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Andrew K. Heidinger

Abstract

An algorithm is developed to rapidly estimate cloud properties for a large area from daytime imager data. In this context, a large area refers to a grid cell composed of many imager pixels. The algorithm assumes a gamma distribution to model the subgrid variability in the optical depth and estimates both the mean and the width of the horizontal distribution of optical depth. Optical depth in this study refers to a vertically integrated value at 0.63 μm. Mean values of the cloud-top effective particle radius and cloud-top temperature are also estimated. Retrievals were performed separately for ice and water cloud layers within a grid cell. Applications of this approach to data from NOAA's Advanced Very High Resolution Radiometer (AVHRR) are presented. Simulations indicate that this method performs well for all retrieved parameters except for thin clouds with very broad distributions of optical depth. Comparison of this approach versus rigorous pixel-level retrieval results for an actual scene with multiple cloud layers indicate that comparable performance is achieved with a two to three orders of magnitude increase in computational efficiency. This approach is being implemented into the Clouds from AVHRR (CLAVR) suite of cloud algorithms at NOAA. The computational efficiency of this approach will allow for efficient reprocessing of the entire data record of the AVHRR.

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Andrew K. Heidinger
and
Stephen K. Cox

Abstract

As numerical weather and climate prediction models demand more accurate treatment of clouds, the role of finite-cloud effects in longwave radiative transfer clearly warrants further study. In this research, finite-cloud effects are defined as the influence of cloud shape, size, and spatial arrangement on longwave radiative transfer. To show the magnitude of these effects, radiometer data collected in 1992 during the Atlantic Stratocumulus Transition Experiment (ASTEX) were analyzed. The ASTEX data showed that radiative transfer calculations that ignored the vertical dimensions of the clouds underestimated the longwave cloud radiative surface forcing by 30%, on average. To study further these finite-cloud effects, a three-dimensional 11-µm radiative transfer model was developed. Results from this model, which neglected scattering, agreed with the measurements taken during ASTEX on 14 June 1992. This model was also used to reiterate that, for optically thick clouds, knowledge of cloud macrophysical properties can be more crucial to the modeling of the transfer of longwave radiation than the detailed description of cloud microphysical properties. Lastly, techniques for the inclusion of these finite-cloud effects in numerical models were explored. Accurate radiative heating rate profiles were achieved with a method that assumed a linear variation of the cloud fraction within the cloud layer. Parameterizations of the finite-cloud effects for the marine stratocumulus observed during ASTEX are presented.

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Andi Walther
and
Andrew K. Heidinger

Abstract

This paper describes the daytime cloud optical and microphysical properties (DCOMP) retrieval for the Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset. Within PATMOS-x, DCOMP is applied to observations from the Advanced Very High Resolution Radiometer and employs the standard bispectral approach to estimate cloud optical depth and particle size. The retrievals are performed within the optimal estimation framework. Atmospheric-correction and forward-model parameters, such as surface albedo and gaseous absorber amounts, are obtained from numerical weather prediction reanalysis data and other climate datasets. DCOMP is set up to run on sensors with similar channel settings and has been successfully exercised on most current meteorological imagers. This quality makes DCOMP particularly valuable for climate research. Comparisons with the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 dataset are used to estimate the performance of DCOMP.

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Michael J. Pavolonis
and
Andrew K. Heidinger

Abstract

Two algorithms for detecting multilayered cloud systems with satellite data are presented. The first algorithm utilizes data in the 0.65-, 11-, and 12-μm regions of the spectrum that are available on the Advanced Very High Resolution Radiometer (AVHRR). The second algorithm incorporates two different techniques to detect cloud overlap: the same technique used in the first algorithm and an additional series of spectral tests that now include data from the 1.38- and 1.65-μm near-infrared regions that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible/Infrared Imager/Radiometer Suite (VIIRS). VIIRS is the imager that will replace the AVHRR on the next generation of polar-orbiting satellites. Both algorithms were derived assuming that a scene with cloud overlap consists of a semitransparent ice cloud that overlaps a cloud composed of liquid water droplets. Each algorithm was tested on three different MODIS scenes. In all three cases, the second (VIIRS) algorithm was able to detect more cloud overlap than the first (AVHRR) algorithm. Radiative transfer calculations indicate that the VIIRS algorithm will be more effective than the AVHRR algorithm when the visible optical depth of the ice cloud is greater than 3. Both algorithms will work best when the visible optical depth of the water cloud is greater than 5. Model sensitivity studies were also performed to assess the sensitivity of each algorithm to various parameters. It was found that the AVHRR algorithm is most sensitive to cloud particle size and the VIIRS near-infrared test is most sensitive to cloud vertical location. When validating each algorithm using cloud radar data, the VIIRS algorithm was shown to be more effective at detecting cloud overlap than the AVHRR algorithm; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection.

