Snow Watch Team status update GCW Steering Group 6th session 26-28 November, Davos

1. Snow Watch Team status update GCW Steering Group 6th session 26-28 November, Davos

2. Team composition Snow Watch Team (November 2018) composition and updates Patricia de Rosnay (ECMWF) co-chair Kari Luojus (Finnish Meteorological Institute) co-chair Ross Brown (Environment and Climate Change Canada), (active member) Chris Derksen (Environment and Climate Change Canada) Sean Helfrich (NOAA/NESDIS/Center for Satellite Applications and Research - STAR) Samantha Pullen (UK Met. Office) Dave Robinson (Rutgers University) Mareile Wolff (MetNo) Stepped down: Vincent Fortin (CMC, Canada) Proposed addition: Frank Lespinas(ECCC) European Centre for Medium-Range Weather Forecasts

3. Updated Terms of Reference proposed • Roles focus on sgsp:snow on the ground and solid precipitation • Assess the maturity, accuracy and homogeneity of sgspobserving systems, data, products and information • Identify priority issues and actions for improved observing of sgspat global, regional and national scales • Provide advice to WMO on issues related to real-time in situ reporting practicessgspas well as for remotely sensed and other sources of real-time information • Liaise with the cryosphere community, and WMO bodies, to maintain up-to-date knowledge of sgspmonitoring technologies, programs, datasets and products • Provide information and advice accessible from the GCW Website on sgsp-related products and issues e.g. anomalous snow cover conditions, extreme events, annual assessments • Contribute to establishing “Guidelines and Best Practices” for sgspobserving practices • Contribute to defining/refining sgsp related terminology • Contribute to the WMO Rolling Review of Requirements database on matters related to sgsp • Provideprogress updates to the Integrated Products WG and/or GCW Secretariat upon request • Provide support to GCW Steering Group, Working Groups and Teams as required European Centre for Medium-Range Weather Forecasts

4. International exchange of snow data - WMO EC-69 (2017), Abridged final report with resolutions and decisions https://library.wmo.int/index.php?lvl=notice_display&id=19919#.W4AgERZG1e5 • Resolution 15 on international exchange of snow data • “…zero snow depth (absence of snow) should be reported …” • ” Requests Members to exchange in situ snow measurements in real time in BUFR through the Global Telecommunication System …” - On going: US SNOTEL, COOP, SCAN data on the GTS (NOAA) with support from ECMWF & SnowWatch, WMO resolution 15 used in support of required resources at NOAA (Sept 2018) – Role of GODEX (Global Obs data Exchange). - OSCAR-> monitoring of observing system

5. Snow Observations Snow SYNOP and National Network data in Europe In situ data available on the GTS ECMWF Snow Depth (cm) X TAC SYNOP X BUFR SYNOP X National data 5 20 50 2018 11 15 at 06UTC European Centre for Medium-Range Weather Forecasts 5

6. Snow reports from Ukraine Ukraine increased number of stations from 30 to more than 160, using BUFR SYNOP. Used in operations since June 2018 at ECMWF https://www.ecmwf.int/en/about/media-centre/news/2018/extra-weather-station-data-improve-ecmwfs-forecasts 6

7. Snow reports from Bulgaria (NIMH) • HarmoSnow COST action ES1404 contribute to improve in situ data exchange for NWP • NIMH: 39 additional stations (BUFR format, routinely produced) • ECMWF data acquisition, 1-month assimilation test • Suitable for operational use 5 20 50 19 January 2016 Snow depth in cm Operational ECMWF Test ECMWF using additional NIMH data Lack of observations in Bulgaria 39 more stations provided by NIMH de Rosnay et al., RD16-178 2016 7

8. In situ snow depth observations GTS Snow depth availability SYNOP TAC + SYNOP BUFR + national BUFR data Status on 10-15 December 2013 8

9. In situ snow depth observations GTS Snow depth availability SYNOP TAC + SYNOP BUFR + national BUFR data Status on 10-15 December 2017 9

