Cloud Detection in Nonpolar Regions for CERES Using TRMM VIRS and Terra and Aqua MODIS Data

Minnis, Patrick; Trepte, Qing Z.; Sun-Mack, S.; Chen, Yan; Doelling, David R.; Young, David F.; Spangenberg, D. A.; Miller, Walter F.; Wielicki, Bruce A.; Brown, R. R.; Gibson, Sharon; Geier, Erika

doi:10.1109/tgrs.2008.2001351

Cited by 179 publications

(159 citation statements)

References 42 publications

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“…Cloud cover varies seasonally, geographically, and diurnally, and as such the revisit frequency required (RFR) in order to satisfy a given reasonably clear view requirement (referred to as the "Effective Temporal Resolution" in Table 1) within a certain period varies throughout the year, with location, and also with the acceptable cloud threshold [3,[79][80][81][82][83][84][85][86][87][88][89]. As the great majority of moderate resolution EO satellites have morning overpasses, the revisit frequency required herein will be presented assuming a morning (10:30 am local solar time) overpass.…”

Section: Input Datasets: How Frequently To Image For (Reasonably) Clementioning

confidence: 99%

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation's (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10-100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of "reasonably clear" are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9-4.8 days (depending on the month), 3.0-4.1 days for 80% clear, OPEN ACCESSRemote Sens. 2015, 7 1462 2.2-3.3 days for 90% clear, and 1.7-2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.

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Section: Input Datasets: How Frequently To Image For (Reasonably) Clementioning

confidence: 99%

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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“…If CloudSat's columnar classification (Sassen and Wang, 2008) is cirrus, altostratus, altocumulus or deep convection, those cells in the column identified as cloud with temperatures < 273 K are classed as ice and r eff is set uniformly to the value inferred from MODIS data (Minnis et al, 2008(Minnis et al, , 2010a(Minnis et al, , 2010b. Warmer cells in the column are set to liquid with r eff = 10 µm.…”

Section: Merged A-train Datamentioning

confidence: 99%

“…Several key cloud properties are inferred directly from MODIS radiances (Minnis et al, 2008). Hence, as an extension to imagery reconstruction, it is instructive to see how well the algorithm can reconstruct MODIS-inferred cloud properties.…”

Section: Image Reconstructionmentioning

confidence: 99%

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Barker

Jerg

Wehr

et al. 2011

Quart J Royal Meteoro Soc

144

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† The contribution of H. W. Barker to this article was prepared as part of his official duties as a Canadian government employee This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active-passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed.The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km) 2 domains constructed from MODIS, CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m −2 for ∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation ∼65% are within ±10 W m −2 .

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“…With regards to the cloud radiative properties, CERES estimates are reasonably accurate (Minnis et al, 2011). In the Arctic, the average difference between the in situ groundmeasured and CERES cloud fraction is ∼ 0.15 (Minnis et al, 2008). The effect of cloud fraction on the insolation adjustment depends on both the tilt angles and directions (Fig.…”

Section: Uncertainty In the Tilt-corrected Insolationmentioning

confidence: 84%

A Retrospective, Iterative, Geometry-Based (RIGB) tilt-correction method for radiation observed by automatic weather stations on snow-covered surfaces: application to Greenland

Wang

Zender

et al. 2016

The Cryosphere

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Abstract. Surface melt and mass loss of the Greenland Ice Sheet may play crucial roles in global climate change due to their positive feedbacks and large fresh-water storage. With few other regular meteorological observations available in this extreme environment, measurements from automatic weather stations (AWS) are the primary data source for studying surface energy budgets, and for validating satellite observations and model simulations. Station tilt, due to irregular surface melt, compaction and glacier dynamics, causes considerable biases in the AWS shortwave radiation measurements. In this study, we identify tilt-induced biases in the climatology of surface shortwave radiative flux and albedo, and retrospectively correct these by iterative application of solar geometric principles. We found, over all the AWS from the Greenland Climate Network (GC-Net), the Kangerlussuaq transect (K-transect) and the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) networks, insolation on fewer than 40 % of clear days peaks within ±0.5 h of solar noon time, with the largest shift exceeding 3 h due to tilt. Hourly absolute biases in the magnitude of surface insolation can reach up to 200 W m −2 , with respect to the well-understood clear-day insolation. We estimate the tilt angles and their directions based on the solar geometric relationship between the simulated insolation at a horizontal surface and the observed insolation by these tilted AWS under clear-sky conditions. Our adjustment reduces the root mean square error (RMSE) against references from both satellite observation and reanalysis by 16 W m −2 (24 %), and raises the correlation coefficients with them to above 0.95. Averaged over the whole Greenland Ice Sheet in the melt season, the adjustment in insolation to compensate station tilt is ∼ 11 W m −2 , enough to melt 0.24 m of snow water equivalent. The adjusted diurnal cycles of albedo are smoother, with consistent semi-smiling patterns. The seasonal cycles and inter-annual variabilities of albedo agree better with previous studies. This tilt-corrected shortwave radiation data set derived using the Retrospective, Iterative, Geometry-Based (RIGB) method provide more accurate observations and validations for surface energy budgets studies on the Greenland Ice Sheet, including albedo variations, surface melt simulations and cloud radiative forcing estimates.

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Cloud Detection in Nonpolar Regions for CERES Using TRMM VIRS and Terra and Aqua MODIS Data

Cited by 179 publications

References 42 publications

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

A Retrospective, Iterative, Geometry-Based (RIGB) tilt-correction method for radiation observed by automatic weather stations on snow-covered surfaces: application to Greenland

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