The Johns Hopkins University Applied Physics Laboratory (JHU/APL) is developing a compact, light-weight, and lowpower midwave-infrared (MWIR) imager called the Compact Midwave Imaging Sensor (CMIS), under the support of the NASA Earth Science Technology Office Instrument Incubator Program. The goal of this CMIS instrument development and demonstration project is to increase the technical readiness of CMIS, a multi-spectral sensor capable of retrieving 3D winds and cloud heights 24/7, for a space mission. The CMIS instrument employs an advanced MWIR detector that requires less cooling than traditional technologies and thus permits a compact, low-power design, which enables accommodation on small spacecraft such as CubeSats. CMIS provides the critical midwave component of a multi-spectral sensor suite that includes a high-resolution Day-Night Band and a longwave infrared (LWIR) imager to provide global cloud characterization and theater weather imagery. In this presentation, an overview of the CMIS project, including the high-level sensor design, the concept of operations, and measurement capability will be presented. System performance for a variety of different scenes generated by a cloud resolving model (CRM) will also be discussed.
The Johns Hopkins University Applied Physics Laboratory (JHU/APL) has created a unique design for a compact, lightweight, and low-power instrument called the Compact Midwave Imaging Sensor (CMIS). Funded by the NASA ESTO Instrument Incubator Program (IIP), the goal of this CMIS development project is to increase the technical readiness of CMIS for retrieval of cloud heights and atmospheric motion vectors using stereo-photometric methods. The low-cost, low size, weight and power (SWaP) CMIS solution will include high operating temperature (HOT) MWIR detectors and a very low power cooler to enable spaceflight in a 6U CubeSat. This paper will provide an overview of the CMIS project to include the high-level sensor design.
We describe an algorithm for creating a virtual, statistically estimated 13.3-μm band for the Visible Infrared Imaging Radiometer Suite (VIIRS), an instrument aboard the National Oceanic and Atmospheric Administration’s (NOAA’s) operational satellite, Suomi NPP. VIIRS does not have a 13.3-μm band, although this band has important applications such as estimating cloud-top pressure. We demonstrate that a reliable estimate of the missing data can be created with a multisensor approach, using other VIIRS bands at 4, 9, 11, and 12 μm, as well as input from the Cross-track Infrared Sounder, on board the same satellite, which produces data at a much finer spectral resolution but lower spatial resolution. In addition, we evaluate the algorithm by applying it to data from the Moderate Resolution Image Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS), both on the Aqua satellite. MODIS and AIRS provide a benchmark for measuring the accuracy of the algorithm since, unlike VIIRS, MODIS makes measurements in the 13.3-μm band.
KEYWORDS: Satellites, Clouds, Solar radiation models, Solar energy, Solar radiation, Brain-machine interfaces, Spatial resolution, Meteorological satellites, Infrared radiation, Radiative transfer
Models to compute global horizontal irradiance (GHI) and direct normal irradiance (DNI) have been in
development over the last three decades. These models can be classified as empirical or physical based on
the approach. Empirical models relate ground-based observations with satellite measurements and use these
relations to compute surface radiation. Physical models consider the physics behind the radiation received
at the satellite and create retrievals to estimate surface radiation. While empirical methods have been
traditionally used for computing surface radiation for the solar energy industry, the advent of faster
computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) is a
physical model that computes DNI and GHI using the visible and infrared channel measurements from a
weather satellite. GSIP uses a two-stage scheme that first retrieves cloud properties and uses those
properties in a radiative transfer model to calculate GHI and DNI. Developed for polar orbiting satellites,
GSIP has been adapted to NOAA's Geostationary Operation Environmental Satellite series and can run
operationally at high spatial resolutions. This method holds the possibility of creating high quality datasets
of GHI and DNI for use by the solar energy industry. We present an outline of the methodology and results
from running the model as well as a validation study using ground-based instruments.
Top-of-atmosphere radiances and adjoint sensitivities for ice clouds at 600-2300 cm-1 are studied using a new fast radiative transfer system (forward, tangent linear, and adjoint) developed for the NASA/NOAA/DOD Joint Center for Satellite Data Assimilation. The radiative transfer model is based on a hybrid solution method for computing thermal radiances that fully accounts for multiple scattering and that allows clouds to be placed at any number of arbitrary layers. Called the successive order of interaction model, it has been shown to be faster in most cases and more accurate than the popular delta-Eddington model. Ice particle scattering properties are obtained from rigorous scattering theory for various particle shapes and sizes. Gas optical depths are derived from line-by-line calculations. Results indicate that top-of-atmosphere brightness temperatures are sensitive to ice water path occurring in multiple cloud layers, which suggests major challenges for retrieving cloud properties under conditions other than single-layered clouds.
