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NASA NTRS2012

The Impact of AMSR-E Soil Moisture Assimilation on Evapotranspiration Estimation

An assessment ofETestimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 CNLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product.

Peters-Lidard, Christa D.↗

NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗

NASA NTRS2016

Update of S-NPP VIIRS Thermal Emissive Bands Radiometric Calibration Stability Monitoring Using the Moon

The Suomi-NPP VIIRS thermal emissive bands (TEB) are radiometrically calibrated on-orbit with reference to an onboard blackbody (BB) regularly operated at approximately 292.5 K. The calibration stability at other temperature ranges can be evaluated based on the observations of remote targets with stable thermal properties, such as the Moon. VIIRS has scheduled viewings of the Moon on a nearly monthly basis at a phase angle of nearly -51 degrees. In this study, the brightness temperatures (BT) of the lunar surface retrieved using the detector gain coefficients calibrated with the BB are trended to monitor the calibration stability of VIIRS TEB. Since the Lunar surface temperatures are spatially non-uniform and vary greatly with the photometric geometry, the BT trending must be based on the same regions of the Moon under the same solar illumination condition. Also, the TEB lunar images are always partially saturated because the highest lunar surface temperatures are beyond the dynamic range of all VIIRS TEB detectors. Therefore, a temporally invariant dynamic mask is designed to clip a fraction of the lunar images corresponding to the regions of the Moon that may saturate the detector at any lunar event. The BT of the remaining hottest pixels are then trended. Results show that, since the launch of VIIRS to mid-2016, the radiometric calibration of all TEB detectors has been stable within 0.4 K at the BT range of as high as 350 K.

Wang, Zhipeng↗

NASA NTRSDate not supplied

TPSAS-NF1676L-18322-DND

MetOp-A satellite was launched on 19 October 2006 and MetOp-B was launched on 17 September 2013. Two satellites fly in complementary orbits and in a sun synchronous morning orbit passing over the Equator at the same local time 9:30 am. MetOp-B phased 50 minutes apart from MetOp-A. Presented here are the global surface IR emissivity spectra retrieved from IASI measurements observed from both MetOp-A and MetOp-B satellites. Inter-comparison of the emissivities obtained from MetOp-A and MetOp-B is performed to ensure the continuity of emissivity monitoring and its trend analysis. Effort on emissivity validation continues with available ground in-situ measurements and retrieval consistency check through radiative transfer model simulations. The spatial resolution of emissivity climatology atlas is now increased from 0.5 to 0.25 degrees latitude-longitude, and available to the community.

Daniel K Zhou↗

NASA NTRSDate not supplied

Forward and Inverse Models for Satellite Remote Sensors using Principal Component Analysis

Satellite remote sensors such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C make millions of observations each day with thousands of spectral channels for each observation; this poses challenges for efficiently inversion of the inherently large dataset as needed to retrieve atmospheric and surface properties. This presentation will illustrate the use of Principal Component Analysis (PCA) to speed up radiative transfer forward model calculations and to stabilize the inversion algorithms. A Principal Component-based radiative transfer model (PCRTM) developed at NASA Langley Research Center can simulate top of atmosphere (TOA) radiance or reflectance spectra from 50 cm-1 to 50000 cm-1 (200 m to 0.20 m quickly and accurately. PCRTM demonstrated very high accuracy relative to reference line-by-line radiative transfer models and it saves orders of magnitude computational time. Examples of the PCRTM model developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, CPF, TEMPO, SBG, OMI, and SCIAMACHY will be presented. In addition to using the PCRTM as forward model, the NASA Langley developed inversion algorithm also uses PCA to compress the state vector into a compressed dimension to speed up and stabilize the inversion process. Examples of retrieved atmospheric temperature, water vapor, CO2, CO, CH4, N2O, and O3 profiles, cloud properties (optical depth, size, phase, and height), and surface properties (surface emissivity spectra and skin temperatures) will be presented. This algorithm is being transitioned to the NASA Sounder SIPS and NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC).

forward model↗

DOE OSTI2025

MOSAiC studies of long-lasting mixed-phase cloud events and analysis of the liquid-phase properties of Arctic clouds

