Engineering topics

Benjamin R. Scarino

Publications and source records attributed to Benjamin R. Scarino.

6 records

NASA NTRSDate not supplied

Langley Automated Sensor Inter-calibration System (LASICS): Open Access Tools for Satellite Imager Inter- Calibration

Satellite imager calibration teams are tasked with maintaining stable measurement records to facilitate reliable monitoring of geophysical parameters and ensure dependable input for forecast models. Satellite imagers are neither uniformly calibrated nor radiometrically scaled to a common reference standard. Consistent inter-calibration between various earth-orbiting satellite imager pairs is a critical step in the creation of seamless earth-scene reflectance data records over time for input to higher level algorithms that retrieve earth system climate-sensitive properties. Each imager inherently by virtue of its optics (and associated properties like spectral response etc.) and orbit will have a unique measurement of the same earth-scene reflected signal. A key part of this is the computationally efficient and optimal identification and prediction of science opportunities where the imager pairs from the irrespective earth-orbits near-simultaneously view the same stable terrestrial targets with nearly identical viewing and solar geometry.

Arun Gopalan↗

NASA NTRSDate not supplied

Additional characterization of Libya-4 in support of post-launch vicarious calibration of satellite imagers

Libya-4 (28.55° N and 23.39° E) is one of the most characterized and utilized Pseudo Invariant Calibration Site (PICS) for post-launch radiometer drift monitoring and sensor pair radiometric scaling. Libya-4 is one of the driest and most reflective PICS located in an extensive sand dune region void of any vegetation with an elevation of 118 m. It is positioned near the northeast border with Egypt. With minimal cloud cover and the monthly mean precipitation of ~ 1mm, Libya-4 is one of the temporally, spectrally, and spatially stable CEOS recommended PICS and Cosnefroy et al. 1996 identified sites. The Libya-4 PICS has been extensively used for post-launch radiometric calibration and validation of high-, medium, and low-resolution satellite imagers, including Landsat, MODIS, VIIRS, and Meteosats. The CERES Imager and Geostationary Calibration Group (IGCG) at NASA LaRC utilizes Libya-4 to perform an independent assessment of the radiometric stability of the MODIS and VIIRS L1B products. The site is also used for absolute radiometric scaling between MODIS, VIIRS, and geostationary imagers to ensure consistent cloud and radiative flux retrievals. Multi-year Terra-MODIS, Aqua-MODIS , NPP-VIIRS and Metoesat-7 observations over Libya-4 show very similar TOA reflectance temporal variability. Although, the surface reflectance and atmospheric column varies seasonally, the inter-annual variability of the seasonal cycle should be small. Especially during 2013, the Libya-4 TOA reflectance was found to be greater than usual in all 4 satellite records. Preliminary comparisons with mean wind speed and aerosol optical depth (AOD) indicate that the year 2013 is marked by elevated levels of both conditions. The goal of this study is to tie the Libya-4 visible reflectance inter-annual variability with corresponding meteorological measurements to further improve the characterization of the site.

David R. Doelling↗

NASA NTRSDate not supplied

Evaluation of Spectral Band Adjustment Factors for Cross-Calibration of Visible Imagers

The CERES EBAF dataset provides TOA SW and LW fluxes for long-term monitoring of the Earth’s energy balance and to validate climate models. The EBAF products, based on the Terra and Aqua CERES instrument observed radiances, rely on coincident measurements from the onboard MODIS imagers to determine cloud properties that are used in the angular distribution model scene selection required to convert the CERES observed radiances into flux values. Once the Terra and Aqua orbits start drifting outside of their 15-minute window, the CERES project will rely solely on the NOAA-20 CERES observations. A seamless transition of fluxes and clouds can only occur if the analogous MODIS and VIIRS channels are properly inter-calibrated. The visible band (0.65 µm) in MODIS (band 1) and VIIRS (M5 or I1) is critical for retrieving the cloud mask and optical depth. The spectral response functions (SRF) of these bands differ noticeably and will require scene dependent spectral band adjustment factors (SBAF) for proper radiometric scaling between them. Fortunately, the VIIRS I1 and M5 bands are calibrated using the same solar diffuser as a reference, which implies that band reflectance differences must be due to the spectral disparity. The coincident I1 and M5 TOA reflectance measurements provide the optimal opportunity to validate SBAFs over many surface and cloud conditions. The CERES project maintains SCIAMACHY, GOME-2, Hyperion, and radiative transfer model-based scene-stratified hyper-spectral reflectance measurements that can be convolved with sensor pair SRFs to compute the corresponding SBAF. This study will evaluate the SCIAMACHY, GOME-2, Hyperion, and RTM based SBAFs over tropical ocean targets used in SNO inter-calibration of MODIS and VIIRS, including clear-sky ocean, deep convective clouds (DCC), and liquid cloud targets in terms of their applicability to absolute intercomparison of visible imagers. The large SCIAMACHY and GOME-2 footprints and the lack of Hyperion cloudy scenes will impact the resulting SBAFs and necessitates the need for future CLARREO hyper-spectral reflectances to decrease SBAF uncertainties Preliminary results indicate that the SCIAMACHY and GOME-2 based SBAFs for the VIIRS I1 and M5 band pairs may differ by 1.5% for some ATO scene types. Because most of the modern GEOs visible band SRFs encompass the VIIRS I1 band SRF, these evaluations are critical to ensure that the MODIS, VIIRS, and GEO clouds and fluxes are consistent. SBAFs for Libya-4, Dome-C and other Earth invariant target will also be evaluated.

