Moderate Resolution Imaging Spectroradiometer Cloud Products Research Articles

Retrieving Vertical Cloud Radar Reflectivity from MODIS Cloud Products with CGAN: An Evaluation for Different Cloud Types and Latitudes

Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity > −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms.

Cloud optical and microphysical properties derived from ground‐based and satellite sensors over a site in the Yangtze Delta region

AbstractComprehensive surface‐based retrievals of cloud optical and microphysical properties were made at Taihu, a highly polluted site in the central Yangtze Delta region, during a research campaign from May 2008 to December 2009. Cloud optical depth (COD), effective radius (Re), and liquid water path (LWP) were retrieved from measurements made with a suite of ground‐based and spaceborne instruments, including an Analytical Spectral Devices spectroradiometer, a multifilter rotating shadowband radiometer, a multichannel microwave radiometer profiler, and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites. Retrievals from zenith radiance measurements capture better the temporal variation of cloud properties than do retrievals from hemispherical fluxes. Annual mean LWP, COD, and Re are 115.8 ± 90.8 g/m2, 28.5 ± 19.2, and 6.9 ± 4.2 µm. Over 90% of LWP values are less than 250 g/m2. Most of the COD values (>90%) fall between 5 and 60, and ~80% of Re values are less than 10 µm. Maximum (minimum) values of LWP and Re occur in summer (winter); COD is highest in winter and spring. Raining and nonraining clouds have significant differences in LWP, COD, and Re. Rainfall frequency is best correlated with LWP, followed by COD and Re. Cloud properties retrieved from multiple ground‐based instruments are also compared with those from satellite retrievals. On average, relative to surface retrievals, mean differences of satellite retrievals in cloud LWP, COD, and Re were −33.6 g/m2 (−26.4%), −5.8 (−31.4%), and 2.9 µm (29.3%) for 11 MODIS‐Terra overpasses and −43.3 g/m2 (−22.3%), −3.0 (−10.0%), and −1.3 µm (−12.0%) for 8 MODIS‐Aqua overpasses, respectively. These discrepancies indicate that MODIS cloud products still suffer from large uncertainties in this region.

AIRS pixel cloud detection using MODIS cloud products

In order to utilize the Moderate Resolution Imaging Spectroradiometer(MODIS) cloud products to detect the properties of Atmospheric Infrared Sounder(AIRS) cloud,this paper first adopted space collocation algorithm to match the MODIS and AIRS,and then combined the MODIS cloud classification mask,cloud phase mask products and the operational algorithm of MODIS cloud retrieval to realize the utilization of MODIS data to detect AIRS single field of the cloud(pixel cloud).The results show that MODIS 1km resolution products can realize AIRS cloud classification(lower,midlevel or high clouds) and cloud phase(water,ice or mixed-phase clouds) detection.

Comparing MODIS and AIRS Infrared-Based Cloud Retrievals

AbstractComparisons are described for infrared-derived cloud products retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) using measured spatial response functions obtained from prelaunch AIRS calibration. One full day (1 January 2005) of global collection-5 MODIS and version-5 AIRS retrievals of cloud-top temperature Tc, effective cloud fraction f, and derived effective brightness temperature Tb,e is investigated. Comparisons of Tb,e demonstrate that MODIS and AIRS are essentially radiatively consistent and that MODIS Tb,e is 0.62 K higher than AIRS Tb,e for all scenes, increasing to 1.43 K for cloud described by AIRS as single layer and decreasing to 0.50 K for two-layer clouds. Somewhat larger differences in Tc and f are observed between the two instruments. The magnitudes of differences depend partly on whether MODIS uses a CO2-slicing or 11-μm brightness temperature window retrieval method. Some cloud- and regime-type differences and similarities between AIRS and MODIS cloud products are traceable to the assumptions made about the number of cloud layers in AIRS and also to the MODIS retrieval method. This (partially) holistic comparison approach should be useful for ongoing algorithm refinements, rigorous assessments of climate applicability, and establishment of the capability of synergistic MODIS and AIRS retrievals for improved cloud quantities and also should be useful for future observations to be made by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP).

