Arctic Sea-Ice Surface Elevation Distribution from NASA’s Operation IceBridge ATM Data

Author(s):
Yi, Donghui; Egido, Alejandro; Smith, Walter H. F.; Connor, Laurence; Buchhaupt, Christopher; Zhang, Dexin
Publication title: Remote Sensing
2022
 | Volume: 14 | Issue: 13
2022
DOI: 10.3390/rs14133011
Abstract:
In this paper, we characterize the sea-ice elevation distribution by using NASA’s Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) L1B data… In this paper, we characterize the sea-ice elevation distribution by using NASA’s Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) L1B data over the Arctic Ocean during 94 Spring campaigns between 2009 and 2019. The ultimate objective of this analysis is to better understand sea-ice topography to improve the estimation of the sea-ice freeboard for nadir-looking altimeters. We first introduce the use of an exponentially modified Gaussian (EMG) distribution to fit the surface elevation probability density function (PDF). The characteristic function of the EMG distribution can be integrated in the modeling of radar altimeter waveforms. Our results indicate that the Arctic sea-ice elevation PDF is dominantly positively skewed and the EMG distribution is better suited to fit the PDFs than the classical Gaussian or lognormal PDFs. We characterize the elevation correlation characteristics by computing the autocorrelation function (ACF) and correlation length (CL) of the ATM measurements. To support the radar altimeter waveform retracking over sea ice, we perform this study typically on 1.5 km ATM along-track segments that reflect the footprint diameter size of radar altimeters. During the studied period, the mean CL values range from 20 to 30 m, which is about 2% of the radar altimeter footprint diameter (1.5 km). more
Application Climate Data Records Cryosphere OSI-SAF

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

Author(s):
Brüning, S.; Niebler, S.; Tost, H.
Publication title: Atmospheric Measurement Techniques
2024
 | Volume: 17 | Issue: 3
2024
DOI: 10.5194/amt-17-961-2024
Abstract:
Satellite instruments provide high-temporal-resolution data on a global scale, but extracting 3D information from current instruments remains a challe… Satellite instruments provide high-temporal-resolution data on a global scale, but extracting 3D information from current instruments remains a challenge. Most observational data are two-dimensional (2D), offering either cloud top information or vertical profiles. We trained a neural network (Res-UNet) to merge high-resolution satellite images from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) with 2D CloudSat radar reflectivities to generate 3D cloud structures. The Res-UNet extrapolates the 2D reflectivities across the full disk of MSG SEVIRI, enabling a reconstruction of the cloud intensity, height, and shape in three dimensions. The imbalance between cloudy and clear-sky CloudSat profiles results in an overestimation of cloud-free pixels. Our root mean square error (RMSE) accounts for 2.99dBZ. This corresponds to 6.6% error on a reflectivity scale between -25 and 20dBZ. While the model aligns well with CloudSat data, it simplifies multi-level and mesoscale clouds in particular. Despite these limitations, the results can bridge data gaps and support research in climate science such as the analysis of deep convection over time and space. © Copyright: more
Application Atmosphere CM-SAF Climate Data Records EUM-Secr Validation

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Author(s):
Safieddine, Sarah; Parracho, Ana Claudia; George, Maya; Aires, Filipe; Pellet, Victor; Clarisse, Lieven; Whitburn, Simon; Lezeaux, Olivier; Thépaut, Jean-Noël; Hersbach, Hans; Radnoti, Gabor; Goettsche, Frank; Martin, Maria; Doutriaux-Boucher, Marie; Coppens, Dorothée; August, Thomas; Zhou, Daniel K.; Clerbaux, Cathy
Publication title: Remote Sensing
2020
 | Volume: 12 | Issue: 17
2020
DOI: 10.3390/rs12172777
Abstract:
Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface… Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis. more
Application Climate Data Records EUM-Secr