D. R. Doelling↗

NASA NTRSDate not supplied

Spaceborne Passive-Microwave and Visible/IR Observations of Severe Weather: Leveraging Multiple Perspectives for Detection, Validation, and Climatologies

Severe weather phenomena not only are responsible for damages to property, infrastructure, and agriculture, they are also difficult to measure in-situ and have been associated with large errors and uncertainties in precipitation estimation that pose challenges to establishing the current climatology of severe storms and their future trend in a changing Earth system. Severe convection exhibits distinct signatures in remote-sensing datasets, where it is manifested as textured or overshooting cloud tops in visible/IR imagery, or resulting a prominent brightness temperature depression in passive-microwave imagery. These distinct deep convective signatures in spaceborne datasets have been leveraged to analyze severe thunderstorms, create climatologies, improve prediction, and provide a method of detection around the globe where traditional ground-based data may be inconsistent or unavailable. Visible/IR and passive-microwave instruments are powerful tools for detecting severe thunderstorms, however, these datasets exhibit key limitations when examined individually: passive-microwave channels have large footprint and exhibit non-uniform beam filling. The visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. We investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together and assess the extent to which these datasets can be combined to improve severe thunderstorm detection. We pair passive microwave, IR, and visible signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis to characterize potentially severe convective storms observed by Aqua MODIS and AMSR-E. We will discuss how MODIS and AMSR-E signatures may be used together to diagnose storm properties and processes, and how the interrelationships between the signatures varies seasonally and geographically.

Sarah D. Bang↗

NASA NTRSDate not supplied

Additional Characterization of Sonoran Pics in Support of A Stable Multi-Sensor Geostationary Imager Record

The Sonoran Desert is the most utilized Pseudo Invariant Calibration Site (PICS) located in the Americas for post-launch radiometer drift monitoring and sensor pair radiometric scaling. The Sonoran Desert is located near the US Arizona and Mexican border with an elevation of 37 m. The site has small spatial and angular variations; however, soil moisture can cause short- term periodic reflectance fluctuations. The NASA Clouds and the Earth’s Radiant Energy System (CERES) project utilizes the site to validate the GOES East and West imager calibration coefficients derived from inter-calibrating GOES with MODIS. Because the daily local noon angular solar and viewing angles repeat every year over the site, a daily exoatmospheric radiance model (DERM) can be constructed over the lifetime of a well-calibrated GOES sensor. DERM based on a well-calibrated GOES imager can then be used to determine the calibration coefficients of successive GOES imagers to provide a stable multi-sensor GOES imager record. This will ensure that the GOES retrieved clouds and derived broadband fluxes can be used to infer the regional diurnal cycle in between Terra and Aqua CERES measurements to accurately compute the daily mean regional fluxes and clouds over the 20-year CERES SYN1deg product. Although the Sonoran Desert surface reflectance and atmospheric column vary seasonally, the inter-annual variability of the seasonal cycle is small, but it adds noise to the resulting DERM calibration coefficients. We seek to reduce the uncertainty of the DERM approach by improving the clear-sky filtering and correlating the observed interannual reflectance variability with atmospheric parameters, such as precipitable water, ozone concentration, and aerosol optical depth. The additional characterization of the Sonoran Desert site DERM for both the GOES East and West positions should improve the accuracy of the CERES SYN1deg product clouds and fluxes.

Prathana Khakurel↗

NASA NTRSDate not supplied

A Census of Severe Weather as Observed From Aqua: Visible/IR and Passive-Microwave Perspectives of Severe Convection

Severe weather phenomena represent the extreme upper end of the spectrum of convection and precipitation and tend to be highly localized and relatively rare compared to the rest of the distribution, but they can cause damage and loss disproportionate to their scale and frequency. Fortunately, severe convection exhibits distinct signatures in spaceborne remote-sensing datasets (e.g. overshooting cloud tops in visible/IR, or brightness temperature depressions in passive-microwave imagery). Leveraging these signatures individually has become a long-established practice to detect, analyze and establish climatologies of severe thunderstorms, especially in instances where traditional ground-based data may be unavailable. Spaceborne visible/IR and passive-microwave approaches are not without their pitfalls, however: passive-microwave channels have large footprints and exhibit non-uniform beam filling. Visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. To address this, we investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together and assess the extent to which these datasets can be combined to improve severe thunderstorm detection. We pair AMSR-E and MODIS signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis in six different geographical regimes throughout the Aqua domain. We present a census of potentially severe convective storms and their environments as seen by multiple instruments simultaneously, investigating how MODIS and AMSR-E signatures may be used together to diagnose storm properties and processes, and how the interrelationships between the signatures varies seasonally and geographically. Using statistical machine learning analysis, we aim to quantify the optimal MODIS and AMSR-E parameter sets for discriminating severe from non-severe storm cells and assess what improvement (if any) in detection results from combining the IR, visible, and microwave datasets.

Sarah Bang↗