The Copernicus Atmosphere Monitoring Service (CAMS)
provides near-real-time forecast and reanalysis of aerosols using the ECMWF
Integrated Forecasting System with atmospheric composition extension,
...constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol
Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this
work is to evaluate two new near-real-time AOD products to prepare for their
assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea
and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B
over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and
NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp
(v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the
modeled AOD were assessed at the model grid resolution (i.e., level-3)
using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is
explained by instrument and retrieval algorithm similarities. VIIRS AOD is
frequently lower over the ocean background and higher over biomass burning
and dust source land regions compared to MODIS. VIIRS shows larger spatial
coverage over land and resolves finer spatial structures such as the
transport of Australian biomass burning smoke over the Pacific, which can be
explained by the use of a heavy aerosol detection test in the retrieval
algorithm. Our results confirm the positive offset over ocean (i) between
Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration
degradation of Terra/MODIS in the Dark Target algorithm and (ii) between
SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective
bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the
rest of the products, which is mainly related to differences in spatial
representativity at the IFS grid spatial resolution due to the stringent
cloud filtering applied to the SLSTR radiances. Finally, the geometry
characteristics of the instrument, which drive the range of scattering
angles sampled by the instrument, can explain a large part of the
differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A.
The climate in the Arctic has warmed much more quickly in the last 2 to 3 decades than at the mid-latitudes, i.e., during the Arctic amplification (AA) period. Radiative forcing in the Arctic is ...influenced both directly and indirectly by aerosols. However, their observation from ground or airborne instruments is challenging, and thus measurements are sparse. In this study, total aerosol optical depth (AOD) is determined from top-of-atmosphere reflectance measurements by the Advanced Along-Track Scanning Radiometer (AATSR) on board ENVISAT over snow and ice in the Arctic using a retrieval called AEROSNOW for the period 2003 to 2011. AEROSNOW incorporates an existing aerosol retrieval algorithm with a cloud-masking algorithm, alongside a novel quality-flagging methodology specifically designed for implementation in the high Arctic region (≥ 72∘ N). We use the dual-viewing capability of the AATSR instrument to accurately determine the contribution of aerosol to the reflection at the top of the atmosphere for observations over the bright surfaces of the cryosphere in the Arctic. The AOD is retrieved assuming that the surface reflectance observed by the satellite can be well parameterized by a bidirectional snow reflectance distribution function (BRDF). The spatial distribution of AOD shows that high values in spring (March, April, May) and lower values in summer (June, July, August) are observed. The AEROSNOW AOD values are consistent with those from collocated Aerosol Robotic Network (AERONET) measurements, with no systematic bias found as a function of time. The AEROSNOW AOD in the high Arctic was validated by comparison with ground-based measurements at the PEARL, OPAL, Hornsund, and Thule stations. The AEROSNOW AOD value is less than 0.15 on average, and the linear regression of AEROSNOW and AERONET total AOD yields a slope of 0.98, a Pearson correlation coefficient of R=0.86, and a root mean square error (RMSE) of =0.01 for the monthly scale in both spring and summer. The AEROSNOW observation of increased AOD values over the high Arctic cryosphere during spring confirms clearly that Arctic haze events were well captured by this dataset. In addition, the AEROSNOW AOD results provide a novel and unique total AOD data product for the springtime and summertime from 2003 to 2011. These AOD values, retrieved from spaceborne observation, provide a unique insight into the high Arctic cryospheric region at high spatial resolution and temporal coverage.
Accurate knowledge of the reflectance from snow/ice-covered surfaces is of fundamental importance for the retrieval of snow parameters and atmospheric constituents from space-based and airborne ...observations.
