Use of Doppler radar to assess ice cloud particle fall velocity-size relations for remote sensing and climate studies

2000 ◽  
Vol 105 (D17) ◽  
pp. 22427-22436 ◽  
Author(s):  
Sergey Y. Matrosov ◽  
Andrew J. Heymsfield
Keyword(s):  
Remote Sensing ◽  
Doppler Radar ◽  
Fall Velocity ◽  
Cloud Particle ◽  
Ice Cloud ◽  
Climate Studies

scholarly journals Developing and bounding ice particle mass- and area-dimension expressions for use in atmospheric models and remote sensing

2016 ◽  
Vol 16 (7) ◽  
pp. 4379-4400 ◽  
Author(s):  
Ehsan Erfani ◽  
David L. Mitchell
Keyword(s):  
Remote Sensing ◽  
Power Laws ◽  
Field Studies ◽  
Particle Mass ◽  
Atmospheric Models ◽  
Cloud Particle ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Probe Measurements

Abstract. Ice particle mass- and projected area-dimension (m-D and A-D) power laws are commonly used in the treatment of ice cloud microphysical and optical properties and the remote sensing of ice cloud properties. Although there has long been evidence that a single m-D or A-D power law is often not valid over all ice particle sizes, few studies have addressed this fact. This study develops self-consistent m-D and A-D expressions that are not power laws but can easily be reduced to power laws for the ice particle size (maximum dimension or D) range of interest, and they are valid over a much larger D range than power laws. This was done by combining ground measurements of individual ice particle m and D formed at temperature T  <  −20 °C during a cloud seeding field campaign with 2-D stereo (2D-S) and cloud particle imager (CPI) probe measurements of D and A, and estimates of m, in synoptic and anvil ice clouds at similar temperatures. The resulting m-D and A-D expressions are functions of temperature and cloud type (synoptic vs. anvil), and are in good agreement with m-D power laws developed from recent field studies considering the same temperature range (−60 °C  <  T  <  −20 °C).

scholarly journals Developing and bounding ice particle mass- and area-dimension expressions for use in atmospheric models and remote sensing

2015 ◽  
Vol 15 (20) ◽  
pp. 28517-28573 ◽  
Author(s):  
E. Erfani ◽  
D. L. Mitchell
Keyword(s):  
Remote Sensing ◽  
Field Measurements ◽  
Power Laws ◽  
Field Studies ◽  
Particle Mass ◽  
Atmospheric Models ◽  
Cloud Particle ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Cloud Properties

Abstract. Ice particle mass- and projected area-dimension (m-D and A-D) power laws are commonly used in the treatment of ice cloud microphysical and optical properties and the remote sensing of ice cloud properties. Although there has long been evidence that a single m-D or A-D power law is often not valid over all ice particle sizes, few studies have addressed this fact. This study develops self-consistent m-D and A-D expressions that are not power laws, but can easily be reduced to power laws for the ice particle size (maximum dimension or D) range of interest, and they are valid over a much larger D range than power laws. This was done by combining field measurements of individual ice particle m and D formed at temperature T < −20 °C with 2-dimensional stereo (2D-S) and Cloud Particle Imager (CPI) probe measurements (or estimates) of D, A and m in synoptic and anvil ice clouds at similar temperatures. The resulting m-D and A-D expressions are functions of temperature and cloud type (synoptic vs. anvil), and are in good agreement with m-D power laws developed from recent field studies considering the same temperature range (−60 °C < T < −20 °C).