10. In situ snow depth observations GTS Snow depth availability SYNOP TAC SYNOP BUFR national BUFR data Status on 15 November 2018 10

11. In situ snow depth observations GTS Snow depth availability 15 December 2013 Improvement in China: About 200 new stations reporting (in snow conditions) in SYNOP BUFR 15 December 2017 11

12. In situ snow depth observations GTS Snow depth availability 15 November 2018 12

13. In situ snow depth observations in the US SNOTEL (Snow Telemetry) network Other networks, inc. National Weather Service Cooperative Observer Program (COOP), or Soil Climate Analysis Network (SCAN) that provide thousands of stations • NOAA working on WMO BUFR conversion of the US national networks to make them available on the GTS • Godex meeting this week in India 13

14. International exchange of snow data - WMO EC-69 (2017), Abridged final report with resolutions and decisions https://library.wmo.int/index.php?lvl=notice_display&id=19919#.W4AgERZG1e5 • Resolution 15 on international exchange of snow data • “…zero snow depth (absence of snow) should be reported …” • ” Requests Members to exchange in situ snow measurements in real time in BUFR through the Global Telecommunication System …” - On going: US SNOTEL, COOP, SCAN data on the GTS (NOAA) with support from ECMWF & SnowWatch, WMO resolution 15 used in support of required resources at NOAA (Sept 2018) – Role of GODEX (Global Obs data Exchange). - OSCAR-> monitoring of observing system - SWE in BUFR (2018): for NRT exchange of SWE data via GTS IPET-CM = Inter-programme expert team on code maintenance New SWE BUFR approved May2018: http://www.wmo.int/pages/prog/www/ISS/Meetings/IPET-CM_Offenbach2018/IPET-CM_DocPlan.html Available to WMO MSs November 2018 Presented at ECMWF TAC (Tech. Adv. Committee) in October 2018 Presented at the IPET-SUP/GODEX meeting this week (India) 14

15. GCW Snow Watch actionsInternational exchange of snow data SnowWaterEquivalent BUFR • New BUFR sequence 3 07 103 & corresponding BUFR table B entries and code Based on the existing 3-07-101 (snow observation) by adding the WIGOS Station Identifier and the required elements to report the Snow Water Equivalent SWE: model prognostic variable  Relevant for data assimilation  Long term benefit for operational NWP & hydrology 15

16. Observing System Experiments Winter 2014-2015 (December to April) - Assess the impact of the snow observing system Impact on T2m Forecasts: Normalized RMSE for T2m FC difference compared to the reference (OL) SYNOP+IMS (1-0) SYNOP+Nat (2-0) SYNOP+Nat+IMS (3-0) -> oper Best T2m Forecast when all observations, combining in situ and IMS, are assimilated. European Centre for Medium-Range Weather Forecasts

17. Impact of National data (case 3-1) All data assimilated (SYNOP+Nat+IMS) compared to SYNOP+IMS assimilation -> Further T2m forecasts error reduction at medium range Contribution& complementarities of each observation types to improve T2m forecasts at short and medium ranges European Centre for Medium-Range Weather Forecasts

18. Snow depth observations in Europe SYNOP + national BUFR data (GTS) • Very good coverage of snow observations in Scandinavia • Impact on extended range forecasts ? • Impact on river discharge? European Centre for Medium-Range Weather Forecasts

19. Observing System Experiments  Extended Range impact OL (No DA) SYNOP+IMS All in situ All obs DA 140 Model too long to melt snow  OL has more snow Ongoing: Impact of snow data assimilation on Seasonal forecast (S4) and river discharge in Scandinavia Earlier melting in S4 with in situ snow DA Next: River discharge for OL and All obs DA Snow depth AN expts 20 D J F M A 2014-2015 160 OL (No DA) All obs DA (oper) System4 Experiment System4 Experiment Snow Water (mm) 0 22 21 21 20 20 Mar Apr May June July 22 21 21 20 20 Mar Apr May June July

