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

2015 ◽  
Vol 120 (15) ◽  
pp. 7661-7681 ◽  
Author(s):  
Lusheng Liang ◽  
Larry Di Girolamo ◽  
Wenbo Sun
Keyword(s):  
Optical Thickness ◽  
Effective Radius ◽  
Oceanic Water ◽  
Cloud Drop ◽  
Thickness Variability

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

2014 ◽  
Vol 14 (4) ◽  
pp. 1943-1958 ◽  
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.

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

2014 ◽  
Vol 7 (11) ◽  
pp. 3873-3890 ◽  
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.

scholarly journals Quantifying the Dependence of Satellite Cloud Retrievals on Instrument Uncertainty

2017 ◽  
Vol 30 (17) ◽  
pp. 6959-6976 ◽  
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.

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

2019 ◽  
Vol 12 (5) ◽  
pp. 2863-2879 ◽  
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.

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

2015 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Li Yi ◽  
Boris Thies ◽  
Suping Zhang ◽  
Xiaomeng Shi ◽  
Jörg Bendix
Keyword(s):  
Optical Thickness ◽  
Yellow Sea ◽  
Effective Radius ◽  
Sea Fog ◽  
Fog Detection

scholarly journals Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Vol 10 (12) ◽  
pp. 4747-4759 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optical Remote Sensing ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multi-pixel radiances. The first DNN method corrects traditional bispectral retrievals based on the plane-parallel homogeneous cloud assumption using the reflectances at the same two wavelengths. The other DNN method uses so-called convolutional layers and retrieves cloud properties directly from the reflectances at four wavelengths. The DNN methods are trained and tested on cloud fields from large-eddy simulations used as input to a 3-D radiative-transfer model to simulate upward radiances. The second DNN-based retrieval, sidestepping the bispectral retrieval step through convolutional layers, is shown to be more accurate. It reduces 3-D radiative-transfer effects that would otherwise affect the radiance values and estimates cloud properties robustly even for optically thick clouds.

scholarly journals Influence of ice crystal shape on retrieval of cirrus optical thickness and effective radius: A case study

2009 ◽  
Vol 114 (D19) ◽  
Author(s):  
H. Eichler ◽  
A. Ehrlich ◽  
M. Wendisch ◽  
G. Mioche ◽  
J.-F. Gayet ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Effective Radius ◽  
Crystal Shape ◽  
Ice Crystal

scholarly journals Testing remote sensing on artificial observations: impact of drizzle and 3-D cloud structure on effective radius retrievals

2010 ◽  
Vol 10 (19) ◽  
pp. 9535-9549 ◽  
Author(s):  
T. Zinner ◽  
G. Wind ◽  
S. Platnick ◽  
A. S. Ackerman
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Radiative Transfer ◽  
Droplet Size ◽  
Optical Thickness ◽  
Marine Boundary Layer ◽  
Effective Radius ◽  
Retrieval Algorithm ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Abstract. Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation. For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES) with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics. We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1) a typical daytime stratocumulus deck at two times in the diurnal cycle and (2) one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three effective radius retrievals is noticed for both cloud scene types for different reasons. For our, presumably typical, overcast stratocumulus scenes with an optical thickness of 8 to 9 and rain rates at cloud bottom up to 0.05 mm/h clear drizzle impact on the retrievals can be excluded. The cumulus scene does not show much drizzle sensitivity either despite extended drizzle areas being directly visible from above (locally >1 mm/h), which is mainly due to technical characteristics of the standard retrieval approach. 3-D effects, on the other hand, produce large discrepancies between the 1.6 and 2.1 μm channel observations compared to 3.7 μm retrievals in the latter case. A general sensitivity of MODIS particle size data to drizzle formation is not corroborated by our case studies.

Sign in / Sign up

Export Citation Format

Share Document