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NASA NTRS2023

Assessing High Resolution Thermal Monitoring of Complex Intertidal Environments From Space: the Case of ECOSTRESS at Rias Baixas, NW Iberia

Currently, thermal sensors on satellites and other orbital platforms with high spatial resolution designed to monitor the oceans are often insufficient to assess the surface temperature of small bodies of water or within the coastal boundary layer. As the accuracy and sensitivity of remote sensing satellites improve, the demand for more accurate and up-to-date basic data sets for calibration increases. The quality of the thermal data collected by ECOSTRESS on the International Space Station allows the characterization of thermal stress levels in coastal ecosystems with a high spatial resolution of 70 m and a return time from hours to 5 days. This study focused on the calibration of ECOSTRESS estimates with in situ surface temperature data from sensors installed at 3 cm depth in the sediment on the intertidal muddy sandflats of three of the Rías Baixas in Galicia, NW Iberian Peninsula, from 2019 to 2021. A final number of 45 ECOSTRESS temperature images were analyzed. From these, 20% contained substantial georeferencing errors which had to be corrected manually with GIS software tools. We applied the Fourier's law of Heat Conduction to derive surface estimates from loggers sub-surface measurements that could be directly compared with ECOSTRESS data. Overall, a good calibration which explained more than 80% of ECOSTRESS temperature estimates for the whole dataset was obtained, but with an intrinsic cold bias around 1.39 °C. When temperatures at a depth of 3 cm were used, the linear fit became worse and the negative bias increased to 1.49 °C. Closer inspection revealed that night measurements were responsible for this larger deviation, as ECOSTRESS estimates became much colder compared to within sediment measurements because of the combined effect of the instrument intrinsic bias and nocturnal surface cooling. The best calibration was obtained when surface estimates were calculated just for the nighttime, as the cold bias decreased to 0.93 °C. More importantly, during hot daytime conditions in emersion above 20 °C, ECOSTRESS data matched surface temperature estimates, probably because of a better performance of ECOSTRESS algorithm at dry surfaces with lower emissivity. Thus, during the most ecologically relevant periods when high temperatures could drive thermal stress in many commercially-important bivalve species, ECOSTRESS provides accurate surface estimates that can be used to derive sub surface temperatures at the depths at which the different burrowing organisms live. We thus conclude that this instrument constitutes an important global tool to examine thermal stress at an unprecedented spatial scale for complex sea-land boundary systems.

ECOSTRESS↗

NASA NTRS2019

Sea Surface Salinity Distribution in the Southern Ocean as Observed from Space

Large-scale spatial and temporal variabilities of sea surface salinity (SSS) in the Southern Ocean from 2011 to 2017 were studied using products derived from microwave sensors on board Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active and Passive (SMAP) satellites. Four products, three from Aquarius and one from SMOS, were evaluated and shown to be generally consistent within 0.3 to 0.6 psu and agree favorably with in situ measurements. However, although the Aquarius products show consistent seasonality of SSS with high values of 34.45 psu in October and low values of 33.40 psu in May, theSMOS and SMAP products lack such seasonal variations. This may be caused by larger uncertainties in the SMOS and SMAP data due in part to the lack of concurrent scatterometer measurements that is used to correct for roughness effects. The four products provide similar spatial distributions of SSS with root mean square difference from 0.25 to 0.58 psu. Differences among Aquarius products are mainly due to varying salinity retrieval algorithms, smoothing, and masking of sea ice, while the SMOS product showed the highest SSS deviation that is likely due to the bias adjustment done on the data set. Our analyses show that SSS in the Southern Ocean region has significant meridional variations with the lowest SSS near the ice edge and highest at lower latitudes. The SSS is also lowest in summer indicating the predominant influence of sea ice and glacial melt, but it stays low near ice edges even during the growth season. Salinity together with temperature is the key parameters that control the state and circulation of the World's oceans. The large‐scale distribution of the sea surface salinity of the Southern Ocean is quantified for the first time using Aquarius, Soil Moisture and Ocean Salinity, and Soil Moisture Active and Passive satellite microwave data. Seasonal and interannual changes in the distribution as affected by the sea ice cover, surface temperature, and precipitation are evaluated. Comparative studies were done, using four different products that are publicly available, in conjunction with in situ observations to gain insights into the true nature of the distribution and how consistently the sea surface salinity is depicted by the different products. There are general consistencies in the products, and discrepancies are attributed to different algorithms, smoothing techniques, and sea ice masking. Aquarius data are shown to have higher accuracy than Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive products in part because of having concurrent scatterometer that provides accurate correction to roughness effects.

