Satellite Radiance Measurements Research Articles

Direct assimilation of AVHRR satellite radiance measurements in a reanalysis system

AbstractThe Advanced Very‐High‐Resolution Radiometer (AVHRR) is a broad‐band, five‐channel scanner sensing in the visible, near‐infrared and thermal infrared portions of the electromagnetic spectrum. AVHRR instruments onboard polar‐orbiting satellites have data records spanning 30 years. The radiances from the infrared channels of AVHRR have been directly assimilated over the ice‐free ocean in the NASA Goddard Earth Observing System (GEOS) since 2017 to constrain skin sea surface temperature (SST). The GEOS system already uses an advanced bulk SST and a skin SST model which incorporates diurnal warming and cool‐skin temperature on the surface of the ocean. The positive contribution of this effort to the Numerical Weather Prediction (NWP) makes it desirable to extend the skin SST data assimilation procedure to reanalysis applications. The AVHRR data from platforms that were previously unexplored for NWP applications are assimilated at the Global Modeling and Assimilation Office (GMAO) at NASA, for an upcoming reanalysis project. This study uses results from reanalysis experiments to assess the impact of assimilating AVHRR radiances over open ocean on the atmospheric state variables, focusing on the analyzed skin temperatures that include the active skin SST model. It is demonstrated that including the skin temperature variations in atmospheric analysis is generally beneficial. The addition of radiance measurements from AVHRR provides further improvements to the overall reanalysis system performance. by helping to correct the diurnal heating and reducing errors in the background departure of hyperspectral radiance observations, which are an essential component of atmospheric reanalyses.

Retrieval of aerosol single scattering albedo using joint satellite and surface visibility measurements

Aerosol single scattering albedo (SSA) measuring the ratio of scattering to extinction is a critical parameter in determining aerosol radiative effect. However, the retrieval of SSA from satellite platforms is limited because most existing passive satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) only provide the measurements of reflected solar radiation at the top of the atmosphere (TOA), which are only capable of retrieving aerosol optical depth (AOD) on the basis of assumed SSA. In this study, we develop a machine learning based algorithm to retrieve SSA using joint surface visibility and satellite radiation measurements. With ancillary information such as meteorological condition, surface visibility can be regarded as a proxy of column AOD. By combining MODIS measured TOA apparent reflectance with ground visibility as well as other auxiliary parameters, we retrieve SSA at over 6000 visibility stations worldwide. The validation results during the period from 2010 to 2019 show high consistency with AERONET retrieved SSA, with daily, monthly, and seasonal retrievals all exhibiting high correlations of 0.68, 0.96, and 0.97, respectively. Daily, monthly, and seasonal RMSE values are 0.044, 0.012, and 0.011, respectively. The mean absolute bias (MAB) of daily retrievals is 0.031 and 62% of the samples fall within the uncertainty interval of ±0.03. Monthly and seasonal MAB values decrease to below 0.01. Over 97% of the monthly and seasonal retrievals are within the error envelope of ±0.03. Our work generates a global aerosol SSA dataset with extensive coverage over land, which can be used for validating and improving climate models and the estimation of aerosol radiative forcing.

Comparison of middle- and low-latitude sodium layer from a ground-based lidar network, the Odin satellite, and WACCM–Na model

Abstract. The ground-based measurements obtained from a lidar network and the 6-year OSIRIS (optical spectrograph and infrared imager system) limb-scanning radiance measurements made by the Odin satellite are used to study the climatology of the middle- and low-latitude sodium (Na) layer. Up to January 2021, four Na resonance fluorescence lidars at Beijing (40.5∘ N, 116.0∘ E), Hefei (31.8∘ N, 117.3∘ E), Wuhan (30.5∘ N, 114.4∘ E), and Haikou (19.5∘ N, 109.1∘ E) collected vertical profiles of Na density for a total of 2136 nights (19 587 h). These large datasets provide multi-year routine measurements of the Na layer with exceptionally high temporal and vertical resolution. The lidar measurements are particularly useful for filling in OSIRIS data gaps since the OSIRIS measurements were not made during the dark winter months because they utilize the solar-pumped resonance fluorescence from Na atoms. The observations of Na layers from the ground-based lidars and the satellite are comprehensively compared with a global model of meteoric Na in the atmosphere (WACCM–Na). The lidars present a unique test of OSIRIS and WACCM (Whole Atmosphere Community Climate Model), because they cover the latitude range along 120∘ E longitude in an unusual geographic location with significant gravity wave generation. In general, good agreement is found between lidar observations, satellite measurements, and WACCM simulations. On the other hand, the Na number density from OSIRIS is larger than that from the Na lidars at the four stations within one standard deviation of the OSIRIS monthly average, particularly in autumn and early winter arising from significant uncertainties in Na density retrieved from much less satellite radiance measurements. WACCM underestimates the seasonal variability of the Na layer observed at the lower latitude lidar stations (Wuhan and Haikou). This discrepancy suggests the seasonal variability of vertical constituent transport modelled in WACCM is underestimated because much of the gravity wave spectrum is not captured in the model.

