the summer monsoon season. It shows that our results are useful for continuous LSA monitoring.

scholarly journals Comparison of optical-equivalent snow grain size estimates under Arctic low Sun conditions during PAMARCMiP 2018

2021 ◽  
Author(s):  
Evelyn Jäkel ◽  
Tim Carlsen ◽  
André Ehrlich ◽  
Manfred Wendisch ◽  
Michael Schäfer ◽  
...  
Keyword(s):  
Grain Size ◽  
Climate Model ◽  
Model Simulation ◽  
Trace Gases ◽  
Regional Climate ◽  
Retrieval Method ◽  
Surface Conditions ◽  
Airborne Measurements ◽  
Reactive Trace Gases ◽  
Size Estimates

Abstract. The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Non-hydrostatic – Aerosols and Reactive Trace gases) simulations. The study is focused on low Sun and partly rough surface conditions encountered during a three-week campaign conducted North of Greenland in March/April 2018 within the framework of the PAMARCMiP (Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project) project. Further, we propose an adjusted airborne retrieval method by using the albedo at 1700 nm wavelength. This reduced the effect of atmospheric masking and improved the sensitivity on ropt. From this approach we achieved a significantly improved uncertainty (

scholarly journals Evaluation of a new middle-lower tropospheric CO<sub>2</sub> product using data assimilation

2012 ◽  
Vol 12 (10) ◽  
pp. 26685-26717
Author(s):  
A. Tangborn ◽  
L. L. Strow ◽  
B. Imbiriba ◽  
L. Ott ◽  
S. Pawson
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Model Simulation ◽  
Lower Troposphere ◽  
Surface Measurements ◽  
Peak Sensitivity ◽  
The Difference ◽  
Using Data ◽  
Mean Differences

Abstract. Atmospheric CO2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the Global Modeling and Assimilation Office (GMAO) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO2 boundary fluxes was also completed. The analyzed and simulated CO2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the retrieved channel contains useful information on CO2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in-situ surface observation locations in more remote areas.

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