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Andrew K. Heidinger
and
Michael J. Pavolonis

Abstract

This paper demonstrates that the split-window approach for estimating cloud properties can improve upon the methods commonly used for generating cloud temperature and emissivity climatologies from satellite imagers. Because the split-window method provides cloud properties that are consistent for day and night, it is ideally suited for the generation of a cloud climatology from the Advanced Very High Resolution Radiometer (AVHRR), which provides sampling roughly four times per day. While the split-window approach is applicable to all clouds, this paper focuses on its application to cirrus (high semitransparent ice clouds), where this approach is most powerful. An optimal estimation framework is used to extract estimates of cloud temperature, cloud emissivity, and cloud microphysics from the AVHRR split-window observations. The performance of the split-window approach is illustrated through the diagnostic quantities generated by the optimal estimation approach. An objective assessment of the performance of the algorithm cloud products from the recently launched space lidar [Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO/CALIOP)] is used to characterize the performance of the AVHRR results and also to provide the constraints needed for the optimal estimation approach.

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Andrew K. Heidinger
and
Graeme L. Stephens

Abstract

The paper analyzes the influence of horizontal variability of clouds on sunlight reflected in a narrow portion of the solar spectrum and how this influence affects the ability to estimate cloud properties from measurements of reflection. This paper is part of a series that examines the use of high-resolution measurements of absorption lines of the oxygen A band located between 0.763 and 0.773 μm as a way of deriving information on cloud structure presently unobtainable from other passive measurements. The effects of spatial heterogeneity on reflectances and pathlength distributions are examined for marine stratocumulus cloud fields derived from Landsat data. The results showed that for the marine stratocumulus fields studied, the radiance errors were on the order of 3%–20%, and the spectral radiance ratio (in-band to out-of-band) errors were on the order of 1%–5%. When these errors are too large, the retrieved quantities possess too much error to be useful. The errors due to horizontal transport of photons and subpixel variability are studied independently as a function of spatial scale. Using both the radiance and radiance ratios, a technique was developed that allows the plane-parallel forward model typically used in retrieval schemes to be able to predict when its retrievals are so influenced by spatial heterogeneity that the results are invalid. This ability would represent a significant step forward in the current abilities of passive retrievals, which cannot determine the effect of cloud spatial variability on their retrievals. Lastly, a method was demonstrated that used domain-averaged measurements of absorption formed by reflection along with plane-parallel theory to estimate the distribution of optical depth throughout the domain. The results for the four simulated cloud fields showed this technique to have significant promise in quickly classifying the level of cloud heterogeneity over a large area.

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Andrew K. Heidinger
and
Graeme L. Stephens

Abstract

This paper explores the feasibility of using O2 A-band reflectance spectra in the retrieval of cloud optical and physical properties. Analyses demonstrate that these reflection spectra are sensitive to optical properties of clouds such as optical depth τ c and phase function, vertical profile information including cloud-top pressure, pressure thickness, and the surface albedo. An estimation method is developed to demonstrate how well this information might be retrieved from synthetic spectra calculated by a line-by-line spectral multiple scattering model assuming realistic instrument parameters (spectral resolution, calibration accuracy, and signal-to-noise properties). The quality of the retrievals is expressed in terms of two indices, one relating to total error and another that quantifies the extent of reliance of the retrieval on the measurement, or conversely on other a priori information. Sources of total error include instrument-related errors, forward model errors including phase function errors, and errors in a priori data.

The retrievals presented show the following: (i) The optical depth, surface albedo, cloud-top pressure, and cloud layer pressure thickness can be retrieved with an accuracy of approximately 5% for most cases of low cloud except when these clouds are optically thin and over bright surfaces. The spectra also contain information about the pressure thickness of the low-level cloud and this information also can be retrieved with an expected accuracy of less than 10% and with little reliance on any a priori data. (ii) Significantly larger errors result for retrievals for high clouds when no attempt is made to constrain the uncertainties associated with the unknown character of the scattering phase function. (iii) Retrieval of a limited amount of information about the phase function is possible under certain circumstances. It is possible to retrieve the asymmetry parameter sufficiently well to improve the accuracy of the forward model. This results in a shrinking of the errors in τ c to less than 10% for τ c > 0.1. (iv) The pressure information about scattering layers inherent in the A-band spectra is shown to provide a limited amount of vertical profiling capability (four to five layers of information at the most) provided the measurements are obtained with a spectral resolution of about 0.5 cm−1 and obtained with an accuracy of 2% or better. A specific example demonstrates the capability of not only detecting the presence of thin high cloud above lower brighter cloud but also the capability of estimating the optical depths of both clouds. (v) The advantage of additional information such as provided by active profilers (radar and lidar) is explored. The advantages of this additional profile information are quantitatively shown to improve not only the retrieval of vertical profiles of extinction but also the optical properties of individual cloud layers.