Effect of the Vertical Distribution of Absorbing Aerosols on the Atmospheric Correction for Satellite Ocean Color Remote Sensing

The vertical distribution of absorbing aerosols has nonnegligible impact on the atmospheric correction of satellite ocean color remote sensing, especially for the water-leaving radiance retrieval at blue and ultraviolet bands. In this study, we investigated the impact of the vertical distribution of absorbing aerosols on the satellite-measured radiance at the top of the atmosphere (TOA) and the retrieved water-leaving radiance. First, the global occurrence frequency of absorbing aerosols was mapped, and it was found that the annual averaged occurrence frequencies of absorbing aerosols were >30% over the coasts of the Sahara and Arabian Desert, China, South-Central Africa, and the Indian Peninsula. Second, a new aerosol classification algorithm was developed to establish absorbing aerosol optical models based on AERosol RObotic NETwork (AERONET) site observations. Finally, the effects of the vertical distribution of absorbing aerosols on the upward radiance at the TOA at 412 nm and the retrieved water-leaving radiance were evaluated. The results showed that the influence of the vertical distribution on the TOA radiance could be up to 8% for dust and 10% for fine-dominated absorbing aerosols (FDAs), which was comparable with the influence of aerosol optical depth. Not considering absorbing aerosols during atmospheric correction might produce <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 4$ </tex-math></inline-formula> %–10% errors in the water-leaving radiance retrieval at 412 nm over turbid waters under the assumption of a Gaussian distribution. Simplified exponential and two-layer atmospheric vertical distribution models can lead to errors of water-leaving radiance retrieval up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 12$ </tex-math></inline-formula> %–15% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 30$ </tex-math></inline-formula> %–40%, respectively. Overall, the imperfect vertical distribution assumption and aerosol model selection might induce uncertainty in water-leaving radiance retrieval from 10% to 80% under absorbing aerosol conditions.

Rethinking the correction for absorbing aerosols in the OMI- and TROPOMI-like surface UV algorithms

Abstract. Satellite estimates of surface UV irradiance have been available since 1978 from the TOMS UV spectrometer and have continued with significantly improved ground resolution using the Ozone Monitoring Instrument (OMI 2004–current) and Sentinel 5 Precursor (S5P 2017–current). The surface UV retrieval algorithm remains essentially the same: it first estimates the clear-sky UV irradiance based on measured ozone and then accounts for the attenuation by clouds and aerosols, applying two consecutive correction factors. When estimating the total aerosol effect in surface UV irradiance, there are two major classes of aerosols to be considered: (1) aerosols that only scatter UV radiation and (2) aerosols that both scatter and absorb UV radiation. The former effect is implicitly included in the measured effective Lambertian-equivalent scene reflectivity (LER), so the scattering aerosol influence is estimated through cloud correction factor. Aerosols that absorb UV radiation attenuate the surface UV radiation more strongly than non-absorbing aerosols of the same extinction optical depth. Moreover, since these aerosols also attenuate the outgoing satellite-measured radiance, the cloud correction factor that treats these aerosols as purely scattering underestimates their aerosol optical depth (AOD), causing underestimation of LER and overestimation of surface UV irradiance. Therefore, for correction of aerosol absorption, additional information is needed, such as a model-based monthly climatology of aerosol absorption optical depth (AAOD). A correction for absorbing aerosols was proposed almost a decade ago and later implemented in the operational OMI and TROPOMI UV algorithms. In this study, however, we show that there is still room for improvement to better account for the solar zenith angle (SZA) dependence and nonlinearity in the absorbing aerosol attenuation, and as a result we propose an improved correction scheme. There are two main differences between the new proposed correction and the one that is currently operational in OMI and TROPOMI UV algorithms. First, the currently operational correction for absorbing aerosols is a function of AAOD only, while the new correction additionally takes the solar zenith angle dependence into account. Second, the second-order polynomial of the new correction takes the nonlinearity in the correction as a function of AAOD better into account, if compared to the currently operational one, and thus better describes the effect by absorbing aerosols over a larger range of AAOD. To illustrate the potential impact of the new correction in the global UV estimates, we applied the current and new proposed correction for global fields of AAOD from the aerosol climatology currently used in OMI UV algorithm, showing a typical differences of ±5 %. This new correction is easy to implement operationally using information of solar zenith angle and existing AAOD climatology.

