Richard A. Frey

The MODIS cloud products: algorithms and examples from Terra

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.

Discriminating clear sky from clouds with MODIS

The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MEDIS is observing clear skies. It will be produced globally at single-pixel resolution; the algorithm uses as many as 14 of the MEDIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MEDIS cloud mask is ancillary input to MEDIS land, ocean, and atmosphere science algorithms to suggest processing options. The MEDIS cloud mask algorithm will operate in near real time in a limited computer processing and storage facility with simple easy-to-follow algorithm paths. The MEDIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests attempts to detect the presence of clouds in the instrument field of view. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions relies on different tests. Tests capable of detecting similar cloud conditions are grouped together. While these groups are arranged so that independence between them is maximized, few, if any, spectral tests are completely independent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of particular cloud types. The product of all the group confidences is used to determine the confidence of finding clear-sky conditions. This paper outlines the MEDIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MEDIS cloud mask, initial validation of some of the individual cloud tests is presented using existing remote sensing data sets.

Cloud Detection with MODIS. Part II: Validation

An assessment of the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm for Terra and Aqua satellites is presented. The MODIS cloud mask algorithm output is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar (AHSRL)], aircraft [Cloud Physics Lidar (CPL)], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms. The comparison with 3 yr of coincident observations of MODIS and combined radar and lidar cloud product from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site in Lamont, Oklahoma, indicates that the MODIS algorithm agrees with the lidar about 85% of the time. A comparison with the CPL and AHSRL indicates that the optical depth limitation of the MODIS cloud mask is approximately 0.4. While MODIS algorithm flags scenes with a cloud optical depth of 0.4 as cloudy, approximately 90% of the mislabeled scenes have optical depths less than 0.4. A comparison with the GLAS cloud dataset indicates that cloud detection in polar regions at night remains challenging with the passive infrared imager approach. In anticipation of comparisons with other satellite instruments, the sensitivity of the cloud mask algorithm to instrument characteristics (e.g., instantaneous field of view and viewing geometry) and thresholds is demonstrated. As expected, cloud amount generally increases with scan angle and instantaneous field of view (IFOV). Nadir sampling represents zonal monthly mean cloud amounts but can have large differences for regional studies when compared to full-swath-width analysis.

Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5

Significant improvements have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask (MOD35 and MYD35) for Collection 5 reprocessing and forward stream data production. Most of the modifications are realized for nighttime scenes where polar and oceanic regions will see marked improvement. For polar night scenes, two new spectral tests using the 7.2-m water vapor absorption band have been added as well as updates to the 3.9–12- and 11–12-m cloud tests. More non-MODIS ancillary input data have been added. Land and sea surface temperature maps provide crucial information for mid- and low-level cloud detection and lessen dependence on ocean brightness temperature variability tests. Sun-glint areas are also improved by use of sea surface temperatures to aid in resolving observations with conflicting cloud versus clear-sky signals, where visible and near-infrared (NIR) reflectances are high, but infrared brightness temperatures are relatively warm. Day and night Arctic cloud frequency results are compared to those created by the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder-Extended (APP-X) algorithm. Day versus night sea surface temperatures derived from MODIS radiances and using only the MODIS cloud mask for cloud screening are contrasted. Frequencies of cloud from sun-glint regions are shown as a function of sun-glint angle to gain a sense of cloud mask quality in those regions. Continuing validation activities are described in Part II of this paper.

MODIS Global Cloud-Top Pressure and Amount Estimation: Algorithm Description and Results

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representa...

An automatic cloud mask algorithm based on time series of MODIS measurements

[1] Quality of aerosol retrievals and atmospheric correction over land depends strongly on accuracy of the cloud mask (CM) algorithm. The heritage CM algorithms developed for AVHRR and MODIS use the latest sensor measurements of spectral reflectance and brightness temperature and perform processing at the pixel level. The algorithms are threshold-based and empirically tuned. They do not explicitly address the classical problem of cloud search, wherein the baseline clear-skies scene is defined for comparison. Here we report on a new land CM algorithm, which explicitly builds and maintains a reference clear-skies image of the surface (refcm) using a time series of MODIS measurements. The new algorithm, developed as part of the multiangle implementation of atmospheric correction (MAIAC) algorithm for MODIS, relies on the fact that clear-skies images of the same surface area have a common textural pattern, defined by the surface topography, boundaries of rivers and lakes, distribution of soils and vegetation, etc. This pattern changes slowly given the daily rate of global Earth observations, whereas clouds introduce high-frequency random disturbances. Under clear skies, consecutive gridded images of the same surface area have a high covariance, whereas in presence of clouds covariance is usually low. This idea is central to initialization of refcm, which is used to derive cloud mask in combination with spectral and brightness temperature tests. The refcm is continuously updated with the latest clear-skies MODIS measurements, thus adapting to seasonal and rapid surface changes. The algorithm is enhanced by an internal dynamic land-water-snow classification coupled with a surface change mask. An initial comparison shows that the new algorithm offers the potential to perform better than the MODIS MOD35 cloud mask in situations where the land surface is changing rapidly and over Earth regions covered by snow and ice.

