scholarly journals Passive Remote Sensing of Ice Cloud Properties at Terahertz Wavelengths Based on Genetic Algorithm

2021 ◽  
Vol 13 (4) ◽  
pp. 735
Author(s):  
Lei Liu ◽  
Chensi Weng ◽  
Shulei Li ◽  
Letu Husi ◽  
Shuai Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Brightness Temperature ◽  
Absolute Error ◽  
Ice Clouds ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
The Absolute ◽  
Ice Water

Ice clouds play a critical role in the balance of the earth–atmosphere radiation system, but there are some limitations in the existing remote sensing methods for ice clouds. Terahertz wave is expected to be the best waveband for retrieving ice clouds, with terahertz wavelengths in the order of the size of typical ice cloud particles. An inversion method for the remote sensing of ice clouds at terahertz wavelengths based on genetic algorithm is proposed in this paper. First, suitable channel sets in the terahertz band, which are mainly a combination of absorption lines and window regions, are determined. Then, to improve the efficiency of the generation of the retrieval database, based on the brightness temperature simulated by the atmospheric radiative transfer simulator (ARTS) for different cloud parameters, a fast forward operator is constructed using three-dimensional interpolation to simulate the brightness temperature difference between clear sky and a cloudy scene. Finally, an inversion model to retrieve the ice cloud base height, the effective particle diameter and the ice water path is established based on the genetic algorithm, and an analysis of the inversion errors is performed. The results show that the forward operator, constructed by the nearest interpolation, can accurately calculate the brightness temperature difference at a high speed. The proposed inversion method at terahertz wavelengths based on the genetic algorithm can achieve the expected scientific requirement. The absolute error of the cloud height is around 0.2 km, and the absolute error of the low ice water path (below 20 g/m2) is small, while the relative error of the high ice water path is generally maintained at around 10%, and the absolute error of the effective particle diameter is mostly around 4 μm.

scholarly journals Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG

2020 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Lucie Leonarski ◽  
Laurent C.-Labonnote ◽  
Mathieu Compiègne ◽  
Jérôme Vidot ◽  
Anthony J. Baran ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Signal To Noise Ratio ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water

The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (IWP) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose IWP is between about 1 and 300 g/m2).

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 Large-Scale Analysis of Relationships between Mineral Dust, Ice Cloud Properties, and Precipitation from Satellite Observations Using a Bayesian Approach: Theoretical Basis and First Results for the Tropical Atlantic Ocean

2017 ◽  
Vol 2017 ◽  
pp. 1-18
Author(s):  
Lars Klüser ◽  
Thomas Popp
Keyword(s):  
Atlantic Ocean ◽  
Bayesian Approach ◽  
Mineral Dust ◽  
Tropical Atlantic ◽  
Ice Crystal ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water ◽  
Dust Load

Mineral dust and ice cloud observations from the Infrared Atmospheric Sounding Interferometer (IASI) are used to assess the relationships between desert dust aerosols and ice clouds over the tropical Atlantic Ocean during the hurricane season 2008. Cloud property histograms are first adjusted for varying cloud top temperature or ice water path distributions with a Bayesian approach to account for meteorological constraints on the cloud variables. Then, histogram differences between dust load classes are used to describe the impact of dust load on cloud property statistics. The analysis of the histogram differences shows that ice crystal sizes are reduced with increasing aerosol load and ice cloud optical depth and ice water path are increased. The distributions of all three variables broaden and get less skewed in dusty environments. For ice crystal size the significant bimodality is reduced and the order of peaks is reversed. Moreover, it is shown that not only are distributions of ice cloud variables simply shifted linearly but also variance, skewness, and complexity of the cloud variable distributions are significantly affected. This implies that the whole cloud variable distributions have to be considered for indirect aerosol effects in any application for climate modelling.

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 Ice water content vertical profiles of high-level clouds: classification and impact on radiative fluxes

2015 ◽  
Vol 15 (12) ◽  
pp. 16325-16369 ◽  
Author(s):  
A. G. Feofilov ◽  
C. J. Stubenrauch ◽  
J. Delanoë
Keyword(s):  
Water Content ◽  
Cloud Base ◽  
Vertical Profiles ◽  
Ice Clouds ◽  
Water Path ◽  
Ice Water Path ◽  
Isosceles Trapezoid ◽  
Ice Water Content ◽  
High Level ◽  
Ice Water

Abstract. In this work, we discuss the shape of ice water content (IWC) vertical profiles in high ice clouds and its effect on radiative properties of these clouds, both in short- and in long-wave bands (SW and LW). Based on the analysis of colocated satellite data, we suggest a minimal set of primitive shapes (rectangular, isosceles trapezoid, lower and upper triangle), which sufficiently well represents the IWC profiles. About 75% of all high-level ice clouds (P < 440 hPa) have an ice water path smaller than 100 g m−2, with a 10% smaller contribution from single layer clouds. Most IWC profiles (80%) can be represented by a rectangular or isosceles trapezoid shape. However, with increasing IWP, the number of lower triangle profiles (IWC rises towards cloud base) increases, reaching up to 40% for IWP values greater than 300 g m−2. The number of upper triangle profiles (IWC rises towards cloud top) is in general small and decreasing with IWP, with the maximum occurrence of 15% in cases of IWP less than 10 g m−2. We propose a statistical classification of the IWC shapes using ice water path (IWP) as a single parameter. We have estimated the radiative effects of clouds with the same IWP and with different IWC profile shapes for five typical atmospheric scenarios and over a broad range of IWP, cloud height, cloud vertical extent, and effective ice crystal diameter (De). We explain changes in outgoing LW fluxes at the top of the atmosphere (TOA) by the cloud thermal radiance while differences in TOA SW fluxes relate to the De vertical profile within the cloud. Absolute differences in net TOA and surface fluxes associated with these parameterized IWC profiles instead of assuming constant IWC profiles are in general of the order of 1–2 W m−2: they are negligible for clouds with IWP < 30 g m−2, but may reach 2 W m−2 for clouds with IWP >300 W m−2.

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

2017 ◽  
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 implemented 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 as well as vegetated surfaces, whereas a surface covered by permanent snow &amp; 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. 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. Only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 gm−2, a liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the ice optical thickness and ice water path respectively. 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 physical implementation, 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.

Remote Sensing of Cloud Ice Water Path from SAPHIR Microwave Sounder Onboard Megha- Tropiques

2019 ◽  
Vol 55 (2) ◽  
pp. 135-144
Author(s):  
Durgesh Nandan Piyush ◽  
J Satapathy ◽  
J. Srinivasan
Keyword(s):  
Remote Sensing ◽  
Water Path ◽  
Ice Water Path ◽  
Cloud Ice ◽  
Ice Water
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