Dataset: ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0#

Dataset identifier: esacci.SNOW.day.L3C.SCFV.multi-sensor.multi-platform.AVHRR_MERGED.2-0.r1
Data store: cciodp

How to open this dataset in AVL JupyterLab  ⧉

cciodp_store = new_data_store('cciodp')
ds = cciodp_store.open_data('esacci.SNOW.day.L3C.SCFV.multi-sensor.multi-platform.AVHRR_MERGED.2-0.r1')

Bounding box map#

Map tiles and data from OpenStreetMap, under the ODbL.

Basic information#

Parameter	Value
Bounding box longitude (°)	-180.0 to 180.0
Bounding box latitude (°)	-90.0 to 90.0
Time range	1982-01-01 to 2018-12-30
Time period	1D

Click here for full dataset metadata.

Variable list#

Click on a variable name to jump to the variable’s full metadata.

Variable	Long name	Units
scfv	Snow Cover Fraction Viewable	percent
scfv_unc	Unbiased Root Mean Square Error for Snow Cover Fraction Viewable	percent
spatial_ref	[none]	[none]

Full variable metadata#

scfv#

Field	Value
_Unsigned	true
long_name	Snow Cover Fraction Viewable
units	percent
standard_name	snow_area_fraction_viewable_from_above
valid_range	0, -2
actual_range	0, 100
flag_values	-51, -50, -46, -41, -2
flag_meanings	Cloud Polar_Night_or_Night Water Permanent_Snow_and_Ice No_Satellite_Acquisition
grid_mapping	spatial_ref
ancillary_variables	scfv_unc
missing_value	-1
orig_data_type	uint8
fill_value	-1
size	347431680000
shape	13404, 3600, 7200
chunk_sizes	1, 1800, 3600
file_chunk_sizes	1, 1800, 3600
data_type	uint8
dimensions	time, lat, lon
file_dimensions	time, lat, lon

scfv_unc#

Field	Value
_Unsigned	true
long_name	Unbiased Root Mean Square Error for Snow Cover Fraction Viewable
units	percent
standard_name	snow_area_fraction_viewable_from_above standard_error
valid_range	0, -2
actual_range	0, 100
flag_values	-51, -50, -46, -41, -2
flag_meanings	Cloud Polar_Night_or_Night Water Permanent_Snow_and_Ice No_Satellite_Acquisition
grid_mapping	spatial_ref
missing_value	-1
orig_data_type	uint8
fill_value	-1
size	347431680000
shape	13404, 3600, 7200
chunk_sizes	1, 1800, 3600
file_chunk_sizes	1, 1800, 3600
data_type	uint8
dimensions	time, lat, lon
file_dimensions	time, lat, lon

spatial_ref#

Field	Value
spatial_ref	GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]]
longitude_of_prime_meridian	0.0
semi_major_axis	6378137.0
inverse_flattening	298.257223563
GeoTransform	-180 0.05 0 90 0 -0.05
grid_mapping_name	latitude_longitude
orig_data_type	int32
fill_value	9223372036854775807
size	1
shape	1
chunk_sizes	1
file_chunk_sizes	1
data_type	int64
dimensions
file_dimensions

Full dataset metadata#

Field	Value
title	ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0
source	AVHRR_NOAA-7_GAC mosaic from ESA Cloud CCI project
history	20211109T162200Z: ESA snow_cci SCF processing line (Revision: 203), version 2.0
references	http://snow-cci.enveo.at/
product_version	2-0
comment	The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.
project	Climate Change Initiative - European Space Agency
ecv	SNOW
institute	University of Bern
processing_level	L3C
product_string	AVHRR_MERGED
data_type	SCFV
abstract	This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 1982-2018. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology. The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.
publication_date	2022-03-17T16:44:03
uuid	763eb87e0682446cafa8c74488dd5fb8
catalog_url	https://catalogue.ceda.ac.uk/uuid/763eb87e0682446cafa8c74488dd5fb8
sensor_id	multi-sensor
platform_id	multi-platform
cci_project	SNOW