![snow wager times snow wager times](https://i.redd.it/7q9a95rqbrh41.png)
But it flooded a dried riverbed, diverted water from a key reservoir that supplies Beijing and resettled hundreds of farmers and their families, all to feed one of the most extensive snow-making operations in the history of the Games. BEIJING - China did not move mountains to host the 2022 Winter Olympics. Basic Methodology: Jouni Pulliainen, Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations, Remote Sensing of Environment, Volume 101, Issue 2, 30 March 2006, Pages 257-269, ISSN 0034-4257. doi: 10.1109/TGRS.2009.2018442 Implementation: Matias Takala, Kari Luojus, Jouni Pulliainen, Chris Derksen, Juha Lemmetyinen, Juha-Petri Kärnä, Jarkko Koskinen, Bojan Bojkov, Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Remote Sensing of Environment, Volume 115, Issue 12, 15 December 2011, Pages 3517-3529, ISSN 0034-4257. Koskinen, "Detection of Snowmelt Using Spaceborne Microwave Radiometer Data in Eurasia From 1979 to 2007," in IEEE Transactions on Geoscience and Remote Sensing, vol. Brodzik, An earth-gridded SSM/I data set for cryospheric studies and global change monitoring, Advances in Space Research, Volume 16, Issue 10, 1995, Pages 155-163, ISSN 0273-1177, (95)00397-W Snowmelt detection: M. Related Documents GlobSnow Homepage GlobSnow2 Final Report Armstrong, R.L. Other project partners involved are NR (Norwegian Computing Centre), ENVEO IT GmbH, GAMMA Remote Sensing AG, Finnish Environment Institute (SYKE), Environment Canada (EC), Northern Research Institute (Norut), University of Bern, Meteoswiss and ZAMG. The project was coordinated by the Finnish Meteorological Institute (FMI). All the other areas show a retrieved SWE value (that is in all cases greater than 0.001 mm). The areas that are determined as snow-free or melted by the melt-detection approach, are denoted with a SWE value of 0 mm. The areas that are identified as wet snow or have no SWE retrieval, but are identified as snow covered with the time-series melt-detection approach, are denoted with a SWE value of 0.001 mm. The areas that have been flagged as snow-free or melted are identified using a time-series melt detection approach described in Takala et al.
SNOW WAGER TIMES FULL
> 0.001 mm denote areas with full snow cover (Snow Extent 100%)
![snow wager times snow wager times](https://magnetismotimes.com/wp-content/uploads/2014/08/Superpacote-Conto-de-Fadas.png)
0.001 mm denote areas with melting snow (Snow Extent undefined between 0% and 100% no SWE retrieval because of the wet state of the snow cover) 0 mm denotes snow-free areas (Snow Extent 0%) The information on snow extent is included in the product by utilizing the following coding for the SWE product, whereby SWE values of: In addition to the SWE retrievals, the SWE products include information on the overall extent of snow cover. The dataset produced in GlobSnow-2 is identified as the GlobSnow SWE v2.0 data record.
![snow wager times snow wager times](https://images.neopets.com/items/toy_mootix_plushie.gif)
The GlobSnow-1 project resulted in two versions of the data record, SWE v1.0 and SWE v1.3 (available from FMI). Monthly Aggregated Snow Water Equivalent (Monthly 元B SWE), a single product for each calendar month, providing the average and maximum SWE, calculated from the weekly aggregated SWE product. Weekly Aggregated Snow Water Equivalent (Weekly 元B SWE), calculated for each day based on a 7-day sliding time window aggregation of the daily SWE product. Daily Snow Water Equivalent (Daily 元A SWE), snow water equivalent (mm) for each grid cell for all evaluated land areas of the Northern Hemisphere. There are three SWE products (all on the EASE model grid see Armstrong and Brodzik, 1995):
SNOW WAGER TIMES SERIES
The GlobSnow-1 and -2 projects have developed a long term data record of SWE products covering the non-alpine Northern Hemisphere, based on a time series of remotely sensed observations from the Nimbus-7 SMMR, DMSP F8/F11/F13/F17 SSM/I(S) instruments and ground-based weather station measurements from 1979 until 2014. This approach was shown to be superior to alternative algorithms which solely utilize satellite data through comparison with extensive ground reference datasets. The GlobSnow SWE record utilizes a novel data-assimilation based approach for SWE estimation which combines weather station measurements of snow depth with satellite passive microwave measurements. The previous existing daily SWE records have spanned a shorter time period (2002-2014) or described the snow conditions on a monthly basis for a similar period (1978-2014). The GlobSnow SWE product is the first satellite based daily SWE dataset for the non-alpine northern hemisphere that extends from 1979 to 2014.