Evaluation of ERA-Interim and MERRA Cloudiness in the Southern Ocean

2014 ◽  
Vol 27 (5) ◽  
pp. 2109-2124 ◽  
Author(s):  
Catherine M. Naud ◽  
James F. Booth ◽  
Anthony D. Del Genio
Keyword(s):  
Southern Ocean ◽  
Cloud Cover ◽  
Warm Season ◽  
Cloud Amount ◽  
Thermodynamic Characteristics ◽  
Cumulus Clouds ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Modern Era

Abstract The Southern Ocean cloud cover modeled by the Interim ECMWF Re-Analysis (ERA-Interim) and Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalyses are compared against Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) observations. ERA-Interim monthly mean cloud amounts match the observations within 5%, while MERRA significantly underestimates the cloud amount. For a compositing analysis of clouds in warm season extratropical cyclones, both reanalyses show a low bias in cloud cover. They display a larger bias to the west of the cyclones in the region of subsidence behind the cold fronts. This low bias is larger for MERRA than for ERA-Interim. Both MODIS and MISR retrievals indicate that the clouds in this sector are at a low altitude, often composed of liquid, and of a broken nature. The combined CloudSat–Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud profiles confirm these passive observations, but they also reveal that low-level clouds in other parts of the cyclones are also not properly represented in the reanalyses. The two reanalyses are in fairly good agreement for the dynamic and thermodynamic characteristics of the cyclones, suggesting that the cloud, convection, or boundary layer schemes are the problem instead. An examination of the lower-tropospheric stability distribution in the cyclones from both reanalyses suggests that the parameterization of shallow cumulus clouds may contribute in a large part to the problem. However, the differences in the cloud schemes and in particular in the precipitation processes, which may also contribute, cannot be excluded.

scholarly journals New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hirofumi Hashimoto ◽  
Weile Wang ◽  
Jennifer L. Dungan ◽  
Shuang Li ◽  
Andrew R. Michaelis ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Seasonal Patterns ◽  
Optical Remote Sensing ◽  
Amazon Forest ◽  
Evergreen Forests ◽  
Geostationary Satellites ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation

AbstractAssessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

scholarly journals Estimation of Daily Sunshine Duration from Terra and Aqua MODIS Data

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
H. M. Kandirmaz ◽  
K. Kaba
Keyword(s):  
Cloud Cover ◽  
Sunshine Duration ◽  
Quadratic Model ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Daily Sunshine ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Relative Sunshine ◽  
Relative Sunshine Duration ◽  
Better Than

Some studies have shown that the estimation of global sunshine duration can be done with the help of geostationary satellites because they can record several images of the same location in a day. In this paper, images obtained from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensors of polar orbiting satellites Aqua and Terra were used to estimate daily global sunshine duration for any region in Turkey. A new quadratic correlation between daily mean cloud cover index and relative sunshine duration was also introduced and compared with the linear correlation. Results have shown that polar orbiting satellites can be used for the estimation of sunshine duration. The quadratic model introduced here works better than the linear model especially for the winter months in which very low sunshine duration values were recorded at the ground stations for many days.

scholarly journals Satellite Observations of Cloud-Related Variations in Aerosol Properties

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 430 ◽  
Author(s):  
Tamás Várnai ◽  
Alexander Marshak
Keyword(s):  
Particle Size ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Cloud Amount ◽  
Orthogonal Polarization ◽  
Radiative Processes ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Aerosol Properties

This paper presents an overview of our efforts to characterize and better understand cloud-related changes in aerosol properties. These efforts primarily involved the statistical analysis of global or regional datasets of Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol and cloud observations. The results show that in oceanic regions, more than half of all aerosol measurements by passive satellite instruments come from near-cloud areas, where clouds and cloud-related processes may significantly modify aerosol optical depth and particle size. Aerosol optical depth is also shown to increase systematically with regional cloud amount throughout the Earth. In contrast, it is shown that effective particle size can either increase or decrease with increasing cloud cover. In bimodal aerosol populations, the sign of changes depends on whether coarse mode or small mode aerosols are most affected by clouds. The results also indicate that over large parts of Earth, undetected cloud particles are not the dominant reason for the satellite-observed changes with cloud amount, and that 3D radiative processes contribute about 30% of the observed near-cloud changes. The findings underline the need for improving our ability to accurately measure aerosols near clouds.

scholarly journals Calibration of global MODIS cloud amount using CALIOP cloud profiles

2020 ◽  
Author(s):  
Andrzej Z. Kotarba
Keyword(s):  
Cloud Amount ◽  
Satellite Observation ◽  
Cloud Fraction ◽  
Orthogonal Polarization ◽  
Polar Regions ◽  
Cloud Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOV) as either confident cloudy, probably cloudy, probably clear, or confident clear. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by NASA assumes that confident clear and probably clear IFOV are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this best guess approach is unreliable, especially on a regional/ local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with MODIS/ Aqua IFOV. Based on 33,793,648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in the polar regions at night, and in selected locations over the northern hemisphere. Applications of MODIS data at ~10 degrees resolution (or finer) should first assess the extent of the error. Uncertainties were related to the efficiency of the cloud masking algorithm. Until the algorithm can be significantly modified, our method is a robust way to calibrate (correct) MODIS estimates. It can be also used for MODIS/ Terra data, and other missions where the footprint is collocated with CALIPSO.