20. Update of Canadian Historical Snow Survey dataset to 2016 • In winter 2016/17 a project was initiated to update the Canadian snow survey dataset for the period from 2004-2016 • Data from 10 provincial agencies were obtained, quality controlled and added to the existing dataset increasing coverage over Quebec and northern Canada • The full dataset is NOT freely available for public access due to a restrictions from the Quebec Government (MELCC) and Hydro-Québec • A 10-km gridded version of the dataset has been developed for sharing with the research community • Trend analysis of March SWE showed evidence of a latitudinal gradient in trends consistent with climate model simulations in response to warming and increased precipitation (Brown et al. 2019 in prep) Comparison of station coverage between the previous and current version of the Canadian historical snow survey dataset. Mar-01 SWE trend (1967-2016) versus latitude. Trend units are % per decade with respect to the 1967-2016 mean SWE. Brown et al. (2019, submitted) 20 Source: R. Brown, ECCC, Nov 19, 2018

21. Snow Watch Team contributions to snow cover assessments • In response to evidence that the 2017/18 snow season was characterized by considerably above-normal SWE over the Arctic, the Snow Watch Team provided an assessment in June 2018 for the GCW website: • Conclusions: • March NH snow mass of 3190 gigatons for 2017/18 ranked as the highest in the GlobSnow period of record (since 1979) • 2017/18 peak SWE over the Arctic found to be the largest in the CMC snow analysis period of record (since 1998) • The results highlight the strong interannual variability in snow cover, which responds quickly to year-to-year variations in atmospheric circulation and corresponding anomalies in air temperature and precipitation 2018 21

22. ESA SnowPEx (GCW Snow Watch) Assessment of satellite snow products Following Snow Watch recommendation, ESA initiated (and funded) a Satellite Snow Products intercomparison and evaluation Exercise – ESA SnowPEx (06/2014 -> 12/2016) • The primary objectives were • Intercompare and evaluate global / hemispheric (pre) operational snow products derived from different EO sensors and generated by means of different algorithms, assessing the product quality by objective means. • Evaluate and intercompare temporal trends of seasonal snow parameters from various EO based products in order to achieve well-founded uncertainty estimates for climate change monitoring. • Elaborate recommendations and needs for further improvements in monitoring seasonal snow parameters from EO data. Slide from Kari Luojus

23. SnowPEx – ParticipatingOrganisations ISSPI-1 WS July 2014 ISSPI-2 WS Sep 2015

24. In-situ Validation of SE products Slide from Kari Luojus SE Product Insitu Snow Depth Conversion to Binary SE: If (FSC>25%) then snow Conversion to Binary SE: If (SD>0cm )then snow Statistical Analysis:F-Score, False Alarm, etc. F-Score [0 to 1 (best)]

25. Monthly Spatial Difference Maps – May 2008 Slide from Kari Luojus IMS24-ASNOW IMS24-JXM10 IMS24-JXM05 IMS24-CRCLIM IMS24-M10C05 IMS24-MEASU

26. Multiannual trends of monthly average snow products Slide from Kari Luojus MAJOR OUTCOME of SNOWPEX: Protocols and methods for inter-comparison and validation by community, available at SnowPEx website: earth.esa.int/web/sppa/activities/qa4eo/snowpex 26

27. Evaluation of SWE and solid precipitation products and datasets over southern Québec Objective: Evaluation of multiple gridded snow water equivalent (SWE) and solid precipitation datasets over the St-Maurice watershed region in southern Quebec over multiple years to obtain new insights into SWE dataset uncertainties. Carried out as a contribution to the GCW SnowPEx project supported by ESA Ground truth: 10-km interpolated (multi-variate kriging) SWE from bi-weekly snow surveys over 1981-2014 including estimates of interpolation error Datasets evaluated:Reanalyses (ERA-interim, MERRA, MERRA-2, CFSR, JRA-55), GlobSnow, CMC analysis, CLASS offline runs, CRCM5, BLEND5 multidataset average 27 Source: R. Brown, ECCC, Nov 19, 2018

