Global Mapping of Microwave Emissivity and Emitting Depth in Arid Areas Using GMI Observations

Author(s):
Favrichon, S.; Prigent, C.; Jimenez, C.; Vogt, R.
Publication title: Earth and Space Science
2023
 | Volume: 10 | Issue: 11
2023
DOI: 10.1029/2022EA002756
Abstract:
Over arid areas, observations of brightness temperatures by passive microwave radiometers are affected by the variation of the emitting depth with wav… Over arid areas, observations of brightness temperatures by passive microwave radiometers are affected by the variation of the emitting depth with wavelengths. When this variation is unaccounted for, it limits the assimilation of passive microwaves over deserts in Numerical Weather Prediction models and it causes large errors in passive microwave retrievals of land surface temperatures. The emitting depths, along with the corresponding emissivities, are estimated from 10 to 89 GHz, using the non-Sun-synchronous observations of the Global Precipitation Mission Microwave Imager to reconstruct the monthly diurnal cycle of brightness temperature. The soil temperature profile is modeled using a two-term Fourier decomposition based on the ERA5 surface temperature. The combination of the observation and the modeled temperature allows for an estimation of the microwave effective emitting depth. The emitting depth is estimated to be up to 25 cm at 36 GHz, resulting in large differences between the surface temperature and the effective emitting temperature. The variation of emitting depth with frequency is parameterized, and a companion data set provides the necessary parameters to calculate the emitting depth for arid areas between 10 and 89 GHz, globally. The benefit of this parameterization is quantified, with an application to the modeling of observations from the Special Sensor Microwave Imager Sounder over arid areas. © 2023 The Authors. more
Application CM-SAF Climate Data Records Land

Global, regional and national trends of atmospheric ammonia derived from a decadal (2008–2018) satellite record

Author(s):
Van Damme, Martin; Clarisse, Lieven; Franco, Bruno; Sutton, Mark A; Erisman, Jan Willem; Wichink Kruit, Roy; van Zanten, Margreet; Whitburn, Simon; Hadji-Lazaro, Juliette; Hurtmans, Daniel; Clerbaux, Cathy; Coheur, Pierre-François
Publication title: Environmental Research Letters
2021
 | Volume: 16 | Issue: 5
2021
DOI: 10.1088/1748-9326/abd5e0
Abstract:
Abstract Excess atmospheric ammonia (NH 3 ) leads to deleterious effects on biodiversity, ecosy… Abstract Excess atmospheric ammonia (NH 3 ) leads to deleterious effects on biodiversity, ecosystems, air quality and health, and it is therefore essential to monitor its budget and temporal evolution. Hyperspectral infrared satellite sounders provide daily NH 3 observations at global scale for over a decade. Here we use the version 3 of the Infrared Atmospheric Sounding Interferometer (IASI) NH 3 dataset to derive global, regional and national trends from 2008 to 2018. We find a worldwide increase of 12.8 ± 1.3 % over this 11-year period, driven by large increases in east Asia (5.80 ± 0.61% increase per year), western and central Africa (2.58 ± 0.23 % yr −1 ), North America (2.40 ± 0.45 % yr −1 ) and western and southern Europe (1.90 ± 0.43 % yr −1 ). These are also seen in the Indo-Gangetic Plain, while the southwestern part of India exhibits decreasing trends. Reported national trends are analyzed in the light of changing anthropogenic and pyrogenic NH 3 emissions, meteorological conditions and the impact of sulfur and nitrogen oxides emissions, which alter the atmospheric lifetime of NH 3 . We end with a short case study dedicated to the Netherlands and the ‘Dutch Nitrogen crisis’ of 2019. more
AC-SAF Application Atmosphere Climate Data Records

GNSS radio occultation excess-phase processing for climate applications including uncertainty estimation

