Comparison of profile total ozone from SBUV (v8.6) with GOME-type and ground-based total ozone for a 16-year period (1996 to 2011)

Author(s):
Chiou, E. W.; Bhartia, P. K.; McPeters, R. D.; Loyola, D. G.; Coldewey-Egbers, M.; Fioletov, V. E.; Van Roozendael, M.; Spurr, R.; Lerot, C.; Frith, S. M.
Publication title: Atmospheric Measurement Techniques
2014
 | Volume: 7 | Issue: 6
2014
DOI: 10.5194/amt-7-1681-2014
Abstract:
Abstract. This paper describes the comparison of the variability of total column ozone inferred from the three independent multi-year data records, na… Abstract. This paper describes the comparison of the variability of total column ozone inferred from the three independent multi-year data records, namely, (i) Solar Backscatter Ultraviolet Instrument (SBUV) v8.6 profile total ozone, (ii) GTO (GOME-type total ozone), and (iii) ground-based total ozone data records covering the 16-year overlap period (March 1996 through June 2011). Analyses are conducted based on area-weighted zonal means for 0–30° S, 0–30° N, 50–30° S, and 30–60° N. It has been found that, on average, the differences in monthly zonal mean total ozone vary between −0.3 and 0.8 % and are well within 1%. For GTO minus SBUV, the standard deviations and ranges (maximum minus minimum) of the differences regarding monthly zonal mean total ozone vary between 0.6–0.7% and 2.8–3.8% respectively, depending on the latitude band. The corresponding standard deviations and ranges regarding the differences in monthly zonal mean anomalies show values between 0.4–0.6% and 2.2–3.5%. The standard deviations and ranges of the differences ground-based minus SBUV regarding both monthly zonal means and anomalies are larger by a factor of 1.4–2.9 in comparison to GTO minus SBUV. The ground-based zonal means demonstrate larger scattering of monthly data compared to satellite-based records. The differences in the scattering are significantly reduced if seasonal zonal averages are analyzed. The trends of the differences GTO minus SBUV and ground-based minus SBUV are found to vary between −0.04 and 0.1% yr−1 (−0.1 and 0.3 DU yr−1). These negligibly small trends have provided strong evidence that there are no significant time-dependent differences among these multi-year total ozone data records. Analyses of the annual deviations from pre-1980 level indicate that, for the 15-year period of 1996 to 2010, all three data records show a gradual increase at 30–60° N from −5% in 1996 to −2% in 2010. In contrast, at 50–30° S and 30° S–30° N there has been a levelling off in the 15 years after 1996. The deviations inferred from GTO and SBUV show agreement within 1%, but a slight increase has been found in the differences during the period 1996–2010. more
AC-SAF Atmosphere Climate Data Records Validation

Comparison of Satellite-Based and Ångström–Prescott Estimated Global Horizontal Irradiance under Different Cloud Cover Conditions in South African Locations

