The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides the scientific community with observed top-of-atmosphere (TOA) shortwave and longwave fluxes for climate monitoring and climate model validation. To achieve this goal, CERES relies on TOA broadband fluxes derived from geostationary satellite (GEO) imagery to account for the diurnal flux variations between the CERES observation intervals. Consistent global flux derivation depends on accurate and consistent cloud retrievals. Scene-dependent spectral measurement inconsistency of the instruments that make up the contiguous ring of GEO observations (GEO-Ring), as well as limb darkening effects, can cause discontinuities in derived cloud properties and radiative fluxes at the boundaries of adjacent imager domains. Although the algorithms utilize radiative transfer models to account for instrument-band-dependent atmospheric correction and viewing zenith angle (VZA) dependency, small discontinuities may persist due to uncertainties inherent to the multiple imager-specific algorithms. Furthermore, while hyperspectral-instrument-based spectral band adjustment factors may effectively account for spectrally induced bias, they are less effective at reducing variance owed to the specific composition of the viewed scene, which is challenging to robustly characterize. As such, this article highlights the use of a deep neural network (DNN) to resolve spectral- and VZA-induced biases between GEO-Ring imagers. The DNN uses available infrared (IR) channels from the GEO instruments, along with viewing and solar illumination geometry, to estimate homogenized, VIIRS-like IR radiances for use in the GEO cloud algorithm. This approach is effective at mitigating scene-dependent spectral variance and VZA dependency, resulting in consistent radiance measurements across the GEO-Ring, thereby leading toward a more seamless global cloud assessment.
The NASA Clouds and the Earth’s Radiant Energy System (CERES) project relies on top-of-atmosphere (TOA) broadband fluxes derived from geostationary (GEO) satellite imagery to account for the diurnal flux variations between the CERES observation intervals, and thereby produce a synoptic gridded (SYN1deg) product based on continuous temporal observations. Consistent broadband flux derivation depends on accurate radiative property measurements and cloud retrievals, which largely determine the radiance-to-flux conversion process. Therefore, it is important to ensure a high quality of cloud property input in order to maintain a reliable broadband flux record. In Edition 4 of the CERES SYN1deg product, a robust automated image anomaly detection algorithm based on inter-line and inter-pixel differences, spatial variance, and 2-D Fourier analysis has been successful in identifying imagery with linear artifacts, but the line-by-line inspection and cleaning process must still be performed by a human. Therefore, further automation of this quality assurance process is warranted, especially considering the excessive amount of additional cleaning necessitated by the GOES-17 Advance Baseline Imager (ABI) cooling system anomaly. As such, this article highlights advancement of the CERES GEO image artifact cleaning approach based on a convolutional neural network (CNN) for classification of bad scanlines. Once trained, the CNN approach is a computationally inexpensive means to ensure greater consistency in cloud retrievals, and therefore broadband flux derivation, based on GOES-17 measurements.
The lack of shortwave (SW, visible, and near-infrared) geostationary satellite data at night results in degradation of many weather forecasts and real-time diagnostic products. We present a method to extrapolate SW GOES-16 advanced baseline imager data through night using nighttime longwave (LW, infrared) observations and the relationships between LW and SW data observed during the previous day. The method is not a forecast since it requires LW nighttime observations but can provide continuity through day, night, and satellite terminator hours. To provide performance statistics, the algorithm is applied during the day so the SW extrapolations can be compared to observations. Typical mean absolute errors (MAEs) range from 1.0% to 12.7% reflectance depending on the SW channel. These MAEs can be predicted using a diagnostic metric called 0-h MAE which quantifies the quality of the algorithm’s input data. In addition to quantitative error statistics, three case studies are presented, including an animation of extrapolated imagery from dusk through dawn. Considerations for future improvements include use of convolutional neural networks and/or object-based extrapolations where mesoscale features are extrapolated individually.
An artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels, the retrieved total cloud visible optical depth, and vertical humidity profiles is trained to detect multilayer (ML) ice-over-water cloud systems as identified by matched CloudSat and CALIPSO (CC) data. The multilayer ANN, or MLANN, algorithm is also trained to retrieve the optical depth and the top and base heights of the upper-layer ice clouds in ML systems. The trained MLANN was applied to independent MODIS data resulting in a combined ML and single layer hit rate of 80% (77%) for nonpolar regions during the day (night). The results are more accurate than currently available methods and the previous version of the MLANN. Upper-layer cloud top and base heights are accurate to ±1.2 km and ±1.6 km, respectively, while the uncertainty in optical depth is ±0.457 and ±0.556 during day and night, respectively. Areas of further improvement and development are identified and will be addressed in future versions of the MLANN.