Vertically resolved observations of the temporal evolution of mixed-phase clouds (MPCs) were performed over the central Arctic during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition, which lasted from October 2019 to September 2020. The research icebreaker Polarstern , drifting with the pack ice for more than 7 months, mostly at latitudes > 85° N, served as a platform for state-of-the-art remote sensing of aerosols and clouds. The use of the recently introduced dual field-of-view (FOV) polarization lidar technique in combination with the well-established lidar-radar retrieval technique provided, for the first time, a robust instrumental basis to monitor the evolution of the liquid and the ice phase of MPCs and the interplay between the two phases. Two long-lasting Arctic MPC events observed close to the North Pole in mid-winter (December 2019) and late summer (September 2020) are discussed to provide new insight into Arctic MPC evolution processes. In the second part of the article, cloud statistics, covering all seasons of a year, are presented. The focus is on the optical and microphysical properties of the liquid phase. These results are solely derived from the dual-FOV lidar observations. The key findings of the study can be summarized as follows: persistent activation of aerosol particles to form water droplets is of great importance for the longevity of MPCs. The observations confirm that ice formation occurs predominantly via immersion freezing. The field studies suggest that the free tropospheric reservoirs of cloud condensation nuclei (CCN) and of ice-nucleating particles (INPs) were always well filled, i.e., the clouds did not exhaust their supply of activatable and activated particles. The observation of long-lasting MPC events, low ice production rates, and a sufficiently large INP reservoir leads to the recommendation to use a time-dependent immersion freezing parameterization in MPC modeling efforts.

Jimenez, Cristofer [Leibniz Inst. for Tropospheric↗

DOE OSTI2026

Trisodium Phosphate Phases and Solubility in Alkaline Solutions Relevant to Radioactive Waste Processing

The U.S. Department of Energy’s Hanford Site faces significant challenges in managing millions of gallons of legacy radioactive waste, where phosphate precipitation can obstruct pipelines during retrieval and processing. To resolve long-standing inconsistencies in reported solubility and clarify factors governing trisodium phosphate hydrates, we examined the Na3PO4:NaOH:H2O system. Powder and single-crystal X-ray diffraction revealed that commercial precursors undergo transformations that produce multiple hydrates, including three previously unreported phases comprising an ordered polymorph of Na3(PO4)·12H2O·1/6NaOH, Na3PO4·5H2O, and Na3PO4·9H2O. Computational modeling indicated that the ordered dodecahydrate is more stable than its disordered counterpart, suggesting kinetic persistence of structural disorder. Solubility measurements were conducted with solutions prepared either from anhydrous Na3PO4 or from Na3(PO4)·12H2O·1/6NaOH and revealed that release of interstitial NaOH from the hydrate precursor elevated solution alkalinity, thereby significantly reducing phosphate solubility relative to solutions prepared with anhydrous Na3PO4 across 20–44 °C. These results begin to reconcile inconsistencies in prior solubility data and clarify how phase composition dictates phosphate precipitation under alkaline conditions.

Graham, Trenton R. (ORCID:0000000189078004)↗

NASA NTRS1996

Planetary Atmosphere Dynamics and Radiative Transfer

This research program has dealt with two projects in the field of planetary atmosphere dynamics and radiative energy transfer, one theoretical and one experimental. The first project, in radiative energy transfer, incorporated the capability to isolate and quantify the contribution of individual atmospheric components to the Venus radiative balance and thermal structure to greatly improve the current understanding of the radiative processes occurring within the Venus atmosphere. This is possible by varying the mixing ratios of each gas species, and the location, number density and aerosol size distributions of the clouds. This project was a continuation of the work initiated under a 1992 University Consortium Agreement. Under the just completed grant, work has continued on the use of a convolution-based algorithm that provided the capability to calculate the k coefficients of a gas mixture at different temperatures, pressures and spectral intervals from the separate k-distributions of the individual gas species. The second primary goal of this research dealt with the Doppler wind retrieval for the Successful Galileo Jupiter probe mission in December, 1995. In anticipation of the arrival of Galileo at Jupiter, software development continued to read the radioscience and probe/orbiter trajectory data provided by the Galileo project and required for Jupiter zonal wind measurements. Sample experiment radioscience data records and probe/orbiter trajectory data files provided by the Galileo Radioscience and Navigation teams at the Jet Propulsion Laboratory, respectively, were used for the first phase of the software development. The software to read the necessary data records was completed in 1995. The procedure by which the wind retrieval takes place begins with initial consistency checks of the raw data, preliminary data reductions, wind recoveries, iterative reconstruction of the probe descent profile, and refined wind recoveries. At each stage of the wind recovery consistency is checked and maintained between the orbiter navigational data, the radioscience data, and the probe descent profile derived by the Atmospheric Instrument Team. Preliminary results show that the zonal winds at Jupiter increase with depth to approximately 150 m/s.

