scholarly journals Implementation of the Daytime Cloud Optical and Microphysical Properties Algorithm (DCOMP) in PATMOS-x

2012 ◽  
Vol 51 (7) ◽  
pp. 1371-1390 ◽  
Author(s):  
Andi Walther ◽  
Andrew K. Heidinger
Keyword(s):  
Weather Prediction ◽  
Atmospheric Correction ◽  
Optimal Estimation ◽  
Reanalysis Data ◽  
Model Parameters ◽  
Cloud Optical Depth ◽  
Microphysical Properties ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Set Up

AbstractThis paper describes the daytime cloud optical and microphysical properties (DCOMP) retrieval for the Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset. Within PATMOS-x, DCOMP is applied to observations from the Advanced Very High Resolution Radiometer and employs the standard bispectral approach to estimate cloud optical depth and particle size. The retrievals are performed within the optimal estimation framework. Atmospheric-correction and forward-model parameters, such as surface albedo and gaseous absorber amounts, are obtained from numerical weather prediction reanalysis data and other climate datasets. DCOMP is set up to run on sensors with similar channel settings and has been successfully exercised on most current meteorological imagers. This quality makes DCOMP particularly valuable for climate research. Comparisons with the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 dataset are used to estimate the performance of DCOMP.

scholarly journals Radiative and Microphysical Properties of Cirrus Cloud Inferred from Infrared Measurements Made by the Moderate Resolution Imaging Spectroradiometer (MODIS). Part I: Retrieval Method

2014 ◽  
Vol 53 (5) ◽  
pp. 1297-1316 ◽  
Author(s):  
Hironobu Iwabuchi ◽  
Soichiro Yamada ◽  
Shuichiro Katagiri ◽  
Ping Yang ◽  
Hajime Okamoto
Keyword(s):  
Brightness Temperature ◽  
Optical Thickness ◽  
Optimal Estimation ◽  
Particle Radius ◽  
Cirrus Cloud ◽  
Microphysical Properties ◽  
Effective Particle ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractAn optimal estimation–based algorithm is developed to infer the global-scale distribution of cirrus cloud radiative and microphysical properties from the measurements made by the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) at three infrared (IR) window bands centered at 8.5, 11, and 12 μm. Cloud-top and underlying surface temperatures, as a priori information, are obtained from the MODIS operational products. A fast-forward model based on semianalytical equations for the brightness temperature is used. The modeling errors in brightness temperature are mainly from the uncertainties in model parameters including surface emissivity, precipitable water, and cloud-base temperature. The total measurement–model errors are well correlated for the three bands, which are considered in the retrieval. The most important factors for the accurate retrieval of cloud optical thickness and the effective particle radius are cloud-top and surface temperatures, whereas model parameter uncertainties constitute a moderately significant error source. The three-band IR method is suitable for retrieving optical thickness and effective radius for cloud optical thicknesses within a range of 0.5–6, where the typical root-mean-square error is less than 20% in optical thickness and less than 40% in effective particle radius. A tropical-region case study demonstrates the advantages of the method—in particular, the ability to be applied to more pixels in optically thin cirrus in comparison with a solar-reflection-based method—and the ability of the optimal estimation framework to produce useful diagnostics of the retrieval quality. Collocated comparisons with spaceborne active remote sensing data exhibit reasonable consistency with respect to retrieved particle size.

scholarly journals Estimating marine aerosol particle volume and number from Maritime Aerosol Network data