Author(s):
Mabasa, Brighton; Lysko, Meena D.; Moloi, Sabata J.
Publication title: Solar
2022
 | Volume: 2 | Issue: 3
2022
DOI: 10.3390/solar2030021
Abstract:
The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradian… The study compares the performance of satellite-based datasets and the Ångström–Prescott (AP) model in estimating the daily global horizontal irradiance (GHI) for stations in South Africa. The daily GHI from four satellites (namely SOLCAST, CAMS, NASA SSE, and CMSAF SARAH) and the Ångström–Prescott (AP) model are evaluated by validating them against ground observation data from eight radiometric stations located in all six macro-climatological regions of South Africa, for the period 2014-19. The evaluation is carried out under clear-sky, all-sky, and overcast-sky conditions. CLAAS-2 cloud fractional coverage data are used to determine clear and overcast sky days. The observed GHI data are first quality controlled using the Baseline Surface Radiation Network methodology and then quality control of the HelioClim model. The traditional statistical benchmarks, namely the relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) provided information about the performance of the datasets. Under clear skies, the estimated datasets showed excellent performance with maximum rMBE, rMAE, and rRMSE less than 6.5% and a minimum R2 of 0.97. In contrast, under overcast-sky conditions there was noticeably poor performance with maximum rMBE (24%), rMAE (29%), rRMSE (39%), and minimum R2 (0.74). For all-sky conditions, good correlation was found for SOLCAST (0.948), CMSAF (0.948), CAMS (0.944), and AP model (0.91); all with R2 over 0.91. The maximum rRMSE for SOLCAST (10%), CAMS (12%), CMSAF (12%), and AP model (11%) was less than 13%. The maximum rMAE for SOLCAST (7%), CAMS (8%), CMSAF (8%), and AP model (9%) was less than 10%, showing good performance. While the R2 correlations for the NASA SSE satellite-based GHI were less than 0.9 (0.896), the maximum rRMSE was 18% and the maximum rMAE was 15%, showing rather poor performance. The performance of the SOLCAST, CAMS, CMSAF, and AP models was almost the same in the study area. CAMS, CMSAF, and AP models are viable, freely available datasets for estimating the daily GHI at South African locations with quantitative certainty. The relatively poor performance of the NASA SSE datasets in the study area could be attributed to their low spatial resolution of 0.5° × 0.5° (~55 km × 55 km). The feasibility of the datasets decreased significantly as the proportion of sky that was covered by clouds increased. The results of the study could provide a basis/data for further research to correct biases between in situ observations and the estimated GHI datasets using machine learning algorithms. more
Application Atmosphere CM-SAF Climate Data Records

Comparison of surface solar irradiance from ground observations and satellite data (1990–2016) over a complex orography region (piedmont—Northwest Italy)

Author(s):
Manara, V.; Stocco, E.; Brunetti, M.; Diolaiuti, G.A.; Fugazza, D.; Pfeifroth, U.; Senese, A.; Trentmann, J.; Maugeri, M.
Publication title: Remote Sensing
2020
 | Volume: 12 | Issue: 23
2020
DOI: https://doi.org/10.3390/rs12233882
Abstract:
Climate Monitoring Satellite Application Facility (CM SAF) surface solar irradiance (SSI) products were compared with ground-based observations over t… Climate Monitoring Satellite Application Facility (CM SAF) surface solar irradiance (SSI) products were compared with ground-based observations over the Piedmont region (north-western Italy) for the period 1990–2016. These products were SARAH-2.1 (Surface Solar Radiation DataSet—Heliosat version 2.1) and CLARA-A2 (Cloud, Albedo and Surface Radiation dataset version A2). The aim was to contribute to the discussion on the representativeness of satellite SSI data including a focus on high-elevation areas. The comparison between SSI averages shows that for low OCI (orographic complexity index) stations, satellite series have higher values than corresponding ground-based observations, whereas for high OCI stations, SSI values for satellite records are mainly lower than for ground stations. The comparison between SSI anomalies highlights that satellite records have an excellent performance in capturing SSI day-to-day variability of ground-based low OCI stations. In contrast, for high OCI stations, the agreement is much lower, due to the higher uncertainty in both satellite and ground-based records. Finally, if the temporal trends are considered, average low-elevation ground-based SSI observations show a positive trend, whereas satellite records do not highlight significant trends. Focusing on high-elevation stations, the observed trends for ground-based and satellite records are more similar with the only exception of summer. This divergence seems to be due to the relevant role of atmospheric aerosols on SSI trends. more
Atmosphere CM-SAF Climate Data Records Validation

Consistency and structural uncertainty of multi-mission GPS radio occultation records