Author(s):
Innerkofler, J.; Kirchengast, G.; Schwärz, M.; Marquardt, C.; Andres, Y.
Publication title: Atmospheric Measurement Techniques
2023
 | Volume: 16 | Issue: 21
2023
DOI: 10.5194/amt-16-5217-2023
Abstract:
Earth observation from space provides a highly valuable basis for atmospheric and climate science, in particular also through climate benchmark data f… Earth observation from space provides a highly valuable basis for atmospheric and climate science, in particular also through climate benchmark data from suitable remote sensing techniques. Measurements by global navigation satellite system (GNSS) radio occultation (RO) qualify to produce such benchmark data records as they globally provide accurate and long-Term stable datasets for essential climate variables (ECVs) such as temperature. This requires a rigorous processing of the raw RO measurements to ECVs, with narrow uncertainties. In order to fully exploit this potential, Wegener Center's Reference Occultation Processing System (rOPS) Level 1a (L1a) processing subsystem includes uncertainty estimation in both precise orbit determination (POD) and excess-phase profile derivation. Here we introduce the new rOPS L1a excess-phase processing, the first step in the RO profiles retrieval down to atmospheric profiles, which extracts the atmospheric excess phase from raw SI-Traceable RO measurements. This excess-phase processing, for itself algorithmically concise, includes integrated quality control and uncertainty estimation, requiring a complex framework of various subsystems that we first introduce before describing the implementation of the core algorithms. The quality control and uncertainty estimation, computed per RO event, are supported by reliable forward-modeled excess-phase profiles based on the POD orbit arcs and collocated short-range forecast profiles of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5). The quality control removes or alternatively flags excess-phase profiles of insufficient or degraded quality. The uncertainty estimation accounts both for relevant random-and systematic-uncertainty components, and the resulting (total) uncertainty profiles serve as a starting point for the subsequent uncertainty propagation through the retrieval processing chain down to the atmospheric ECV profiles. We also evaluated the quality and reliability of the resulting excess-phase profiles based on Metop-A/B/C (Meteorological Operational) RO datasets for three 3-month periods in 2008, 2013, and 2020 by way of a sensitivity analysis for three representative atmospheric layers (tropo-, strato-, mesosphere), investigating consistency with ERA5-derived profiles, influences of different orbit and clock inputs, and consistency across the different Metop satellites. These consistencies range from centimeter to submillimeter levels, indicating that the new processing can provide highly accurate and robust excess-phase profiles. Furthermore, cross-evaluation and intercomparison with excess-phase data from the established data providers EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) and UCAR (University Corporation for Atmospheric Research) revealed subtle discrepancies but overall very close agreement, with larger differences compared to UCAR in the boundary layer. The new rOPS L1a processing can hence be considered capable of producing reliable long-Term data records including uncertainty estimation for the benefit of climate applications. © Copyright: more
Atmosphere Climate Data Records EUM-Secr Validation

Ground-based validation of the MetOp-A and MetOp-B GOME-2 OClO measurements

Author(s):
Pinardi, Gaia; Van Roozendael, Michel; Hendrick, François; Richter, Andreas; Valks, Pieter; Alwarda, Ramina; Bognar, Kristof; Frieß, Udo; Granville, José; Gu, Myojeong; Johnston, Paul; Prados-Roman, Cristina; Querel, Richard; Strong, Kimberly; Wagner, Thomas; Wittrock, Folkard; Yela Gonzalez, Margarita
Publication title: Atmospheric Measurement Techniques
2022
 | Volume: 15 | Issue: 11
2022
DOI: 10.5194/amt-15-3439-2022
Abstract:
Abstract. This paper reports on ground-based validation of the atmospheric OClO data record produced within the framework of EUMETSAT's Satellite Appl… Abstract. This paper reports on ground-based validation of the atmospheric OClO data record produced within the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC SAF) using the Global Ozone Monitoring Experiment (GOME)-2A and GOME-2B instrument measurements, covering the 2007–2016 and 2013–2016 periods, respectively. OClO slant column densities are compared to correlative measurements collected from nine Zenith-Scattered-Light Differential Optical Absorption Spectroscopy (ZSL-DOAS) instruments from the Network for the Detection of Atmospheric Composition Change (NDACC) distributed in both the Arctic and Antarctic. Sensitivity tests are performed on the ground-based data to estimate the impact of the different OClO DOAS analysis settings. On this basis, we infer systematic uncertainties of about 25 % (i.e., about 3.75×1013 molec. cm−2) between the different ground-based data analyses, reaching total uncertainties ranging from about 26 % to 33 % for the different stations (i.e., around 4 to 5×1013 molec. cm−2). Time series at the different sites show good agreement between satellite and ground-based data for both the inter-annual variability and the overall OClO seasonal behavior. GOME-2A results are found to be noisier than those of GOME-2B, especially after 2011, probably due to instrumental degradation effects. Daily linear regression analysis for OClO-activated periods yield correlation coefficients of 0.8 for GOME-2A and 0.87 for GOME-2B, with slopes with respect to the ground-based data ensemble of 0.64 and 0.72, respectively. Satellite minus ground-based offsets are within 8×1013 molec. cm−2, with some differences between GOME-2A and GOME-2B depending on the station. Overall, considering all the stations, a median offset of about -2.2×1013 molec. cm−2 is found for both GOME-2 instruments. more
AC-SAF Atmosphere Climate Data Records Validation