2012 ◽  
Vol 12 (6) ◽  
pp. 14959-15007
Author(s):  
A. M. Sayer ◽  
A. Smirnov ◽  
N. C. Hsu ◽  
L. A. Munchak ◽  
B. N. Holben
Keyword(s):  
Transport Model ◽  
Surface Concentration ◽  
Open Ocean ◽  
Similar Data ◽  
Particle Volume ◽  
Aerosol Composition ◽  
Microphysical Properties ◽  
Present Collection ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. As well as spectral aerosol optical depth (AOD), aerosol composition and concentration (number, volume, or mass) are of interest for a variety of applications. However, remote sensing of these quantities is more difficult than for AOD, as it is more sensitive to assumptions relating to aerosol composition. This study uses spectral AOD measured on Maritime Aerosol Network (MAN) cruises, with the additional constraint of a microphysical model for unpolluted maritime aerosol based on analysis of Aerosol Robotic Network (AERONET) inversions, to estimate these quantities over open ocean. When the MAN data are subset to those likely to be comprised of maritime aerosol, number and volume concentrations obtained are physically reasonable. Attempts to estimate surface concentration from columnar abundance, however, are shown to be limited by uncertainties in vertical distribution. Columnar AOD at 550 nm and aerosol number for unpolluted maritime cases are also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) data, for both the present Collection 5.1 and forthcoming Collection 6. MODIS provides a best-fitting retrieval solution, as well as the average for several different solutions, with different aerosol microphysical models. The "average solution" MODIS dataset agrees more closely with MAN than the "best solution" dataset. Terra tends to retrieve lower aerosol number than MAN, and Aqua higher, linked with differences in the aerosol models commonly chosen. Collection 6 AOD is likely to agree more closely with MAN over open ocean than Collection 5.1. In situations where spectral AOD is measured accurately, and aerosol microphysical properties are reasonably well-constrained, estimates of aerosol number and volume using MAN or similar data would provide for a greater variety of potential comparisons with aerosol properties derived from satellite or chemistry transport model data.

scholarly journals Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2771 ◽  
Author(s):  
Lu She ◽  
Hankui Zhang ◽  
Weile Wang ◽  
Yujie Wang ◽  
Yun Shi
Keyword(s):  
Mean Squared Error ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Japan Meteorological Agency ◽  
Absolute Bias ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated ◽  
New Generation

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

scholarly journals SST Comparison of AVHRR and MODIS Time Series in the Western Mediterranean Sea

2020 ◽  
Vol 12 (14) ◽  
pp. 2241
Author(s):  
María José López García
Keyword(s):  
Time Series ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
Western Mediterranean ◽  
Western Mediterranean Sea ◽  
Temporal And Spatial Variability ◽  
Moderate Resolution ◽  
Long Term Trends ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Development And Validation

Sea Surface Temperature (SST) is a key parameter for understanding atmospheric and oceanic processes. Since the late 1980s, infrared satellite images have been used to complement in situ records for studying the temporal and spatial variability of SST. The Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)’s satellite was the first sensor successfully used to compute SST following the development and validation of the atmospheric correction algorithm known as “split-window”. More recently, the MODerate-resolution Imaging Spectroradiometer (MODIS) on board the National Aeronautics and Space Administration (NASA)’s Terra and Aqua satellites, launched in 1999 and 2002, respectively, also provides SST products which can be combined with AVHRR series to complete the analysis of time series. This paper presents a comparison of the monthly SST data derived from both sensors, AVHRR and MODIS, in a series of ten years (2000–2009) in the Western Mediterranean basins. The results showed a high correlation (R2 = 0.99) between the sensors when averaged values at the regional scale were compared. SST obtained from AVHRR were slightly higher (+0.18 °C ± 0.2 °C, on average) than SST from MODIS. The series were most similar during winter and spring (+0.09 °C ± 0.1 °C for January to May) with a greater difference from June to December (+0.24 °C ± 0.2 °C). The comparative analysis showed that the two sensors can be used jointly to estimate long-term trends at the regional scale.

