LSA SAF Meteosat FRP products – Part 2: Evaluation and demonstration for use in the Copernicus Atmosphere Monitoring Service (CAMS)

Author(s):
Roberts, G.; Wooster, M. J.; Xu, W.; Freeborn, P. H.; Morcrette, J.-J.; Jones, L.; Benedetti, A.; Jiangping, H.; Fisher, D.; Kaiser, J. W.
Publication title: Atmospheric Chemistry and Physics
2015
 | Volume: 15 | Issue: 22
2015
DOI: 10.5194/acp-15-13241-2015
Abstract:
Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation … Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS. more
Climate Data Records LSA-SAF Land Validation

Making better sense of the mosaic of environmental measurement networks: a system-of-systems approach and quantitative assessment

Author(s):
Thorne, Peter W.; Madonna, Fabio; Schulz, Joerg; Oakley, Tim; Ingleby, Bruce; Rosoldi, Marco; Tramutola, Emanuele; Arola, Antti; Buschmann, Matthias; Mikalsen, Anna C.; Davy, Richard; Voces, Corinne; Kreher, Karin; De Maziere, Martine; Pappalardo, Gelsomina
Publication title: Geoscientific Instrumentation, Methods and Data Systems
2017
 | Volume: 6 | Issue: 2
2017
DOI: 10.5194/gi-6-453-2017
Abstract:
Abstract. There are numerous networks and initiatives concerned with the non-satellite-observing segment of Earth observation. These are owned and ope… Abstract. There are numerous networks and initiatives concerned with the non-satellite-observing segment of Earth observation. These are owned and operated by various entities and organisations often with different practices, norms, data policies, etc. The Horizon 2020 project GAIA–CLIM is working to improve our collective ability to use an appropriate subset of these observations to rigorously characterise satellite observations. The first fundamental question is which observations from the mosaic of non-satellite observational capabilities are appropriate for such an application. This requires an assessment of the relevant, quantifiable aspects of the measurement series which are available. While fundamentally poor or incorrect measurements can be relatively easily identified, it is metrologically impossible to be sure that a measurement series is correct. Certain assessable aspects of the measurement series can, however, build confidence in their scientific maturity and appropriateness for given applications. These are aspects such as that it is well documented, well understood, representative, updated, publicly available and maintains rich metadata. Entities such as the Global Climate Observing System have suggested a hierarchy of networks whereby different subsets of the observational capabilities are assigned to different layers based on such assessable aspects. Herein, we make a first attempt to formalise both such a system-of-systems networks concept and a means by which to, as objectively as possible, assess where in this framework different networks may reside. In this study, we concentrate on networks measuring primarily a subset of the atmospheric Essential Climate Variables of interest to GAIA–CLIM activities. We show assessment results from our application of the guidance and how we plan to use this in downstream example applications of the GAIA–CLIM project. However, the approach laid out should be more widely applicable across a broad range of application areas. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs and in highlighting to network owners and operators areas for potential improvement. more
EUM-Secr International cooperation Validation

Mapping long-term and high-resolution global gridded photosynthetically active radiation using the ISCCP H-series cloud product and reanalysis data

Author(s):
Tang, Wenjun; Qin, Jun; Yang, Kun; Jiang, Yaozhi; Pan, Weihao
Publication title: EARTH SYSTEM SCIENCE DATA
2022
 | Volume: 14 | Issue: 4
2022
DOI: 10.5194/essd-14-2007-2022
Abstract:
Photosynthetically active radiation (PAR) is a fundamental physiological variable for research in the ecological, agricultural, and global change fiel… Photosynthetically active radiation (PAR) is a fundamental physiological variable for research in the ecological, agricultural, and global change fields. In this study, we produced a 35-year (1984-2018) high-resolution (3 h, 10 km) global gridded PAR dataset using an effective physical-based model. The main inputs of the model were the latest International Satellite Cloud Climatology Project (ISCCP) H-series cloud products, MERRA-2 aerosol data, ERA5 surface routine variables, and MODIS and CLARRA-2 albedo products. Our gridded PAR product was evaluated against surface observations measured at 7 experimental stations of the SURFace RADiation budget network (SURFRAD), 42 experimental stations of the National Ecological Observatory Network (NEON), and 38 experimental stations of the Chinese Ecosystem Research Network (CERN). Instantaneous PAR was validated against SURFRAD and NEON data; mean bias errors (MBE) and root mean square errors (RMSE) were, on average 5.8 and 44.9 W m(-2), respectively, and the correlation coefficient (R) was 0.94 at the 10 km scale. When upscaled to 30 km, the errors were markedly reduced. Daily PAR was validated against SURFRAD, NEON, and CERN data, and the RMSEs were 13.2, 13.1, and 19.6 W m(-2,) respectively, at the 10 km scale. The RMSEs were slightly reduced when upscaled to 30 km. Compared with the well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES), our PAR product was found to be a more accurate dataset with higher resolution. This new dataset is now available at https://doi.org/10.11888/RemoteSen.tpdc.271909 (Tang, 2021). more
Application CM-SAF Climate Data Records Land

LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis

Author(s):
Wooster, M. J.; Roberts, G.; Freeborn, P. H.; Xu, W.; Govaerts, Y.; Beeby, R.; He, J.; Lattanzio, A.; Fisher, D.; Mullen, R.
Publication title: Atmospheric Chemistry and Physics
2015
 | Volume: 15 | Issue: 22
2015
DOI: 10.5194/acp-15-13217-2015
Abstract:
Abstract. Characterizing changes in landscape fire activity at better than hourly temporal resolution is achievable using thermal observations of acti… Abstract. Characterizing changes in landscape fire activity at better than hourly temporal resolution is achievable using thermal observations of actively burning fires made from geostationary Earth Observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from geostationary EO data, often with the aim of supporting biomass burning fuel consumption and trace gas and aerosol emission calculations. Such Fire Radiative Power (FRP) products are generated operationally from Meteosat by the Land Surface Analysis Satellite Applications Facility (LSA SAF) and are available freely every 15 min in both near-real-time and archived form. These products map the location of actively burning fires and characterize their rates of thermal radiative energy release (FRP), which is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the full spatio-temporal resolution FRP data set derivable from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) imager onboard Meteosat at a 3 km spatial sampling distance (decreasing away from the west African sub-satellite point), whilst the FRP-GRID product is an hourly summary at 5° grid resolution that includes simple bias adjustments for meteorological cloud cover and regional underestimation of FRP caused primarily by underdetection of low FRP fires. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) detection algorithm used to deliver these products and detail the methods used to generate the atmospherically corrected FRP and per-pixel uncertainty metrics. Using SEVIRI scene simulations and real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe certain sensor and data pre-processing characteristics that influence SEVIRI's active fire detection and FRP measurement capability, and use these to specify parameters in the FTA algorithm and to make recommendations for the forthcoming Meteosat Third Generation operations in relation to active fire measures. We show that the current SEVIRI FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel and that it appears more sensitive to fire than other algorithms used to generate many widely exploited active fire products. Finally, we briefly illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity; the companion paper (Roberts et al., 2015) provides a full product performance evaluation and a demonstration of product use within components of the Copernicus Atmosphere Monitoring Service (CAMS). more
Climate Data Records LSA-SAF Land Method

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972-1977

Author(s):
Kolbe, WM; Tonboe, RT; Stroeve, J
Publication title: EARTH SYSTEM SCIENCE DATA
2024
 | Volume: 16 | Issue: 3
2024
DOI: 10.5194/essd-16-1247-2024
Abstract:
The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured t… The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature ( T-B ) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km 2 , respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km 2 . Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at 10.5285/34a15b96f1134d9e95b9e486d74e49cf .(Tonboe et al., 2023) more
Application Climate Data Records Cryosphere OSI-SAF

Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island

Author(s):
Li, Qi; Bessafi, Miloud; Li, Peng
Publication title: Atmosphere
2023
 | Volume: 14 | Issue: 9
2023
DOI: 10.3390/atmos14091331
Abstract:
This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiati… This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° and the temporal coverage is from 2007 to 2016. The first five years (2007–2011) are used as training data, and the remaining five years (2012–2016) are used as test data in the prediction model. Datasets were detrended, de-seasonalized, and normalized before being applied to multiple linear regression (MLR), principal component regression (PCR), stepwise regression (SR), and partial least squares regression (PLSR), which are used to perform prediction mapping. The statistical analysis using MAE, MSE, and RMSE shows that the PCR model had the smallest MAE, MSE, and RMSE as compared to the other three models. The PCR model seems better for SSR mapping prediction over Reunion Island. Although the PCR model provides better prediction results, its MAE, MSE, and RMSE are quite large. more
Application Atmosphere CM-SAF Climate Data Records