High-resolution (1 km) satellite rainfall estimation from SM2RAIN applied to Sentinel-1: Po River basin as a case study

Author(s):
Filippucci, Paolo; Brocca, Luca; Quast, Raphael; Ciabatta, Luca; Saltalippi, Carla; Wagner, Wolfgang; Tarpanelli, Angelica
Publication title: Hydrology and Earth System Sciences
2022
 | Volume: 26 | Issue: 9
2022
DOI: 10.5194/hess-26-2481-2022
Abstract:
Abstract. The use of satellite sensors to infer rainfall measurements has become a widely used practice in recent years, but their spatial resolution … Abstract. The use of satellite sensors to infer rainfall measurements has become a widely used practice in recent years, but their spatial resolution usually exceeds 10 km, due to technological limitations. This poses an important constraint on its use for applications such as water resource management, index insurance evaluation or hydrological models, which require more and more detailed information. In this work, the algorithm SM2RAIN (Soil Moisture to Rain) for rainfall estimation is applied to two soil moisture products over the Po River basin: a high-resolution soil moisture product derived from Sentinel-1, named S1-RT1, characterized by 1 km spatial resolution (500 m spacing), and a 25 (12.5 km spacing) product derived from ASCAT, resampled to the same grid as S1-RT1. In order to overcome the need for calibration and to allow for its global application, a parameterized version of SM2RAIN algorithm was adopted along with the standard one. The capabilities in estimating rainfall of each obtained product were then compared, to assess both the parameterized SM2RAIN performances and the added value of Sentinel-1 high spatial resolution. The results show that good estimates of rainfall are obtainable from Sentinel-1 when considering aggregation time steps greater than 1 d, since the low temporal resolution of this sensor (from 1.5 to 4 d over Europe) prevents its application for infer daily rainfall. On average, the ASCAT-derived rainfall product performs better than S1-RT1, even if the performances are equally good when 30 d accumulated rainfall is considered (resulting in a mean Pearson correlation for the parameterized SM2RAIN product of 0.74 and 0.73, respectively). Notwithstanding this, the products obtained from Sentinel-1 outperform those from ASCAT in specific areas, like in valleys inside mountain regions and most of the plains, confirming the added value of the high-spatial-resolution information in obtaining spatially detailed rainfall. Finally, the performances of the parameterized products are similar to those obtained with the calibrated SM2RAIN algorithm, confirming the reliability of the parameterized algorithm for rainfall estimation in this area and fostering the possibility to apply SM2RAIN worldwide, even without the availability of a rainfall benchmark product. more
Atmosphere Climate Data Records H-SAF Method

Glyoxal tropospheric column retrievals from TROPOMI - multi-satellite intercomparison and ground-based validation