Garcia-Eidell, Cynthia↗

NASA NTRSDate not supplied

Improvements to the Simulated CubeSpark Satellite Constellation and Their Effects on Lightning Geolocation Accuracy from Orbit

The CubeSpark mission concept is being developed as a constellation of up to six satellites in low-Earth orbit (LEO) for high resolution 3D lightning mapping using optical and radio frequency (RF) sensors. Individual lightning VHF signals are simulated from Earth’s atmosphere through the ionosphere to each satellite, using their arrival times to reconstruct source locations. Here we present recent updates to these simulations based on improved ionospheric modeling, with a focus on the expected three-dimensional resolution. These studies include testing from the equator up to high latitudes, with varying vertical total electron content (vTEC), and using between one and six orbiting stations. In addition to the more robust ionosphere model, the constellation formation has also been updated to reduce its resulting errors and increase the effective range of VHF geolocation from space. The goals of CubeSpark include mapping thundercloud charge structure as well as lightning channel lengths relevant to climatology, meteorology, and more. These applications require location uncertainty less than 1-2 km in each dimension. This improved algorithm shows sufficient resolution up to high latitudes, including significantly larger areas having 3D resolution less than 1 km. Analysis of the distributions of biases in simulated arrival times has also revealed the unexpected relationship between the shape of those distributions and the resulting uncertainties. This work helps to refine our understanding of the sources of error in lightning geolocation and reinforces the potential for post-processing improvements in this and other similar systems.

Lightning↗

NASA NTRS2018

Sea Surface Salinity Distribution in the Southern Ocean as Observed from Space

Large scale spatial and temporal variabilities of sea surface salinity (SSS) in the Southern Ocean from 2011 to 2017 were studied using products derived from microwave sensors on board Aquarius/SAC-D, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active and Passive (SMAP) satellites. Four products, three from Aquarius and one from SMOS, were evaluated and shown to be generally consistent within 0.3 to 0.6 psu and agree favorably with in situ measurements. However, although the Aquarius products show consistent seasonality of SSS with high values of 34.45 in October and low values of 33.40 in May, the SMOS and SMAP products lack such seasonal variations. This may be caused by larger uncertainties in the SMOS and SMAP data due in part to the lack of concurrent scatterometer measurements that is used to correct for roughness effects. The four products provide similar spatial distributions of SSS with root-mean-square-errors from 0.25 to 0.58 psu. Differences among Aquarius products are mainly due to varying salinity retrieval algorithms, smoothing, and masking of sea ice, while the SMOS product showed the highest SSS deviation that is likely due to the bias-adjustment done on the dataset. Our analyses show that SSS in the Southern Ocean region has significant meridional variations with the lowest SSS near the ice edge and highest at lower latitudes. The SSS is also lowest in summer indicating the predominant influence of sea ice and glacial melt, but it stays low near ice edges even during the growth season.

SSS↗

NASA NTRSDate not supplied

Fine Optimization of TES Design for the X-IFU Instrument on ATHENA

The X-ray Integral Field Unit (X-IFU) instrument on the Advanced Telescope for High ENergy Astrophysics (ATHENA) is baselined to have 3168 transition-edge sensor (TES) microcalorimeter pixels. These pixels will be DC biased and readout using time division multiplexing. We recently reported a demonstration of the required pixel and readout performance in 252 pixels from a 1 kilo-pixel array (e.g. 2.16 eV resolution for 6.9 keV X-rays). Having achieved the required performance, the 50um square TES design in this demonstration array is considered the baseline pixel design for X-IFU. However, by making small adjustments to the TES design, we may be able to make small but consequential improvements to the instrument performance, while staying within the many constraints of the X-IFU requirements. In this presentation, we will show results from newly fabricated devices with subtle changes from the baseline TES design to further optimize the performance for X-IFU. We will discuss optimizing the TES sheet resistance, normal metal features, X-ray absorber attachment points, TES aspect ratio, and heat capacity. These minor design changes can have significant effects on thermo-electric time constants, thermal fluctuation noise, resistive transition shape and uniformity, and non-linearity of X-ray event pulses. In addition, full characterization of these new TES designs allows greater understanding of the relevant physics within the TES and gives routes for further optimization.

