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.

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

2018 ◽  
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 those cloud scenes is studied. For spatial scales near 45 km, only 16 out of 256 possible cloud scenes account for 90 % of all observations and contain either one, two, or three cloud types. The number of possible cloud scenes is shown to depend on spatial scale with a maximum number of 194 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 modelled 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.

Surface Irradiances of Edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product

2018 ◽  
Vol 31 (11) ◽  
pp. 4501-4527 ◽  
Author(s):  
Seiji Kato ◽  
Fred G. Rose ◽  
David A. Rutan ◽  
Tyler J. Thorsen ◽  
Norman G. Loeb ◽  
...  
Keyword(s):  
Lagrange Multiplier ◽  
Spatial Scales ◽  
Radiant Energy ◽  
Energy System ◽  
Specific Humidity ◽  
Adjustment Process ◽  
Polar Regions ◽  
Atmospheric Infrared Sounder ◽  
Data Product ◽  
Surface Data

Abstract The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m−2 and decrease by 0.2 W m−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m−2.

scholarly journals Estrutura microfísica das nuvens em diferentes ecossistemas da América do Sul

2009 ◽  
Vol 32 (2) ◽  
pp. 33-41
Author(s):  
Bruno Muniz Duarte ◽  
José Ricardo de Almeida França
Keyword(s):  
Deep Convection ◽  
Effective Radius ◽  
Atmospheric Models ◽  
Cloud Type ◽  
Synoptic Condition ◽  
Cloud Particle ◽  
Microphysical Properties ◽  
Four Seasons ◽  
Frontal Systems

Clouds directly affect meteorological conditions of the planet, by interacting with electromagnetic radiation from the sun, the earth's surface and the atmosphere. Each cloud type interacts in a particular way, being extremely important that the total set of clouds at any location is well represented in atmospheric models, in order to generate more accurate results. The purpose of this paper is to initiate a characterization of cloud types as a function of their microphysical properties and evaluate the dependence on ecosystem and synoptic condition. The data were obtained through remote sensing, using the MODIS sensor and the variables: cloud particle effective radius, optical thickness, pressure and temperature of the cloud top. Several forms of distributions were found for six different ecosystems for the four seasons. It was noted that narrow and concentrated effective radius spectra are linked to deep convection clouds, while broader distributions can be usually associated to cold frontal systems. The amount of events analyzed was not enough to show clear patterns, although the results can lead to other directions in a future and more focused work.

scholarly journals The structure of turbulence and mixed-phase cloud microphysics in a highly supercooled altocumulus cloud

2020 ◽  
Vol 20 (4) ◽  
pp. 1921-1939 ◽  
Author(s):  
Paul A. Barrett ◽  
Alan Blyth ◽  
Philip R. A. Brown ◽  
Steven J. Abel
Keyword(s):  
Prediction Models ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
In Situ Measurements ◽  
Cloud Layer ◽  
Mixed Phase ◽  
Ice Cloud ◽  
Cloud Properties

Abstract. Observations of vertically resolved turbulence and cloud microphysics in a mixed-phase altocumulus cloud are presented using in situ measurements from an instrumented aircraft. The turbulence spectrum is observed to have an increasingly negative skewness with distance below cloud top, suggesting that long-wave radiative cooling from the liquid cloud layer is an important source of turbulence kinetic energy. Turbulence measurements are presented from both the liquid cloud layer and ice virga below. Vertical profiles of both bulk and microphysical liquid and ice cloud properties indicate that ice is produced within the liquid layer cloud at a temperature of −30 ∘C. These high-resolution in situ measurements support previous remotely sensed observations from both ground-based and space-borne instruments and could be used to evaluate numerical model simulations of altocumulus clouds at spatial scales from eddy-resolving models to global numerical weather prediction models and climate simulations.

scholarly journals Convective formation of pileus cloud near the tropopause

2006 ◽  
Vol 6 (5) ◽  
pp. 1185-1200 ◽  
Author(s):  
T. J. Garrett ◽  
J. Dean-Day ◽  
C. Liu ◽  
B. Barnett ◽  
G. Mace ◽  
...  
Keyword(s):  
Gravity Wave ◽  
Deep Convection ◽  
Cloud Microphysics ◽  
In Situ Measurements ◽  
Ice Crystals ◽  
Total Water ◽  
Wave Analysis ◽  
Cold Temperatures ◽  
Face Experiment

Abstract. Pileus clouds form where humid, vertically stratified air is mechanically displaced ahead of rising convection. This paper describes convective formation of pileus cloud in the tropopause transition layer (TTL), and explores a possible link to the formation of long-lasting cirrus at cold temperatures. The study examines in detail in-situ measurements from off the coast of Honduras during the July 2002 CRYSTAL-FACE experiment that showed an example of TTL cirrus associated with, and penetrated by, deep convection. The TTL cirrus was enriched with total water compared to its surroundings, but was composed of extremely small ice crystals with effective radii between 2 and 4 μm. Through gravity wave analysis, and intercomparison of measured and simulated cloud microphysics, it is argued that the TTL cirrus originated neither from convectively-forced gravity wave motions nor environmental mixing alone. Rather, it is hypothesized that a combination of these two processes was involved in which, first, a pulse of convection forced pileus cloud to form from TTL air; second, the pileus layer was punctured by the convective pulse and received larger ice crystals through interfacial mixing; third, the addition of this condensate inhibited evaporation of the original pileus ice crystals where a convectively forced gravity wave entered its warm phase; fourth, through successive pulses of convection, a sheet of TTL cirrus formed. While the general incidence and longevity of pileus cloud remains unknown, in-situ measurements, and satellite-based Microwave Limb Sounder retrievals, suggest that much of the tropical TTL is sufficiently humid to be susceptible to its formation. Where these clouds form and persist, there is potential for an irreversible repartition from water vapor to ice at cold temperatures.

Optimized intelligent algorithm for classifying cloud particles recorded by a Cloud Particle Imager

2021 ◽  
Author(s):  
Zepei Wu ◽  
Shuo Liu ◽  
Delong Zhao ◽  
Ling Yang ◽  
Zixin Xu ◽  
...  
Keyword(s):  
Processing Speed ◽  
Cloud Microphysics ◽  
Parameter Analysis ◽  
Ice Crystals ◽  
Support Vector ◽  
Svm Classifier ◽  
Ice Crystal ◽  
Cloud Particle ◽  
Average Accuracy ◽  
Cloud Particles

AbstractCloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis, to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on ten types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2,000 images/s. In actual application, the algorithm should consider the processing speed, because the images are in the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2,000 images/s. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics.

scholarly journals A daytime climatological distribution of high opaque ice cloud classes over the Indian summer monsoon region observed from 25-year AVHRR data

2009 ◽  
Vol 9 (12) ◽  
pp. 4185-4196 ◽  
Author(s):  
A. Devasthale ◽  
H. Grassl
Keyword(s):  
Summer Monsoon ◽  
Indian Subcontinent ◽  
Deep Convection ◽  
Temporal Distribution ◽  
Radiative Balance ◽  
Ice Cloud ◽  
Brightness Temperatures ◽  
Spatio Temporal ◽  
Very High ◽  
Summer Monsoon Region

Abstract. A daytime climatological spatio-temporal distribution of high opaque ice cloud (HOIC) classes over the Indian subcontinent (0–40° N, 60° E–100° E) is presented using 25-year data from the Advanced Very High Resolution Radiometers (AVHRRs) for the summer monsoon months. The HOICs are important for regional radiative balance, precipitation and troposphere-stratosphere exchange. In this study, HOICs are sub-divided into three classes based on their cloud top brightness temperatures (BT). Class I represents very deep convection (BT<220 K). Class II represents deep convection (220 K

scholarly journals Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals

2016 ◽  
Vol 16 (8) ◽  
pp. 5075-5090 ◽  
Author(s):  
Robert E. Holz ◽  
Steven Platnick ◽  
Kerry Meyer ◽  
Mark Vaughan ◽  
Andrew Heidinger ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Spatial Scales ◽  
Radiative Properties ◽  
Radiation Balance ◽  
Data Set ◽  
Mean Values ◽  
Ice Cloud ◽  
Lidar Ratio ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light-scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of 2 bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single-scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g ≈ 0.75 in the mid-visible spectrum, 5–15 % smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products. This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28 %), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single-habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

scholarly journals Impacts of Shallow Convection on MJO Simulation: A Moist Static Energy and Moisture Budget Analysis

2013 ◽  
Vol 26 (8) ◽  
pp. 2417-2431 ◽  
Author(s):  
Qiongqiong Cai ◽  
Guang J. Zhang ◽  
Tianjun Zhou
Keyword(s):  
Boundary Layer ◽  
Deep Convection ◽  
Lower Troposphere ◽  
Longwave Radiation ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Moist Static Energy ◽  
Shallow Convection ◽  
Budget Analysis ◽  
Static Energy

Abstract The role of shallow convection in Madden–Julian oscillation (MJO) simulation is examined in terms of the moist static energy (MSE) and moisture budgets. Two experiments are carried out using the NCAR Community Atmosphere Model, version 3.0 (CAM3.0): a “CTL” run and an “NSC” run that is the same as the CTL except with shallow convection disabled below 700 hPa between 20°S and 20°N. Although the major features in the mean state of outgoing longwave radiation, 850-hPa winds, and vertical structure of specific humidity are reasonably reproduced in both simulations, moisture and clouds are more confined to the planetary boundary layer in the NSC run. While the CTL run gives a better simulation of the MJO life cycle when compared with the reanalysis data, the NSC shows a substantially weaker MJO signal. Both the reanalysis data and simulations show a recharge–discharge mechanism in the MSE evolution that is dominated by the moisture anomalies. However, in the NSC the development of MSE and moisture anomalies is weaker and confined to a shallow layer at the developing phases, which may prevent further development of deep convection. By conducting the budget analysis on both the MSE and moisture, it is found that the major biases in the NSC run are largely attributed to the vertical and horizontal advection. Without shallow convection, the lack of gradual deepening of upward motion during the developing stage of MJO prevents the lower troposphere above the boundary layer from being preconditioned for deep convection.

scholarly journals Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2

2020 ◽  
Vol 13 (9) ◽  
pp. 4107-4157 ◽  
Author(s):  
Shin-ichiro Shima ◽  
Yousuke Sato ◽  
Akihiro Hashimoto ◽  
Ryohei Misumi
Keyword(s):  
Method Development ◽  
Cloud Condensation Nuclei ◽  
Deep Convection ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Computational Time ◽  
Ice Particles ◽  
Mass Dimension ◽  
Mixed Phase Clouds ◽  
Droplet Method

Abstract. The super-droplet method (SDM) is a particle-based numerical scheme that enables accurate cloud microphysics simulation with lower computational demand than multi-dimensional bin schemes. Using SDM, a detailed numerical model of mixed-phase clouds is developed in which ice morphologies are explicitly predicted without assuming ice categories or mass–dimension relationships. Ice particles are approximated using porous spheroids. The elementary cloud microphysics processes considered are advection and sedimentation; immersion/condensation and homogeneous freezing; melting; condensation and evaporation including cloud condensation nuclei activation and deactivation; deposition and sublimation; and coalescence, riming, and aggregation. To evaluate the model's performance, a 2-D large-eddy simulation of a cumulonimbus was conducted, and the life cycle of a cumulonimbus typically observed in nature was successfully reproduced. The mass–dimension and velocity–dimension relationships the model predicted show a reasonable agreement with existing formulas. Numerical convergence is achieved at a super-particle number concentration as low as 128 per cell, which consumes 30 times more computational time than a two-moment bulk model. Although the model still has room for improvement, these results strongly support the efficacy of the particle-based modeling methodology to simulate mixed-phase clouds.

Sign in / Sign up

Export Citation Format

Share Document