The Prospect for Remote Sensing of Cirrus Clouds with a Submillimeter-Wave Spectrometer

Cirrus clouds have only recently been recognized as having a significant influence on weather and climate through their impact on the radiative energy budget of the atmosphere. In addition, the unique difficulties presented by the study of cirrus put them on the “back burner” of atmospheric research for much of the twentieth century. Foremost, because they inhabit the frigid upper troposphere, their inaccessibility has hampered intensive research. Other factors have included a lack of in situ instrumentation to effectively sample the clouds and environment, and basic uncertainties in the underlying physics of ice cloud formation, growth, and maintenance. Cloud systems that produced precipitation, severe weather, or hazards to aviation were deemed more worthy of research support until the mid- 1980s. Beginning at this time, however, major field research programs such as the First ISCCP (International Satellite Cloud Climatology Program) Regional Experiment (FIRE; Cox et al. 1987), International Cirrus Experiment (ICE; Raschke et al. 1990), Experimental Cloud Lidar Pilot Study (ECLIPS; Platt et al. 1994), and the Atmospheric Radiation Measurement (ARM) Program (Stokes and Schwartz 1994) have concentrated on cirrus cloud research, relying heavily on ground-based remote sensing observations combined with research aircraft. What has caused this change in research emphasis is an appreciation for the potentially significant role that cirrus play in maintaining the radiation balance of the earth-atmosphere system (Liou 1986). As climate change issues were treated more seriously, it was recognized that the effects, or feedbacks, of extensive high-level ice clouds in response to global warming could be pivotal. This fortunately came at a time when new generations of meteorological instrumentation were becoming available. Beginning in the early 1970s, major advancements were made in the fields of numerical cloud modeling and cloud measurements using aircraft probes, satellite multispectral imaging, and remote sensing with lidar, short-wavelength radar, and radiometers, all greatly facilitating cirrus research. Each of these experimental approaches have their advantages and drawbacks, and it should also be noted that a successful cloud modeling effort relies on field data for establishing boundary conditions and providing case studies for validation. Although the technologies created for in situ aircraft measurements can clearly provide unique knowledge of cirrus cloud thermodynamic and microphysical properties (Dowling and Radke 1990), available probes may suffer from limitations in their response to the wide range of cirrus particles and actually sample a rather small volume of cloud during any mission.

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Satellite Remote Sensing of Cirrus

Cirrus ◽

10.1093/oso/9780195130720.003.0011 ◽

2002 ◽

Author(s):

Patrick Minnis

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Climate Models ◽

Cirrus Clouds ◽

Data Sets ◽

Visible Radiation ◽

Accurate Identification ◽

Spatial And Temporal Scales ◽

Sensing Systems ◽

Particle Size And Shape

The determination of cirrus properties over large spatial and temporal scales will, in most instances, require the use of satellite data. Global coverage at resolutions as fine as several meters are attainable with instruments on Landsat, and temporal coverage at 1-min intervals is now available with the latest Geostationary Operational Environmental Satellite (GOES) imagers. Extracting information about cirrus clouds from these satellite data sets is often difficult because of variations in background, similarities to other cloud types, and the frequently semitransparent nature of cirrus clouds. From the surface, cirrus clouds are readily discerned by the human observer via the patterns of scattered visible radiation from the sun, moon, and stars. The relatively uniform background presented by the sky facilitates cloud detection and the familiar textures, structures, and apparent altitude of cirrus distinguish it from other cloud types. From satellites, cirrus can also be detected from scattered visible radiation, but the demands of accurate identification for different surface backgrounds over the entire diurnal cycle and quantification of the cirrus properties require the analysis of radiances scattered or emitted over a wide range of the electromagnetic spectrum. Many of these spectra and high-resolution satellite data can be used to understand certain aspects of cirrus clouds in particular situations. Intensive study of well-measured cases can yield a wealth of information about cirrus properties on fine scales (e.g., Minnis et al. 1990; Westphal et al. 1996). Production of a global climatology of cirrus clouds, however, requires compromises in spatial, temporal, and spectral coverage (e.g., Schiffer and Rossow 1983). This chapter summarizes both the state of the art and the potential for future passive remote sensing systems to aid the understanding of cirrus processes and to acquire sufficient statistics for constraining and refining weather and climate models. Theoretically, many different aspects of cirrus can be determined from passive sensing systems. A limited number of quantities are the focus of most efforts to describe cirrus clouds. These include the areal coverage, top and base altitude or pressure, thickness, top and base temperatures, optical depth, effective particle size and shape, vertical ice water path, and size, shape and spacing of the cloud cells.

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Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Atmospheric Measurement Techniques ◽

10.5194/amt-10-4317-2017 ◽

2017 ◽

Vol 10 (11) ◽

pp. 4317-4339 ◽

Cited By ~ 5

Author(s):

Johan Strandgren ◽

Jennifer Fricker ◽

Luca Bugliaro

Keyword(s):

Remote Sensing ◽

Optical Thickness ◽

Cirrus Clouds ◽

Cirrus Cloud ◽

Retrieval Error ◽

Water Path ◽

Retrieval Accuracy ◽

Ice Water Path ◽

Retrieval Errors ◽

Ice Water

Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate large-scale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary. In this paper the CiPS (Cirrus Properties from SEVIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m−2. For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.

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