Nick Wakeham↗

NASA NTRS2002

TOMS UV Algorithm: Problems and Enhancements

Satellite instruments provide global maps of surface ultraviolet (UV) irradiance by combining backscattered radiance measurements with radiative transfer models. The models are limited by uncertainties in input parameters of the atmosphere and the surface. We evaluate the effects of possible enhancements of the current Total Ozone Mapping Spectrometer (TOMS) surface UV irradiance algorithm focusing on effects of diurnal variation of cloudiness and improved treatment of snow/ice. The emphasis is on comparison between the results of the current (version 1) TOMS UV algorithm and each of the changes proposed. We evaluate different approaches for improved treatment of pixel average cloud attenuation, with and without snow/ice on the ground. In addition to treating clouds based only on the measurements at the local time of the TOMS observations, the results from other satellites and weather assimilation models can be used to estimate attenuation of the incident UV irradiance throughout the day. A new method is proposed to obtain a more realistic treatment of snow covered terrain. The method is based on a statistical relation between UV reflectivity and snow depth. The new method reduced the bias between the TOMS UV estimations and ground-based UV measurements for snow periods. The improved (version 2) algorithm will be applied to re-process the existing TOMS UV data record (since 1978) and to the future satellite sensors (e.g., Quik/TOMS, GOME, OMI on EOS/Aura and Triana/EPIC).

Krotkov, Nickolay↗

NASA NTRS2008

Ground-based Network and Supersite Measurements for Studying Aerosol Properties and Aerosol-Cloud Interactions

From radiometric principles, it is expected that the retrieved properties of extensive aerosols and clouds from reflected/emitted measurements by satellite (and/or aircraft) should be consistent with those retrieved from transmitted/emitted radiance observed at the surface. Although space-borne remote sensing observations contain large spatial domain, they are often plagued by contamination of surface signatures. Thus, ground-based in-situ and remote-sensing measurements, where signals come directly from atmospheric constituents, the sun, and the Earth-atmosphere interactions, provide additional information content for comparisons that confirm quantitatively the usefulness of the integrated surface, aircraft, and satellite datasets. The development and deployment of AERONET (AErosol RObotic NETwork) sunphotometer network and SMART-COMMIT (Surface-sensing Measurements for Atmospheric Radiative Transfer - Chemical, Optical & Microphysical Measurements of In-situ Troposphere) mobile supersite are aimed for the optimal utilization of collocated ground-based observations as constraints to yield higher fidelity satellite retrievals and to determine any sampling bias due to target conditions. To characterize the regional natural and anthropogenic aerosols, AERONET is an internationally federated network of unique sunphotometry that contains more than 250 permanent sites worldwide. Since 1993, there are more than 480 million aerosol optical depth observations and about 15 sites have continuous records longer than 10 years for annual/seasonal trend analyses. To quantify the energetics of the surface-atmosphere system and the atmospheric processes, SMART-COMMIT instrument into three categories: flux radiometer, radiance sensor and in-situ probe. Through participation in many satellite remote-sensing/retrieval and validation projects over eight years, SMART-COMMIT have gradually refine( and been proven vital for field deployment. In this paper, we will demonstrate the capability of AERONET SMART-COMMIT in current Asian Monsoon Year-2008 campaigns that are designed and being executed to study the compelling variability in temporal scale of both anthropogenic and natural aerosols (e.g., airborne dust, smoke, mega-city pollutant). Feedback mechanisms between aerosol radiative effects and monsoon dynamics have been recently proposed, however there is a lack of consensus on whether aerosol forcing would be more likely to enhance or reduce the strength of the monsoon circulation. We envision robust approaches which well-collocated ground-based measurements and space-borne observations will greatly advance our understanding of absorbing aerosols (e.g., "Global Dimming" vs. "Elevated Heat-Pump" effects) on aerosol cloud water cycle interactions.

