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Proceedings ArticleDOI

Development of multi-sensor global cloud and radiance composites for earth radiation budget monitoring from DSCOVR

02 Oct 2017-Vol. 10424, pp 21-33

AbstractThe Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95% of the globe.

Topics: Geostationary orbit (53%), Satellite (53%), Radiance (53%), Radiometer (51%)

Summary (2 min read)

1. INTRODUCTION

  • The Deep Space Climate Observatory was launched in February 2015 to reach a looping halo orbit around Lagrangian point 1 (L1) with a spacecraft-Earth-Sun angle varying from 4 to 15 degrees [1, 2].
  • This goal implies calculation of the albedo and the outgoing longwave radiation using a combination of NISTAR, EPIC, and other imager-based products.
  • The process involves, first, deriving and merging cloud properties and radiation estimates from low earth orbit (LEO) and geosynchronous (GEO) satellite imagers.
  • These properties are then spatially averaged and collocated to match the EPIC pixels to provide the scene identification needed to select anisotropic directional models (ADMs).
  • These datasets have 409 pixels by about 12800 lines matching the 4 km/pixel resolution and temporal coverage of the original AVHRR Level 1B GAC data.

2. GENERATION OF GLOBAL GEO/LEO COMPOSITES

  • The input GEO, AVHRR, and MODIS datasets are carefully pre-processed using a variety of data quality control algorithms including both automated and human-analyzed techniques.
  • If a satellite observation occurred long before or long after the nominal time, then that data sample is given a lower rating and thus will likely be replaced with data from another source that was observed closer in time.
  • As an inherent result of the merging process, the obtained composite may reveal uneven boundaries between data that originate from two different satellites .
  • Most programming interfaces provide a uniformly distributed random variable, but converting it to a normally distributed number involves two logarithms, two square roots, and two trigonometric functions, severely impairing the computational performance.

3. EPIC-VIEW COMPOSITES

  • EPIC's total field of view (FOV) is 0.61 full angle and the recorded image dimensions are 20482048 pixels for high resolution channels, which translates to about nominal 7.8 km pixel spacing at nadir.
  • To minimize the under-sampling of the global composite data and to improve the accuracy of the PSF sampling, the decision was made to double the dimensions of the EPIC domain by creating a virtual grid of 40964096 pixels at 3.9 km/pix resolution.
  • On the new grid, the PSF can be sampled with half-pixel accuracy and so the matrix of PSF weights becomes 1212 size with the largest four weights at the center (corresponding to the former central pixel) of 0.07598.
  • The process of remapping and convolution can be summarized in 5 steps: 1) Convert the global composite data (where applicable); 2) Remap to the virtual grid with bilinear interpolation;.
  • The actual FOV fractions (sums of the PSF weights screened out by the same masks), which describe the percent ratio of a particular cloud phase in a given FOV, are also calculated and stored accordingly.

4. DISCUSSION AND CONCLUSION

  • A very good spatial collocation can be seen when comparing the two images.
  • Overall, the global composite data files provide well-characterized and consistent regional and global cloud and surface property datasets covering all time and space scales to match with EPIC.
  • The EPIC-view composites are useful for many applications including: Inter-calibration of non-UV EPIC channels; Provide high-resolution independent scene identification for each EPIC pixel; Convolve with EPIC radiances and CERES ADMs to compute daytime fluxes from NISTAR; Serve as a comparison source for EPIC cloud retrievals; Provide cloud mask for other retrievals based on EPIC radiances.
  • Spatial variability and continuity of the global composite data have been analyzed to assess the performance of the merging criteria.
  • The described algorithm has demonstrated seamless global coverage for any requested time of day with a temporal lag of under 2 hours in over 95% of the globe.