Cirrus cloud simulations using WRF with improved radiation parameterization and increased vertical resolution

[1] The capability of Weather Research and Forecasting (WRF) model in the simulation of cirrus clouds has been examined, with a focus on the effects of radiative processes and vertical model resolution. We incorporate in WRF a new radiation module, referred to as the Fu‐Liou‐Gu scheme, which is an improvement and refinement based on the Fu‐Liou scheme, particularly in reference to parameterization of the single‐scattering properties of ice crystal size and shape. We conducted a number of real‐time WRF simulations for cirrus cases that were observed in the coastal and western United States on 29–30 March 2007, and we compared these with available observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and GOES visible and IR images over the same areas. Simulation results show that WRF is capable of generating reasonable cirrus cloud fields and their movement and dissipation processes, especially those associated with the large‐scale frontal system. Radiative processes are important in cirrus cloud simulations by affecting the vertical thermal structure and hence convection. The newly implemented radiation module, the Fu‐Liou‐Gu scheme, has been demonstrated to work well in WRF and can be effectively used for studies related to cirrus cloud formation and evolution and aerosol‐cloud‐radiation interactions. With the newly implemented radiation scheme, the simulations of cloud cover and cloud and ice water path (CWP and IWP) have been improved for cirrus clouds, with a more consistent comparison with the corresponding MODIS observations in terms of CWP and IWP means and CWP frequency distribution, especially for optically thin cirrus with an improvement of about 20% in simulated mean IWP. The model‐simulated ice crystal sizes have also been shown to be comparable to those determined from MODIS cloud products. Finally, we have demonstrated that model vertical resolution plays a significant role in cirrus cloud simulation in terms of altering vertical velocity field and the associated regional circulation.

Remote sensing of cirrus cloud vertical size profile using MODIS data

This paper describes an algorithm for inferring cirrus cloud top and cloud base effective particle sizes and cloud optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS) 0.645, 1.64 and 2.13, and 3.75 μm band reflectances/radiances. This approach uses a successive minimization method based on a look‐up library of precomputed reflectances/radiances from an adding‐doubling radiative transfer program, subject to corrections for Rayleigh scattering at the 0.645 μm band, above‐cloud water vapor absorption, and 3.75 μm thermal emission. The algorithmic accuracy and limitation of the retrieval method were investigated by synthetic retrievals subject to the instrument noise and the perturbation of input parameters. The retrieval algorithm was applied to three MODIS cirrus scenes over the Atmospheric Radiation Measurement Program's southern Great Plain site, north central China, and northeast Asia. The reliability of retrieved cloud optical thicknesses and mean effective particle sizes was evaluated by comparison with MODIS cloud products and qualitatively good correlations were obtained for all three cases, indicating that the performance of the vertical sizing algorithm is comparable with the MODIS retrieval program. Retrieved cloud top and cloud base ice crystal effective sizes were also compared with those derived from the collocated ground‐based millimeter wavelength cloud radar for the first case and from the Cloud Profiling Radar onboard CloudSat for the other two cases. Differences between retrieved and radar‐derived cloud properties are discussed in light of assumptions made in the collocation process and limitations in radar remote sensing characteristics.

Cirrus cloud top temperatures retrieved from radiances in the National Polar-Orbiting Operational Environmental Satellite System--Visible Infrared Imager Radiometer Suite 855 and 120 μm bandpasses

We describe what is believed to be a new approach developed for the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) to retrieve pixel-level, cirrus cloud top temperatures (CTTs) from radiances observed in the 8.55 and 12.0 microm bandpasses. The methodology solves numerically a set of nonlinear algebraic equations derived from the theory of radiative transfer based upon the correlation between emissivities in these two bandpasses. This new approach has been demonstrated using NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) as a proxy to Visible Infrared Imager Radiometer Suite (VIIRS) data. Many scenes have been analyzed covering a wide range of geophysical conditions, including single-layered and multilayered cirrus cloud situations along with diverse backgrounds and seasons. For single-layer clouds, the new approach compares very favorably with the MODIS 5 km resolution cloud products; the mean CTT for both methods are very close, while the standard deviation for the new approach is smaller. However, in multilayered cloud situations, the mean CTTs for the new approach appear to be colder than the CTTs from MODIS cloud products, which are acknowledged to be too warm. Finally, partly because the new approach is applied at the pixel level, CTTs do not increase toward cloud edges as is seen in the MODIS products. Based upon these initial results, the new approach to retrieve improved VIIRS cloud top properties has been incorporated into the ground-based data processing segment of the NPOESS system where prelaunch testing of all VIIRS algorithms continues.

AIRS Subpixel Cloud Characterization Using MODIS Cloud Products

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the Earth Observing System's (EOS's) Aqua satellite enable improved global monitoring of the distribution of clouds. MODIS is able to provide, at high spatial resolution (∼1–5 km), a cloud mask, surface and cloud types, cloud phase, cloud-top pressure (CTP), effective cloud amount (ECA), cloud particle size (CPS), and cloud optical thickness (COT). AIRS is able to provide CTP, ECA, CPS, and COT at coarser spatial resolution (∼13.5 km at nadir) but with much better accuracy using its high-spectral-resolution measurements. The combined MODIS–AIRS system offers the opportunity for improved cloud products over those possible from either system alone. The key steps for synergistic use of imager and sounder radiance measurements are 1) collocation in space and time and 2) imager cloud amount, type, and phase determination within the sounder pixel. The MODIS and AIRS measurements from ...

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.