Mapping vessel traffic patterns in the ice-covered waters of the Pacific Arctic

Author(s):
Kapsar, Kelly; Gunn, Grant; Brigham, Lawson; Liu, Jianguo
Publication title: Climatic Change
2023
 | Volume: 176 | Issue: 7
2023
DOI: 10.1007/s10584-023-03568-3
Abstract:
Recent climate change has caused declines in ice coverage which have lengthened the open water season in the Arctic and increased access to resources … Recent climate change has caused declines in ice coverage which have lengthened the open water season in the Arctic and increased access to resources and shipping routes. These changes have resulted in more vessel activity in seasonally ice-covered regions. While traffic is increasing in the ice-free season, the amount of vessel activity in the marginal ice zone (ice concentration 15–80%) or in pack ice (>80% concentration) remains unclear. Understanding patterns of vessel activities in ice is important given increased safety challenges and environmental impacts. Here, we couple high-resolution ship tracking information with sea ice thickness and concentration data to quantify vessel activity in ice-covered areas of the Pacific Arctic (northern Bering, Chukchi, and western Beaufort Seas). This region is a geo-strategically critical area that contains globally important commercial fisheries and serves as a corridor for Arctic access for wildlife and vessels. We find that vessel traffic in the marginal ice zone is widely distributed across the study area while vessel traffic in pack ice is concentrated along known shipping routes and in areas of natural resource development. Of the statistically significant relationships between vessel traffic and both sea ice concentration and thickness, over 99% are negative, indicating that increasing sea ice is associated with decreasing vessel traffic on a monthly time scale. Furthermore, there is substantial vessel traffic in areas of high concentration for bowhead whales (Balaena mysticetus), and traffic in these areas increased four-fold during the study period. Fishing vessels dominate vessel traffic at low ice concentrations, but vessels categorized as Other, likely icebreakers, are the most common vessel type in pack ice. These findings indicate that vessel traffic in areas of ice coverage is influenced by distant policy and resource development decisions which should be taken into consideration when trying to predict future vessel-ice interactions in a changing climate. more
Application Climate Data Records Cryosphere OSI-SAF Ocean

Marine aerosol properties over the Southern Ocean in relation to the wintertime meteorological conditions

Author(s):
Thomas, Manu Anna; Devasthale, Abhay; Kahnert, Michael
Publication title: ATMOSPHERIC CHEMISTRY AND PHYSICS
2022
 | Volume: 22 | Issue: 1
2022
DOI: 10.5194/acp-22-119-2022
Abstract:
Given the vast expanse of oceans on our planet, marine aerosols (and sea salt in particular) play an important role in the climate system via multitud… Given the vast expanse of oceans on our planet, marine aerosols (and sea salt in particular) play an important role in the climate system via multitude of direct and indirect effects. The efficacy of their net impact, however, depends strongly on the local meteorological conditions that influence their physical, optical and chemical properties. Understanding the coupling between aerosol properties and meteorological conditions is therefore important. It has been historically difficult to statistically quantify this coupling over larger oceanic areas due to the lack of suitable observations, leading to large uncertainties in the representation of aerosol processes in climate models. Perhaps no other region shows higher uncertainties in the representation of marine aerosols and their effects than the Southern Ocean. During winter the Southern Ocean boundary layer is dominated by sea salt emissions. Here, using 10 years of austral winter period (June, July and August, 2007-2016) space-based aerosol profiling by CALIOP-CALIPSO in combination with meteorological reanalysis data, we investigated the sensitivity of marine aerosol properties over the Southern Ocean (40-65 degrees S) to various meteorological parameters, such as vertical relative humidity (RH), surface wind speed and sea surface temperature (SST) in terms of joint histograms. The sensitivity study is done for the climatological conditions and for the enhanced cyclonic and anticyclonic conditions in order to understand the impact of large-scale atmospheric circulation on the aerosol properties. We find a clear demarcation in the 532 nm aerosol backscatter and extinction at RH around 60 %, irrespective of the state of the atmosphere. The backscatter and extinction increase at higher relative humidity as a function of surface wind speed. This is mainly because of the water uptake by the wind-driven sea salt aerosols at high RH near the ocean surface resulting in an increase in size, which is confirmed by the decreased depolarization for the wet aerosols. An increase in aerosol backscatter and extinction is observed during the anticyclonic conditions compared to cyclonic conditions for the higher wind speeds and relative humidity, mainly due to aerosols being confined to the boundary layer, and their proximity to the ocean surface facilitates the growth of the particles. We further find a very weak dependency of aerosol backscatter on SSTs at lower wind speeds. However, when the winds are stronger than about 12 m s(-1), the backscattering coefficient generally increases with SST. When aerosol properties are investigated in terms of aerosol verticality and in relation to meteorological parameters, it is seen that the aerosol backscatter values in the free troposphere (pressure 60 %, low depolarization values are noticeable in the lower troposphere, which is an indication of the dominance of water-coated and mostly spherical sea salt particles. For RH < 60 %, there are instances when the aerosol depolarization increases in the boundary layer; this is more prominent in the mean and anticyclonic cases, which can be associated with the presence of drier aerosol particles. Based on the joint histograms investigated here, we provide third-degree polynomials to obtain aerosol extinction and backscatter as a function of wind speed and relative humidity. Additionally, backscattering coefficient is also expressed jointly in terms of wind speed and sea surface temperature. Furthermore, depolarization is expressed as a function of relative humidity. These fitting functions would be useful to test and improve the parameterizations of sea salt aerosols in the climate models. We also note some limitations of our study. For example, interpreting the verticality of aerosol properties (especially depolarization) in relation to the meteorological conditions in the free and upper troposphere (pressure more
Application Atmosphere Climate Data Records Cryosphere OSI-SAF

Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model

Author(s):
Niehaus, H.; Istomina, L.; Nicolaus, M.; Tao, R.; Malinka, A.; Zege, E.; Spreen, G.
Publication title: Cryosphere
2024
 | Volume: 18 | Issue: 2
2024
DOI: 10.5194/tc-18-933-2024
Abstract:
The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scal… The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scale observations of melt pond coverage and sea ice albedo are crucial to investigate the role of sea ice for Arctic amplification and its representation in global climate models. We present the new Melt Pond Detection 2 (MPD2) algorithm, which retrieves melt pond, sea ice, and open-ocean fractions as well as surface albedo from Sentinel-3 visible and near-infrared reflectances. In contrast to most other algorithms, our method uses neither fixed values for the spectral albedo of the surface constituents nor an artificial neural network. Instead, it aims for a fully physical representation of the reflective properties of the surface constituents based on their optical characteristics. The state vector X, containing the optical properties of melt ponds and sea ice along with the area fractions of melt ponds and open ocean, is optimized in an iterative procedure to match the measured reflectances and describe the surface state. A major problem in unmixing a compound pixel is that a mixture of half open water and half bright ice cannot be distinguished from a homogeneous pixel of darker ice. In order to overcome this, we suggest constraining the retrieval with a priori information. Initial values and constraint of the surface fractions are derived with an empirical retrieval which uses the same spectral reflectances as implemented in the physical retrieval. © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. more
Application Climate Data Records Cryosphere OSI-SAF

Merging high-resolution satellite surface radiation data with meteorological sunshine duration observations over China from 1983 to 2017

Author(s):
Feng, F.; Wang, K.
Publication title: Remote Sensing
2021
 | Volume: 13 | Issue: 4
2021
DOI: 10.3390/rs13040602
Abstract:
Surface solar radiation (Rs ) is essential to climate studies. Thanks to long-term records from the Advanced Very High-Resolution Radiometers (AVHRR),… Surface solar radiation (Rs ) is essential to climate studies. Thanks to long-term records from the Advanced Very High-Resolution Radiometers (AVHRR), the recent release of International Satellite Cloud Climatology Project (ISCCP) HXG cloud products provide a promising opportunity for building long-term Rs data with high resolutions (3 h and 10 km). In this study, we compare three satellite Rs products based on AVHRR cloud products over China from 1983 to 2017 with direct observations of Rs and sunshine duration (SunDu)-derived Rs . The results show that SunDu-derived Rs have higher accuracy than the direct observed Rs at time scales of a month or longer by comparing with the satellite Rs products. SunDu-derived Rs is available from the 1960s at more than 2000 stations over China, which provides reliable decadal estimations of Rs . However, the three AVHRR-based satellite Rs products have significant biases in quantifying the trend of Rs from 1983 to 2016 (−4.28 W/m2/decade to 2.56 W/m2/decade) due to inhomogeneity in satellite cloud products and the lack of information on atmospheric aerosol optical depth. To adjust the inhomogeneity of the satellite Rs products, we propose a geographically weighted regression fusion method (HGWR) to merge ISCCP-HXG Rs with SunDu-derived Rs . The merged Rs product over China from 1983 to 2017 with a spatial resolution of 10 km produces nearly the same trend as that of the SunDu-derived Rs . This study makes a first attempt to adjust the inhomogeneity of satellite Rs products and provides the merged high-resolution Rs product from 1983 to 2017 over China, which can be downloaded freely. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. more
Atmosphere CM-SAF Climate Data Records Validation
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