Global evaluation of fast radiative transfer model coefficients for early meteorological satellite sensors

Author(s):
Silveira, B.B.; Turner, E.C.; Vidot, J.
Publication title: Atmospheric Measurement Techniques
2024
 | Volume: 17 | Issue: 4
2024
DOI: 10.5194/amt-17-1279-2024
Abstract:
RTTOV (the Radiative Transfer for TOVS code, where TOVS is the TIROS Operational Vertical Sounder) coefficients are evaluated using a large, independe… RTTOV (the Radiative Transfer for TOVS code, where TOVS is the TIROS Operational Vertical Sounder) coefficients are evaluated using a large, independent dataset of 25000 atmospheric model profiles as a robust test of the diverse 83 training profiles typically used. The study is carried out for nine historical satellite instruments: the InfraRed Interferometer Spectrometer D (IRIS-D), Satellite Infrared Spectrometer B (SIRS-B), Medium Resolution Infrared Radiometer (MRIR) and High Resolution Infrared Radiometer (HRIR) for the infrared part of the spectrum, and the Microwave Sounding Unit (MSU), Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave - Humidity (SSM/T-2), Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager/Sounder (SSMI/S) for the microwave. Simulated channel brightness temperatures show similar statistics for both the independent and the 83-profile datasets, confirming that it is acceptable to validate the RTTOV coefficients with the same profiles used to generate the coefficients. Differences between the RTTOV and the line-by-line models are highest in water vapour channels, where mean values can reach up to 0.4±0.2K for the infrared and 0.04±0.13K for the microwave. Examination of the latitudinal dependence of the bias reveals different patterns of variability for similar channels on different instruments, such as the channel centred at 679cm-1 on both IRIS-D and SIRS-B, showing the importance of the specification of the instrumental spectral response functions (ISRFs). Maximum differences of up to several kelvin are associated with extremely non-typical profiles, such as those in polar or very hot regions. © Copyright: more
Atmosphere NWP-SAF Software for CDR development Validation

Global Cloudiness and Cloud Top Information from AVHRR in the 42-Year CLARA-A3 Climate Data Record Covering the Period 1979–2020

Author(s):
Karlsson, Karl-Göran; Devasthale, Abhay; Eliasson, Salomon
Publication title: Remote Sensing
2023
 | Volume: 15 | Issue: 12
2023
DOI: 10.3390/rs15123044
Abstract:
This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo a… This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results. more
Atmosphere CM-SAF Climate Data Records Validation

Global long-term monitoring of the ozone layer – a prerequisite for predictions

Author(s):
Loyola, D. G.; Coldewey-Egbers, R. M.; Dameris, M.; Garny, H.; Stenke, A.; Van Roozendael, M.; Lerot, C.; Balis, D.; Koukouli, M.
Publication title: International Journal of Remote Sensing
2009
 | Volume: 30 | Issue: 15-16
2009
DOI: 10.1080/01431160902825016
Abstract:
Although the Montreal Protocol now controls the production and emission of ozone depleting substances, the timing of ozone recovery is unclear. There … Although the Montreal Protocol now controls the production and emission of ozone depleting substances, the timing of ozone recovery is unclear. There are many other factors affecting the ozone layer, in particular climate change is expected to modify the speed of re-creation of the ozone layer. Therefore, long-term observations are needed to monitor the further evolution of the stratospheric ozone layer. Measurements from satellite instruments provide global coverage and are supplementary to selective ground-based observations. The combination of data derived from different space-borne instruments is needed to produce homogeneous and consistent long-term data records. They are required for robust investigations including trend analysis. For the first time global total ozone columns from three European satellite sensors GOME (ERS-2), SCIAMACHY (ENVISAT), and GOME-2 (METOP-A) are combined and added up to a continuous time series starting in June 1995. On the one hand it is important to monitor the consequences of the Montreal Protocol and its amendments; on the other hand multi-year observations provide the basis for the evaluation of numerical models describing atmospheric processes, which are also used for prognostic studies to assess the future development. This paper gives some examples of how to use satellite data products to evaluate model results with respective data derived from observations, and to disclose the abilities and deficiencies of atmospheric models. In particular, multi-year mean values derived from the Chemistry-Climate Model E39C-A are used to check climatological values and the respective standard deviations. more
AC-SAF Atmosphere Climate Data Records Validation

Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns

Author(s):
Chan, K.L.; Valks, P.; Heue, K.-P.; Lutz, R.; Hedelt, P.; Loyola, D.; Pinardi, G.; Van Roozendael, M.; Hendrick, F.; Wagner, T.; Kumar, V.; Bais, A.; Piters, A.; Irie, H.; Takashima, H.; Kanaya, Y.; Choi, Y.; Park, K.; Chong, J.; Cede, A.; Frieß, U.; Richter, A.; Ma, J.; Benavent, N.; Holla, R.; Postylyakov, O.; Rivera Cárdenas, C.; Wenig, M.
Publication title: Earth System Science Data
2023
 | Volume: 15 | Issue: 4
2023
DOI: 10.5194/essd-15-1831-2023
Abstract:
We introduce the new Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 product of total column ozone (O3), total and tropospheri… We introduce the new Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 product of total column ozone (O3), total and tropospheric column nitrogen dioxide (NO2), total column water vapour, total column bromine oxide (BrO), total column formaldehyde (HCHO), and total column sulfur dioxide (SO2) (daily products 10.15770/EUM-SAF-AC-0048, ; monthly products 10.15770/EUM-SAF-AC-0049, ). The GOME-2 level-3 products aim to provide easily translatable and user-friendly data sets to the scientific community for scientific progress as well as to satisfy public interest. The purpose of this paper is to present the theoretical basis as well as the verification and validation of the GOME-2 daily and monthly level-3 products. The GOME-2 level-3 products are produced using the overlapping area-weighting method. Details of the gridding algorithm are presented. The spatial resolution of the GOME-2 level-3 products is selected based on the sensitivity study. The consistency of the resulting level-3 products among three GOME-2 sensors is investigated through time series of global averages, zonal averages, and bias. The accuracy of the products is validated by comparison to ground-based observations. The verification and validation results show that the GOME-2 level-3 products are consistent with the level-2 data. Small discrepancies are found among three GOME-2 sensors, which are mainly caused by the differences in the instrument characteristic and level-2 processor. The comparison of GOME-2 level-3 products to ground-based observations in general shows very good agreement, indicating that the products are consistent and fulfil the requirements to serve the scientific community and general public. © Copyright: more
AC-SAF Atmosphere Climate Data Records Validation

Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions

Author(s):
Kim, Hyunglok; Wigneron, Jean-Pierre; Kumar, Sujay; Dong, Jianzhi; Wagner, Wolfgang; Cosh, Michael H.; Bosch, David D.; Collins, Chandra Holifield; Starks, Patrick J.; Seyfried, Mark; Lakshmi, Venkataraman
Publication title: Remote Sensing of Environment
2020
 | Volume: 251
2020
DOI: 10.1016/j.rse.2020.112052
Abstract:
Over the past four decades, satellite systems and land surface models have been used to estimate global-scale surface soil moisture (SSM). However, in… Over the past four decades, satellite systems and land surface models have been used to estimate global-scale surface soil moisture (SSM). However, in areas such as densely vegetated and irrigated regions, obtaining accurate SSM remains challenging. Before using satellite and model-based SSM estimates over these areas, we should understand the accuracy and error characteristics of various SSM products. Thus, this study aimed to compare the error characteristics of global-scale SSM over vegetated and irrigated areas as obtained from active and passive satellites and model-based data: Advanced Scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), and Global Land Data Assimilation System (GLDAS). We employed triple collocation analysis (TCA) and caluclated conventional error metrics from in-situ SSM measurements. We also considered all possible triplets from 6 different products and showed the viability of considering the standard deviation of TCA-based numbers in producing robust results. Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets. The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%. more
Climate Data Records H-SAF Land Validation

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
Subscribe to