Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection

An observed sea fog event over the Eastern Yellow Sea on 15–16 April 2012 was reproduced in the Weather Research and Forecasting (WRF) simulation with high-resolution to investigate the roles of physical processes and synoptic-scale flows on advection fog with phase transition. First, it was verified by a satellite-based fog detection algorithm and in situ observation data. In the simulation, longwave (infrared) radiative cooling (LRC) with a downward turbulent sensible heat flux (SHF), due to the turbulence after sunset, triggered cloud formation over the surface when warm-moist air advection occurred. At night, warm air advection with continuous cooling due to longwave radiation and SHF near the surface modulated the change of the SHF from downward to upward, resulting in a drastic increase in the turbulent latent heat flux (LHF) that provided sufficient moisture at the lower atmosphere (self-moistening). This condition represents a transition from cold-sea fog to warm-sea fog. Enhanced turbulent mixing driven by a buoyancy force increased the depth of the sea fog and the marine atmospheric boundary layer (MABL) height, even at nighttime. In addition, cold air advection with a prevailing northerly wind at the top of the MABL led to a drastic increase in turbulent mixing and the MABL height and rapid growth of the height of sea fog. After sunrise, shortwave radiative warming in the fog layers offsetting the LRC near the surface weakened thermal instability, which contributed to the reduction in the MABL height, even during the daytime. In addition, dry advection of the northerly wind induced dissipation of the fog via evaporation. An additional sensitivity test of sea surface salinity showed weaker and shallower sea fog than the control due to the decrease in both the LHF and local self-moistening. Detailed findings from the simulated fog event can help to provide better guidance for fog detection using remote sensing.

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Spatial and Temporal Characteristics of Sea Fog in Yellow Sea and Bohai Sea Based on Active and Passive Remote Sensing

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324028 ◽

2020 ◽

Author(s):

Jianhua Wan ◽

Jing Su ◽

Hui Sheng ◽

Shanwei Liu ◽

JiaJia Li

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Bohai Sea ◽

Temporal Characteristics ◽

Passive Remote Sensing ◽

Sea Fog ◽

Spatial And Temporal Characteristics

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Influence of local surface albedo variability and ice crystal shape on passive remote sensing of thin cirrus

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-1943-2014 ◽

2014 ◽

Vol 14 (4) ◽

pp. 1943-1958 ◽

Cited By ~ 12

Author(s):

C. Fricke ◽

A. Ehrlich ◽

E. Jäkel ◽

B. Bohn ◽

M. Wirth ◽

...

Keyword(s):

Optical Thickness ◽

Surface Albedo ◽

Near Infrared ◽

Effective Radius ◽

High Spectral Resolution ◽

Spectral Radiance ◽

Crystal Shape ◽

Frequency Distributions ◽

Airborne Measurements ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Airborne measurements of solar spectral radiance reflected by cirrus are performed with the HALO-Solar Radiation (HALO-SR) instrument onboard the High Altitude and Long Range Research Aircraft (HALO) in November 2010. The data are used to quantify the influence of surface albedo variability on the retrieval of cirrus optical thickness and crystal effective radius. The applied retrieval of cirrus optical properties is based on a standard two-wavelength approach utilizing measured and simulated reflected radiance in the visible and near-infrared spectral region. Frequency distributions of the surface albedos from Moderate resolution Imaging Spectroradiometer (MODIS) satellite observations are used to compile surface-albedo-dependent lookup tables of reflected radiance. For each assumed surface albedo the cirrus optical thickness and effective crystal radius are retrieved as a function of the assumed surface albedo. The results for the cirrus optical thickness are compared to measurements from the High Spectral Resolution Lidar (HSRL). The uncertainty in cirrus optical thickness due to local variability of surface albedo in the specific case study investigated here is below 0.1 and thus less than that caused by the measurement uncertainty of both instruments. It is concluded that for the retrieval of cirrus optical thickness the surface albedo variability is negligible. However, for the retrieval of crystal effective radius, the surface albedo variability is of major importance, introducing uncertainties up to 50%. Furthermore, the influence of the bidirectional reflectance distribution function (BRDF) on the retrieval of crystal effective radius was investigated and quantified with uncertainties below 10%, which ranges below the uncertainty caused by the surface albedo variability. The comparison with the independent lidar data allowed for investigation of the role of the crystal shape in the retrieval. It is found that if assuming aggregate ice crystals, the HSRL observations fit best with the retrieved optical thickness from HALO-SR.