Atkinson, David H.↗

NASA NTRS2022

Polarization performance simulation for the GeoXO atmospheric composition instrument: NO2 retrieval impacts

NOAA’s Geostationary Extended Observations (GeoXO) constellation will continue and expand on the capabilities of the current generation of geostationary satellite systems to support US weather, ocean, atmosphere, and climate operations. It is planned to consist of a dedicated atmospheric composition instrument (ACX) to support air quality forecasting and monitoring by providing similar capabilities to missions such as TEMPO (Tropospheric Emission: Monitoring Pollution), currently planned to launch in 2023, and Ozone Monitoring Instrument (OMI), TROPOMI (TROPOspheric Monitoring Instrument), and GEMS (Geostationary Environment Monitoring Spectrometer) currently in operation. As the early phases of ACX development are progressing, design trade-offs are being considered to understand the relationship between instrument design choices and trace gas retrieval impacts. Some of these choices will affect the instrument polarization sensitivity (PS), which can have radiometric impacts on environmental satellite observations. We conducted a study to investigate how such radiometric impacts can affect NO2 retrievals by exploring their sensitivities to time of day, location, and scene type with an ACX instrument model that incorporates PS. The study addresses the basic steps of operational NO2 retrievals: the spectral fitting step and the conversion of slant column to vertical column via the air mass factor (AMF). The spectral fitting step was performed by generating at-sensor radiance from a clear sky scene with a known NO2 amount, the application of an instrument model including both instrument PS and noise, and a physical retrieval. The spectral fitting step was found to mitigate the impacts of instrument PS. The AMF-related step was considered for clear sky and partially cloudy scenes, where instrument PS can lead to errors in interpreting the cloud content, propagating to AMF errors and finally to NO2 retrieval errors. For this step, the NO2 retrieval impacts were small but non-negligible for high NO2 amounts; we estimated that a typical high NO2 amount can cause maximum of 0.25 x 10^15 molecules/cm^2 for a PS of 5%. These simulation capabilities were designed to aid in the development of a GeoXO atmospheric composition instrument that will improve our ability to monitor and understand the Earth’s atmosphere.

trace gases↗

NASA NTRS2022

Polarization Performance Simulation for the GeoXO Atmospheric Composition Instrument: NO2 Retrieval Impacts

NOAA's Geostationary Extended Observations (GeoXO) constellation will continue and expand on the capabilities of the current generation of geostationary satellite systems to support US weather, ocean, atmosphere, and climate operations. It is planned to consist of a dedicated atmospheric composition instrument (ACX) to support air quality forecasting and monitoring by providing capabilities similar to missions such as TEMPO (Tropospheric Emission: Monitoring Pollution), currently planned to launch in 2023, as well as OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument), and GEMS (Geostationary Environment Monitoring Spectrometer) currently in operation. As the early phases of ACX development are progressing, design trade-offs are being considered to understand the relationship between instrument design choices and trace gas retrieval impacts. Some of these choices will affect the instrument polarization sensitivity (PS), which can have radiometric impacts on environmental satellite observations. We conducted a study to investigate how such radiometric impacts can affect NO2 retrievals by exploring their sensitivities to time of day, location, and scene type with an ACX instrument model that incorporates PS. The study addresses the basic steps of operational NO2 retrievals: the spectral fitting step and the conversion of slant column to vertical column via the air mass factor (AMF). The spectral fitting step was performed by generating at-sensor radiance from a clear-sky scene with a known NO2 amount, the application of an instrument model including both instrument PS and noise, and a physical retrieval. The spectral fitting step was found to mitigate the impacts of instrument PS. The AMF-related step was considered for clear-sky and partially cloudy scenes, for which instrument PS can lead to errors in interpreting the cloud content, propagating to AMF errors and finally to NO2 retrieval errors. For this step, the NO2 retrieval impacts were small but non-negligible for high NO2 amounts; we estimated that a typical high NO2 amount can cause a maximum retrieval error of 0.25×1015 molec. cm−2 for a PS of 5 %. These simulation capabilities were designed to aid in the development of a GeoXO atmospheric composition instrument that will improve our ability to monitor and understand the Earth's atmosphere.