Assessing climatic benefits from forestation potential in semi-arid lands

Author(s):
Rohatyn, Shani; Rotenberg, Eyal; Yakir, Dan; Carmel, Yohay
Publication title: ENVIRONMENTAL RESEARCH LETTERS
2021
 | Volume: 16 | Issue: 10
2021
DOI: 10.1088/1748-9326/ac29e9
Abstract:
Forestation actions are a major tool for both climate-change mitigation and biodiversity conservation. We address two weaknesses in this approach: the… Forestation actions are a major tool for both climate-change mitigation and biodiversity conservation. We address two weaknesses in this approach: the little attention given to the negative effects of reduced albedo associated with forestation in many regions, and ignoring the potential of drylands that account for 40% of the global potential land area for forestation. We propose an approach to identify suitable land for forestation and quantify its `net equivalent carbon stock change' over 80 years of forest lifetime (NESC), accounting for both carbon sequestration and albedo changes. We combined remote-sensing tools with data-based estimates of surface parameters and with published climate matrices, to identify suitable land for forestation actions. We then calculated the cumulative (over 80 years) `net sequestration potential' (Delta SP), the `emission equivalent of shortwave radiation forcing' (EESF) due to changes in surface albedo, and, in turn, the combined NESC = Delta SP-EESF, of planting forests with >40% tree-cover. Demonstrating our approach in a large climatically diverse state (Queensland), we identified 14.5 million hectares of potential forestation land in its semi-arid land and show that accounting for the EESF, reduces the climatic benefits of the Delta SP by almost 50%. Nevertheless, it results in a total NESC of 0.72 Gt C accumulated by the end of the century, and 80 years of forestation cycle. This estimated NESC is equivalent to 15% of the projected carbon emissions for the same period in Queensland, for a scenario of no change in emission rates during that period. Our approach extends restoration efforts by identifying new land for forestation and carbon sequestration but also demonstrates the importance of quantifying the climatic value of forestation in drylands. more
Application CM-SAF Climate Data Records Land

Assessing Crop Water Requirement and Yield by Combining ERA5-Land Reanalysis Data with CM-SAF Satellite-Based Radiation Data and Sentinel-2 Satellite Imagery

Author(s):
Pelosi, Anna; Belfiore, Oscar Rosario; D’Urso, Guido; Chirico, Giovanni Battista
Publication title: Remote Sensing
2022
 | Volume: 14 | Issue: 24
2022
DOI: 10.3390/rs14246233
Abstract:
The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest da… The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop–water balance models, thereby improving the crop water requirements (CWR) and yield estimates from the field to the regional scale. Satellite imagery and numerical weather prediction outputs offer high resolution (in time and space) gridded data that can compensate for the paucity of crop parameter field measurements and ground weather observations, as required for assessments of CWR and yield. In this study, the AquaCrop model was used to assess CWR and yield of tomato on a farm in Southern Italy by assimilating Sentinel-2 (S2) canopy cover imagery and using CM-SAF satellite-based radiation data and ERA5-Land reanalysis as forcing weather data. The prediction accuracy was evaluated with field data collected during the irrigation season (April–July) of 2021. Satellite estimates of canopy cover differed from ground observations, with a RMSE of about 11%. CWR and yield predictions were compared with actual data regarding irrigation volumes and harvested yield. The results showed that S2 estimates of crop parameters represent added value, since their assimilation into crop growth models improved CWR and yield estimates. Reliable CWR and yield estimates can be achieved by combining the ERA5-Land and CM-SAF weather databases with S2 imagery for assimilation into the AquaCrop model. more
Application Atmosphere CM-SAF Climate Data Records Land

Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations

Author(s):
Risse, N.; Mech, M.; Prigent, C.; Spreen, G.; Crewell, S.
Publication title: Cryosphere
2024
 | Volume: 18 | Issue: 9
2024
DOI: 10.5194/tc-18-4137-2024
Abstract:
Upcoming submillimeter wave satellite missions require an improved understanding of sea ice emissivity to separate atmospheric and surface microwave s… Upcoming submillimeter wave satellite missions require an improved understanding of sea ice emissivity to separate atmospheric and surface microwave signals under dry polar conditions. This work investigates hectometer-scale observations of airborne sea ice emissivity between 89 and 340 GHz, combined with high-resolution visual imagery from two Arctic airborne field campaigns that took place in summer 2017 and spring 2019 northwest of Svalbard, Norway. Using k-means clustering, we identify four distinct sea ice emissivity spectra that occur predominantly across multiyear ice, first-year ice, young ice, and nilas. Nilas features the highest emissivity, and multiyear ice features the lowest emissivity among the clusters. Each cluster exhibits similar nadir emissivity distributions from 183 to 340 GHz. To relate hectometer-scale airborne measurements to kilometer-scale satellite footprints, we quantify the reduction in the variability of airborne emissivity as footprint size increases. At 340 GHz, the emissivity interquartile range decreases by almost half when moving from the hectometer scale to a footprint of 16 km, typical of satellite instruments. Furthermore, we collocate the airborne observations with polar-orbiting satellite observations. After resampling, the absolute relative bias between airborne and satellite emissivities at similar channels lies below 3 %. Additionally, spectral variations in emissivity at nadir on the satellite scale are low, with slightly decreasing emissivity from 183 to 243 GHz, which occurs for all hectometer-scale clusters except those predominantly composed of multiyear ice. Our results will enable the development of microwave retrievals and assimilation over sea ice in current and future satellite missions, such as the Ice Cloud Imager (ICI) and EUMETSAT Polar System - Sterna (EPS-Sterna). © 2024 Nils Risse et al. more
Application Atmosphere NWP-SAF Software for CDR development

Assessing prior emergent constraints on surface albedo feedback in CMIP6

Author(s):
Thackeray, C.W.; Hall, A.; Zelinka, M.D.; Fletcher, C.G.
Publication title: Journal of Climate
2021
 | Volume: 34 | Issue: 10
2021
DOI: 10.1175/JCLI-D-20-0703.1
Abstract:
An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of cli… An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAFsnow) and sea ice (SAFice). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAFsnow EC is equally strong in CMIP6 as it was in past generations, while the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 6 0.05 W m22 K21, or;61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. The NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAFsnow and SAFice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAFice, and to a lesser extent SAFsnow. Ó 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy more
Application CM-SAF Climate Data Records Cryosphere

Assessing the consistency of satellite derived upper tropospheric humidity measurements

Author(s):
Shi, Lei; Schreck III, Carl J.; John, Viju O.; Chung, Eui-Seok; Lang, Theresa; Buehler, Stefan A.; Soden, Brian J.
2022
2022
DOI: 10.5194/amt-15-6949-2022
Abstract:
Abstract. Four upper tropospheric humidity (UTH) datasets derived from satellite sounders are evaluated to assess their consistency as part of the act… Abstract. Four upper tropospheric humidity (UTH) datasets derived from satellite sounders are evaluated to assess their consistency as part of the activities for the Global Energy and Water Exchanges (GEWEX) water vapor assessment project. The datasets include UTH computed from brightness temperature measurements of the 183.31 ± 1 GHz channel of the Special Sensor Microwave – Humidity (SSM/T-2), Advanced Microwave Sounding Unit-B (AMSU-B), and Microwave Humidity Sounder (MHS), and from channel 12 of the High-Resolution Infrared Radiation Sounder (HIRS). The four datasets are consistent in the interannual temporal and spatial variability of the tropics. Large positive anomalies peaked over the central equatorial Pacific region during El Niño events in the same phase with the increase of sea surface temperature. Conversely, large negative anomalies were obtained during El Niño events when the tropical domain average is taken. The weakened ascending branch of the Pacific Walker circulation in the western Pacific and the enhanced descending branches of the local Hadley circulation along the Pacific subtropics largely contributed to widespread drying areas and thus negative anomalies in the upper troposphere during El Niño events as shown in all four datasets. Due to differences in retrieval definitions, calibration methods, and sensor limitations, there are differences in spatial anomalies and temporal change rates, where more significant anomaly values are usually found in the microwave UTH data. more
Application Atmosphere CM-SAF Climate Data Records EUM-Secr Validation

Assessing the Fitness of Satellite Albedo Products for Monitoring Snow Albedo Trends

Author(s):
Urraca, R; Lanconelli, C; Cappucci, F; Gobron, N
Publication title: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
2023
 | Volume: 61
2023
DOI: 10.1109/TGRS.2023.3281188
Abstract:
Accurate monitoring of albedo trends over snow is essential to evaluate the consequences of the global snow cover retreat on Earth's energy budget. Sa… Accurate monitoring of albedo trends over snow is essential to evaluate the consequences of the global snow cover retreat on Earth's energy budget. Satellite observations provide the best way to monitor these trends globally, but their uncertainty increases over snow. Besides, different products sometimes show diverging trends. A better assessment of the fitness of satellite products for monitoring snow albedo trends is needed. We analyze the consistency of black-sky albedo estimates from global long-term products over snow: advanced very-high-resolution radiometer (AVHRR)-based (CLARA-A2.1, GLASS-v4.2), moderate resolution imaging spectroradiometer (MODIS)-based (MCD43C3-v6.1/v6, GLASS-v4.2), multiangle imaging spectro radiometer (MISR)-based (MIL3MLSN-v4), and multisensor (C3S-v1/v2). We use MCD43C3-6.1 as the reference based on a previous comparison against in situ measurements. CLARA-A2.1 is the one most consistent with MCD43C3, but has a low coverage in high latitudes and an artificial albedo decrease since 2015. The study shows the limitations of MIL3MLSN, Global Land Surface Satellite (GLASS), and Copernicus Climate Change Service (C3S) multisensor products over snow. MIL3MLSN has a too-low coverage of albedo over snow. GLASS-AVHRR overestimates albedo in regions with seasonal snow due to delayed snowmelt and underestimates it in permanently snow-covered regions. GLASS-MODIS is more consistent with MCD43C3 at mid-latitudes, and also underestimates albedo in regions with permanent snow and has an increase in missing values after 2011. Both the GLASS datasets are temporally inconsistent with the other products. Despite the improvements from v1 to v2, C3S-v2 has the largest negative bias over snow and discontinuities in the transitions between sensors. The study evidences the difficulties of AVHRR products to provide stable snow albedo estimates in polar regions, particularly before 2000. more
CM-SAF Climate Data Records Land Validation

Assessing the Influences of Cloud Top Height Information on Passive Microwave Retrieval of Cloud Liquid Water Path

Author(s):
Li, J.; Liu, C.; Yao, B.; Zhao, Y.; Dou, F.; Hu, X.; Weng, F.; Sohn, B.-J.
Publication title: IEEE Transactions on Geoscience and Remote Sensing
2024
 | Volume: 62
2024
DOI: 10.1109/TGRS.2024.3493598
Abstract:
Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passiv… Cloud liquid water path (LWP) quantifies liquid water amount within the atmosphere and is closely related to water cycle, weather, and climate. Passive microwave (MW) observations are powerful tools for retrieving LWP. An empirical relationship between the LWPs and MW brightness temperatures (BTs) can be obtained for conventional retrievals, which consider only the influence of LWP on BTs. However, besides LWP, the cloud vertical extent [e.g., cloud top height (CTH)] can affect MW emission, absorption, and corresponding channel BTs, but it is ignored in conventional retrievals. This study investigates the influences of CTH on MW LWP retrievals, and a CTH-dependent algorithm is developed using CTHs from infrared retrievals. Synthetic radiative transfer simulations are performed to quantify CTH effects on MW channel BTs and to establish the CTH-dependent retrieval coefficients. We use the Advanced MW Scanning Radiometer 2 (AMSR2) observations. Cloud products from Moderate Resolution Imaging Spectroradiometer (MODIS) are collocated to provide the necessary CTH information. Thus, we develop an LWP retrieval algorithm by combining AMSR2 BTs with MODIS CTHs. The results indicate that incorporating CTH information into LWP retrievals enhances the consistency between MW and visible/infrared retrievals. Specifically, the CTH-dependent algorithm showed an improvement in the intraclass correlation coefficient (ICC) and a reduction in mean relative differences (MRDs) by approximately 4% (from 18% to 14%) compared to AMSR2 operational retrievals. The CTH-dependent results are slightly more consistent with the MODIS results than the CTH-independent ones, though it remains important to note that the CTH-dependent retrievals introduce less differences compared to their CTH-independent retrievals. © 2024 IEEE. more
Application Atmosphere NWP-SAF Software for CDR development
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