Author(s):
Steiner, Andrea K.; Ladstädter, Florian; Ao, Chi O.; Gleisner, Hans; Ho, Shu-Peng; Hunt, Doug; Schmidt, Torsten; Foelsche, Ulrich; Kirchengast, Gottfried; Kuo, Ying-Hwa; Lauritsen, Kent B.; Mannucci, Anthony J.; Nielsen, Johannes K.; Schreiner, William; Schwärz, Marc; Sokolovskiy, Sergey; Syndergaard, Stig; Wickert, Jens
Publication title: Atmospheric Measurement Techniques
2020
 | Volume: 13 | Issue: 5
2020
DOI: 10.5194/amt-13-2547-2020
Abstract:
Abstract. Atmospheric climate monitoring requires observations of high quality that conform to the criteria of the Global Climate Observing System (GC… Abstract. Atmospheric climate monitoring requires observations of high quality that conform to the criteria of the Global Climate Observing System (GCOS). Radio occultation (RO) data based on Global Positioning System (GPS) signals are available since 2001 from several satellite missions with global coverage, high accuracy, and high vertical resolution in the troposphere and lower stratosphere. We assess the consistency and long-term stability of multi-satellite RO observations for use as climate data records. As a measure of long-term stability, we quantify the structural uncertainty of RO data products arising from different processing schemes. We analyze atmospheric variables from bending angle to temperature for four RO missions, CHAMP, Formosat-3/COSMIC, GRACE, and Metop, provided by five data centers. The comparisons are based on profile-to-profile differences aggregated to monthly medians. Structural uncertainty in trends is found to be lowest from 8 to 25 km of altitude globally for all inspected RO variables and missions. For temperature, it is more
Atmosphere Climate Data Records ROM-SAF Validation

Consistency of surface anisotropy characterization with meteosat observations

Author(s):
Lattanzio, A.; Govaerts, Y.M.; Pinty, B.
Publication title: Advances in Space Research
2007
 | Volume: 39 | Issue: 1
2007
DOI: 10.1016/j.asr.2006.02.049
Abstract:
The purpose of this paper is to present the results of the evaluation of the Meteosat Surface Albedo (MSA) product, including the effects due to instr… The purpose of this paper is to present the results of the evaluation of the Meteosat Surface Albedo (MSA) product, including the effects due to instrument changes and associated calibration uncertainties. To this end, observations acquired by two adjacent geostationary spacecrafts, Meteosat-7 and Meteosat-5 have been processed with the MSA algorithm. These satellites are located, respectively, at 0° and 63° East. Data acquired by these two instruments overlap over a large area encompassing most of Africa and the Arabian peninsula. The consistency of the surface anisotropy retrieval is evaluated through a reconstruction of the Meteosat-5 (-7) observations with the Meteosat-7 (-5) surface anisotropy characterization. Some differences slightly higher than the calibration accuracy have been found. This effect has no significant impact on the albedo retrieval which indicates that MSA is a reliable algorithm to produce albedo datasets. more
Climate Data Records EUM-Secr Method

Contrasting extremely warm and long-lasting cold air anomalies in the North Atlantic sector of the Arctic during the HALO-(AC)3 campaign

Author(s):
Walbröl, A.; Michaelis, J.; Becker, S.; Dorff, H.; Ebell, K.; Gorodetskaya, I.; Heinold, B.; Kirbus, B.; Lauer, M.; Maherndl, N.; Maturilli, M.; Mayer, J.; Müller, H.; Neggers, R.A.J.; Paulus, F.M.; Röttenbacher, J.; Rückert, J.E.; Schirmacher, I.; Slättberg, N.; Ehrlich, A.; Wendisch, M.; Crewell, S.
Publication title: Atmospheric Chemistry and Physics
2024
 | Volume: 24 | Issue: 13
2024
DOI: 10.5194/acp-24-8007-2024
Abstract:
How air masses transform during meridional transport into and out of the Arctic is not well represented by numerical models. The airborne field campai… How air masses transform during meridional transport into and out of the Arctic is not well represented by numerical models. The airborne field campaign HALO-(AC)3 applied the High Altitude and Long-range Research Aircraft (HALO) within the framework of the collaborative research project on Arctic amplification (AC)3 to address this question by providing a comprehensive observational basis. The campaign took place from 7 March to 12 April 2022 in the North Atlantic sector of the Arctic, a main gateway of atmospheric transport into and out of the Arctic. Here, we investigate to which degree the meteorological and sea ice conditions during the campaign align with the long-term climatology (1979–2022). For this purpose, we use the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis v5 (ERA5), satellite data, and measurements at Ny-Ålesund, including atmospheric soundings. The observations and reanalysis data revealed two distinct periods with different weather conditions during HALO-(AC)3: the campaign started with a warm period (11–20 March 2022) where strong southerly winds prevailed that caused poleward transport of warm and moist air masses, so-called moist and warm air intrusions (WAIs). Two WAI events were identified as atmospheric rivers (ARs), which are narrow bands of strong moisture transport. These warm and moist air masses caused the highest measured 2 m temperatures (5.5 °C) and daily precipitation rates (42 mm d−1) at Ny-Ålesund for March since the beginning of the record (1993). Over the sea ice northwest of Svalbard, ERA5 indicated record-breaking rainfall. After the passage of a strong cyclone on 21 March 2022, a cold period followed. Northerly winds advected cold air into the Fram Strait, causing marine cold air outbreaks (MCAOs) until the end of the campaign. This second phase included one of the longest MCAO events found in the ERA5 record (19 d). On average, the entire campaign period was warmer than the climatological mean due to the strong influence of the ARs. In the Fram Strait, the sea ice concentration was well within the climatological variability over the entire campaign duration. However, during the warm period, a large polynya opened northeast of Svalbard, untypical for this season. Compared to previous airborne field campaigns focusing on the evolution of (mixed-phase) clouds, a larger variety of MCAO conditions was observed during HALO-(AC)3. In summary, air mass transport into and out of the Arctic was more pronounced than usual, providing exciting prospects for studying air mass transformation using HALO-(AC)3 © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. more
Application Climate Data Records Cryosphere OSI-SAF