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Andrew K. Heidinger
and
Michael J. Pavolonis

Abstract

Data from the National Oceanic and Atmospheric Administration’s (NOAA’s) Advanced Very High Resolution Radiometer (AVHRR) instrument are used to provide the mean July and January global daytime distributions of multilayer cloud, where multilayer cloud is defined as cirrus overlapping one or more lower layers. The AVHRR data were taken from multiple years that were chosen to provide data with a constant local equator crossing time of 1430–1500 local time. The cloud overlap detection algorithm is used in NOAA’s Extended Clouds from AVHRR (CLAVR-x) processing system. The results between 60°N and 60°S indicated that roughly 20% of all clouds and roughly 40% of all ice clouds were classified as cirrus overlapping lower cloud (cirrus overlap). The results show a strong July–January pattern that is consistent with the seasonal cycle in convection. In some regions, cirrus overlap is found to be the dominant type of cloud observed. The distributions of overlapping cirrus cloud presented here are compared with results from other studies based on rawinsondes and manual surface observations. Comparisons are also made with another satellite-derived study that used coincident infrared and microwave observations over the tropical oceans during a 6-month period

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Charles H. White
,
Andrew K. Heidinger
, and
Steven A. Ackerman

Abstract

Satellite low-Earth-orbiting (LEO) and geostationary (GEO) imager estimates of cloud-top pressure (CTP) have many applications in both operations and in studying long-term variations in cloud properties. Recently, machine learning (ML) approaches have shown improvement upon physically based algorithms. However, ML approaches, and especially neural networks, can suffer from a lack of interpretability, making it difficult to understand what information is most useful for accurate predictions of cloud properties. We trained several neural networks to estimate CTP from the infrared channels of the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Baseline Imager (ABI). The main focus of this work is assessing the relative importance of each instrument’s infrared channels in neural networks trained to estimate CTP. We use several ML explainability methods to offer different perspectives on feature importance. These methods show many differences in the relative feature importance depending on the exact method used, but most agree on a few points. Overall, the 8.4- and 8.6-μm channels appear to be the most useful for CTP estimation on ABI and VIIRS, respectively, with other native infrared window channels and the 13.3-μm channel playing a moderate role. Furthermore, we find that the neural networks learn relationships that may account for properties of clouds such as opacity and cloud-top phase that otherwise complicate the estimation of CTP.

Significance Statement

Model interpretability is an important consideration for transitioning machine learning models to operations. This work applies several explainability methods in an attempt to understand what information is most important for estimating the pressure level at the top of a cloud from satellite imagers in a neural network model. We observe much disagreement between approaches, which motivates further work in this area but find agreement on the importance of channels in the infrared window region around 8.6 and 10–12 μm, informing future cloud property algorithm development. We also find some evidence suggesting that these neural networks are able to learn physically relevant variability in radiation measurements related to key cloud properties.

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Sarah M. Thomas
,
Andrew K. Heidinger
, and
Michael J. Pavolonis

Abstract

A comparison is made between a new operational NOAA Advanced Very High Resolution Radiometer (AVHRR) global cloud amount product to those from established satellite-derived cloud climatologies. The new operational NOAA AVHRR cloud amount is derived using the cloud detection scheme in the extended Clouds from AVHRR (CLAVR-x) system. The cloud mask within CLAVR-x is a replacement for the Clouds from AVHRR phase 1 (CLAVR-1) cloud mask. Previous analysis of the CLAVR-1 cloud climatologies reveals that its utility for climate studies is reduced by poor high-latitude performance and the inability to include data from the morning orbiting satellites. This study demonstrates, through comparison with established satellite-derived cloud climatologies, the ability of CLAVR-x to overcome the two main shortcomings of the CLAVR-1-derived cloud climatologies. While systematic differences remain in the cloud amounts from CLAVR-x and other climatologies, no evidence is seen that these differences represent a failure of the CLAVR-x cloud detection scheme. Comparisons for July 1995 and January 1996 indicate that for most latitude zones, CLAVR-x produces less cloud than the International Satellite Cloud Climatology Project (ISCCP) and the University of Wisconsin High Resolution Infrared Radiation Sounder (UW HIRS). Comparisons to the Moderate Resolution Imaging Spectroradiometer (MODIS) for 1–8 April 2003 also reveal that CLAVR-x tends to produce less cloud. Comparison of the seasonal cycle (July–January) of cloud difference with ISCCP, however, indicates close agreement. It is argued that these differences may be due to the methodology used to construct a cloud amount from the individual pixel-level cloud detection results. Overall, the global cloud amounts from CLAVR-x appear to be an improvement over those from CLAVR-1 and compare well to those from established satellite cloud climatologies. The CLAVR‐x cloud detection results have been operational since late 2003 and are available in real time from NOAA.

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