A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT

Climate change is largely determined by the radiation budget imbalance at the Top Of the Atmosphere (TOA), which is generated by the increasing concentrations of greenhouse gases (GHGs). As a result, the Earth Energy Imbalance (EEI) is considered as an Essential Climate Variable (ECV) that has to be monitored continuously from space. However, accurate TOA radiation measurements remain very challenging. Ideally, EEI monitoring should be performed with a constellation of satellites in order to resolve as much as possible spatio-temporal fluctuations in EEI which contain important information on the underlying mechanisms driving climate change. The monitoring of EEI and its components (incoming solar, reflected solar, and terrestrial infrared fluxes) is the main objective of the UVSQ-SAT pathfinder nanosatellite, the first of its kind in the construction of a future constellation. UVSQ-SAT does not have an active determination system of its orientation with respect to the Sun and the Earth (i.e., the so-called attitude), a prerequisite in the calculation of EEI from the satellite radiation measurements. We present a new effective method to determine the UVSQ-SAT’s in-orbit attitude using its housekeeping and scientific sensors measurements and a well-established deep learning algorithm. One of the goals is to estimate the satellite attitude with a sufficient accuracy for retrieving the radiative fluxes (incoming solar, reflected solar, terrestrial infrared) on each face of the satellite with an uncertainty of less than ±5 Wm−2 (1σ). This new method can be extended to any other satellites with no active attitude determination or control system. To test the accuracy of the method, a ground-based calibration experiment with different attitudes is performed using the Sun as the radiative flux reference. Based on the deep learning estimation of the satellite ground-based attitude, the uncertainty on the solar flux retrieval is about ±16 Wm−2 (1σ). The quality of the retrieval is mainly limited by test conditions and the number of data samples used in training the deep learning system during the ground-based calibration. The expected increase in the number of training data samples will drastically decrease the uncertainty in the retrieved radiative fluxes. A very similar algorithm will be implemented and used in-orbit for UVSQ-SAT.

Toward an atlas of the number of visible stars

Modelling techniques for the propagation of light pollution in the atmosphere allow the computation of maps of artificial night sky brightness in any direction of the sky, involving a large number of details from satellite data. Cinzano et al. (2001a) introduced a method of mapping naked eye star visibility at the zenith from large areas based on satellite radiance measurements and Garstang models of the propagation of light pollution. It takes into account the altitude of each land area from digital elevation data, natural sky brightness in the chosen sky direction based on the Garstang approach, eye capability after Garstang and Schaefer, and atmospheric extinction in the visual photometric band. Here we discuss how to use these methods to obtain maps of the average number of visible stars when looking at the night sky hemisphere, finally answering, site by site, the question of how many stars are visible in the sky. This is not trivial, as the number of stars visible depends on the limiting magnitude in each direction in the sky, and this depends on sky brightness in that direction, atmospheric extinction at that zenith distance and the observer's visual acuity and experience. We present, as an example, a map of the number of visible stars in Italy to an average observer on clear nights with a resolution of approximately 1 km.

Machine Learning Nowcasting of PV Energy Using Satellite Data

Satellite-measured radiances are obviously of great interest for photovoltaic (PV) energy prediction. In this work we will use them together with clear sky irradiance estimates for the nowcasting of PV energy productions over peninsular Spain. We will feed them directly into two linear Machine Learning models, Lasso and linear Support Vector Regression (SVR), and two highly non-linear ones, Deep Neural Networks (in particular, Multilayer Perceptrons, MLPs) and Gaussian SVRs. We shall also use a simple clear sky-based persistence model for benchmarking purposes. We consider prediction horizons of up to 6 h, with Gaussian SVR being statistically better than the other models at each horizon, since its errors increase slowly with time (with an average of 1.92% for the first three horizons and of 2.89% for the last three). MLPs performance is close to that of the Gaussian SVR for the longer horizons (with an average of 3.1%) but less so at the initial ones (average of 2.26%), being nevertheless significantly better than the linear models. As it could be expected, linear models give weaker results (in the initial horizons, Lasso and linear SVR have already an error of 3.21% and 3.46%, respectively), but we will take advantage of the spatial sparsity provided by Lasso to try to identify the concrete areas with a larger influence on PV energy nowcasts.

Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation

Abstract. Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from the underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon the Moderate Resolution Imaging Spectroradiometer (MODIS, on board Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, on board Suomi-NPP) sensors, use wavelength bands in “window” regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper, we use the High-resolution TRANsmission (HITRAN) database and Line-By-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are “similar”, they are different enough that applying MODIS-specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases of up to 0.07. As recent studies have been attempting to create a long-term data record by joining multiple satellite data sets, including MODIS and VIIRS, the consistency of gas correction has become even more crucial.

The stochastic Beer–Lambert–Bouguer law for discontinuous vegetation canopies

The 3D distribution of canopy foliage affects the radiation regime and retrievals of canopy biophysical parameters. The gap fraction is one primary indicator of a canopy structure. Historically the Beer–Lambert–Bouguer law and the linear mixture model have served as a basis for multiple technologies for retrievals of the gap (or vegetation) fraction and Leaf Area Index (LAI). The Beer–Lambert–Bouguer law is a form of the Radiative Transfer (RT) equation for homogeneous canopies, which was later adjusted for a correlation between fitoelements using concept of the clumping index. The Stochastic Radiative Transfer (SRT) approach has been developed specifically for heterogeneous canopies, however the approach lacks a proper model of the vegetation fraction. This study is focused on the implementation of the stochastic version of the Beer–Lambert–Bouguer law for heterogeneous canopies, featuring the following principles: 1) two mechanisms perform photon transport- transmission through the turbid medium of foliage crowns and direct streaming through canopy gaps, 2) the radiation field is influenced by a canopy structure (quantified by the statistical moments of a canopy structure) and a foliage density (quantified by the gap fraction as a function of LAI), 3) the notions of canopy transmittance and gap fraction are distinct. The derived stochastic Beer–Lambert–Bouguer law is consistent with the Geometrical Optical and Radiative Transfer (GORT) derivations. Analytical and numerical analysis of the stochastic Beer–Lambert–Bouguer law presented in this study provides the basis to reformulate widely used technologies for retrievals of the gap fraction and LAI from ground and satellite radiation measurements.

A one‐year study of the diurnal cycle of meteorology, clouds and radiation in the West African Sahel region

The diurnal cycles of meteorological and radiation variables are analysed during the wet and dry seasons over the Sahel region of West Africa during 2006 using surface data collected by the Atmospheric Radiation Measurement (ARM) programme's Mobile Facility, satellite radiation measurements from the Geostationary Earth Radiation Budget (GERB) instrument aboard Meteosat 8, and reanalysis products from the National Centers for Environmental Prediction (NCEP). The meteorological analysis builds upon past studies of the diurnal cycle in the region by incorporating diurnal cycles of lower tropospheric wind profiles, thermodynamic profiles, integrated water vapour and liquid water measurements, and cloud radar measurements of frequency and location. These meteorological measurements are complemented by 3 h measurements of the diurnal cycles of the top‐of‐atmosphere (TOA) and surface short‐wave (SW) and long‐wave (LW) radiative fluxes and cloud radiative effects (CREs), and the atmospheric radiative flux divergence (RFD) and atmospheric CREs. Cirrus cloudiness during the dry season is shown to peak in coverage in the afternoon, while convective clouds during the wet season are shown to peak near dawn and have an afternoon minimum related to the rise of the lifting condensation level into the Saharan Air Layer. The LW and SW RFDs and CREs exhibit diurnal cycles during both seasons, but there is a relatively small difference in the LW cycles during the two seasons (10 − 30 W m−2 depending on the variable and time of day). Small differences in the TOA CREs during the two seasons are overwhelmed by large differences in the surface SW CREs, which exceed 100 W m−2. A significant surface SW CRE during the wet season combined with a negligible TOA SW CRE produces a diurnal cycle in the atmospheric CRE that is modulated primarily by the SW surface CRE, peaks at midday at ∼150 W m−2, and varies widely from day to day.