For accurate cloud ceiling information, a data fusion approach is proposed that utilizes satellite data to extend surface station information to much wider areas. Cloud base height (CBH) retrieved from satellite observations provides for much larger spatial coverage and higher resolution. The direct comparison of GOES-16 CBH with surface station ceiling yields a local bias that has to be corrected for in the initial GOES-16 cloud base information. This sparsely sampled bias correction presents an irregular 2D mesh of control points, which is then interpolated by constructing a continuous smooth field using polyharmonic splines. The influence of remote stations is restricted by grouping the control points into clusters depending on an effective distance. This cluster-based approach allows for constructing separate spline surfaces corresponding to physically different clouds. The obtained continuous bias correction function is then applied to the entire GOES-16 pixel level CBH except for areas far away from surface stations in data sparse regions such as offshore. The described method is currently being tested using daytime-only observations over the central and eastern United States. Overall, this approach has potential to provide more accurate, high spatial resolution cloud ceiling information for the aviation community.
Determining whether a scene observed with a satellite imager is composed of a thin cirrus over a water cloud or thick cirrus contiguous with underlying layers of ice and water clouds is often difficult because of similarities in the observed radiance values. In this paper an artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels and the retrieved total cloud visible optical depth, is trained to detect multilayer ice-over-water cloud systems as identified by matched April 2009 CloudSat and CALIPSO (CC) data. The CC lidar and radar profiles provide the vertical structure that serves as output truth for a multilayer ANN, or MLANN, algorithm. Applying the trained MLANN to independent July 2008 MODIS data resulted in a combined ML and single layer hit rate of 75% (72%) for nonpolar regions during the day (night). The results are comparable to or more accurate than currently available methods. Areas of improvement are identified and will be addressed in future versions of the MLANN.
Ultra-spectral sounders (USS) in low earth orbit have significantly improved weather forecast accuracy in recent years, and their impact could be significantly improved with reduced revisit times. The GeoMetWatch, Inc.1 Sounding and Tracking Observatory for Regional Meteorology (STORMTM) program is designed to place a constellation of six USS units in spaced geostationary (GEO) positions around the earth. From GEO, the repeat time for a specific weather feature can be reduced to minutes, and the vertical temperature, water vapor and wind profiles can provide detailed warnings not available by any other means. The STORMTM sensor, a derivative of the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) EDU that was designed and built for NASA by Utah State University (USU) and rigorously tested in 2006, will be launched on a commercial geostationary satellite in late 2016. It combines advanced technologies to provide improved performance and reliability over the original EDU. From GEO the USS can observe surface thermal properties and atmospheric weather and chemistry variables in four dimensions. This paper provides an overview of the STORMTM instrument and the measurement concept. STORMTM’s USS will provide data of the same quality as the current LEO satellite sounders (AIRS, CrIS, and IASI) but with the ability to track storm development with soundings and images at any desired rate. Wind profiles obtained from a time sequence of STORMTM water vapor retrieval images will provide additional input to now casting and regional models.
The ultraspectral infrared radiances obtained from satellite observations provide atmospheric, surface, and/or cloud
information. The intent of the measurement of the thermodynamic state is the initialization of weather and climate
models. Great effort has been given to retrieving and validating these atmospheric, surface, and/or cloud properties. Error
Consistency Analysis Scheme (ECAS), through fast radiative transfer model (RTM) forward and inverse calculations,
has been developed to estimate the error budget in terms of absolute and standard deviation of differences in both
spectral radiance and retrieved geophysical parameter domains. The retrieval error is assessed through ECAS without
assistance of other independent measurements such as radiosonde data. ECAS re-evaluates instrument random noise, and
establishes the link between radiometric accuracy and retrieved geophysical parameter accuracy. ECAS can be applied to
measurements of any ultraspectral instrument and any retrieval scheme with associated RTM. In this paper, ECAS is
described and demonstration is made with the measurements of the METOP-A satellite Infrared Atmospheric Sounding
Interferometer (IASI).
A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze
other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base
height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES-
10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to
normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms.
Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties
are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected
domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level
results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the
products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for
aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have
potential for use in many other applications.