Near-global total cloud frequencies and multilayered cloud frequencies derived from AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), and GLAS (Geoscience Laser Altimeter System) were analyzed and compared. The GLAS retrievals can be used to quantify the amount of cloud that may go undetected from satellite imagers such AVHRR and MODIS and to help validate satellite cloud overlap detection algorithms. Model sensitivity studies indicate that clouds with a total column optical depth of 0.5 or less may often go undetected by AVHRR and MODIS. The GLAS data show that such cloudy observations comprised 18.3% (14.5%) of all cloudy GLAS footprints during the most convectively active (least convectively active) portion of the day. Where the most (least) convectively active time period is defined as local solar noon plus (minus) 12 hours. It was also shown that the zonal mean total cloud frequency from GLAS and AVHRR and GLAS and MODIS are well correlated but often differ in magnitude because of thin clouds or small-scale cloud systems that are missed by the AVHRR and MODIS cloud detection algorithms. With the exception of the polar regions, the AVHRR and GLAS and the MODIS (via the Visible/Infrared Imager/Radiometer Suite algorithm) and GLAS multilayered cloud frequencies are in good agreement.
Validation of the CLAVR-x cloud detection algorithm over ocean is presented in this paper. CLAVR-x is the latest AVHRR processor developed at the NOAA/NESDIS Office of Research and Applications, with much improved cloud detection algorithm than its CLAVR predecessors. As our first validation study, we have selected sea surface temperature obtained from the NOAA-16 AVHRR GAC data for July 2001. The SST data was matched-up with global buoy data, which was assumed to be ground truth. Both fixed and drifting buoys were considered. This analysis indicates that the CLAVR-x cloud masking allows for generation of sea surface temperature values that are in good agreement with in situ buoys.
The NOAA/NESDIS Office of Research and Applications (ORA) has embarked on a pilot data stewardship project aimed at improving the data record from the Advanced Very High Resolution Radiometer (AVHRR). One part of this larger project includes the generation of a new cloud climatology from the Extended AVHRR Pathfinder Atmospheres (PATMOS-x) data set. Included within the PATMOS-x data-stream is a full suite of cloud products including various cloud amounts. This paper compares the PATMOS-x cloud amount time series for all July data (1982-2004) to the cloud amount time series from the International Satellite Cloud Climatology Project (ISCCP) and University of Wisconsin High Resolution Infrared Sounder (UW/HIRS) data sets. The results indicate that the large intersatellite discontinuities in the total amount seen in the original PATMOS are reduced in PATMOS-x. The total cloud for July time series from PATMOS-x, UW/HIRS and PATMOS show little trend over the period studied but that ISCCP time series does indicate a continuous downward trend When comparing the time series of high cloud amount, it was that PATMOS-x shows no significant trend in high cloud from 20S to 20N.
Radiances and brightness temperatures from three near-infrared/infrared channels that are available on most current and past satellite imagers were used to develop automated algorithms for identifying multilayered cloud systems (cloud overlap) and cirrus clouds at night. The cloud overlap algorithm uses information from the 3.75 micron, 11 micron, and 12 micron regions of the spectrum and the cirrus algorithm uses 3.75 micron and 11 micron channel data. The cloud overlap algorithm was developed assuming that a scene with cloud overlap consists of a semitransparent ice cloud that overlaps a lower cloud composed of liquid water droplets. Cirrus clouds are taken to be high ice clouds with a visible optical depth of 5.0 or less. The algorithms are applied to single satellite pixels that are already assumed to be cloudy based on cloud mask information. The utility of each algorithm was demonstrated on two different Moderate Resolution Imaging Spectroradiometer (MODIS) scenes and the cloud overlap algorithm was validated against millimeter radar-derived cloud boundaries. Overall the results show that both algorithms have the potential to be very useful for nighttime cloud studies.
A new, fast radiative transfer model including scattering
has been developed for the purpose of microwave radiance assimilation
in cloudy and precipitating areas. The model uses a technique called
successive order of interaction (SOI) which is based on a blending of the doubling and the successive order of scattering techniques. An adjoint and tangent linear version of the model are also available. Within this paper we present first applications of the SOI model. We compare brightness temperatures simulated from NCEP's Global Forecasting System (GFS) using a non-scattering version of the SOI model with global satellite data obtained by the Advanced Scanning Microwave Radiometer (AMSR-E) onboard NASA's Aqua spacecraft. Additionally, we show first sensitivity studies using the
adjoint model for cases that include scattering by liquid and frozen
precipitation.
Near nadir observations in the 11μm and 12 μm bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the TERRA spacecraft and the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-16 spacecraft are collected at their orbit intersections, where both MODIS and AVHRR view the Earth and its atmosphere at the same location within 30 seconds in the Arctic region. Sample data with 1 km resolution from spatially uniform areas are taken for direct inter-comparison of the scene radiance and brightness temperatures at around 270 K. Then a pixel-by-pixel match between the MODIS and AVHRR observations is performed to evaluate their correlation at different scene temperatures. The results show that MODIS and AVHRR observations agree well (difference less than 0.3 K) for both the 11μm and 12 μm bands, although their band correlation exhibits a slightly non-linear trend for scene temperatures greater than 285 K. The performance of MODIS is considered a good predictor of the performance of the National Polar-orbiting Operational Environmental Satellite System (NPOESS)/Visible Infrared Imager/Radiometer Suite (VIIRS), the future replacement of AVHRR. The direct comparison of MODIS and AVHRR observations is therefore considered a risk reduction study for VIIRS.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.