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Development of Multi-Sensor Global Cloud and Radiance Composites for Earth
Radiation Budget Monitoring from DSCOVR
Konstantin Khlopenkov
*a
, David Duda
a
, Mandana Thieman
a
,
Patrick Minnis
a
, Wenying Su
b
, and Kristopher Bedka
b
a
Science Systems and Applications Inc., Hampton, VA 23666;
b
NASA Langley Research Center, Hampton, VA 23681.
ABSTRACT
The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the
onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced
Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary
satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic
directional models needed to calculate shortwave and longwave fluxes.
A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple
satellite imagers to obtain seamless global hourly composites at 5-km resolution. An aggregated rating is employed to
incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial
accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel
resampling.
The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC
point spread function defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are
computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any
requested time of day with a temporal lag of under 2 hours in over 95% of the globe.
Keywords: DSCOVR, radiation budget, cloud properties, image processing, composite, subpixel, point spread function.
1. INTRODUCTION
The Deep Space Climate Observatory (DSCOVR) was launched in February 2015 to reach a looping halo orbit around
Lagrangian point 1 (L1) with a spacecraft-Earth-Sun angle varying from 4 to 15 degrees [1, 2]. The Earth science
instruments consist of the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and
Technology Advanced Radiometer (NISTAR) [3]. The vantage point from L1, unique to Earth science, provides for
continuous monitoring of the Earth’s reflected and emitted radiation and enables analysis of the daytime Earth radiation
budget. This goal implies calculation of the albedo and the outgoing longwave radiation using a combination of
NISTAR, EPIC, and other imager-based products. The process involves, first, deriving and merging cloud properties and
radiation estimates from low earth orbit (LEO) and geosynchronous (GEO) satellite imagers. These properties are then
spatially averaged and collocated to match the EPIC pixels to provide the scene identification needed to select
anisotropic directional models (ADMs). Shortwave (SW) and longwave (LW) anisotropic factors are finally computed
for each EPIC pixel and convolved with EPIC reflectances to determine single average SW and LW factors used to
convert the NISTAR-measured radiances to global daytime SW and LW fluxes [4, 5].
EPIC imager delivers 20482048 pixel imagery in 10 spectral bands, all shortwave, from 317 to 780 nm, while NISTAR
measures the full Earth disk radiance at the top-of-atmosphere (TOA) in three broadband spectral windows: 0.2100,
0.24, and 0.74 m. Although NISTAR can provide accurate top-of-atmosphere (TOA) radiance measurements, the
low resolution of EPIC imagery (discussed futher below) and its lack of infrared channels diminish its usefulness in
obtaining details on small-scale surface and cloud properties [6]. Previous studies [7] have shown that these properties
*
konstantin.khlopenkov@nasa.gov; phone 1 757 951-1914; fax 1 757 951-1900; www.ssaihq.com