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FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations

Atmospheric Measurement Techniques ◽

10.5194/amt-7-3873-2014 ◽

2014 ◽

Vol 7 (11) ◽

pp. 3873-3890 ◽

Cited By ~ 6

Author(s):

C. K. Carbajal Henken ◽

R. Lindstrot ◽

R. Preusker ◽

J. Fischer

Keyword(s):

Optical Thickness ◽

Single Layer ◽

Cloud Water ◽

Effective Radius ◽

Cloud Property ◽

Microphysical Properties ◽

Microphysical Property ◽

Resolution Imaging ◽

Cloud Optical Thickness ◽

Cloud Water Path

Abstract. A newly developed daytime cloud property retrieval algorithm, FAME-C (Freie Universität Berlin AATSR MERIS Cloud), is presented. Synergistic observations from the Advanced Along-Track Scanning Radiometer (AATSR) and the Medium Resolution Imaging Spectrometer (MERIS), both mounted on the polar-orbiting Environmental Satellite (Envisat), are used for cloud screening. For cloudy pixels two main steps are carried out in a sequential form. First, a cloud optical and microphysical property retrieval is performed using an AATSR near-infrared and visible channel. Cloud phase, cloud optical thickness, and effective radius are retrieved, and subsequently cloud water path is computed. Second, two cloud top height products are retrieved based on independent techniques. For cloud top temperature, measurements in the AATSR infrared channels are used, while for cloud top pressure, measurements in the MERIS oxygen-A absorption channel are used. Results from the cloud optical and microphysical property retrieval serve as input for the two cloud top height retrievals. Introduced here are the AATSR and MERIS forward models and auxiliary data needed in FAME-C. Also, the optimal estimation method, which provides uncertainty estimates of the retrieved property on a pixel basis, is presented. Within the frame of the European Space Agency (ESA) Climate Change Initiative (CCI) project, the first global cloud property retrievals have been conducted for the years 2007–2009. For this time period, verification efforts are presented, comparing, for four selected regions around the globe, FAME-C cloud optical and microphysical properties to cloud optical and microphysical properties derived from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. The results show a reasonable agreement between the cloud optical and microphysical property retrievals. Biases are generally smallest for marine stratocumulus clouds: −0.28, 0.41 μm and −0.18 g m−2 for cloud optical thickness, effective radius and cloud water path, respectively. This is also true for the root-mean-square deviation. Furthermore, both cloud top height products are compared to cloud top heights derived from ground-based cloud radars located at several Atmospheric Radiation Measurement (ARM) sites. FAME-C mostly shows an underestimation of cloud top heights when compared to radar observations. The lowest bias of −0.3 km is found for AATSR cloud top heights for single-layer clouds, while the highest bias of −3.0 km is found for AATSR cloud top heights for multilayer clouds. Variability is low for MERIS cloud top heights for low-level clouds, and high for MERIS cloud top heights for mid-level and high-level single-layer clouds, as well as for both AATSR and MERIS cloud top heights for multilayer clouds.

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Bias in MODIS cloud drop effective radius for oceanic water clouds as deduced from optical thickness variability across scattering angles

Journal of Geophysical Research Atmospheres ◽

10.1002/2015jd023256 ◽

2015 ◽

Vol 120 (15) ◽

pp. 7661-7681 ◽

Cited By ~ 17

Author(s):

Lusheng Liang ◽

Larry Di Girolamo ◽

Wenbo Sun

Keyword(s):

Optical Thickness ◽

Effective Radius ◽

Oceanic Water ◽

Cloud Drop ◽

Thickness Variability

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Quantifying the Dependence of Satellite Cloud Retrievals on Instrument Uncertainty

Journal of Climate ◽

10.1175/jcli-d-16-0429.1 ◽

2017 ◽

Vol 30 (17) ◽

pp. 6959-6976 ◽

Cited By ~ 6

Author(s):