Convection-Permitting ICON-LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

Author(s):
Chen, S.; Poll, S.; Hendricks Franssen, H.-J.; Heinrichs, H.; Vereecken, H.; Goergen, K.
Publication title: Journal of Geophysical Research: Atmospheres
2024
 | Volume: 129 | Issue: 6
2024
DOI: 10.1029/2023JD039569
Abstract:
Renewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub-Saharan Africa, wh… Renewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub-Saharan Africa, where three-quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON-NWP) model in its Limited Area Mode (ICON-LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection-permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON-LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets. © 2024. The Authors. more
Application Atmosphere CM-SAF Climate Data Records Validation

Contribution of changes in snow cover extent to shortwave radiation perturbations at the top of the atmosphere over the northern hemisphere during 2000–2019

Author(s):
Chen, X.; Yang, Y.; Yin, C.
Publication title: Remote Sensing
2021
 | Volume: 13 | Issue: 23
2021
DOI: 10.3390/rs13234938
Abstract:
Snow-induced radiative forcing (SnRF), defined as the instantaneous perturbation of the Earth’s shortwave radiation at the top of the atmosphere (TOA)… Snow-induced radiative forcing (SnRF), defined as the instantaneous perturbation of the Earth’s shortwave radiation at the top of the atmosphere (TOA), results from variations in the terrestrial snow cover extent (SCE), and is critical for the regulation of the Earth’s energy budget. However, with the growing seasonal divergence of SCE over the Northern Hemisphere (NH) in the past two decades, novel insights pertaining to SnRF are lacking. Consequently, the contribution of SnRF to TOA shortwave radiation anomalies still remains unclear. Utilizing the latest datasets of snow cover, surface albedo, and albedo radiative kernels, this study investigated the distribution of SnRF over the NH and explored its changes from 2000 to 2019. The 20-year averaged annual mean SnRF in the NH was −1.13 ± 0.05 W m−2, with a weakening trend of 0.0047 Wm−2 yr−1 (p < 0.01) during 2000–2019, indicating that an extra 0.094 W m−2 of shortwave radiation was absorbed by the Earth climate system. Moreover, changes in SnRF were highly correlated with satellite-observed TOA shortwave flux anomalies (r = 0.79, p < 0.05) during 2000–2019. Additionally, a detailed contribution analysis revealed that the SnRF in snow accumulation months, from March to May, accounted for 58.10% of the annual mean SnRF variability across the NH. These results can assist in providing a better understanding of the role of snow cover in Earth’s climate system in the context of climate change. Although the rapid SCE decline over the NH has a hiatus for the period during 2000–2019, SnRF continues to follow a weakening trend. Therefore, this should be taken into consideration in current climate change models and future climate projections. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. more
Application CM-SAF Climate Data Records Cryosphere
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