The accurate identification of the presence of cloud in the
ground scenes observed by remote-sensing satellites is an end in itself. The
lack of knowledge of cloud at high latitudes increases the ...error and
uncertainty in the evaluation and assessment of the changing impact of
aerosol and cloud in a warming climate. A prerequisite for the accurate
retrieval of aerosol optical thickness (AOT) is the knowledge of the presence
of cloud in a ground scene. In our study, observations of the upwelling radiance in the visible (VIS),
near infrared (NIR), shortwave infrared (SWIR) and the thermal infrared
(TIR), coupled with solar extraterrestrial irradiance, are used to determine
the reflectance. We have developed a new cloud identification algorithm for
application to the reflectance observations of the Advanced Along-Track Scanning
Radiometer (AATSR) on European Space Agency (ESA)-Envisat and Sea and Land
Surface Temperature Radiometer (SLSTR) on board the ESA Copernicus Sentinel-3A
and -3B. The resultant AATSR–SLSTR cloud identification algorithm (ASCIA)
addresses the requirements for the study AOT at high latitudes and utilizes
time-series measurements. It is assumed that cloud-free surfaces have
unchanged or little changed patterns for a given sampling period, whereas
cloudy or partly cloudy scenes show much higher variability in space and
time. In this method, the Pearson correlation coefficient (PCC) parameter is
used to measure the “stability” of the atmosphere–surface system observed
by satellites. The cloud-free surface is classified by analysing the PCC
values on the block scale 25×25 km2. Subsequently, the
reflection at 3.7 µm is used for accurate cloud identification at
scene level: with areas of either 1×1 or 0.5×0.5 km2.
The ASCIA data product has been validated by comparison with independent
observations, e.g. surface synoptic observations (SYNOP), the data from
AErosol RObotic NETwork (AERONET) and the following satellite products:
(i) the ESA standard cloud product from AATSR L2 nadir cloud flag; (ii) the
product from a method based on a clear-snow spectral shape developed at IUP
Bremen (Istomina et al., 2010), which we call ISTO; and (iii) the Moderate
Resolution Imaging Spectroradiometer (MODIS) products. In comparison to
ground-based SYNOP measurements, we achieved a promising agreement better
than 95 % and 83 % within ±2 and ±1 okta respectively. In
general, ASCIA shows an improved performance in comparison to other
algorithms applied to AATSR measurements for the identification of clouds in
a ground scene observed at high latitudes.
Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC)(3) project was established in 2016 (www.ac3-tr.de/). It comprises modeling and ...data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, shipborne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data. For example, a distinct atmospheric moistening, an increase of regional storm activities, an amplified winter warming in the Svalbard and North Pole regions, and a decrease of sea ice thickness in the Fram Strait and of snow depth on sea ice have been identified. A positive trend of tropospheric bromine monoxide (BrO) column densities during polar spring was verified. Local marine/biogenic sources for cloud condensation nuclei and ice nucleating particles were found. Atmospheric-ocean and radiative transfer models were advanced by applying new parameterizations of surface albedo, cloud droplet activation, convective plumes and related processes over leads, and turbulent transfer coefficients for stable surface layers. Four modes of the surface radiative energy budget were explored and reproduced by simulations. To advance the future synthesis of the results, cross-cutting activities are being developed aiming to answer key questions in four focus areas: lapse rate feedback, surface processes, Arctic mixed-phase clouds, and airmass transport and transformation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC) 3 project was established in 2016 ( www.ac3-tr.de/ ). It comprises ...modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, shipborne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data. For example, a distinct atmospheric moistening, an increase of regional storm activities, an amplified winter warming in the Svalbard and North Pole regions, and a decrease of sea ice thickness in the Fram Strait and of snow depth on sea ice have been identified. A positive trend of tropospheric bromine monoxide (BrO) column densities during polar spring was verified. Local marine/biogenic sources for cloud condensation nuclei and ice nucleating particles were found. Atmospheric–ocean and radiative transfer models were advanced by applying new parameterizations of surface albedo, cloud droplet activation, convective plumes and related processes over leads, and turbulent transfer coefficients for stable surface layers. Four modes of the surface radiative energy budget were explored and reproduced by simulations. To advance the future synthesis of the results, cross-cutting activities are being developed aiming to answer key questions in four focus areas: lapse rate feedback, surface processes, Arctic mixed-phase clouds, and airmass transport and transformation.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
7.