At NASA Langley Research Center (LaRC), radiances from multiple satellites are analyzed in near real-time to produce
cloud products over many regions on the globe. These data are valuable for many applications such as diagnosing
aircraft icing conditions and model validation and assimilation. This paper presents an overview of the multiple products
available, summarizes the content of the online database, and details web-based satellite browsers and tools to access
satellite imagery and products.
The international experiment EAQUATE (European AQUA Thermodynamic Experiment) was held in September 2004 in Italy and in the United Kingdom. The Italian phase, performed in the period 6-10 September 2004, was mainly devoted to assessment and validation of performances of new IR hyperspectral sensors and benefits from data and results of measurements of AQUA and in particular of AIRS. It is also connected with the preparatory actions of MetOp mission with particular attention to calibration and validation of IASI products (as water vapour and temperature profiles), characterization of semitransparent clouds and study of radiative balance, demonstrating the role of ground-based and airborne systems in validation operations.
The Italian phase of the campaign was carried out within a cooperation between NASA Langley Research Center, University of Wisconsin, the Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), the Mediterranean Agency for Remote Sensing (MARS) and the Universities of Basilicata, Bologna and Napoli. It involved the participation of the Scaled Composites Proteus aircraft (with NAST thermal infrared interferometer and microwave radiometer, the Scanning HIS infrared interferometer, the FIRSC far-IR interferometer), an Earth Observing System-Direct Readout Station and several ground based instruments: four lidar systems, a microwave radiometer, two infrared spectrometers, and a ceilometer. Radiosonde launches for measurements of PTU and wind velocity and direction were also performed as ancillary observations. Four flights were successfully completed with two different AQUA overpasses. The aircraft flew over the Napoli, Potenza and Tito Scalo ground stations several times allowing the collection of coincident aircraft and in- situ observations.
KEYWORDS: Black bodies, Calibration, Temperature metrology, Monte Carlo methods, Electronics, Fourier transforms, Imaging systems, Aluminum, Spectroscopy, Resistors
The NASA New Millennium Program's Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) instrument provides enormous advances in water vapor, wind, temperature, and trace gas profiling from geostationary orbit. The top-level instrument calibration requirement is to measure brightness temperature to better than 1 K (3 sigma) over a broad range of atmospheric brightness temperatures, with a reproducibility of ±0.2 K. For in-flight radiometric calibration, GIFTS uses views of two on-board blackbody sources (290 K and 255 K) along with cold space, sequenced at regular programmable intervals. The blackbody references are cavities that follow the UW Atmospheric Emitted Radiance Interferometer (AERI) design, scaled to the GIFTS beam size. The cavity spectral emissivity is better than 0.998 with an absolute uncertainty of less than 0.001. Absolute blackbody temperature uncertainties are estimated at 0.07 K. This paper describes the detailed design of the GIFTS on-board calibration system that recently underwent its Critical Design Review. The blackbody cavities use ultra-stable thermistors to measure temperature, and are coated with high emissivity black paint. Monte Carlo modeling has been performed to calculate the cavity emissivity. Both absolute temperature and emissivity measurements are traceable to NIST, and detailed uncertainty budgets have been developed and used to show the overall system meets accuracy requirements. The blackbody controller is housed on a single electronics board and provides precise selectable set point temperature control, thermistor resistance measurement, and the digital interface to the GIFTS instrument. Plans for the NIST traceable ground calibration of the on-board blackbody system have also been developed and are presented in this paper.
Development in the mid 80s of the High-resolution Interferometer Sounder (HIS) instrument for the high altitude NASA ER2 aircraft demonstrated the capability for advanced atmospheric temperature and water vapor sounding and set the stage for new satellite instruments that are now becoming a reality [AIRS(2002), CrIS(2006), IASI(2006), GIFTS(200?), HES(2013)]. Follow-on developments at the University of Wisconsin that employ Fourier Transform Infrared (FTIR) for Earth observations include the ground-based Atmospheric Emitted Radiance Interferometer (AERI) and the new Scanning HIS aircraft instrument.
The Scanning HIS is a smaller version of the original HIS that uses cross-track scanning to enhance spatial coverage. Scanning HIS and its close cousin, the NPOESS Airborne Sounder Testbed (NAST), are being used for satellite instrument validation and for atmospheric research. A novel detector configuration on Scanning HIS allows the incorporation of a single focal plane and cooler with three or four spectral bands that view the same spot on the ground. The calibration accuracy of the S-HIS and results from recent field campaigns are presented, including validation comparisons with the NASA EOS infrared observations (AIRS and MODIS).