Simulation of remote sensing of clouds and humidity from space using a combined platform of radar and multi-frequency microwave radiometers

2020 ◽  
Author(s):  
Jonathan Jiang ◽  
Hui Su ◽  
Qing Yue ◽  
Pekka Kangaslahti
Keyword(s):  
Remote Sensing ◽  
Effective Diameter ◽  
Cloud Particle ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Thermodynamic Conditions ◽  
Ice Water Content ◽  
Microwave Radiometers ◽  
Cloud Ice ◽  
Ice Water

&lt;p&gt;We present a simulated simultaneous retrieval of mass mean cloud ice particle effective diameter, ice water content, water vapor, and temperature profiles using a combination of a 94-GHz cloud radar and multi-frequency (118, 183, 240, 310, 380, 664, and 850 GHz) millimeter- and submillimeter-wave radiometers from a space platform. The retrieval capabilities and uncertainties of the combined radar and microwave radiometers are quantified. We show that this combined active and passive remote sensing approach with SmallSat technologies addresses a gap in the current state-of-the-art remote sensing measurements of ice cloud properties, especially deriving vertical profiles of ice cloud particle sizes in the atmosphere together with the ambient thermodynamic conditions. Therefore, this new approach can serve as a plausible candidate for future missions that target cloud and precipitation processes to improve weather forecasts and climate predictions. &amp;#160;&lt;/p&gt;

Refinements to Ice Particle Mass Dimensional and Terminal Velocity Relationships for Ice Clouds. Part II: Evaluation and Parameterizations of Ensemble Ice Particle Sedimentation Velocities

2007 ◽  
Vol 64 (4) ◽  
pp. 1068-1088 ◽  
Author(s):  
Andrew J. Heymsfield ◽  
Gerd-Jan van Zadelhoff ◽  
David P. Donovan ◽  
Frederic Fabry ◽  
Robin J. Hogan ◽  
...  
Keyword(s):  
Climate Models ◽  
Doppler Radar ◽  
Terminal Velocity ◽  
Fall Velocity ◽  
Particle Sedimentation ◽  
Cloud Properties ◽  
Wide Range ◽  
Single Moment ◽  
Ice Water Content ◽  
Ice Water

Abstract This two-part study addresses the development of reliable estimates of the mass and fall speed of single ice particles and ensembles. Part I of the study reports temperature-dependent coefficients for the mass-dimensional relationship, m = aDb, where D is particle maximum dimension. The fall velocity relationship, Vt = ADB, is developed from observations in synoptic and low-latitude, convectively generated, ice cloud layers, sampled over a wide range of temperatures using an assumed range for the exponent b. Values for a, A, and B were found that were consistent with the measured particle size distributions (PSD) and the ice water content (IWC). To refine the estimates of coefficients a and b to fit both lower and higher moments of the PSD and the associated values for A and B, Part II uses the PSD from Part I plus coincident, vertically pointing Doppler radar returns. The observations and derived coefficients are used to evaluate earlier, single-moment, bulk ice microphysical parameterization schemes as well as to develop improved, statistically based, microphysical relationships. They may be used in cloud and climate models, and to retrieve cloud properties from ground-based Doppler radar and spaceborne, conventional radar returns.

The THz imager instrument for ice cloud remote sensing

2021 ◽  
Author(s):  
Shuai Dong ◽  
Zhenzhan Wang ◽  
Shengwei Zhang ◽  
Wensi Zhai ◽  
Bin Li
Keyword(s):  
Remote Sensing ◽  
Ice Cloud

scholarly journals Ice Microphysics Observations in Hurricane Humberto: Comparison with Non-Hurricane-Generated Ice Cloud Layers

2006 ◽  
Vol 63 (1) ◽  
pp. 288-308 ◽  
Author(s):  
Andrew J. Heymsfield ◽  
Aaron Bansemer ◽  
Stephen L. Durden ◽  
Robert L. Herman ◽  
T. Paul Bui
Keyword(s):  
Doppler Radar ◽  
Deep Convection ◽  
Particle Growth ◽  
Dual Wavelength ◽  
Size Distributions ◽  
Temperature Growth ◽  
Radar Observations ◽  
Ice Cloud ◽  
Ice Particles ◽  
High Concentrations