THE ARCTIC CLOUD PUZZLE Wendisch, Manfred; Macke, Andreas; Ehrlich, André ...
Bulletin of the American Meteorological Society,
05/2019, Letnik:
100, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Clouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. ...However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC) project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Polar Multi-Sensor Aerosol product (PMAp) is based on the synergistic use of three instruments from the Metop platform, GOME-2, AVHRR, and IASI. The retrieval algorithm includes three major ...steps: a pre-identification of the aerosol class, a selection of the aerosol model, and a calculation of the Aerosol Optical Depth (AOD). This paper provides a detailed description of the PMAp retrieval, which combines information provided by the three instruments. The retrieved AOD is qualitatively evaluated, and a good temporal as well as spatial performance is observed, including for the transition between ocean and land. More quantitatively, the performance is evaluated by comparison to AERONET in situ measurements. Very good consistency is also observed when compared to other space-based data such as MODIS or VIIRS. The paper demonstrates the ability of this first generation of synergistic products to derive reliable AOD, opening the door for the development of synergistic products from the instruments to be embarked on the coming Metop Second Generation platform. PMAp has been operationally distributed in near-real-time since 2014 over ocean, and 2016 over land.
•A novel satellite-based full physical retrieval to derive Aerosol Optical Thickness and Surface parameters over vegetation dominated scenes has been developed.•The approach is based on the SCIATRAN ...and relies on multi-spectral and multi-viewing capabilities of the used satellite instrument.•The minimization is done by solving the Quadratic Programming Problem (QPP).•The retrieval has been applied to POLDER/PARASOL data and provides very convincing results at native spatial resolution.•A first, brief validation with ARM ground based measurements shows good and partly very good agreement (R = 0.84).
We present a novel approach to derive Aerosol Optical Thickness (AOT) at 0.5 µm and the surface reflectance for five spectral channels at native spatial resolution from the measurements of the Polarization and Directionality of Earth’s Reflectances-3 (POLDER) instrument aboard the Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. POLDER had multi-spectral and multi-viewing capabilities. This publication describes the first step in the design of a high-quality AOT and surface property retrieval algorithm, enabling the global evaluation of future missions providing multi-spectral and multi-viewing space-borne measurements. The developed retrieval approach is based on the radiative transfer and retrieval model SCIATRAN using an analytical linearized retrieval mode. The surface is parametrized according to the Ross-Li model and the aerosol typing was using prescribed types based on the approach by Levy et al. 26. The minimization using SCIATRAN has been done by solving the quadratic programming problem which minimizes the considered system of equations (typically) based on linear constraints. In this study we constrained the retrieval so far only by assuming that the retrieved AOT at 0.5 µm is larger than 0.01 and smaller than 1.5. Until now, the retrieval is based only upon unpolarized POLDER data and shows promisingly weak dependence on a priori information.
As a first validation, the retrieved AOT values have been compared with ground based measurements over Atmospheric Radiation Measurement Climate Research Facilities (ARM) in the Southern Great Plains (SGP). The SCIATRAN retrievals are mostly homogeneous and consistent in the region around the US ARM/SGP measurement stations. The retrieval results agree well with the ARM ground based measurements of AOT. The correlation coefficient was R=0.84and part of the remaining differences between ARM measurements and SCIATRAN retrievals can be attributed to unmasked clouds. Histogram based analysis of the AOT values showed reasonable distributions using SCIATRAN. Most distributions based on SCIATRAN AOT retrievals within a radius of 25 km around the stations are significantly broader than those derived from ARM measurements during 30 minutes but are well centered around the ARM AOT distributions.
The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, ...constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019-February 2020 and March-May 2020).
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