Tsay, Si-Chee↗

NASA NTRS2019

Global Validation of Columnar Water Vapor Derived from EOS MODIS-MAIAC Algorithm Against the Ground-Based AERONET Observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from the Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1-kilometer resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000-2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS (Root Mean Square) error lower than 0.250 centimeters. The binned error analysis revealed an underestimation (approximately 10 percent) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 centimeters. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 centimeters. While the accuracy is relatively similar between 1.0 and 5.0 centimeters for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (less than 5.0 centimeters). The expected error was defined as plus or minus 15 percent, with less than 68 percent of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (approximately 0.2 centimeters per decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.

MAIAC↗

DOE OSTI2024

Sensor response and radiation damage effects for 3D pixels in the ATLAS IBL Detector

Pixel sensors in 3D technology equip the outer ends of the staves of the Insertable B Layer (IBL), the innermost layer of the ATLAS Pixel Detector, which was installed before the start of LHC Run 2 in 2015. 3D pixel sensors are expected to exhibit more tolerance to radiation damage and are the technology of choice for the innermost layer in the ATLAS tracker upgrade for the HL-LHC programme. While the LHC has delivered an integrated luminosity of ≃ 235 fb -1 since the start of Run 2, the 3D sensors have received a non-ionising energy deposition corresponding to a fluence of ≃ 8.5 × 10 14 1 MeV neutron-equivalent cm -2 averaged over the sensor area. This paper presents results of measurements of the 3D pixel sensors' response during Run 2 and the first two years of Run 3, with predictions of its evolution until the end of Run 3 in 2025. Data are compared with radiation damage simulations, based on detailed maps of the electric field in the Si substrate, at various fluence levels and bias voltage values. These results illustrate the potential of 3D technology for pixel applications in high-radiation

47 OTHER INSTRUMENTATION↗

NASA NTRS2012

Retrieval of Vertical LAI Profiles Over Tropical Rain Forests using Waveform Lidar at La Selva, Costa Rica

This study explores the potential of waveform lidar in mapping the vertical and spatial distributions of leaf area index (LAI) over the tropical rain forest of La Selva Biological Station in Costa Rica. Vertical profiles of LAI were derived at 0.3 m height intervals from the Laser Vegetation Imaging Sensor (LVIS) data using the Geometric Optical and Radiative Transfer (GORT) model. Cumulative LAI profiles obtained from LVIS were validated with data from 55 ground to canopy vertical transects using a modular field tower to destructively sample all vegetation. Our results showed moderate agreement between lidar and field derived LAI (r2=0.42, RMSE=1.91, bias=−0.32), which further improved when differences between lidar and tower footprint scales (r2=0.50, RMSE=1.79, bias=0.27) and distance of field tower from lidar footprint center (r2=0.63, RMSE=1.36, bias=0.0) were accounted for. Next, we mapped the spatial distribution of total LAI across the landscape and analyzed LAI variations over different land cover types. Mean values of total LAI were 1.74, 5.20, 5.41 and 5.62 over open pasture, secondary forests, regeneration forests after selective-logging and old-growth forests respectively. Lastly, we evaluated the sensitivities of our LAI retrieval model to variations in canopy/ground reflectance ratio and to waveform noise such as induced by topographic slopes. We found for both, that the effects were not significant for moderate LAI values (about 4). However model derivations of LAI might be inaccurate in areas of high-slope and high LAI (about 8) if ground return energies are low. This research suggests that large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the high LAI levels in tropical rain forests and may be a useful tool for understanding the light transmittance within these canopies.

