Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitating Clouds: Results from In Situ Observations in TRMM Field Campaigns

Heymsfield, Andrew J.; Bansemer, Aaron; Field, Paul R.; Durden, Stephen L.; Stith, Jeffrey L.; Dye, J. E.; Hall, Warren A.; Grainger, C. A.

doi:10.1175/1520-0469(2002)059<3457:oapops>2.0.co;2

Cited by 278 publications

(348 citation statements)

References 35 publications

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“…As revealed by the photos of ice particles, their sizes range from microns to millimeters and their habits (or shapes) vary from simple pristine hexagonal columns and plates to highly irregular aggregates and polycrystals (Weickmann, 1947;Heymsfield et al, 2002;Heymsfield, 2003). This makes the development of ice particle models that quantitatively replicate the microphysical and associated optical properties of ice particles very difficult.…”

Section: Introductionmentioning

confidence: 99%

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

et al. 2009

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Abstract. The influence is investigated of the assumed ice particle microphysical and optical model on inferring ice cloud optical thickness (τ ) from satellite measurements of the Earth's reflected shortwave radiance. Ice cloud τ are inferred, and subsequently compared, using products from MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER (POLarization and Directionality of the Earth's Reflectances). POLDER τ values are found to be substantially smaller than those from collocated MODIS data. It is shown that this difference is caused primarily by the use of different ice particle bulk scattering models in the two retrievals, and more specifically, the scattering phase function. Furthermore, the influence of the ice particle model on the derivation of ice cloud radiative forcing (CRF) from satellite retrievals is studied. Three sets of shortwave CRF are calculated using different combinations of the retrieval and associated ice particle models. It is shown that the uncertainty associated with an ice particle model may lead to two types of errors in estimating CRF from satellite retrievals. One stems from the retrieval itself and the other is due to the optical properties, such as the asymmetry factor, used for CRF calculations. Although a comparison of the CRFs reveals that these two types of errors tend to cancel each other, significant differences are still found between the three CRFs, which indicates that the ice particle model affects notCorrespondence to: Z. Zhang (zzbatmos@umbc.edu) only optical thickness retrievals but also CRF calculations. In addition to CRF, the effect of the ice particle model on the derivation of seasonal variation of τ from satellite measurements is discussed. It is shown that optical thickness retrievals based on the same MODIS observations, but derived using different assumptions of the ice particle model, can be substantially different. These differences can be divided into two parts. The first-order difference is mainly caused by the differences in the asymmetry factor. The second-order difference is related to seasonal changes in the sampled scattering angles and therefore dependent on the sun-satellite viewing geometry. Because of this second-order difference, the use of different ice particle models may lead to a different understanding of the seasonal variation of τ .

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Section: Introductionmentioning

confidence: 99%

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

et al. 2009

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“…Unimodal fits are used to compute all other bulk quantities. Unimodal fits were performed via the method of moments (in a manner similar to Heymsfield et al, 2002). Both the method of moments and an expectation maximization algorithm (Moon, 1996;Schwartz, 2014) were used for the bimodal fits -the more accurate of those two fits (as determined by whether fit provided the smaller binned AndersonDarling test statistic; Demortier, 1995) being kept.…”

Section: Parametric Fitting Of Psdsmentioning

confidence: 99%

“…If the smallest ice particles are indeed always present in such large numbers, then their effects on cloud microphysical and radiative properties are pronounced. For instance, Heymsfield et al (2002) reported small particles dominating total particle concentrations (N T s) at all times during multiple Tropical Rainfall Measuring Mission (TRMM) field campaigns, while Field (2000) noted the same phenomenon in midlatitude cirrus. Lawson et al (2006) reported N T s in midlatitude cirrus ranging from ∼ .03 to 8 cm −3 and estimated that particles smaller than 50 µm were responsible for 99 % of N T , 69 % of shortwave extinction, and 40 % of ice water content (IWC).…”

Section: Introductionmentioning

confidence: 99%

A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC4, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets

Schwartz

2017

Atmos. Meas. Tech.