A potentially universal algorithm for estimating aerosol scattering reflectance from satellite remote sensing data

Accuracy of aerosol scattering reflectance in atmospheric correction is crucial for satellite ocean color remote sensing. A new approach is developed to estimate aerosol scattering reflectance from satellite data based on a look-up table of in situ water-leaving reflectance. A decision rule is set to determine the water-leaving component of the total satellite-measured radiance from the table under an assumption that aerosol scattering reflectance follows the Angstrom law. The epsilon spectrum, obtained from the ratio of aerosol scattering reflectance, is used to select the two closest aerosol models for determining the corrected epsilon values, which is adjusted to new values according to the reference wavelength. The actual aerosol scattering reflectance is obtained from the mean value of the reflectance and the new epsilon spectrum. The performance of the model is evaluated using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery, showing that the component of aerosol scattering reflectance can be completely separated from the total satellite-measured radiance, and effects of non-zero water-leaving reflectance can be eliminated over highly turbid waters. The accuracy is validated by in situ measured aerosol optical thickness (AOT) using a handheld multi-band sun photometer and the Aerosol Robotic Network (AERONET) measured AOT data during the 2006 winter cruise and 2007 autumn cruise over the East China Sea (ECS). The analysis gives the mean relative errors of 23.73% and 32.04%, respectively. Our analysis shows that aerosol scattering reflectance can be significantly overestimated over turbid waters when using the atmospheric correction method based on the black ocean assumption (BOA). It may also have some errors over oceanic waters because a small 1% bias of satellite-measured reflectance in the near-infrared (NIR) bands will lead to a relatively large error of ~10% in aerosol scattering reflectance in Band 1. This error will spread to water-leaving reflectance, possibly leading to negative values of water-leaving reflectance. This amplifying effect of aerosol scattering reflectance can be limited by matching aerosol models using epsilon spectrum in our model. Because the assumption of aerosol scattering reflectance following the Angstrom law can eliminate effects of different types of waters including open ocean, coastal regions, lakes and rivers, our model offers a potentially universal algorithm for estimating aerosol scattering reflectance from satellite remote sensing data.

Month-long simulations of gravity waves over North America and North Atlantic in comparison with satellite observations

Mesoscale simulations of gravity waves in the upper troposphere and lower stratosphere over North America and North Atlantic Ocean in January 2003 are compared with satellite radiance measurements from the Advanced Microwave Sounding Unit-A (AMSU-A). Four regions of strong gravity wave (GW) activities are found in the model simulations and the AMSU-A observations: the northwestern Atlantic, the U.S. Rockies, the Appalachians, and Greenland. GWs over the northwestern Atlantic Ocean are associated with the midlatitude baroclinic jet-front system, while the other three regions are apparently related to high topography. Model simulations are further used to analyze momentum fluxes in the zonal and meridional directions. It is found that strong westward momentum fluxes are prevalent over these regions over the whole period. Despite qualitative agreement between model simulations and satellite measurements, sensitivity experiments demonstrate that the simulated GWs are sensitive to the model spin-up time.

Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Abstract. This study compares different methods to retrieve the specific surface area (SSA) of snow from satellite radiance measurements in mountainous terrain. It aims at addressing the effect on the retrieval of topographic corrections of reflectance, namely slope and aspect of terrain, multiple reflections on neighbouring slopes and accounting (or not) for the anisotropy of snow reflectance. Using MODerate resolution Imaging Spectrometer (MODIS) data for six different clear sky scenes spanning a wide range of snow conditions during the winter season 2008–2009 over a domain of 46 × 50 km in the French Alps, we compared SSA retrievals with and without topographic correction, with a spherical or non-spherical snow reflectance model and, in spherical case, with or without anisotropy corrections. The retrieved SSA values were compared to field measurements and to the results of the detailed snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis. It was found that the difference in terms of surface SSA between retrieved values and SAFRAN-Crocus output was minimal when the topographic correction was taken into account, when using a retrieval method assuming disconnected spherical snow grains. In this case, the root mean square deviation was 9.4 m2 kg−1 and the mean difference was 0.1 m2 kg−1, based on 3170 pairs of observation and simulated values. The added-value of the anisotropy correction was not significant in our case, which may be explained by the presence of mixed pixels and surface roughness. MODIS retrieved data show SSA variations with elevation and aspect which are physically consistent and in good agreement with SAFRAN-Crocus outputs. The variability of the MODIS retrieved SSA within the topographic classes of the model was found to be relatively small (3.9 m2 kg−1). This indicates that semi-distributed snowpack simulations in mountainous terrain with a sufficiently large number of classes provides a representation of the snowpack variability consistent with the scale of MODIS 500 m pixels.