Author(s):
Lerot, Christophe; Hendrick, Francois; Van Roozendael, Michel; Alvarado, Leonardo M. A.; Richter, Andreas; De Smedt, Isabelle; Theys, Nicolas; Vlietinck, Jonas; Yu, Huan; Van Gent, Jeroen; Stavrakou, Trissevgeni; Muller, Jean-Francois; Valks, Pieter; Loyola, Diego; Irie, Hitoshi; Kumar, Vinod; Wagner, Thomas; Schreier, Stefan F.; Sinha, Vinayak; Wang, Ting; Wang, Pucai; Retscher, Christian
Publication title: ATMOSPHERIC MEASUREMENT TECHNIQUES
2021
 | Volume: 14 | Issue: 12
2021
DOI: 10.5194/amt-14-7775-2021
Abstract:
We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPO-spheric Monitoring Instrument (TROPOMI) on board the S… We present the first global glyoxal (CHOCHO) tropospheric column product derived from the TROPO-spheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite. Atmospheric glyoxal results from the oxidation of other non-methane volatile organic compounds (NMVOCs) and from direct emissions caused by combustion processes. Therefore, this product is a useful indicator of VOC emissions. It is generated with an improved version of the BIRA-IASB scientific retrieval algorithm relying on the differential optical absorption spectroscopy (DOAS) approach. Among the algorithmic updates, the DOAS fit now includes corrections to mitigate the impact of spectral misfits caused by scene brightness inhomogeneity and strong NO2 absorption. The product comes along with a full error characterization, which allows for providing random and systematic error estimates for every observation. Systematic errors are typically in the range of 1 x 10(14)-3 x 10(14) molec.cm(-2) (similar to 30 %-70 % in emission regimes) and originate mostly from a priori data uncertainties and spectral interferences with other absorbing species. The latter may be at the origin, at least partly, of an enhanced glyoxal signal over equatorial oceans, and further investigation is needed to mitigate them. Random errors are large (> 6 x 10(14) molec. cm(-2)) but can be reduced by averaging observations in space and/or time. Benefiting from a high signal-to-noise ratio and a large number of small-size observations, TROPOMI provides glyoxal tropospheric column fields with an unprecedented level of detail. Using the same retrieval algorithmic baseline, glyoxal column data sets are also generated from the Ozone Monitoring Instrument (OMI) on Aura and from the Global Ozone Monitoring Experiment-2 (GOME-2) on board Metop-A and Metop-B. Those four data sets are intercompared over large-scale regions worldwide and show a high level of consistency. The satellite glyoxal columns are also compared to glyoxal columns retrieved from ground-based Multi-AXis DOAS (MAX-DOAS) instruments at nine stations in Asia and Europe. In general, the satellite and MAX-DOAS instruments provide consistent glyoxal columns both in terms of absolute values and variability. Correlation coefficients between TROPOMI and MAX-DOAS glyoxal columns range between 0.61 and 0.87. The correlation is only poorer at one mid-latitude station, where satellite data appear to be biased low during wintertime. The mean absolute glyoxal columns from satellite and MAX-DOAS generally agree well for low/moderate columns with differences of less than 1 x 10(14) molec.cm(-2). A larger bias is identified at two sites where the MAX-DOAS columns are very large. Despite this systematic bias, the consistency of the satellite and MAX-DOAS glyoxal seasonal variability is high. more
AC-SAF Atmosphere Climate Data Records Validation

High-resolution (1km) all-sky net radiation over Europe enabled by the merging of land surface temperature retrievals from geostationary and polar-orbiting satellites

Author(s):
Rains, D.; Trigo, I.; Dutra, E.; Ermida, S.; Ghent, D.; Hulsman, P.; Gómez-Dans, J.; Miralles, D.G.
Publication title: Earth System Science Data
2024
 | Volume: 16 | Issue: 1
2024
DOI: 10.5194/essd-16-567-2024
Abstract:
Surface net radiation (SNR) is a vital input for many land surface and hydrological models. However, most of the current remote sensing datasets of SN… Surface net radiation (SNR) is a vital input for many land surface and hydrological models. However, most of the current remote sensing datasets of SNR come mostly at coarse resolutions or have large gaps due to cloud cover that hinder their use as input in models. Here, we present a downscaled and continuous daily SNR product across Europe for 2018-2019. Long-wave outgoing radiation is computed from a merged land surface temperature (LST) product in combination with Meteosat Second Generation emissivity data. The merged LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel-3A satellite. This approach makes use of the medium spatial (approx. 5-7km) but high temporal (30min) resolution, gap-free data from MSG along with the low temporal (2-3d) but high spatial (1km) resolution of the Sentinel-3 LST retrievals. The resulting 1km and daily LST dataset is based on an hourly merging of both datasets through bias correction and Kalman filter assimilation. Short-wave outgoing radiation is computed from the incoming short-wave radiation from MSG and the downscaled albedo using 1km PROBA-V data. MSG incoming short-wave and long-wave radiation and the outgoing radiation components at 1km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the mean squared error by ca. 7% with an increase in spatial detail compared to the original MSG product. The resulting pan-European SNR dataset, as well as the merged LST product, can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future. The datasets can be downloaded from 10.5281/zenodo.8332222 and 10.5281/zenodo.8332128 . © 2024 Dominik Rains et al. more
Application Atmosphere Climate Data Records LSA-SAF