have a strong influence on the anisotropy of the radiation at the TOA, and ignoring such effects can result in large TOA-
flux errors. To overcome this problem, high-resolution scene identification is derived from the radiance observations and
cloud properties retrievals from LEO (including NASA Terra and Aqua MODIS, and NOAA AVHRR) and from a
global constellation of GEO satellite imagers, which include Geostationary Operational Environmental Satellites
(GOES) operated by the NOAA, Meteosat satellites (by EUMETSAT), and the Multifunctional Transport Satellites
(MTSAT) and Himawari-8 satellites operated by Japan Meteorological Agency (JMA).
The NASA Clouds and the Earth's Radiant Energy System (CERES) [8] project was designed to monitor the Earth’s
energy balance in the shortwave and longwave broadband wavelengths. For the SYN1deg Edition 4 product [9],
1-hourly imager radiances are obtained from 5 contiguous GEO satellite positions (including GOES-13 and -15,
METEOSAT-7 and -10, MTSAT-2, and Himawari-8). The GEO data utilized for CERES was obtained from the Man
computer Interactive Data Access System (McIDAS) [10] archive, which collects data from its antenna systems in near
real time. The imager radiances are then used to retrieve cloud and radiative properties using the CERES Cloud
Subsystem group algorithms [11, 12]. Radiative properties are derived from GEO and MODIS following the method
described in [13] to calculate broadband shortwave albedo, and following a modified version of the radiance-based
approach of [9] to calculate broadband longwave flux. For the 1st generation GEO imagers, the visible (0.65 µm), water
vapor (WV) (6.7 µm) and IR window (11 µm) channels are utilized in the cloud algorithm. For the 2nd generation GEO
imagers, the solar IR (SIR) (3.9µm) and split-window (12 µm) channels are added. In GOES-12 through 15, the split
window is replaced with a CO
2
slicing channel (13.2 µm). The native nominal pixel resolution varies as a function of
wavelength and by satellite. The channel data is sub-sampled to a ~810 km/pixel resolution. For MODIS, the algorithm
uses every fourth 1-km pixel and every other scanline and yields hourly datasets of 339 pixels by about 12000 lines. A
long-term cloud and radiation property data product has also been developed using AVHRR Global Area Coverage
(GAC) imagery [13], which has been georeferenced to sub-pixel accuracy [14]. These datasets have 409 pixels by about
12800 lines matching the 4 km/pixel resolution and temporal coverage of the original AVHRR Level 1B GAC data.
This work presents an algorithm for optimal merging of selected radiances and cloud properties derived from multiple
satellite imagers to obtain a seamless global composite product at 5-km resolution. These composite images are
produced for each observation time of the EPIC instrument (typically 300500 composites per month). In the next step,
for accurate collocation with EPIC pixels, the global composites are remapped into the EPIC-view domain using
geolocation information supplied in EPIC Level 1B data.
2. GENERATION OF GLOBAL GEO/LEO COMPOSITES
The input GEO, AVHRR, and MODIS datasets are carefully pre-processed using a variety of data quality control
algorithms including both automated and human-analyzed techniques. This is especially true of the GEO satellites that
frequently contain bad scan lines and/or other artifacts that result in data quality issues despite the radiance values being
within a physical range. Some of the improvements to the quality of the imager data include detector de-striping
algorithm [15] and an automated system for detection and filtering of transmission noise and corrupt data [16]. The
CERES Clouds Subsystem has pioneered these and other data quality tests to ensure the removal of as many satellite
artifacts as possible.
The global composites are produced on a rectangular latitude/longitude grid with a constant angle spacing of exactly
1/22 degree, which is about 5 km per pixel near the Equator, resulting in 79203960 grid dimensions. Each source data
image is remapped onto this grid by means of inverse mapping, which uses the latitude and longitude coordinates of a
pixel in the output grid for searching for the corresponding sample location in the input data domain (provided that the
latitude and longitude are known for every pixel of the input data). This process employs the concurrent gradient search
described in [17], which uses local gradients of latitude and longitude fields of the input data to locate the sought sample.
This search is very computationally efficient and yields a fractional row/column position, which is then used to
interpolate the adjacent data samples (such as reflectance or brightness temperature) by means of a 66 point resampling
function. At the image boundaries, the missing contents of the 66 window are padded by replicating the edge pixel
values. The image resampling operations are implemented as Lanczos filtering [18] extended to the 2D case with the
parameter a = 3. This interpolation method is based on the sinc filter, which is known to be an optimal reconstruction
filter for band-limited signals, e.g. digital imagery. For discrete input data, such as cloud phase or surface type, the
nearest-neighbor sampling is used instead of interpolation. The described remapping process allows us to preserve as

