Summary

The impact of snow DA changes has been investigated for 49r1. Snow depth on the Tibetan Plateau and the Rocky Mountains is reduced by assimilating IMS except on complex orography. RMSE of 2m temperature is reduced by 0.5% for winter and 1% for spring in the Northern Hemisphere. In addition, computational time for the 'snow' task is reduced about 30 seconds at 0 UTC by reducing radius for observation scanning and number of IMS input in 2D-OI.

List of snow DA changes

	current system	candidate for 49r1
IMS mask	based on altitude (>1500m)	based on SDFOR (>200)
IMS thinning	select 1 from every 36	select nearest IMS on a gaussian grid of 31km (TL639)
IMS rejection on IMS mask	No	Yes
Cap value for snow depth	1.4m	3.0m
RSCALE_Z	800m	500m
SCAN_RAD	3000km	300km
ODB		Improve number of O-A

IMS mask

In the current operational system, IMS is not assimilated on mountainous areas over 1500m (the same as ERA5). However, Orsolini et al.(2019) found:

The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations
ERA-Interim, which assimilates IMS on mountainous areas over 1500m, has smaller biases of snow depth on the Tibetan Plateau than ERA5.

In order to improve the biases, the condition to assimilate IMS has been changed. In the latest experiment, IMS is not assimilated if standard deviation of filtered sub-grid orography (SDFOR) is more than 200. This is based on the idea that IMS snow cover from satellite observations could be less accurate on complex orography. The following figures show IMS masks for the current system and the latest experiment.

IMS mask based on altitude (>1500m)	IMS mask based on SDFOR (>200)

IMS thinning

In the current operational system, a data thinning is applied to IMS by using "bufr_filter" to select 1 from every 36 raw observations. However, it leads to inhomogeneous coverage of IMS, especially in some areas. In order to improve it, new data thinning has been tested by using "bufr_grid_screen_parallel" as same as other satellite observations. The thinning selects nearest observations on a gaussian grid of 31km. As a result, the coverage becomes homogeneous and number of observations is reduced from 251926 to 109223.

Simple thinning (select 1 from every 36)	Updated thinning (select nearest IMS on a gaussian grid of 31km)

Cap value for snow depth analysis

Snow water equivalent (SWE) in ERA5, ERA5-Snow and ERA5-Land has been validated against in situ observations (snow course in Canada) by Chris Derksen (ECCC). He pointed out that ERA5 and ERA5-Snow don't represent SWE more than 500mm (personal communication). We found it is caused by cap value of 1.4m in snow depth analysis. It has been changed to 3.0m in the latest experiment.