A comparison of global marine surface‐specific humidity datasets from in situ observations and atmospheric reanalysis

ABSTRACTThe accurate estimation of near‐surface marine‐specific humidity is necessary for climate and air–sea interaction applications. Available estimates of monthly mean‐specific humidity spanning the past 50‐years are based on a variety of sources including in situ observations, atmospheric reanalyses and datasets that blend many different data sources. Eight specific humidity datasets are compared and little consensus emerges as to mean values, regional variations and changes over time. For large area averages the datasets do show consistency in their interannual variations and, in the Extratropics, in their seasonal cycles. Adjustments applied to in situ observations from ships are shown to be smaller than differences among the datasets and in well‐sampled regions and periods the in situ data are able to highlight biases in the reanalysis‐based specific humidity estimates. Near surface‐specific humidity estimates from two recent atmospheric reanalysis projects show markedly different responses in Tropical‐specific humidity to the assimilation of satellite radiance measurements that became available in 1999. There is less confidence in reanalysis‐based estimates of specific humidity over the ocean than over land. However the in situ‐based humidity analyses have suffered in recent years with a reduction in observation numbers and lack of information on observation methods and heights. Consequently near‐surface‐specific humidity remains relatively poorly known over the oceans.

Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site)

Models based on the multivariate partial least squares (PLS) regression technique are developed for the retrieval of phytoplankton size structure from measured light absorption spectra (BOUSSOLE site, northwestern Mediterranean Sea). PLS-models trained with data from the Mediterranean Sea showed good accuracy in retrieving, over the nine-year BOUSSOLE time series, the concentrations of total chlorophyll a [Tchl a], of the sum of seven diagnostic pigments and of pigments associated with micro, nano, and picophytoplankton size classes separately. PLS-models trained using either total particle or phytoplankton absorption spectra performed similarly, and both reproduced seasonal variations of biomass and size classes derived by high performance liquid chromatography. Satisfactory retrievals were also obtained using PLS-models trained with a data set including various locations of the world's oceans, with however a lower accuracy. These results open the way to an application of this method to absorption spectra derived from hyperspectral and field satellite radiance measurements.

Comparative study of aerosol and cloud detected by CALIPSO and OMI

The Ozone Monitoring Instrument (OMI) on the Aura Satellite detects the presence of desert dust and smoke particles (also known as aerosols) in terms of a parameter known as the UV Aerosol Index (UV AI). The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission measures the vertical distribution of aerosols and clouds. Aerosols and clouds play important roles in the atmosphere and climate system. Accurately detecting their presence, altitude, and properties using satellite radiance measurements is a very important task. This paper presents a comparative analysis of the CALIPSO Version 2 Vertical Feature Mask (VFM) product with the (OMI) UV Aerosol Index (UV AI) and reflectivity datasets for a full year of 2007. The comparison is done at regional and global scales. Based on CALIPSO arid OMI observations, the vertical and horizontal extent of clouds and aerosols are determined and the effects of aerosol type selection, load, cloud fraction on aerosol identification are discussed. It was found that the spatial-temporal correlation found between CALIPSO and OMI observations, is strongly dependent on aerosol types and cloud contamination. CALIPSO is more sensitivity to cloud and often misidentifies desert dust aerosols as cloud, while some small scale aerosol layers as well as some pollution aerosols are unidentified by OMI UV AI. Large differences in aerosol distribution patterns between CALIPSO and OMI are observed, especially for the smoke and pollution aerosol dominated areas. In addition, the results found a significant correlation between CALIPSO lidar 1064 nm backscatter and the OMI UV AI over the study regions.