much as possible the spatial accuracy of the input imagery, which has a nominal resolution of 48 km/pix at the satellite
nadir.
After the remapping, the new data are merged with data in the global composite. Composite pixels are replaced with the
new samples only if their quality is lower compared to that of the new data. The quality is measured by a specially
designed rating R, which incorporates five parameters: nominal satellite resolution F
resolution
, pixel time relative to the
EPIC time, viewing zenith angle , distance from day/night terminator F
terminator
, and sun glint factor F
glint
:
2
5.1
glintterminatorresolution
)τ/(1
θcos8.02.0
t
FFFR
(1)
where t is the absolute difference, in hours, between the original observation time of a given sample and the EPIC
observation time. The latter defines the nominal time of the whole composite. If a satellite observation occurred long
before or long after the nominal time, then that data sample is given a lower rating and thus will likely be replaced with
data from another source that was observed closer in time. The characteristic time controls the attenuation rate of the
rating R with increasing time difference. Here = 5 hours is used, which will be justified further below. One can see that
a time difference of 2.8 hours decreases the rating by a factor of about 2. Observations with a time difference of more
than 4 hours have not been processed at all.
Observations with a larger viewing zenith angle (VZA) have a lower spatial resolution, and so the numerator in Equation
(1) reduces the rating accordingly. At a very high VZA, observations may still be usable when there is no alternative
data source to fill in the gap, and therefore the numerator does not decrease to zero. The F
resolution
factor describes a
subjective preference in choosing a particular satellite due to its nominal resolutions or other factors. It is set to 100 for
METEOSAT-7, 220 for MTSAT-1R, -2, and Himawari-8, 210 for all other GEO satellites, 185 for MODIS Terra and
Aqua, and 140 for all NOAA satellites. This is designed to prefer GEO satellites in equatorial and mid-latitude regions
and to help the overall continuity of the composite. Also, AVHRR lacks the water vapor channel (6.7 m), which is
critical for correct retrieval of cloud properties, and therefore AVHRR data are assigned a lower initial rating. The F
glint
factor is designed to reduce the pixel's rating in the vicinity of sun glint and is calculated as follows:
2
2
glint
92.01
1
15.01
b
F
(2)
otherwise92.0
92.0cosifcos
b
and
cosθsinθsinθcosθcosβcos
00
(3)
where
0
is the solar zenith angle and
is the relative azimuth angle.
Finally, the F
terminator
factor is designed to give lower priority to pixels around the day/time terminator, which may have
lower quality of the cloud property retrievals obtained by the daytime algorithm. It is calculated as:
)5.88(cos375.0625.0
0terminator
F
(4)
where
0
is taken in degrees but the argument of cosine is treated as radians and is clipped to the range of [].
Overall, the use of such an aggregated rating allows merging of multiple input factors into a single number that can be
compared and enables higher flexibility in choosing between two candidate pixels. A fixed-threshold approach would be
more difficult to implement for multiple factors, harder to fine-tune and achieve reliable and consistent results, and it
would still cause discontinuities in the final composite. For example, a moderate resolution off-nadir observation may
still be usable when all other candidates occurred too far in time. An opposite situation is also possible. The way all the
factors are accounted for in Equation (1) allows for an optimal compromise solution to this problem.
Once the rating comparison indicates that a pixel in the composite is to be replaced with the input data, all parameters
(already remapped) associated with that particular satellite observation are copied to the composite pixel. A list of those
parameters is shown in Table 1. There are a few limitations here that are worth mentioning. First, the Near-Infrared

(NIR) channel is absent on GEO imagers and so reflectance in the 0.86 m band is flagged as a missing value for
composite pixels that originate from GEO satellites. Similarly, the 6.7 m water vapor band is absent for AVHRR pixels.
On GOES-12, -13, -14, and -15, the split-window brightness temperature (BT) is measured in the 13.5 m band instead
of 12 m. For this reason, BT in 12.0 m is also flagged as a missing value for composite pixels originating from those
four satellites. The surface type information is retrieved from the International Geosphere-Biosphere Programme (IGBP)
map [19], with the addition of snow and ice flags taken from the NOAA daily snow and ice cover maps at 1/6 deg spatial
Table 1. List of parameters included in global composite.
Parameter AVHRR
MODIS
GEOs
25 Satellite ID
Global
Composite
± 3.5 hours maximum
from IGBP + snow/ice flags
Figure 1. Global composite map of the brightness temperature in channel 10.8 m generated for Sep-15-2015
13:23UTC. The continuous coverage leaves no gaps and apparent disruptions in the temperature field.