28. Annual maximum SWE (SWEM) evaluation resultsBrown, R., Tapsoba, D. and Derksen, C., 2018. Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec. Hydrological Processes, 32(17), pp.2748-2764 Systematic under-prediction of SWEM by nearly all estimates • Insufficient and inconsistent solid precipitation the main reasons for systematic underprediction of SWEM and for much of the variability in dataset performance and spread • None of the datasets evaluated provide estimates of annual maximum SWE within the operational requirements of Hydro-Québec (±15%) • The results underscored the need for investing more effort in improving solid precipitation estimates 28 Source: R. Brown, ECCC, Nov 19, 2018

29. PSTG (January 2018) recommendations http://www.wmo.int/pages/prog/sat/documents/FinalReport-SnowRadarScienceMeeting_January2018.pdf • Status of snow data products: Advance the development of new satellite mission concepts through coordinated engagement of technical, scientific, programmatic, and applications- focused elements. • Identifying priority science drivers: Improve communication and linkages between snow mission development activities to strengthen proposal development for both mission concepts and supporting scientific activities. • Mission Requirements Maturity and Technical Readiness: Endorsement of a wide-swath, moderate spatial resolution Ku-band radar concept as one approach to address snow, ice, and ocean winds applications. Continue to develop the potential viability of other options, including InSAR-based (single and repeat pass) approaches. • Supporting Experimental Campaigns and Modelling Requirements: Continue coordinated campaign planning and data sharing between ESA, CSA/ECCC, NASA, and other agencies. • Data assimilation: Coordinate progress between operational centers on coupling physical snow models with forward radar models; identify priority research areas (i.e. OSSEs, required model development) to advance the capacity to assimilate radar measurements over snow covered areas. • Potential Secondary Parameters for Snow Radar Missions: Emphasize variables in addition to terrestrial snow in mission proposal documents; increase engagement of sea ice and ocean winds scientific and user communities. • Inter-Agency Programmatics and Collaboration: Use existing programs and coordination frameworks (distributed globally) to ensure coherent and cohesive advancement of scientific and technological challenges related to the monitoring of snow cover - building on existing technology development and scientific advancement programs. European Centre for Medium-Range Weather Forecasts

30. Role of Snow Watch in the GCW preop phase • Promote improved/new observations of snow and data sharing in real time internationally (e.g. SWE, snow depth on the ground on the GTS), and link to the data portal. Focus also on regions with sparse observations • Exploring ways to address long-term global decline in ground station networks, e.g. by highlighting role and impacts of snow monitoring, and assimilation, demonstration of the value of the snow observing systems. • Providing global and regional assessments of changes of the cryosphere, and tracking anomalies and extremes (snow, glacier, permafrost, sea ice); • Exploring innovative ways to address the lack of long-term observations in specific areas (high elevation) e.g. by highlighting role and impacts of snow/glacier/sea ice and permafrost monitoring, and assimilation, contributing to the demonstration of the value of the snow/glacier /sea ice/permafrost observing systems. 30

31. Role of Snow Watch in the GCW preop phase • Develop/recommend more linkages between snow cover and snow water equivalent (SWE) monitoring and impacts, e.g. global freshwater resources, snow hazards and extreme events. • Support international efforts towards new satellite snow mission, to address the global moderate-resolution SWE observing gap • Contribute to the publication of a biannual State of the Cryosphere bulletin, to provide updates, trends, characterization of cryosphere extremes, and seasonal • Discussion on the expected role of Snow Watch for: • Promote and facilitate satellite product intercomparisons for all cryospheric components, with a first priority on sea ice • Assessing the impact of long-term use of citizen observations (and link to WMO and possible GTS availability), for snow cover in particular, e.g. CoCoRaHS program, (http://cocorahs.org), gathering snow observations in the US and Canada ? 31