Influence of land surface effects on MODIS aerosol retrieval using the BAER method over Korea

2006 ◽  
Vol 27 (22) ◽  
pp. 5125-5125 ◽  
Author(s):  
Kwon Ho Lee ◽  
Young Joon Kim ◽  
Wolfgang Von Hoyningen‐Huene ◽  
John P. Burlow
Keyword(s):  
Land Surface ◽  
Surface Effects ◽  
Aerosol Retrieval

Influence of land surface effects on MODIS aerosol retrieval using the BAER method over Korea

2006 ◽  
Vol 27 (14) ◽  
pp. 2813-2830 ◽  
Author(s):  
Lee Kwon Ho ◽  
Kim Young Joon ◽  
Wolfgang von Hoyningen‐Huene ◽  
John P. Burrow
Keyword(s):  
Land Surface ◽  
Surface Effects ◽  
Aerosol Retrieval

scholarly journals GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during DRAGON-NE Asia 2012 campaign

2015 ◽  
Vol 8 (9) ◽  
pp. 9565-9609 ◽  
Author(s):  
M. Choi ◽  
J. Kim ◽  
J. Lee ◽  
M. Kim ◽  
Y. Je Park ◽  
...  
Keyword(s):  
Optical Properties ◽  
Land Surface ◽  
Pearson Correlation ◽  
Ocean Color ◽  
Single Scattering ◽  
Surface Reflectance ◽  
Robotic Networks ◽  
Deep Blue ◽  
Aerosol Retrieval ◽  
Highly Correlated

Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorology Satellites (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm over ocean and land together with validation results during the DRAGON-NE Asia 2012 campaign. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type from selected aerosol models in calculating AOD. Assumed aerosol models are compiled from global Aerosol Robotic Networks (AERONET) inversion data, and categorized according to AOD, FMF, and SSA. Nonsphericity is considered, and unified aerosol models are used over land and ocean. Different assumptions for surface reflectance are applied over ocean and land. Surface reflectance over the ocean varies with geometry and wind speed, while surface reflectance over land is obtained from the 1–3 % darkest pixels in a 6 km × 6 km area during 30 days. In the East China Sea and Yellow Sea, significant area is covered persistently by turbid waters, for which the land algorithm is used for aerosol retrieval. To detect turbid water pixels, TOA reflectance difference at 660 nm is used. GOCI YAER products are validated using other aerosol products from AERONET and the MODIS Collection 6 aerosol data from "Dark Target (DT)" and "Deep Blue (DB)" algorithms during the DRAGON-NE Asia 2012 campaign from March to May 2012. Comparison of AOD from GOCI and AERONET gives a Pearson correlation coefficient of 0.885 and a linear regression equation with GOCI AOD =1.086 × AERONET AOD – 0.041. GOCI and MODIS AODs are more highly correlated over ocean than land. Over land, especially, GOCI AOD shows better agreement with MODIS DB than MODIS DT because of the choice of surface reflectance assumptions. Other GOCI YAER products show lower correlation with AERONET than AOD, but are still qualitatively useful.

scholarly journals Twice‐daily monsoon precipitation maxima in the Himalayas driven by land surface effects

2021 ◽  
Author(s):  
Hatsuki Fujinami ◽  
Koji Fujita ◽  
Nobuhiro Takahashi ◽  
Tomonori Sato ◽  
Hironari Kanamori ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Effects ◽  
Monsoon Precipitation

scholarly journals REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

2017 ◽  
Vol 10 (8) ◽  
pp. 2905-2923 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Tingjun Zhang
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Surface Effects ◽  
Model Parameters ◽  
Fine Scale ◽  
Mountain Areas ◽  
Near Surface ◽  
Cold Air ◽  
The Difference

Abstract. In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

scholarly journals A NEW ALGORITHM FOR THE AOD INVERSION FROM NOAA/AVHRR DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1587-1588
Author(s):  
L. Sun ◽  
R. Li ◽  
H. Yu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Data Retrieval ◽  
Modis Ndvi ◽  
Noaa Avhrr ◽  
Aerosol Retrieval ◽  
Avhrr Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13 μm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74 % of the retrieved AOD are within the expected error range of ±(0.05 + 0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution.

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