resolution. The time relative to EPIC observation is the t from Equation (1) but converted to seconds and stored as a
signed integer number. Satellite ID is an integer number unique for each satellite which is designed to indicate the
origination of a given composite pixel.
A typical result of the compositing algorithm is presented in Figure 1, which shows an example of the map of brightness
temperature in channel 10.8 m composited for Sep-15-2015 13:23UTC. The composite image presents a continuous
coverage with no gaps and no artificial breaks or disruptions in the temperature data. A corresponding map of satellite
ID is shown in Figure 2, which represents typical spatial coverage from different satellites. Most of the equatorial
regions are covered by GEO satellites, while the polar regions are represented by Terra and Aqua, because MODIS was
initially given a higher F
resolution
factor than AVHRR. Similarly, the MET-7 data were assigned a lower F
resolution
factor
in order to correct for their lower quality, and therefore the MET-7 coverage automatically shrinks and is more likely to
Figure 2. A map of satellite coverage in a global composite generated for Sep-15-2015 13:23UTC.
Figure 3. A map of the time relative to nominal for the case of Sep-15-2015 13:23UTC. Pale colors correspond to a
lower difference in time and bright colors indicate a larger difference.

Figures (12)
Citations
More filters

Journal ArticleDOI
TL;DR: Comparison with co-located cloud retrievals from geosynchronous earth orbit (GEO) and lowearth orbit (LEO) satellites shows that the EPIC cloud product algorithms are performing well and are consistent with theoretical expectations.
Abstract: . This paper presents the physical basis of the Earth Polychromatic Imaging Camera (EPIC) cloud product algorithms and an initial evaluation of their performance. Since June 2015, EPIC has been providing observations of the sunlit side of the Earth with its 10 spectral channels ranging from the UV to the near-infrared. A suite of algorithms has been developed to generate the standard EPIC Level 2 cloud products that include cloud mask, cloud effective pressure/height, and cloud optical thickness. The EPIC cloud mask adopts the threshold method and utilizes multichannel observations and ratios as tests. Cloud effective pressure/height is derived with observations from the O2 A-band (780 and 764 nm) and B-band (680 and 688 nm) pairs. The EPIC cloud optical thickness retrieval adopts a single-channel approach in which the 780 and 680 nm channels are used for retrievals over ocean and over land, respectively. Comparison with co-located cloud retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the EPIC cloud product algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the Atmospheric Science Data Center at the NASA Langley Research Center for climate studies and for generating other geophysical products that require cloud properties as input.

22 citations


Journal ArticleDOI
Abstract: The direct and diffuse components of downward shortwave radiation (SW), and photosynthetically active radiation (PAR) at the Earth surface play an essential role in biochemical (e.g. photosynthesis) and physical (e.g. energy balance) processes that control weather and climate conditions, and many ecological processes. Space-based observations have the unique advantage of providing reliable estimates of SW and PAR globally with sufficient accuracy for constructing Earth's radiation budget and estimating land-surface fluxes that control these processes. However, most existing space-based SW and PAR estimations from sensors onboard polar-orbiting and geostationary satellites have inherently low temporal resolution and/or limited spatial coverage of the entire Earth surface. The unique location/orbit of Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) provides an unprecedented opportunity to obtain global estimates of SW and PAR accurately at a high temporal resolution of about 1–2 h. In this study, we developed and used a model (random forest, RF) to estimate global hourly SW and PAR at 0.1° × 0.1° (about 10 km at equator) spatial resolution based on EPIC measurements. We used a combination of EPIC Level-2 products, including solar zenith angle, aerosol optical depth, cloud optical thickness, cloud fraction, total column ozone and surface pressure with their associated quality flags to drive the RF model for estimating SW and PAR. We evaluated the model results against in situ observations from the Baseline Surface Radiation Network (BSRN) and Surface Radiation Budget Network (SURFRAD). We found the EPIC SW and PAR estimates at both hourly and daily time scales to be highly correlated and consistent with these independently obtained in situ measurements. The RMSEs for estimated daily diffuse SW, direct SW, total SW, and total PAR were 19.10, 38.47, 33.52, and 14.09 W/m2, respectively, and the biases for these estimates were 1.71, −0.77, 1.04 and 4.11 W/m2, respectively. We further compared the estimated SW and PAR with the Clouds and the Earth's Radiant Energy System Synoptic 1° × 1° (CERES SYN1deg) products and found a good correlation and consistency in their accuracy, spatial patterns and latitudinal gradient. The EPIC SW and PAR estimates provide a unique dataset (i.e. observations from single instrument from pole-to-pole for the entire sunlit portion of Earth) for characterizing their diurnal cycles and their potential impact on photosynthesis and evapotranspiration processes.