Yolanda L. Shea ◽

Bruce A. Wielicki ◽

Sunny Sun-Mack ◽

Patrick Minnis

Keyword(s):

Climate Change ◽

Optical Thickness ◽

Indirect Effect ◽

Effective Temperature ◽

Radiative Forcing ◽

Cloud Fraction ◽

Effective Radius ◽

Aerosol Indirect Effect ◽

Sources Of Uncertainty ◽

Water Cloud

Cloud response to Earth’s changing climate is one of the largest sources of uncertainty among global climate model (GCM) projections. Two of the largest sources of uncertainty are the spread in equilibrium climate sensitivity (ECS) and uncertainty in radiative forcing due to uncertainty in the aerosol indirect effect. Satellite instruments with sufficient accuracy and on-orbit stability to detect climate change–scale trends in cloud properties will improve confidence in the understanding of the relationship between observed climate change and cloud property trends, thus providing information to better constrain ECS and radiative forcing. This study applies a climate change uncertainty framework to quantify the impact of measurement uncertainty on trend detection times for cloud fraction, effective temperature, optical thickness, and water cloud effective radius. Although GCMs generally agree that the total cloud feedback is positive, disagreement remains on its magnitude. With the climate uncertainty framework, it is demonstrated how stringent measurement uncertainty requirements for reflected solar and infrared satellite measurements enable improved constraint of SW and LW cloud feedbacks and the ECS by significantly reducing trend uncertainties for cloud fraction, optical thickness, and effective temperature. The authors also demonstrate improved constraint on uncertainty in the aerosol indirect effect by reducing water cloud effective radius trend uncertainty.

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Interannual variability in summertime sea fog over the northern Yellow Sea and its association with the local sea surface temperature

Journal of Geophysical Research Atmospheres ◽

10.1029/2020jd034439 ◽

2021 ◽

Author(s):

Jing‐Wu Liu ◽

Yue Sun ◽

Liu Yang

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Interannual Variability ◽

Yellow Sea ◽

Sea Surface ◽

Sea Fog ◽

Northern Yellow Sea

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Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers

Atmospheric Measurement Techniques ◽

10.5194/amt-12-2863-2019 ◽

2019 ◽

Vol 12 (5) ◽

pp. 2863-2879 ◽

Cited By ~ 3

Author(s):

Nikos Benas ◽

Jan Fokke Meirink ◽

Martin Stengel ◽

Piet Stammes

Keyword(s):

Size Distribution ◽

Optical Thickness ◽

Droplet Size Distribution ◽

Effective Radius ◽

High Temporal Resolution ◽

Climate Data ◽

Cloud Properties ◽

Data Record ◽

Stratocumulus Clouds ◽

Cloud Optical Thickness

Abstract. Retrievals of cloud properties from geostationary satellite sensors offer extensive spatial and temporal coverage and resolution. The high temporal resolution allows the observation of diurnally resolved cloud properties. However, retrievals are sensitive to varying illumination and viewing geometries, including cloud glory and cloud bow conditions, which can lead to irregularities in the diurnal data record. In this study, these conditions and their effects on liquid cloud optical thickness and effective radius retrievals are analyzed using the Cloud Physical Properties (CPP) algorithm. This analysis is based on the use of Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectances and products from Meteosat-8 and Meteosat-10, which are located over the Indian and Atlantic Ocean, respectively, and cover an extensive common area under different viewing angles. Comparisons of the retrievals from two full days, over ocean and land, and using different spectral combinations of visible and shortwave-infrared channels, are performed, to assess the importance of these factors in the retrieval process. The sensitivity of the cloud-bow- and cloud-glory-related irregularities to the width of the assumed droplet size distribution is analyzed by using different values of the effective variance of the size distribution. The results suggest for marine stratocumulus clouds an effective variance of around 0.05, which implies a narrower size distribution than typically assumed in satellite-based retrievals. For the case with continental clouds a broader size distribution (effective variance around 0.15) is obtained. This highlights the importance of appropriate size distribution assumptions and provides a way to improve the quality of cloud products in future climate data record releases.

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