Spatial‐temporal variations in phytoplankton size and colored detrital matter absorption at global and regional scales, as derived from twelve years of SeaWiFS data (1998–2009)

A procedure has been proposed by Ciotti and Bricaud (2006) to retrieve spectral absorption coefficients of phytoplankton and colored detrital matter (CDM) from satellite radiance measurements. This was also the first procedure to estimate a size factor for phytoplankton, based on the shape of the retrieved algal absorption spectrum, and the spectral slope of CDM absorption. Applying this method to the global ocean color data set acquired by SeaWiFS over twelve years (1998–2009), allowed for a comparison of the spatial variations of chlorophyll concentration ([Chl]), algal size factor (Sf), CDM absorption coefficient (acdm) at 443 nm, and spectral slope of CDM absorption (Scdm). As expected, correlations between the derived parameters were characterized by a large scatter at the global scale. We compared temporal variability of the spatially averaged parameters over the twelve‐year period for three oceanic areas of biogeochemical importance: the Eastern Equatorial Pacific, the North Atlantic and the Mediterranean Sea. In all areas, both Sf and acdm(443) showed large seasonal and interannual variations, generally correlated to those of algal biomass. The CDM maxima appeared in some occasions to last longer than those of [Chl]. The spectral slope of CDM absorption showed very large seasonal cycles consistent with photobleaching, challenging the assumption of a constant slope commonly used in bio‐optical models. In the Equatorial Pacific, the seasonal cycles of [Chl], Sf, acdm(443) and Scdm, as well as the relationships between these parameters, were strongly affected by the 1997–98 El Niño/La Niña event.

Long-term stability of TES satellite radiance measurements

Abstract. The utilization of Tropospheric Emission Spectrometer (TES) Level 2 (L2) retrieval products for the purpose of assessing long term changes in atmospheric trace gas composition requires knowledge of the overall radiometric stability of the Level 1B (L1B) radiances. The purpose of this study is to evaluate the stability of the radiometric calibration of the TES instrument by analyzing the difference between measured and calculated brightness temperatures in selected window regions of the spectrum. The Global Modeling and Assimilation Office (GMAO) profiles for temperature and water vapor and the Real-Time Global Sea Surface Temperature (RTGSST) are used as input to the Optimal Spectral Sampling (OSS) radiative transfer model to calculate the simulated spectra. The TES reference measurements selected cover a 4-year period of time from mid 2005 through mid 2009 with the selection criteria being; observation latitudes greater than −30° and less than 30°, over ocean, Global Survey mode (nadir view) and retrieved cloud optical depth of less than or equal to 0.01. The TES cloud optical depth retrievals are used only for screening purposes and no effects of clouds on the radiances are included in the forward model. This initial screening results in over 55 000 potential reference spectra spanning the four year period. Presented is a trend analysis of the time series of the residuals (observation minus calculations) in the TES 2B1, 1B2, 2A1, and 1A1 bands, with the standard deviation of the residuals being approximately equal to 0.6 K for bands 2B1, 1B2, 2A1, and 0.9 K for band 1A1. The analysis demonstrates that the trend in the residuals is not significantly different from zero over the 4-year period. This is one method used to demonstrate that the relative radiometric calibration is stable over time, which is very important for any longer term analysis of TES retrieved products (L2), particularly well-mixed species such as carbon dioxide and methane.

A Satellite-Based Assessment of Upper-Tropospheric Water Vapor Measurements during AFWEX

Abstract Consistency of upper-tropospheric water vapor measurements from a variety of state-of-the-art instruments was assessed using collocated Geostationary Operational Environmental Satellite-8 (GOES-8) 6.7-μm brightness temperatures as a common benchmark during the Atmospheric Radiation Measurement Program (ARM) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX). To avoid uncertainties associated with the inversion of satellite-measured radiances into water vapor quantity, profiles of temperature and humidity observed from in situ, ground-based, and airborne instruments are inserted into a radiative transfer model to simulate the brightness temperature that the GOES-8 would have observed under those conditions (i.e., profile-to-radiance approach). Comparisons showed that Vaisala RS80-H radiosondes and Meteolabor Snow White chilled-mirror dewpoint hygrometers are systemically drier in the upper troposphere by ∼30%–40% relative to the GOES-8 measured upper-tropospheric humidity (UTH). By contrast, two ground-based Raman lidars (Cloud and Radiation Test Bed Raman lidar and scanning Raman lidar) and one airborne differential absorption lidar agree to within 10% of the GOES-8 measured UTH. These results indicate that upper-tropospheric water vapor can be monitored by these lidars and well-calibrated, stable geostationary satellites with an uncertainty of less than 10%, and that correction procedures are required to rectify the inherent deficiencies of humidity measurements in the upper troposphere from these radiosondes.