21 citations



Journal ArticleDOI
Abstract: . The National Institute of Standards and Technology Advanced Radiometer (NISTAR) onboard the Deep Space Climate Observatory (DSCOVR) provides continuous full-disk global broadband irradiance measurements over most of the sunlit side of the Earth. The three active cavity radiometers measure the total radiant energy from the sunlit side of the Earth in shortwave (SW; 0.2–4 µ m), total (0.4–100 µ m), and near-infrared (NIR; 0.7–4 µ m) channels. The Level 1 NISTAR dataset provides the filtered radiances (the ratio between irradiance and solid angle). To determine the daytime top-of-atmosphere (TOA) shortwave and longwave radiative fluxes, the NISTAR-measured shortwave radiances must be unfiltered first. An unfiltering algorithm was developed for the NISTAR SW and NIR channels using a spectral radiance database calculated for typical Earth scenes. The resulting unfiltered NISTAR radiances are then converted to full-disk daytime SW and LW flux by accounting for the anisotropic characteristics of the Earth-reflected and emitted radiances. The anisotropy factors are determined using scene identifications determined from multiple low-Earth orbit and geostationary satellites as well as the angular distribution models (ADMs) developed using data collected by the Clouds and the Earth's Radiant Energy System (CERES). Global annual daytime mean SW fluxes from NISTAR are about 6 % greater than those from CERES, and both show strong diurnal variations with daily maximum–minimum differences as great as 20 Wm −2 depending on the conditions of the sunlit portion of the Earth. They are also highly correlated, having correlation coefficients of 0.89, indicating that they both capture the diurnal variation. Global annual daytime mean LW fluxes from NISTAR are 3 % greater than those from CERES, but the correlation between them is only about 0.38.

11 citations


Journal ArticleDOI
Abstract: . Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

8 citations


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Journal ArticleDOI
TL;DR: This paper documents the CERES Edition-2 cloud property retrieval system used to analyze data from the Tropical Rainfall Measuring Mission Visible and Infrared Scanner and by the MODerate-resolution Imaging Spectrometer instruments on board the Terra and Aqua satellites covering the period 1998 through 2007.
Abstract: The National Aeronautics and Space Administration's Clouds and the Earth's Radiant Energy System (CERES) Project was designed to improve our understanding of the relationship between clouds and solar and longwave radiation. This is achieved using satellite broad-band instruments to map the top-of-atmosphere radiation fields with coincident data from satellite narrow-band imagers employed to retrieve the properties of clouds associated with those fields. This paper documents the CERES Edition-2 cloud property retrieval system used to analyze data from the Tropical Rainfall Measuring Mission Visible and Infrared Scanner and by the MODerate-resolution Imaging Spectrometer instruments on board the Terra and Aqua satellites covering the period 1998 through 2007. Two daytime retrieval methods are explained: the Visible Infrared Shortwave-infrared Split-window Technique for snow-free surfaces and the Shortwave-infrared Infrared Near-infrared Technique for snow or ice-covered surfaces. The Shortwave-infrared Infrared Split-window Technique is used for all surfaces at night. These methods, along with the ancillary data and empirical parameterizations of cloud thickness, are used to derive cloud boundaries, phase, optical depth, effective particle size, and condensed/frozen water path at both pixel and CERES footprint levels. Additional information is presented, detailing the potential effects of satellite calibration differences, highlighting methods to compensate for spectral differences and correct for atmospheric absorption and emissivity, and discussing known errors in the code. Because a consistent set of algorithms, auxiliary input, and calibrations across platforms are used, instrument and algorithm-induced changes in the data record are minimized. This facilitates the use of the CERES data products for studying climate-scale trends.