Aaron Pearlman↗

NASA NTRS2005

Retrieval of Dry Snow Parameters from Radiometric Data Using a Dense Medium Model and Genetic Algorithms

In this paper, GA-based techniques are used to invert the equations of an electromagnetic model based on Dense Medium Radiative Transfer Theory (DMRT) under the Quasi Crystalline Approximation with Coherent Potential to retrieve snow depth, mean grain size and fractional volume from microwave brightness temperatures. The technique is initially tested on both noisy and not-noisy simulated data. During this phase, different configurations of genetic algorithm parameters are considered to quantify how their change can affect the algorithm performance. A configuration of GA parameters is then selected and the algorithm is applied to experimental data acquired during the NASA Cold Land Process Experiment. Snow parameters retrieved with the GA-DMRT technique are then compared with snow parameters measured on field.

Tedesco, Marco↗

NASA NTRS1990

Technology for an intelligent, free-flying robot for crew and equipment retrieval in space

Crew rescue and equipment retrieval is a Space Station Freedom requirement. During Freedom's lifetime, there is a high probability that a number of objects will accidently become separated. Members of the crew, replacement units, and key tools are examples. Retrieval of these objects within a short time is essential. Systems engineering studies were conducted to identify system requirements and candidate approaches. One such approach, based on a voice-supervised, intelligent, free-flying robot was selected for further analysis. A ground-based technology demonstration, now in its second phase, was designed to provide an integrated robotic hardware and software testbed supporting design of a space-borne system. The ground system, known as the EVA Retriever, is examining the problem of autonomously planning and executing a target rendezvous, grapple, and return to base while avoiding stationary and moving obstacles. The current prototype is an anthropomorphic manipulator unit with dexterous arms and hands attached to a robot body and latched in a manned maneuvering unit. A precision air-bearing floor is used to simulate space. Sensor data include two vision systems and force/proximity/tactile sensors on the hands and arms. Planning for a shuttle file experiment is underway. A set of scenarios and strawman requirements were defined to support conceptual development. Initial design activities are expected to begin in late 1989 with the flight occurring in 1994. The flight hardware and software will be based on lessons learned from both the ground prototype and computer simulations.

Erickson, J. D.↗

NASA NTRS1990

Summary of results and conclusions based on analysis of volume imaging and high spectral resolution lidar data acquired during FIRE phase 1, part 1

The collection of long term global statistics on cloud cover may be most easily accomplished with satellite based observations; however, measurements derived from passive satellite retrieval methods must be calibrated and verified by in situ or ground based remote sensor observations. Verification is not straight forward, however, because the highly variable nature of cloud altitude, morphology, and optical characteristics complicates the scaling of point measurements to satellite footprint sized areas. This is particularly evident for cirrus clouds which may be organized on horizontal scales of 10's of meters to 8 km or more, and have optical depths ranging from less than .003 to greater than 3. Cirrus clouds can strongly influence earths' radiative balance, but, because they are often transmissive, cirrus clouds are difficult to detect and characterize from satellite measurements. Because of its precise ranging capabilities, spatial resolution and sensitivity, lidar observations have played an important role in the detection, depiction, and characterization of cirrus clouds. Some of the characteristics of cirrus clouds are summarized which observed the High Spectral Resolution and Volume Imaging Lidars during the phase 1 IFO and ETO periods.

Grund, Christian J.↗

NASA NTRS1993

Inference of cirrus cloud properties using satellite-observed visible and infrared radiances. II - Verification of theoretical cirrus radiative properties

The study presents a methodology developed to apply a parameterization of radiative transfer calculations to satellite analyses of cirrus clouds. Cloud heights and optical depths are derived from visible and IR window measurements taken during FIRE when cirrus clouds were present. Geostationary satellite retrievals are compared to lidar-derived cloud heights and retrievals from a polar-orbiting satellite taken at different angles to determine which theoretical models of scattering phase function and single-scattering albedo best represent actual cirrus clouds. Models using small scattering phase function and single-scattering albedo best represent actual cirrus clouds. It is concluded that interpretation of cirrus reflectance with water-droplet models leads to biased results. The cloud-height and optical depth biases can be minimized with the aid of the C20 or cirrostratus models.