Tang, Hao↗

NASA NTRS1994

Observations of cloud liquid water path over oceans: Optical and microwave remote sensing methods

Published estimates of cloud liquid water path (LWP) from satellite-measured microwave radiation show little agreement, even about the relative magnitudes of LWP in the tropics and midlatitudes. To understand these differences and to obtain more reliable estimate, optical and microwave LWP retrieval methods are compared using the International Satellite Cloud Climatology Project (ISCCP) and special sensor microwave/imager (SSM/I) data. Errors in microwave LWP retrieval associated with uncertainties in surface, atmosphere, and cloud properties are assessed. Sea surface temperature may not produce great LWP errors, if accurate contemporaneous measurements are used in the retrieval. An uncertainty of estimated near-surface wind speed as high as 2 m/s produces uncertainty in LWP of about 5 mg/sq cm. Cloud liquid water temperature has only a small effect on LWP retrievals (rms errors less than 2 mg/sq cm), if errors in the temperature are less than 5 C; however, such errors can produce spurious variations of LWP with latitude and season. Errors in atmospheric column water vapor (CWV) are strongly coupled with errors in LWP (for some retrieval methods) causing errors as large as 30 mg/sq cm. Because microwave radiation is much less sensitive to clouds with small LWP (less than 7 mg/sq cm) than visible wavelength radiation, the microwave results are very sensitive to the process used to separate clear and cloudy conditions. Different cloud detection sensitivities in different microwave retrieval methods bias estimated LWP values. Comparing ISCCP and SSM/I LWPs, we find that the two estimated values are consistent in global, zonal, and regional means for warm, nonprecipitating clouds, which have average LWP values of about 5 mg/sq cm and occur much more frequently than precipitating clouds. Ice water path (IWP) can be roughly estimated from the differences between ISCCP total water path and SSM/I LWP for cold, nonprecipitating clouds. IWP in the winter hemisphere is about 3 times the LWP but only half the LWP in the summer hemisphere. Precipitating clouds contribute significantly to monthly, zonal mean LWP values determined from microwave, especially in the intertropical convergence zone (ITCZ), because they have almost 10 times the liquid water (cloud plus precipitation) of nonprecipitating clouds on average. There are significant differences among microwave LWP estimates associated with the treatment of precipitating clouds.

Lin, Bing↗

NASA NTRS2017

Effects of Surface BRDF on the OMI Cloud and NO2 Retrievals: A New Approach Based on Geometry-Dependent Lambertian Equivalent Reflectivity (GLER) Derived from MODIS

The Ozone Monitoring Instrument (OMI) cloud and NO2 algorithms use a monthly gridded surface reflectivity climatology that does not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (GLER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. GLER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from MODIS over land and the Cox Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare GLER and climatological LER at 466 nm, which is used in the OMI O2-O2cloud algorithm to derive effective cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and GLERs is carried out. GLER and corresponding retrieved cloud products are then used as input to the OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with GLERs can increase NO2 vertical columns by up to 50 % in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

MODIS BRDF OMI clouds NO2 GLER↗

NASA NTRS2021

Seasonal Bias in Global Ocean Color Observations

In this study we identify a seasonal bias in the ocean color satellite derived remote sensing reflectances (Rrs(λ); sr-1) at the ocean color validation site, MOBY (Marine Optical BuoY). The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including VIIRS (Visible Infrared Imaging Radiometer Suite), SeaWiFS (Sea-viewing Wide Field-of-view Sensor), and MODIS (Moderate Resolution Imaging Spectrometer). The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder) satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs (λ) and we recommend more simulations to discern these influence Rrs (λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are or will be affected until a solution is implemented.