High-resolution electricity generation model demonstrates suitability of high-altitude floating solar power

Author(s):
Eyring, Nicholas; Kittner, Noah
Publication title: ISCIENCE
2022
 | Volume: 25 | Issue: 6
2022
DOI: 10.1016/j.isci.2022.104394
Abstract:
This paper develops a meteorological site selection algorithm to quantify the electricity generation potential of floating solar design configurations… This paper develops a meteorological site selection algorithm to quantify the electricity generation potential of floating solar design configurations on alpine water bodies in Switzerland. Using European power market demand patterns, we estimate the technical and economic potential of 82 prospective high-altitude floating solar sites co-located with existing Swiss hydropower. We demonstrate that the amount of solar energy radiating from high-altitude Swiss water bodies could meet total national electricity demand while significantly reducing carbon emissions and addressing seasonal supply/demand deficits. We construct a global map overlaying sites on each continent where high-altitude floating solar could provide low-carbon, land-sparing electricity. Our results present a compelling motivation to develop alpine floating solar installations. However, significant innovations are still needed to couple floating solar with existing hydropower operations or low-cost energy storage. As the industry matures, high-altitude floating solar technology could become a high-value, low-carbon electricity source. more
Application Atmosphere CM-SAF Climate Data Records

High-resolution satellite products improve hydrological modeling in northern Italy

Author(s):
Alfieri, Lorenzo; Avanzi, Francesco; Delogu, Fabio; Gabellani, Simone; Bruno, Giulia; Campo, Lorenzo; Libertino, Andrea; Massari, Christian; Tarpanelli, Angelica; Rains, Dominik; Miralles, Diego G.; Quast, Raphael; Vreugdenhil, Mariette; Wu, Huan; Brocca, Luca
Publication title: Hydrology and Earth System Sciences
2022
 | Volume: 26 | Issue: 14
2022
DOI: 10.5194/hess-26-3921-2022
Abstract:
Abstract. Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing… Abstract. Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications. more
Atmosphere Climate Data Records H-SAF Land Validation

HOAPS and ERA-Interim precipitation over the sea: validation against shipboard in situ measurements

Author(s):
Bumke, Karl; König-Langlo, Gert; Kinzel, Julian; Schröder, Marc
Publication title: Atmospheric Measurement Techniques
2016
 | Volume: 9 | Issue: 5
2016
DOI: 10.5194/amt-9-2409-2016
Abstract:
Abstract. The satellite-derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) and ECMWF (European Centre for Medium-Range… Abstract. The satellite-derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) and ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim reanalysis data sets have been validated against in situ precipitation measurements from ship rain gauges and optical disdrometers over the open ocean by applying a statistical analysis for binary estimates. For this purpose collocated pairs of data were merged within a certain temporal and spatial threshold into single events, according to the satellites' overpass, the observation and the ERA-Interim times. HOAPS detects the frequency of precipitation well, while ERA-Interim strongly overestimates it, especially in the tropics and subtropics. Although precipitation rates are difficult to compare because along-track point measurements are collocated with areal estimates and the number of available data are limited, we find that HOAPS underestimates precipitation rates, while ERA-Interim's Atlantic-wide average precipitation rate is close to measurements. However, when regionally averaged over latitudinal belts, deviations between the observed mean precipitation rates and ERA-Interim exist. The most obvious ERA-Interim feature is an overestimation of precipitation in the area of the intertropical convergence zone and the southern subtropics over the Atlantic Ocean. For a limited number of snow measurements by optical disdrometers, it can be concluded that both HOAPS and ERA-Interim are suitable for detecting the occurrence of solid precipitation. more
Atmosphere CM-SAF Climate Data Records Validation
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