Tropical ice cloud optical depth, ice water path, and frequency fields inferred from the MODIS level-3 data

2007 ◽  
Vol 85 (2) ◽  
pp. 171-182 ◽  
Author(s):  
Kerry Meyer ◽  
Ping Yang ◽  
Bo-Cai Gao
Keyword(s):  
Optical Depth ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Level 3 ◽  
Ice Water

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

2014 ◽  
Vol 7 (6) ◽  
pp. 1873-1890 ◽  
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 in determining cloud radiative and thermodynamical properties in 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 the Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the empirical 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 the 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 the normalized probability density function (PDF). Compared to the empirical model, current operational radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

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.

Variational Assimilation of Cloud Liquid/Ice Water Path and Its Impact on NWP

2015 ◽  
Vol 54 (8) ◽  
pp. 1809-1825 ◽  
Author(s):  
Yaodeng Chen ◽  
Hongli Wang ◽  
Jinzhong Min ◽  
Xiang-Yu Huang ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Liquid Water ◽  
Weather Prediction ◽  
Wrf Model ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Water Path ◽  
Ice Water Path ◽  
Ice Water ◽  
The Impact

AbstractAnalysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

scholarly journals The Impacts of Atmospheric and Surface Parameters on Long-Term Variations in the Planetary Albedo

2018 ◽  
Vol 31 (21) ◽  
pp. 8705-8718 ◽  
Author(s):  
Bida Jian ◽  
Jiming Li ◽  
Guoyin Wang ◽  
Yongli He ◽  
Ying Han ◽  
...  
Keyword(s):  
Liquid Water ◽  
Surface Albedo ◽  
Cloud Fraction ◽  
Liquid Water Path ◽  
Water Path ◽  
Ice Water Path ◽  
Planetary Albedo ◽  
Ice Water ◽  
Long Term Variations

Planetary albedo (PA; shortwave broadband albedo) and its long-term variations, which are controlled in a complex way by various atmospheric and surface properties, play a key role in controlling the global and regional energy budget. This study investigates the contributions of different atmospheric and surface properties to the long-term variations of PA based on 13 years (2003–15) of albedo, cloud, and ice coverage datasets from the Clouds and the Earth’s Radiant Energy System (CERES) Single Scanner Footprint edition 4A product, vegetation product from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface albedo product from the Cloud, Albedo, and Radiation dataset, version 2 (CLARA-A2). According to the temporal correlation analysis, statistical results indicate that variations in PA are closely related to the variations of cloud properties (e.g., cloud fraction, ice water path, and liquid water path) and surface parameters (e.g., ice/snow percent coverage and normalized difference vegetation index), but their temporal relationships vary among the different regions. Generally, the stepwise multiple linear regression models can capture the observed PA anomalies for most regions. Based on the contribution calculation, cloud fraction dominates the variability of PA in the mid- and low latitudes while ice/snow percent coverage (or surface albedo) dominates the variability in the mid- and high latitudes. Changes in cloud liquid water path and ice water path are the secondary dominant factor over most regions, whereas change in vegetation cover is the least important factor over land. These results verify the effects of atmospheric and surface factors on planetary albedo changes and thus may be of benefit for improving the parameterization of the PA and determining the climate feedbacks.

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