satellite ocean color↗

NASA NTRS2017

The Effects of Normal Metal Stripes on TES Performance

Exploring the effects of size and geometry of normal metal features on the transition shapes and performance of transition-edge sensor microcalorimeters. The spectral resolution of transition-edge sensor (TES) microcalorimeters is very sensitive to the specific dependencies of the resistance R in the superconducting transition on the current I, magnetic field B, and temperature T. In particular, it has been shown that transitions that are very steep in (R,T) space lead to a significant noise term, in excess of conventional expectations. This so-called unexplained noise is known to be reduced by the addition of normal metal stripes across the TES perpendicular to the direction of current flow. These normal metal stripes have been shown to drastically alter the oscillatory patterns seen in measurements of the critical current as a function of magnetic field. However, there are many remaining questions about the exact impact of the stripes on current distributions within the TES, the Fraunhofer pattern and, therefore, the shape of the R(I, B, T) surface. Through measurements of the resistance under DC bias of TES devices of various sizes, with different stripe patterns and dimensions, we will discuss how these stripes can affect the R(I, B, T) surface. In addition, using measurements and analysis of the noise spectra of various devices we will present how these changes to the stripe pattern may affect the performance of the TES. In particular, we will discuss strategies to reduce the presence of localized discontinuities in the derivative of R, associated with increased noise, while maintaining the globally low levels of unexplained noise currently achieved with conventional metal stripe patterns. Implementing these strategies is a path towards producing large arrays with highly uniform transitions and high spectral resolution. These large uniform arrays will be required for future x-ray astronomy applications, such as the X-IFU on ATHENA.

TES↗

NASA NTRS2015

Lightning Imaging Sensor (LIS) for the International Space Station (ISS): Mission Description and Science Goals

In recent years, the NASA Marshall Space Flight Center, the University of Alabama in Huntsville, and their partners have developed and demonstrated space-based lightning observations as an effective remote sensing tool for Earth science research and applications. The Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) continues to acquire global observations of total (i.e., intracloud and cloud-to-ground) lightning after 17 years on-orbit. However, TRMM is now low on fuel, so this mission will soon be completed. As a follow on to this mission, a space-qualified LIS built as the flight spare for TRMM has been selected for flight as a science mission on the International Space Station (ISS). The ISS LIS will be flown as a hosted payload on the Department of Defense Space Test Program (STP) H5 mission, which has a January 2016 baseline launch date aboard a SpaceX launch vehicle for a 2-4 year or longer mission. The LIS measures the amount, rate, and radiant energy of total lightning over the Earth. More specifically, it measures lightning during both day and night, with storm scale resolution (approx. 4 km), millisecond timing, and high, uniform detection efficiency, without any land-ocean bias. Lightning is a direct and most impressive response to intense atmospheric convection. It has been found that lightning measured by LIS can be quantitatively related to thunderstorm and other geophysical processes. Therefore, the ISS LIS lightning observations will continue to provide important gap-filling inputs to pressing Earth system science issues across a broad range of disciplines, including weather, climate, atmospheric chemistry, and lightning physics. A unique contribution from the ISS platform will be the availability of real-time lightning data, especially valuable for operational applications over data sparse regions such as the oceans. The ISS platform will also uniquely enable LIS to provide simultaneous and complementary observations with other ISS payloads such as the European Space Agency's Atmosphere-Space Interaction Monitor (ASIM) that will be exploring the connection between thunderstorms and lightning with terrestrial gamma-ray flashes (TGFs) and the Japan Aerospace Exploration Agency's Global LIghtning and Sprites MeasurementS (GLIMS) with its focus on global lightning and sprite connections. Another important function of the ISS LIS will be to provide cross-sensor calibration/validation with a number of other payloads, including the TRMM LIS and the next generation geostationary lightning mappers such as the GOES-R Geostationary Lightning Mapper (GLM) and Meteosat Third Generation Lightning Imager (MTG LI), as well as with ground-based lightning detection systems. These inter-calibrations will improve the long term climate monitoring record provided by all these systems. Finally, the ISS LIS will extend the time-series climate record of LIS lightning observations and expand the latitudinal coverage of LIS lightning to the climate significant upper middle-latitudes.

Blakeslee, R. J.↗

NASA NTRS1999

Ocean Wave Studies with Applications to Ocean Modeling and Improvement of Satellite Altimeter Measurements