Abstract Measurements are presented that were acquired from the National Aeronautics and Space Administration (NASA) DC-8 aircraft during an intensive 3-day study of Tropical Storm/Hurricane Humberto on 22, 23, and 24 September 2001. Particle size distributions, particle image information, vertical velocities, and single- and dual-wavelength Doppler radar observations were obtained during repeated sampling of the eyewall and outer eye regions. Eyewall sampling temperatures ranged from −22° to −57°C and peak updraft velocities from 4 to 15 m s−1. High concentrations of small ice particles, in the order 50 cm−3 and above, were observed within and around the updrafts. Aggregates, some larger than 7 mm, dominated the larger sizes. The slope of the fitted exponential size distributions λ was distinctly different close to the eye than outside of that region. Even at low temperatures, λ was characteristic of warm temperature growth (λ &lt; 30 cm−1) close to the eye and characteristic of low temperature growth outside of it as well (λ &gt; 100 cm−1). The two modes found for λ are shown to be consistent with observations from nonhurricane ice cloud layers formed through deep convection, but differ markedly from ice cloud layers generated in situ. It is shown that the median, mass-weighted, terminal velocities derived for the Humberto data and from the other datasets are primarily a function of λ. Microphysical measurements and dual wavelength radar observations are used together to infer and interpret particle growth processes. Rain in the lower portions of the eyewall extended up to the 6- or 7-km level. In the outer eye regions, aggregation progressed downward from between 8.5 and 11.9 km to the melting layer, with some graupel noted in rainbands. Homogeneous ice nucleation is implicated in the high concentrations of small ice particles observed in the vicinity of the updrafts.

scholarly journals An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4285 ◽  
Author(s):  
Shubha Sathyendranath ◽  
Robert Brewin ◽  
Carsten Brockmann ◽  
Vanda Brotas ◽  
Ben Calton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Marine Ecosystem ◽  
Ocean Colour ◽  
Remote Sensing Reflectance ◽  
Resolution Imaging ◽  
In Situ Observations ◽  
Climate Studies ◽  
Reflectance Data

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

scholarly journals Footprint-scale cloud type mixtures and their impacts on Atmospheric Infrared Sounder cloud property retrievals

2019 ◽  
Vol 12 (8) ◽  
pp. 4361-4377 ◽  
Author(s):  
Alexandre Guillaume ◽  
Brian H. Kahn ◽  
Eric J. Fetzer ◽  
Qing Yue ◽  
Gerald J. Manipon ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Deep Convection ◽  
Cloud Microphysics ◽  
Specific Humidity ◽  
Phase Detection ◽  
Cloud Type ◽  
Atmospheric Infrared Sounder ◽  
Cloud Particle ◽  
Cloud Property ◽  
Ice Cloud

Abstract. A method is described to classify cloud mixtures of cloud top types, termed cloud scenes, using cloud type classification derived from the CloudSat radar (2B-CLDCLASS). The scale dependence of the cloud scenes is quantified. For spatial scales at 45 km (15 km), only 18 (10) out of 256 possible cloud scenes account for 90 % of all observations and contain one, two, or three cloud types. The number of possible cloud scenes is shown to depend on spatial scale with a maximum number of 210 out of 256 possible scenes at a scale of 105 km and fewer cloud scenes at smaller and larger scales. The cloud scenes are used to assess the characteristics of spatially collocated Atmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloud property retrievals within scenes of varying cloud type complexity. The likelihood of ice and liquid-phase detection strongly depends on the CloudSat-identified cloud scene type collocated with the AIRS footprint. Cloud scenes primarily consisting of cirrus, nimbostratus, altostratus, and deep convection are dominated by ice-phase detection, while stratocumulus, cumulus, and altocumulus are dominated by liquid- and undetermined-phase detection. Ice cloud particle size and optical thickness are largest for cloud scenes containing deep convection and cumulus and are smallest for cirrus. Cloud scenes with multiple cloud types have small reductions in information content and slightly higher residuals of observed and modeled radiance compared to cloud scenes with single cloud types. These results will help advance the development of temperature, specific humidity, and cloud property retrievals from hyperspectral infrared sounders that include cloud microphysics in forward radiative transfer models.

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