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 a radius for observation scanning and number of IMS input in 2D-OI.

Jira tickets

• IFS-2463 - Getting issue details... STATUS
• IFS-2464 - Getting issue details... STATUS
• IFS-2465 - Getting issue details... STATUS

Contents

1. List of snow DA changes

1.1. 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 300. 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.

1.2. IMS thinning

In the current operational system, a simple data thinning is applied to IMS by "bufr_filter" (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 "bufr_grid_screen_parallel" as same as other satellite observations. The thinning selects nearest observations on a reduced gaussian grid of 31km (TL639). As a result, the coverage becomes homogeneous and number of observations is reduced from 251926 to 109223.

1.3. 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 that it is caused by a cap value of 1.4m in snow depth analysis. It has been changed to 3.0m in the latest experiment.

1.4. Suspicious increments accumulated on IMS mask

The usage of IMS is a bit tricky in the current operational system. IMS is not assimilated on model grids of IMS mask instead of rejecting IMS directly. So large departures can be calculated on IMS mask and the impact propagates outside IMS mask. I found that suspicious increments can be accumulated on IMS mask by the following processes:

• Positive increments by in situ observation outside IMS mask at 6, 12, 18 UTC and propagate to grids on IMS mask
• Negative increments around in situ observation by assimilating IMS at 0 UTC (large departures are also used outside IMS mask)
• Positive increments again by in situ observation at 6, 12, 18 UTC ...

In order to reduce such suspicious increments, the following changes have been applied:

• Reject IMS on IMS mask
• IMS thinning at a resolution of 31km (a bit larger than previous setting)
• Reduce vertical correlation (from green line to red line)

Suspicious increments are accumulated on IMS mask before these modifications but they are reduced after the modifications.

1.5. Radius for observation scanning in 2D-OI

SCAN_RAD has been changed from 3000km to 300km. The horizontal correlation at a distance of 300km is 0.028 and it is enough small. This change reduces computational cost for sorting observations.

2. Experiment list

• In 9-month experiments from Dec 2020 to Aug 2021, operational EDA is used for hybrid-B since March 2021 instead of control EDA.

3. Results

3.1. Links to IVER and scorecard

• Iver
  • https://sites.ecmwf.int/dako/iver/Cy48r1p0_SnowDA1_2021_DJF/
  • https://sites.ecmwf.int/dako/iver/Cy48r1p0_SnowDA1_2020_JJA/
• Scorecard
  • https://sites.ecmwf.int/dako/quaver/hut8_vs_hxni_winter/scorecard.html
  • https://sites.ecmwf.int/dako/quaver/hut7_vs_hzbc_summer/scorecard.html

3.2. Results for winter 2020/21 and summer 2020

The following figures show snow depth averaged in Feb 2021. Snow depth on the Tibetan Plateau and the Rocky Mountains is reduced by assimilating IMS. Snow depth on the other mountains are increased by the cap value change. 

RMSE of 2m temperature is reduced by about 0.5% for winter in the Northern Hemisphere (especially in North America).

3.3. Results of 9-month experiments from Dec 2020 to Aug 2021

4. Computational cost

• Computational time for the 'snow' task is reduced about 30 seconds (2:00 → 1:30) at 0 UTC by reducing radius for observation scanning and number of IMS input in 2D-OI. 
• "bufr_grid_screen_parallel" for IMS thinning takes about 25 seconds, a bit longer than "bufr_filter" (15 seconds).
• Both tasks are not on the critical path, so total computational time is almost the same as before.