Combining analysis of satellite data (altimeter, scatterometer, high-resolution visible and infrared images, etc.) with mathematical modeling of non-linear wave processes, we investigate various ocean wave fields (on scales from capillary to planetary), their role in ocean dynamics and turbulent transport (of heat and biogeochemical quantities), and their effects on satellite altimeter measuring accuracy. In 1998 my attention was focused on long internal gravity waves (10 to 1000 km), known also as baroclinic inertia-gravity (BIG) waves. We found these waves to be a major factor of altimeter measurements "noise," resulting in a greater uncertainty [up to 10 cm in terms of sea surface height (SSH) amplitude] in the measured SSH signal than that caused by the sea state bias variations (up to 5 cm or so). This effect still remains largely overlooked by the satellite altimeter community. Our studies of BIG waves address not only their influence on altimeter measurements but also their role in global ocean dynamics and in transport and turbulent diffusion of biogeochemical quantities. In particular, in collaboration with Prof Peter Weichman, Caltech, we developed a theory of turbulent diffusion caused by wave motions of most general nature. Applied to the problem of horizontal turbulent diffusion in the ocean, the theory yielded the effective diffusion coefficient as a function of BIG wave parameters obtainable from satellite altimeter data. This effort, begun in 1997, has been successfully completed in 1998. We also developed a theory that relates spatial fluctuations of scalar fields (such as sea surface temperature, chlorophyll concentration, drifting ice concentration, etc.) to statistical characteristics of BIG waves obtainable from altimeter measurements. A manuscript is in the final stages of preparation. In order to verify the theoretical predictions and apply them to observations, we are now analyzing Sea-viewing Wide Field of view Sensor (SeaWiFS) and Field of view Sensor (SeaWiFS) and Advanced Very High-Resolution Radiometer (AVHRR) data on sea surface temperature (SST) and chlorophyll concentration jointly with TOPEX/POSEIDON data on SSH variations.

Glazman, Roman E.↗

NASA NTRS2008

Array-scale performance of TES X-ray Calorimeters Suitable for Constellation-X

Having developed a transition-edge-sensor (TES) calorimeter design that enables high spectral resolution in high fill-factor arrays, we now present array-scale results from 32-pixel arrays of identical closely packed TES pixels. Each pixel in such an array contains a Mo/Au bilayer with a transition temperature of 0.1 K and an electroplated Au or Au/Bi xray absorber. The pixels in an array have highly uniform physical characteristics and performance. The arrays are easy to operate due to the range of bias voltages and heatsink temperatures over which solution better than 3 eV at 6 keV can be obtained. Resolution better than 3 eV has also been obtained with 2x8 time-division SQUID multiplexing. We will present the detector characteristics and show spectra acquired through the read-out chain from the multiplexer electronics through the demultiplexer software to real-time signal processing. We are working towards demonstrating this performance over the range of count rates expected in the observing program of the Constellation-X observatory. We mill discuss the impact of increased counting rate on spectral resolution, including the effects of crosstalk and optimal-filtering dead time.

Kilbourne, C. A.↗

NASA NTRS2010

Comparison of Satellite-Derived TOA Shortwave Clear-Sky Fluxes to Estimates from GCM Simulations Constrained by Satellite Observations of Land Surface Characteristics

Clear-sky, upwelling shortwave flux at the top of the atmosphere (S(sub TOA raised arrow)), simulated using the atmospheric and land model components of the Community Climate System Model 3 (CCSM3), is compared to corresponding observational estimates from the Clouds and Earth's Radiant Energy System (CERES) sensor. Improvements resulting from the use of land surface albedo derived from Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the simulations are also examined. Compared to CERES observations, CCSM3 overestimates global, annual averaged S(sub TOA raised arrow) over both land and oceans. However, regionally, CCSM3 overestimates S(sub TOA raised arrow) over some land and ocean areas while underestimating it over other sites. CCSM3 underestimates S(sub TOA raised arrow) over the Saharan and Arabian Deserts and substantial differences exist between CERES observations and CCSM3 over agricultural areas. Over selected sites, after using groundbased observations to remove systematic biases that exist in CCSM computation of S(sub TOA raised arrow), it is found that use of MODIS albedo improves the simulation of S(sub TOA raised arrow). Inability of coarse resolution CCSM3 simulation to resolve spatial heterogeneity of snowfall over high altitude sites such as the Tibetan Plateau causes overestimation of S(sub TOA raised arrow) in these areas. Discrepancies also exist in the simulation of S(sub TOA raised arrow) over ocean areas as CCSM3 does not account for the effect of wind speed on ocean surface albedo. This study shows that the radiative energy budget at the TOA is improved through the use of MODIS albedo in Global Climate Models.

Anantharaj, Valentine G.↗