scholarly journals CORRECTION FOR THE CIRRUS CLOUD TOP HEIGHT OF MODIS BASED ON CALIPSO IN BEIJING-TIANJIN-HEBEI REGION

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 203-210
Author(s):  
B. Y. Yang ◽  
J. Liu ◽  
X. Jia
Keyword(s):  
Remote Sensing ◽  
Satellite Observation ◽  
Orthogonal Polarization ◽  
Cirrus Cloud ◽  
Weather System ◽  
Cloud Optical Depth ◽  
Lower Cloud ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Abstract. Cirrus plays an important role in atmospheric radiation. It affects weather system and climate change. Satellite remote sensing is an important kind of observation for cloud. As a passive remote sensing instrument, large bias was found for thin cirrus cloud top height retrieval from MODIS (Moderate Resolution Imaging Spectroradiometer). Comparatively, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) aboard CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) which is an active remote sensing instrument can acquire more accurate characteristics of thin cirrus cloud. In this study, CALIPSO cirrus cloud top height data was used to correct MODIS cirrus cloud top height. The data analysis area was selected in Beijing-Tianjin-Hebei region and data came from 2013 to 2017. Linear fitting method was selected based on cross-validation method between MODIS and CALIPSO data. The results shows that the difference between MODIS and CALIPSO changes from −3~2 km to −2.0~2.5 km, and the maximum difference changes from about −0.8 km to about 0.2 km. In the context of different vertical levels and cloud optical depth, MODIS cirrus cloud top height is improved after correcting, which is more obvious at lower cloud top height and optical thinner cirrus.

scholarly journals UV Reflectance of the Ocean from DSCOVR/EPIC: Comparisons with a Theoretical Model and Aura/OMI Observations

2019 ◽  
Vol 36 (11) ◽  
pp. 2087-2099 ◽  
Author(s):  
Alexander Vasilkov ◽  
Alexei Lyapustin ◽  
B. Greg Mitchell ◽  
Dong Huang
Keyword(s):  
Radiative Transfer ◽  
Chlorophyll Concentration ◽  
Atmospheric Correction ◽  
Ocean Water ◽  
Cloud Detection ◽  
Ozone Monitoring Instrument ◽  
Inherent Optical Properties ◽  
Uv Reflectance ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractUltraviolet (UV) data collected over the ocean by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) are used. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm adapted for EPIC processing performs cloud detection, aerosol retrievals, and atmospheric correction providing the water-leaving reflectance of the ocean at 340 and 388 nm. The water-leaving reflectance is an indicator of the presence of absorbing and scattering constituents in seawater. The retrieved water-leaving reflectance is compared with full radiative transfer calculations based on a model of inherent optical properties (IOP) of ocean water in UV. The model is verified with data collected on the Aerosol Characterization Experiments (ACE) Asia cruise supported by the NASA Sensor Intercomparison for Marine Biological and Interdisciplinary Ocean Studies (SIMBIOS) project. The model assumes that the ocean water IOPs are parameterized through a chlorophyll concentration. The radiative transfer simulations were carried out using the climatological chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. The EPIC-derived water-leaving reflectance is also compared with climatological Lambertian-equivalent reflectivity (LER) of the ocean derived from measurements of the Ozone Monitoring Instrument (OMI) on board the NASA polar-orbiting Aura satellite. The EPIC reflectance agrees well (within 0.01) with the model reflectance except for oligotrophic oceanic areas. For those areas, the model reflectance is biased low by about 0.01 at 340 nm and up to 0.03 at 388 nm. The OMI-derived climatological LER is significantly higher than the EPIC water-leaving reflectance, largely due to the surface glint contribution. The globally averaged difference is about 0.04.

scholarly journals Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

2016 ◽  
Author(s):  
G. Wind ◽  
A. M. da Silva ◽  
P. M. Norris ◽  
S. Platnick ◽  
S. Mattoo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Weather Prediction ◽  
Model Parameters ◽  
Model Output ◽  
Aerosol Retrieval ◽  
Numerical Experimentation ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sensor Cloud ◽  
Aerosol Module ◽  
Aerosol Properties

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces synthetic radiance data from model output as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing an atmospheric column. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows user to insert a mixture of up to 15 different aerosol species in any of the 36 available simulated layers. The MCRS code is currently known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms; but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, thus potentially improving model skill. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of impacts of various atmospheric components can be conducted in a controlled fashion. The aerosol properties used in MCARS are directly ingested from GEOS-5 model output. They are prepared using the same model subgrid variability management methods as are used for cloud and atmospheric properties profiles, namely the Independent Column Approximation (ICA) technique. After MCRS computes sensor radiances equivalent to their observed counterparts, these radiances are substituted into an operational remote sensing algorithm. Specifically, the MCRS computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04) that would normally be produced from actual sensor output. We show direct application of this synthetic product in analysis of performance of the MOD04 operational algorithm. We use biomass burning case studies employed in a recent Working Group on Numerical Experimentation (WGNE) -sponsored study of aerosol impacts on Numerical Weather Prediction (Freitas et al. 2016). We show that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

scholarly journals A Global MODIS Water Vapor Database for the Operational Atmospheric Correction of Historic and Recent Landsat Imagery

2019 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
David Frantz ◽  
Marion Stellmes ◽  
Patrick Hostert
Keyword(s):  
Water Vapor ◽  
Landsat Imagery ◽  
Atmospheric Correction ◽  
Atmospheric Conditions ◽  
Wet Season ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Daily Water ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Analysis Ready Data (ARD) have undergone the most relevant pre-processing steps to satisfy most user demands. The freely available software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring) is capable of generating Landsat ARD. An essential step of generating ARD is atmospheric correction, which requires water vapor data. FORCE relies on a water vapor database obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). However, two major drawbacks arise from this strategy: (1) The database has to be compiled for each study area prior to generating ARD; and (2) MODIS and Landsat commissioning dates are not well aligned. We have therefore compiled an application-ready global water vapor database to significantly increase the operational readiness of ARD production. The free dataset comprises daily water vapor data for February 2000 to July 2018 as well as a monthly climatology that is used if no daily value is available. We systematically assessed the impact of using this climatology on surface reflectance outputs. A global random sample of Landsat 5/7/8 imagery was processed twice (i) using daily water vapor (reference) and (ii) using the climatology (estimate), followed by computing accuracy, precision, and uncertainty (APU) metrics. All APU measures were well below specification, thus the fallback usage of the climatology is generally a sound strategy. Still, the tests revealed that some considerations need to be taken into account to help quantify which sensor, band, climate, and season are most or least affected by using a fallback climatology. The highest uncertainty and bias is found for Landsat 5, with progressive improvements towards newer sensors. The bias increases from dry to humid climates, whereas uncertainty increases from dry and tropic to temperate climates. Uncertainty is smallest during seasons with low variability, and is highest when atmospheric conditions progress from a dry to a wet season (and vice versa).

Retrieval of Cirrus Microphysical Properties with a Suite of Algorithms for Airborne and Spaceborne Lidar, Radar, and Radiometer Data

2006 ◽  
Vol 45 (12) ◽  
pp. 1665-1689 ◽  
Author(s):  
Yuying Zhang ◽  
Gerald G. Mace
Keyword(s):  
Satellite Observation ◽  
Sensor Data ◽  
Regional Study ◽  
Remote Sensors ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Atmospheric Radiation Measurement ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Water

Abstract Algorithms are developed to convert data streams from multiple airborne and spaceborne remote sensors into layer-averaged cirrus bulk microphysical properties. Radiometers such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) observe narrowband spectral radiances, and active remote sensors such as the lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the millimeter radar on CloudSat will provide vertical profiles of attenuated optical backscatter and radar reflectivity. Equivalent airborne remote sensors are also routinely flown on the NASA WB-57F and ER-2 aircraft. Algorithms designed to retrieve cirrus microphysical properties from remote sensor data must be able to handle the natural variability of cirrus that can range from optically thick layers that cause lidar attenuation to tenuous layers that are not detected by the cloud radar. An approach that is adopted here is to develop an algorithm suite that has internal consistency in its formulation and assumptions. The algorithm suite is developed around a forward model of the observations and is inverted for layer-mean cloud properties using a variational technique. The theoretical uncertainty in the retrieved ice water path retrieval is 40%–50%, and the uncertainty in the layer-mean particle size retrieval ranges from 50% to 90%. Two case studies from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) field campaign as well as ground-based cases from the Atmospheric Radiation Measurement Program (ARM) are used to show the efficacy and error characteristics of the algorithms.

Sign in / Sign up

Export Citation Format

Share Document