scholarly journals Examination of POLDER/PARASOL and MODIS/Aqua Cloud Fractions and Properties Representativeness

2011 ◽  
Vol 24 (16) ◽  
pp. 4435-4450 ◽  
Author(s):  
Shan Zeng ◽  
Frédéric Parol ◽  
Jérôme Riedi ◽  
Céline Cornet ◽  
François Thieuleux
Keyword(s):  
Cloud Cover ◽  
Temporal Variations ◽  
Cloud Fraction ◽  
Atmospheric Sciences ◽  
Cloud Parameters ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Level 2

Abstract The Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar (PARASOL) and Aqua are two satellites on sun-synchronous orbits in the A-Train constellation. Aboard these two platforms, the Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) provide quasi simultaneous and coincident observations of cloud properties. The similar orbits but different detecting characteristics of these two sensors call for a comparison between the derived datasets to identify and quantify potential uncertainties in retrieved cloud properties. To focus on the differences due to different sensor spatial resolution and coverage, while minimizing sampling and weighting issues, the authors have recomputed monthly statistics directly from the respective official level-2 products. The authors have developed a joint dataset that contains both POLDER and MODIS level-2 cloud products collocated on a common sinusoidal grid. The authors have then computed and analyzed monthly statistics of cloud fractions corresponding either to the total cloud cover or to the “retrieved” cloud fraction for which cloud optical properties are derived. These simple yet crucial cloud statistics need to be clearly understood to allow further comparison work of the other cloud parameters. From this study, it is demonstrated that on average POLDER and MODIS datasets capture correctly the main characteristics of global cloud cover and provide similar spatial distributions and temporal variations. However, each sensor has its own advantages and weaknesses in discriminating between clear and cloudy skies in particular situations. Also it is shown that significant differences exist between the MODIS total cloud fraction (day mean) and the “retrieved” cloud fraction (combined mean). This study found a global negative difference of about 10% between POLDER and MODIS day-mean cloud fraction. On the contrary, a global positive difference of about 10% exists between POLDER and MODIS combined-mean cloud fraction. These statistical biases show both global and regional distributions that can be driven by sensors characteristics, environmental factors, and also carry potential information on cloud cover structure. These results provide information on the quality of cloud cover derived from POLDER and MODIS and should be taken into account for the use of other cloud products.

scholarly journals Validation of MODIS C6.1 and MERRA-2 AOD Using AERONET Observations: A Comparative Study over Turkey

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 905
Author(s):  
Midyan Aldabash ◽  
Filiz Bektas Balcik ◽  
Paul Glantz
Keyword(s):  
Absolute Error ◽  
Coefficient Of Determination ◽  
Space Agency ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
The Mean ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Modern Era ◽  
Relative Root ◽  
Better Than

This study validated MODIS (Moderate Resolution Imaging Spectroradiometer) of the National Aeronautics and Space Agency, USA, Aqua and Terra Collection 6.1, and MERRA-2 (Modern-ERA Retrospective Analysis for Research and Application) Version 2 of aerosol optical depth (AOD) at 550 nm against AERONET (Aerosol Robotic Network) ground-based sunphotometer observations over Turkey. AERONET AOD data were collected from three sites during the period between 2013 and 2017. Regression analysis showed that overall, seasonally and daily statistics of MODIS are better than MERRA-2 by the mean of coefficient of determination (R2), mean absolute error (MAE), and relative root mean square deviation (RMSDrel). MODIS combined Terra/Aqua AOD and MERRA-2 AOD corresponding to morning and noon hours resulted in better results than individual sub datasets. A clear annual cycle in AOD was detected by the three platforms. However, overall, MODIS and MERRA-2 tend to overestimate and underestimate AOD, respectively, in comparison with AERONET. MODIS showed higher efficiency in detecting extreme events than MERRA-2. There was no clear relation found between the accuracy in MODIS/MERRA-2 AOD and surface relative humidity (RH).

scholarly journals Analysis of Near-Cloud Changes in Atmospheric Aerosols Using Satellite Observations and Global Model Simulations

2021 ◽  
Vol 13 (6) ◽  
pp. 1151
Author(s):  
Tamás Várnai ◽  
Alexander Marshak
Keyword(s):  
Atmospheric Aerosols ◽  
Large Scale ◽  
Climate Models ◽  
Orthogonal Polarization ◽  
Atmospheric Models ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Modern Era

This paper examines cloud-related variations of atmospheric aerosols that occur in partly cloudy regions containing low-altitude clouds. The goal is to better understand aerosol behaviors and to help better represent the radiative effects of aerosols on climate. For this, the paper presents a statistical analysis of a multi-month global dataset that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite instruments with data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis. Among other findings, the results reveal that near-cloud enhancements in lidar backscatter (closely related to aerosol optical depth) are larger (1) over land than ocean by 35%, (2) near optically thicker clouds by substantial amounts, (3) for sea salt than for other aerosol types, with the difference from dust reaching 50%. Finally, the study found that mean lidar backscatter is higher near clouds not because of large-scale variations in meteorological conditions, but because of local processes associated with individual clouds. The results help improve our understanding of aerosol-cloud-radiation interactions and our ability to represent them in climate models and other atmospheric models.

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