Aircraft comparisons of this type provide a mechanism for periodically testing the absolute calibration of spacecraft instruments with instrumentation for which the calibration can be carefully maintained on the ground. This capability is especially valuable for assuring the long-term consistency and accuracy of climate observations, including those from the NASA EOS spacecrafts (Terra, Aqua and Aura) and the new complement of NPOESS operational instruments. It is expected that aircraft flights of the S-HIS and the NAST will be used to check the long-term stability of AIRS and the NPOESS operational follow-on sounder, the Cross-track Infrared Sounder (CrIS), over the life of the mission.
The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) sensor has been designed to provide highly accurate radiometric and spectral radiances in order to meet the requirements of remote sensing of atmospheric motion from a geostationary orbit. The GIFTS sensor was developed under NASA New Millenium Program funding to demonstrate the tracking of infrared water vapor features in the atmosphere with high vertical resolution. A calibration concept has been developed for the GIFTS instrument design which meets the top level requirement to measure brightness temperature to better than 1 K. The in-flight radiometric calibration is performed using views of two on-board blackbody sources along with cold space. For the GIFTS design, the spectral calibration is established by the highly stable diode laser used as the reference for interferogram sampling, and verified with comparisons to atmospheric absorption line positions. The status of the GIFTS on-orbit calibration approach is described and accuracy estimates are provided. GIFTS is a collaborative activity among NASA Langley Research Center, Utah State Space Dynamics Laboratory, and the University of Wisconsin Space Science and Engineering Center.
Modern Infrared satellite sensors such as AIRS, CrIS, TES, GIFTS and IASI are all capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, super fast radiative transfer models are needed. This paper presents a novel radiative transfer model based on principal component analysis. The model is very accurate and flexible. Its execution speed is a factor of 3-30 times faster than channel-based fast models. Due to its high speed and compressed spectral information format, it has great potential for super fast one-dimensional physical retrievals and for Numerical Weather Prediction (NWP) large volume radiance data assimilation applications. The model has been successfully developed for the NAST-I and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is suitable to include multiple scattering calculations to account for clouds and aerosols.
The ability to accurately validate high spectral resolution infrared radiance measurements from space using comparisons with aircraft spectrometer observations has been successfully demonstrated. The demonstration is based on an under-flight of the Atmospheric Infrared Sounder (AIRS) on the NASA Aqua spacecraft by the Scanning High resolution Interferometer Sounder (S-HIS) on the NASA ER-2 high altitude aircraft on 21 November 2002 and resulted in brightness temperature differences approaching 0.1K for most of the spectrum. This paper presents the details of this AIRS/S-HIS validation case and also presents comparisons of Aqua AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) radiance observations. Aircraft comparisons of this type provide a mechanism for periodically testing the absolute calibration of spacecraft instruments with instrumentation for which the calibration can be carefully maintained on the ground. This capability is especially valuable for assuring the long-term consistency and accuracy of climate observations. It is expected that aircraft flights of the S-HIS and its close cousin the National Polar Orbiting Environmental Satellite System (NPOESS) Atmospheric Sounder Testbed (NAST) will be used to check the long-term stability of the NASA EOS spacecrafts (Terra, Aqua and Aura) and the follow-on complement of operational instruments, including the Cross-track Infrared Sounder (CrIS).
Imagers on many of the current and future operational meteorological satellites in geostationary Earth orbit (GEO) and lower Earth orbit (LEO) have enough spectral channels to derive cloud microphysical properties useful for a variety of applications. The products include cloud amount, phase, optical depth, temperature, height and pressure, thickness, effective particle size, and ice or liquid water path, shortwave albedo, and outgoing longwave radiation for each imager pixel. Because aircraft icing depends on cloud temperature, droplet size, and liquid water content as well as aircraft variables, it is possible to estimate the potential icing conditions from the cloud phase, temperature, effective droplet size, and liquid water path. A prototype icing index is currently being derived over the contiguous USA in near-real time from Geostationary Operational Environmental Satellite (GOES-10 and 12) data on a half-hourly basis and from NOAA-16 Advanced Very High Resolution (AVHRR) data when available. Because the threshold-based algorithm is sensitive to small errors and differences in satellite imager and icing is complex process, a new probability based icing diagnosis technique is developed from a limited set of pilot reports. The algorithm produces reasonable patterns of icing probability and intensities when compared with independent model and pilot report data. Methods are discussed for improving the technique for incorporation into operational icing products.
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