386 citations


"Development of multi-sensor global ..." refers methods in this paper

  • ...The imager radiances are then used to retrieve cloud and radiative properties using the CERES Cloud Subsystem group algorithms [11, 12]....

    [...]


01 Jan 1994
Abstract: The Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community 20 years of globally observed top of the atmosphere (TOA) fluxes critical for climate and cloud feedback studies. The CERES Flux By Cloud Type (FBCT) product contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and also reveal more insight into the interactions between clouds and climate. The FBCT product provides 1° regional daily and monthly shortwave (SW) and longwave (LW) cloud-type fluxes and cloud properties sorted by 7 pressure layers and 6 optical depth bins. Historically, cloud-type fluxes have been computed using radiative transfer models based on observed cloud properties. Instead of relying on radiative transfer models, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. These biases are mitigated by constraining the cloud-type fluxes within each footprint with the CERES Single Scanner Footprint (SSF) observed flux. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. Several examples of FBCT data are presented to highlight its utility for scientific applications.

127 citations


"Development of multi-sensor global ..." refers methods in this paper

  • ...These variables are retrieved from the CERES archival product Meteorological, Ozone, and Aerosol Data (MOA) [20], which includes hourly maps of several meteorological variables at 1 spatial resolution....

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  • ...These variables are retrieved from the CERES archival product Meteorological, Ozone, and Aerosol Data (MOA) [20], which includes hourly maps of several meteorological variables at 1° spatial resolution....

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Journal ArticleDOI
TL;DR: Cloud properties were retrieved by applying the Clouds and Earth's Radiant Energy System (CERES) project Edition-2 algorithms to 3.5 years of Tropical Rainfall Measuring Mission Visible and Infrared Scanner data and 5.5 and 8 years of MODerate Resolution Imaging Spectroradiometer (MODIS) data from Aqua and Terra.
Abstract: Cloud properties were retrieved by applying the Clouds and Earth's Radiant Energy System (CERES) project Edition-2 algorithms to 3.5 years of Tropical Rainfall Measuring Mission Visible and Infrared Scanner data and 5.5 and 8 years of MODerate Resolution Imaging Spectroradiometer (MODIS) data from Aqua and Terra, respectively. The cloud products are consistent quantitatively from all three imagers; the greatest discrepancies occur over ice-covered surfaces. The retrieved cloud cover (~59%) is divided equally between liquid and ice clouds. Global mean cloud effective heights, optical depth, effective particle sizes, and water paths are 2.5 km, 9.9, 12.9 μm , and 80 g·m-2, respectively, for liquid clouds and 8.3 km, 12.7, 52.2 μm, and 230 g·m-2 for ice clouds. Cloud droplet effective radius is greater over ocean than land and has a pronounced seasonal cycle over southern oceans. Comparisons with independent measurements from surface sites, the Ice Cloud and Land Elevation Satellite, and the Aqua Advanced Microwave Scanning Radiometer-Earth Observing System are used to evaluate the results. The mean CERES and MODIS Atmosphere Science Team cloud properties have many similarities but exhibit large discrepancies in certain parameters due to differences in the algorithms and the number of unretrieved cloud pixels. Problem areas in the CERES algorithms are identified and discussed.

122 citations


"Development of multi-sensor global ..." refers methods in this paper

  • ...The imager radiances are then used to retrieve cloud and radiative properties using the CERES Cloud Subsystem group algorithms [11, 12]....

    [...]


Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "Development of multi-sensor global cloud and radiance composites for earth radiation budget monitoring from dscovr" ?

In this paper, a new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution.