Studies of cloud ice water path and optical thickness during FIRE-II and ASTEX

1995 ◽  
Author(s):  
Sergey Y. Matrosov ◽  
Jack B. Snider
Keyword(s):  
Optical Thickness ◽  
Water Path ◽  
Ice Water Path ◽  
Cloud Ice ◽  
Ice Water

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

2017 ◽  
Vol 10 (11) ◽  
pp. 4317-4339 ◽  
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.

scholarly journals CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

2013 ◽  
Vol 6 (5) ◽  
pp. 8187-8233 ◽  
Author(s):  
J. Gong ◽  
D. L. Wu
Keyword(s):  
Climate Models ◽  
Cloud Microphysics ◽  
Water Path ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Ice Water Path ◽  
Satellite Sensors ◽  
Key Variables ◽  
Cloud Ice ◽  
Ice Water

Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables to determine cloud radiative and thermodynamical properties in the climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the forward models between collocated-and-coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up-table (LUT) of Tcir–IWP relationships as a function of ht and frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of normalized probability density function (PDF). Compared to the empirical model, current radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains as an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

scholarly journals Assessing observed and modelled spatial distributions of ice water path using satellite data

2011 ◽  
Vol 11 (1) ◽  
pp. 375-391 ◽  
Author(s):  
S. Eliasson ◽  
S. A. Buehler ◽  
M. Milz ◽  
P. Eriksson ◽  
V. O. John
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
Climate Models ◽  
Water Path ◽  
Ice Water Path ◽  
The Individual ◽  
Cloud Ice ◽  
The Tropics ◽  
Ipcc Ar4 ◽  
Ice Water

Abstract. The climate models used in the IPCC AR4 show large differences in monthly mean ice water path (IWP). The most valuable source of information that can be used to potentially constrain the models is global satellite data. The satellite datasets also have large differences. The retrieved IWP depends on the technique used, as retrievals based on different techniques are sensitive to different parts of the cloud column. Building on the foundation of Waliser et al. (2009), this article provides a more comprehensive comparison between satellite datasets. IWP data from the CloudSat cloud profiling radar provide the most advanced dataset on clouds. For all its unmistakable value, CloudSat data are too short and too sparse to assess climatic distributions of IWP, hence the need to also use longer datasets. We evaluate satellite datasets from CloudSat, PATMOS-x, ISCCP, MODIS and MSPPS in terms of monthly mean IWP, in order to determine the differences and relate them to the sensitivity of the instrument used in the retrievals. This information is also used to evaluate the climate models, to the extent that is possible. ISCCP and MSPPS were shown to have comparatively low IWP values. ISCCP shows particularly low values in the tropics, while MSPPS has particularly low values outside the tropics. MODIS and PATMOS-x were in closest agreement with CloudSat in terms of magnitude and spatial distribution, with MODIS being the better of the two. Additionally PATMOS-x and ISCCP, which have a temporal range long enough to capture the inter-annual variability of IWP, are used in conjunction with CloudSat IWP (after removing profiles that contain precipitation) to assess the IWP variability and mean of the climate models. In general there are large discrepancies between the individual climate models, and all of the models show problems in reproducing the observed spatial distribution of cloud-ice. Comparisons consistently showed that ECHAM-5 is probably the GCM from IPCC AR4 closest to satellite observations.

scholarly journals Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

2017 ◽  
Vol 10 (9) ◽  
pp. 3547-3573 ◽  
Author(s):  
Johan Strandgren ◽  
Luca Bugliaro ◽  
Frank Sehnke ◽  
Leon Schröder
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Optical Thickness ◽  
Cirrus Clouds ◽  
Percentage Error ◽  
Cirrus Cloud ◽  
Water Path ◽  
Ice Water Path ◽  
Ice Water

Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth–atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m−2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

Retrieval of cloud ice water path using SAPHIR on board Megha-Tropiques over the tropical ocean

2017 ◽  
Vol 59 (7) ◽  
pp. 1895-1906 ◽  
Author(s):  
Durgesh Nandan Piyush ◽  
Jayesh Goyal ◽  
J. Srinivasan
Keyword(s):  
Tropical Ocean ◽  
Water Path ◽  
Ice Water Path ◽  
Cloud Ice ◽  
Ice Water

scholarly journals A concept for a satellite mission to measure cloud ice water path, ice particle size, and cloud altitude

2007 ◽  
Vol 133 (S2) ◽  
pp. 109-128 ◽  
Author(s):  
S. A. Buehler ◽  
C. Jiménez ◽  
K. F. Evans ◽  
P. Eriksson ◽  
B. Rydberg ◽  
...  
Keyword(s):  
Particle Size ◽  
Satellite Mission ◽  
Water Path ◽  
Ice Water Path ◽  
Cloud Ice ◽  
Ice Water

scholarly journals The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON

2021 ◽  
Vol 21 (6) ◽  
pp. 4285-4318
Author(s):  
Harald Rybka ◽  
Ulrike Burkhardt ◽  
Martin Köhler ◽  
Ioanna Arka ◽  
Luca Bugliaro ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Sets ◽  
Motion Field ◽  
Moist Convection ◽  
Water Path ◽  
Ice Water Path ◽  
Large Eddy ◽  
Deep Moist Convection ◽  
Cloud Ice ◽  
Ice Water

Abstract. Current state-of-the-art regional numerical weather prediction (NWP) models employ kilometer-scale horizontal grid resolutions, thereby simulating convection within the grey zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the ICOsahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection and distinguish between scattered, large-scale dynamically forced, and frontal convection. We use different ground- and satellite-based observational data sets, which supply information on ice water content and path, ice cloud cover, and cloud-top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations. We find that the timing and geometric extent of the convectively generated cloud shield agree well with observations, while the lifetime of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates a cloud ice water path that lies between the different observational data sets, but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). Modifications of parameters within the microphysical scheme have little effect on the bias in the frozen water path and the longevity of the anvil. In particular, one of our convective days appeared to be very sensitive to the initial and boundary conditions, which had a large impact on the convective triggering but little impact on the high frozen water path and long anvil lifetime bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters. Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding the timing, geometrical structure, and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.

scholarly journals The behavior of high-CAPE summer convection in large-domain large-eddy simulations with ICON

2020 ◽  
Author(s):  
Harald Rybka ◽  
Ulrike Burkhardt ◽  
Martin Köhler ◽  
Ioanna Arka ◽  
Luca Bugliaro ◽  
...  
Keyword(s):  
Large Scale ◽  
Life Time ◽  
Data Sets ◽  
Motion Field ◽  
Moist Convection ◽  
Water Path ◽  
Ice Water Path ◽  
Deep Moist Convection ◽  
Cloud Ice ◽  
Ice Water

Abstract. Current state of the art regional numerical weather prediction (NWP) models employ kilometre scale horizontal grid resolutions thereby simulating convection within its grey-zone. Increasing resolution leads to resolving the 3D motion field and has been shown to improve the representation of clouds and precipitation. Using a hectometer-scale model in forecasting mode on a large domain therefore offers a chance to study processes that require the simulation of the 3D motion field at small horizontal scales, such as deep summertime moist convection, a notorious problem in NWP. We use the Icosahedral Nonhydrostatic weather and climate model in large-eddy simulation mode (ICON-LEM) to simulate deep moist convection distinguishing between scattered, large scale dynamically forced and frontal convection. We use different ground and satellite based observational data sets, that supply information on ice water content and path, ice cloud cover and cloud top height on a similar scale as the simulations, in order to evaluate and constrain our model simulations. We find that the timing and geometric extent of the convectively generated cloud shield agrees well with observations while the life time of the convective anvil was, at least in one case, significantly overestimated. Given the large uncertainties of individual ice water path observations, we use a suite of observations in order to better constrain the simulations. ICON-LEM simulates cloud ice water path that lies in-between the different observational data sets but simulations appear to be biased towards a large frozen water path (all frozen hydrometeors). The bias in frozen water path and the longevity of the anvil are little affected by modifications of parameters within the microphysical scheme. In particular one of our convective days appeared to be very sensitive to the initial and boundary conditions which had a large impact on the convective triggering, but little impact on the high frozen water path and long anvil life time bias. Based on this limited set of sensitivity experiments, the evolution of locally forced convection appears to depend more on the uncertainty of the large-scale dynamical state based on data assimilation than of microphysical parameters. Overall, we judge ICON-LEM simulations of deep moist convection to be very close to observations regarding timing, geometrical structure and cloud ice water path of the convective anvil, but other frozen hydrometeors, in particular graupel, are likely overestimated. Therefore, ICON-LEM supplies important information for weather forecasting and forms a good basis for parameterization development based on physical processes or machine learning.

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