Errors in Cloud Detection over the Arctic Using a Satellite Imager and Implications for Observing Feedback Mechanisms

Abstract Arctic sea ice extent has decreased dramatically over the last 30 years, and this trend is expected to continue through the twenty-first century. Changes in sea ice extent impact cloud cover, which in turn influences the surface energy budget. Understanding cloud feedback mechanisms requires an accurate determination of cloud cover over the polar regions, which must be obtained from satellite-based measurements. The accuracy of cloud detection using observations from space varies with surface type, complicating any assessment of climate trends as well as the understanding of ice–albedo and cloud–radiative feedback mechanisms. To explore the implications of this dependence on measurement capability, cloud amounts from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with those from the CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) satellites in both daytime and nighttime during the time period from July 2006 to December 2008. MODIS is an imager that makes...

High-Spatial-Resolution Surface and Cloud-Type Classification from MODIS Multispectral Band Measurements

Abstract A method for automated classification of surface and cloud types using Moderate Resolution Imaging Spectroradiometer (MODIS) radiance measurements has been developed. The MODIS cloud mask is used to define the training sets. Surface and cloud-type classification is based on the maximum likelihood (ML) classification method. Initial classification results define training sets for subsequent iterations. Iterations end when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The mean vector in the spectral and spatial domain within a class is used for class identification, and a final 1-km-resolution classification mask is generated for such a field of view in a MODIS granule. This automated classification refines the output of the cloud mask algorithm and enables further applications such as clear atmospheric profile or cloud parameter retrievals from MODIS and Atmospheric Infrared Sounder (AIRS) radiance measurements. The advantages of...

A comparison of cloud top heights computed from airborne lidar and MAS radiance data using CO 2 slicing

Data from two instruments onboard the National Aeronautics and Space Administration (NASA) ER-2 high-altitude aircraft have been utilized in the largest validation study to date in assessing the accuracy of the CO2-slicing cloud height algorithm. Infrared measurements of upwelling radiance from the MODIS (Moderate- Resolution Imaging Spectroradiometer) airborne simulator (MAS) were used to generate cloud top heights and then compared to those derived from the Cloud Lidar System (CLS), operating with dual polarization at 0.532 mm. The comparisons were performed for 10 flight days during the Subsonic Aircraft Contrail and Cloud Effects Special Study (SUCCESS) field experiment during April and May 1996 which included various single- layer and multilayer cloud conditions. Overall, the CO2-slicing method retrieved cloud heights to within 6500 m and to within 61500 m of the lidar heights in 32 and 64% of the cases, respectively. From a simulation of cloud height errors as a function of various error sources in the CO2-slicing algorithm, it was concluded that the problem of multilayer clouds is secondary to that of proper specification of clear-sky radiances.

Automated cloud detection and classification of data collected by the Visible Infrared Imager Radiometer Suite (VIIRS)

The Visible Infrared Imager Radiometer Suite (VIIRS) is a high‐resolution Earth imager of the United States National Polar‐orbiting Operational Environmental Satellite System (NPOESS). VIIRS has its heritage in three sensors currently collecting imagery of the Earth—the Advanced Very High Resolution Radiometer, the Moderate Resolution Imaging Spectroradiometer, and the Operational Linescan Sensor. The first launch of the VIIRS sensor is on NASAs NPOESS Preparatory Project (NPP). Data collected by VIIRS will provide products to a variety of users, supporting applications from real‐time to long‐term climate change timescales. VIIRS has been uniquely designed to satisfy this full range of requirements. Cloud masks derived from the automated analyses of VIIRS data are critical data products for the NPOESS program. In this paper, the VIIRS cloud mask (VCM) performance requirements are highlighted, along with the algorithm developed to satisfy these requirements. The expected performance of the VCM algorithm is established using global synthetic cloud simulations and manual cloud analyses of VIIRS proxy imagery. These results show the VCM analyses will satisfy the performance expectations of products created from it, including land and ocean surface products, cloud microphysical products, and automated cloud forecast products. Finally, minor deficiencies that remain in the VCM algorithm logic are identified along with a mitigation plan to resolve each prior to NPP launch or shortly thereafter.