Minnis, Patrick↗

NASA NTRS2007

Variability in Global Top-of-Atmosphere Shortwave Radiation Between 2000 and 2005

Measurements from various instruments and analysis techniques are used to directly compare changes in Earth-atmosphere shortwave (SW) top-of-atmosphere (TOA) radiation between 2000 and 2005. Included in the comparison are estimates of TOA reflectance variability from published ground-based Earthshine observations and from new satellite-based CERES, MODIS and ISCCP results. The ground-based Earthshine data show an order-of-magnitude more variability in annual mean SW TOA flux than either CERES or ISCCP, while ISCCP and CERES SW TOA flux variability is consistent to 40%. Most of the variability in CERES TOA flux is shown to be dominated by variations global cloud fraction, as observed using coincident CERES and MODIS data. Idealized Earthshine simulations of TOA SW radiation variability for a lunar-based observer show far less variability than the ground-based Earthshine observations, but are still a factor of 4-5 times more variable than global CERES SW TOA flux results. Furthermore, while CERES global albedos exhibit a well-defined seasonal cycle each year, the seasonal cycle in the lunar Earthshine reflectance simulations is highly variable and out-of-phase from one year to the next. Radiative transfer model (RTM) approaches that use imager cloud and aerosol retrievals reproduce most of the change in SW TOA radiation observed in broadband CERES data. However, assumptions used to represent the spectral properties of the atmosphere, clouds, aerosols and surface in the RTM calculations can introduce significant uncertainties in annual mean changes in regional and global SW TOA flux.

Loebe, Norman G.↗

NASA NTRS2019

Diurnal Cycle of IMERG V06 Precipitation

This study demonstrates the maturing ability of the half-hourly precipitation estimates from the Integrated Multi-satellitE Retrievals for GPM (IMERG) for use in global analyses of the diurnal cycle. The refined intercalibration and interpolation between the sensors in V06 leads to greater consistency in the precipitation retrievals over different hours of the day. Evaluation against ground measurements suggests a slight lag in the diurnal phase of only +0.59 h. We demonstrate the diurnal cycle over different regions around the globe, including the Maritime Continent, where accurate representation of precipitation variability in global models remains a challenge. Using examples over Singapore, Bangladesh, and Lake Victoria, we reveal the intricate interplay between diurnal and seasonal variability. This study demonstrates the unprecedented capability of IMERG in capturing the diurnal cycle of precipitation globally, potentially advancing our understanding in regions of sparse ground measurements and supporting improvements in its representation in global models.

Jackson Tan↗

NASA NTRS2016

Evaluation of GLAMR-Based Calibration for SI-Traceable Field Reflectance Retrievals

The reflected solar instrument that is part of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is being formulated with a goal of providing SI-traceable measurement of radiance that is an order of magnitude more accurate than the current imaging sensors. The Goddard Laser for Absolute Measurement of Radiance (GLAMR) is a key element to reaching such accuracy along with transferring the laboratory calibration to on-orbit measurements. Results from field reflectance retrievals using three separate instruments all of which have been calibrated using GLAMR are shown. The instruments include a commercial field spectrometer and a portable version of CLARREO's calibration demonstration system. The third instrument is NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) which is an airborne system. All three were operated during a March 2013 measurement campaign at Red Lake Playa, Arizona as part of the on-orbit commissioning phase of Landsat 8. Reflectance is derived from near-coincident measurements by the three sensors for a small area of the playa. The retrieved results are SI-traceable and demonstrate the ability to transfer the GLAMR calibration to the field. Use of the G-LiHT data in the calibration of Landsat-7 and -8 sensors permits them both to be placed on the GLAMR-scale as well.

Angal, Amit↗

NASA NTRS2010

Ten Years of Cloud Optical and Microphysical Retrievals from MODIS

The MODIS cloud optical properties algorithm (MOD06/MYD06 for Terra and Aqua MODIS, respectively) has undergone extensive improvements and enhancements since the launch of Terra. These changes have included: improvements in the cloud thermodynamic phase algorithm; substantial changes in the ice cloud light scattering look up tables (LUTs); a clear-sky restoral algorithm for flagging heavy aerosol and sunglint; greatly improved spectral surface albedo maps, including the spectral albedo of snow by ecosystem; inclusion of pixel-level uncertainty estimates for cloud optical thickness, effective radius, and water path derived for three error sources that includes the sensitivity of the retrievals to solar and viewing geometries. To improve overall retrieval quality, we have also implemented cloud edge removal and partly cloudy detection (using MOD35 cloud mask 250m tests), added a supplementary cloud optical thickness and effective radius algorithm over snow and sea ice surfaces and over the ocean, which enables comparison with the "standard" 2.1 11m effective radius retrieval, and added a multi-layer cloud detection algorithm. We will discuss the status of the MOD06 algorithm and show examples of pixellevel (Level-2) cloud retrievals for selected data granules, as well as gridded (Level-3) statistics, notably monthly means and histograms (lD and 2D, with the latter giving correlations between cloud optical thickness and effective radius, and other cloud product pairs).

Platnick, Steven↗