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Abstract. This paper addresses two straightforward questions. First, how similar are the statistics of cirrus particle size distribution (PSD) datasets collected using the TwoDimensional Stereo (2D-S) probe to cirrus PSD datasets collected using older Particle Measuring Systems (PMS) 2-D Cloud (2DC) and 2-D Precipitation (2DP) probes? Second, how similar are the datasets when shatter-correcting postprocessing is applied to the 2DC datasets? To answer these questions, a database of measured and parameterized cirrus PSDs -constructed from measurements taken during the Small Particles in Cirrus (SPARTICUS); Mid-latitude Airborne Cirrus Properties Experiment (MACPEX); and Tropical Composition, Cloud, and Climate Coupling (TC 4 ) flight campaigns -is used.Bulk cloud quantities are computed from the 2D-S database in three ways: first, directly from the 2D-S data; second, by applying the 2D-S data to ice PSD parameterizations developed using sets of cirrus measurements collected using the older PMS probes; and third, by applying the 2D-S data to a similar parameterization developed using the 2D-S data themselves. This is done so that measurements of the same cloud volumes by parameterized versions of the 2DC and 2D-S can be compared with one another. It is thereby seen -given the same cloud field and given the same assumptions concerning ice crystal cross-sectional area, density, and radar cross section -that the parameterized 2D-S and the parameterized 2DC predict similar distributions of inferred shortwave extinction coefficient, ice water content, and 94 GHz radar reflectivity. However, the parameterization of the 2DC based on uncorrected data predicts a statistically significantly higher number of total ice crystals and a larger ratio of small ice crystals to large ice crystals than does the parameterized 2D-S. The 2DC parameterization based on shatter-corrected data also predicts statistically different numbers of ice crystals than does the parameterized 2D-S, but the comparison between the two is nevertheless more favorable. It is concluded that the older datasets continue to be useful for scientific purposes, with certain caveats, and that continuing field investigations of cirrus with more modern probes is desirable.

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“…Based on cloud observations from the CALIPSO mission (Winker et al, 2010), we also introduce a small amount of ice clouds to regions near the tropopause. The optical properties of both aerosols and ice clouds are calculated using Mie theory with parameterized Gamma particle size distributions (Heymsfield et al, 2002 and 2S-ESS for retrieval). Both models take in the same optical depths of all the absorbing and scattering species (GHG, aerosols, and ice clouds) and compute the upwelling radiances based on the viewing geometry, the solar irradiance, and other auxiliary parameters.…”

Section: Forward Modelmentioning

confidence: 99%

Simulated retrievals for the remote sensing of CO2, CH4, CO, and H2O from geostationary orbit

Natraj

Shia

et al. 2015

Atmos. Meas. Tech.

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Abstract. The Geostationary Fourier Transform Spectrometer (GeoFTS) is designed to measure high-resolution spectra of reflected sunlight in three near-infrared bands centered around 0.76, 1.6, and 2.3 µm and to deliver simultaneous retrievals of column-averaged dry air mole fractions of CO 2 , CH 4 , CO, and H 2 O (denoted XCO 2 , XCH 4 , XCO, and XH 2 O, respectively) at different times of day over North America. In this study, we perform radiative transfer simulations over both clear-sky and all-sky scenes expected to be observed by GeoFTS and estimate the prospective performance of retrievals based on results from Bayesian error analysis and characterization.We find that, for simulated clear-sky retrievals, the average retrieval biases and single-measurement precisions are < 0.2 % for XCO 2 , XCH 4 , and XH 2 O, and < 2 % for XCO, when the a priori values have a bias of 3 % and an uncertainty of 3 %. In addition, an increase in the amount of aerosols and ice clouds leads to a notable increase in the retrieval biases and slight worsening of the retrieval precisions. Furthermore, retrieval precision is a strong function of signal-to-noise ratio and spectral resolution. This simulation study can help guide decisions on the design of the GeoFTS observing system, which can result in cost-effective measurement strategies while achieving satisfactory levels of retrieval precisions and biases. The simultaneous retrievals at different times of day will be important for more accurate estimation of carbon sources and sinks on fine spatiotemporal scales and for studies related to the atmospheric component of the water cycle.

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Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitating Clouds: Results from In Situ Observations in TRMM Field Campaigns

Cited by 278 publications

References 35 publications

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC<sup>4</sup>, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets

Simulated retrievals for the remote sensing of CO<sub>2</sub>, CH<sub>4</sub>, CO, and H<sub>2</sub>O from geostationary orbit

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Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitating Clouds: Results from In Situ Observations in TRMM Field Campaigns

Cited by 278 publications

References 35 publications

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

Influence of ice particle model on satellite ice cloud retrieval: lessons learned from MODIS and POLDER cloud product comparison

A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC&lt;sup&gt;4&lt;/sup&gt;, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets

Simulated retrievals for the remote sensing of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, CO, and H&lt;sub&gt;2&lt;/sub&gt;O from geostationary orbit

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A statistical comparison of cirrus particle size distributions measured using the 2-D stereo probe during the TC<sup>4</sup>, SPARTICUS, and MACPEX flight campaigns with historical cirrus datasets

Simulated retrievals for the remote sensing of CO<sub>2</sub>, CH<sub>4</sub>, CO, and H<sub>2</sub>O from geostationary orbit