These gridded surface cloud fractional radiation nuclei (GCF-CRKs) were created by integrating refined estimates of sinking surface shortwave radiation (DSSR) and high-precision cloud fractions (CF). Using these GCF-CRKs, the spatiotemporal characteristics of Arctic surface shortwave CRE were estimated over a 21 year period (2000-2020).
There are five separate files in total. SFC_SW_Kernel_Src.nc "is used for CRK of all clouds," SFC_SW_0wcloud_Kernel_Src.nc "is used for CRK of low-level clouds," SFC_SW_idlowcloud_Kernel_Src. nc "is used for CRK of mid to low-level clouds," SFC_SW_idhigcloud_Kernel_Src.nc "is used for CRK of mid to high-level clouds, and" SFC_SW_ighcloud_Kernel_Src.nc "is used for CRK of high-level clouds. The four cloud layers are derived from four pressure layers (from the surface to 700 hPa, 700-500 hPa, 500-300 hPa, and 300-50 hPa, representing low clouds, medium low clouds, medium high clouds, and high clouds, respectively) according to the CERES-SYN stratification criteria. The file format is netcdf4, created by Matlab. To read these files, any software that supports netcdf4 can be used. These documents only cover sunny months from April to September between 2000 and 2020, with a longitude range of -180 °~180 ° and a latitude range of 60 ° N~90 ° N.
| collect time | 2000/01/01 - 2020/09/30 |
|---|---|
| collect place | arctic |
| data size | 52.0 MiB |
| data format | nc |
| Coordinate system |
DSSR is calibrated by a CF dependent model that utilizes the correlation between atmospheric top (TOA) shortwave radiation parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources.
A CF dependent model was constructed to improve DSSR estimation by utilizing the correlation between shortwave radiation parameters at the top of the atmosphere (TOA) and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources. Based on this model, use CF as the sole perturbation parameter to construct GCF CRKs for isolating CF CRE.
The results indicate that this method significantly improves the accuracy of DSSR estimation under partially cloudy conditions (0
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| # | title | file size |
|---|---|---|
| 1 | Introduction.docx | 15.7 KiB |
| 2 | SFC_SW_Kernel_Arc.nc | 10.4 MiB |
| 3 | SFC_SW_highcloud_Kernel_Arc.nc | 10.4 MiB |
| 4 | SFC_SW_lowcloud_Kernel_Arc.nc | 10.4 MiB |
| 5 | SFC_SW_midhighcloud_Kernel_Arc.nc | 10.4 MiB |
| 6 | SFC_SW_midlowcloud_Kernel_Arc.nc | 10.4 MiB |
| 7 | _ncdc_meta_.json | 5.3 KiB |
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
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