Juanzhen Sun (RAL/MMM) ICE-POP 2018: PyeongChang Winter Olympics R&D to Enhance Winter Weather Research Juanzhen Sun (RAL/MMM) Zhuming Ying (MMM)

OUTLINE Overview of ICE-POP 2018 NCAR’s participation: wind and precipitation nowcast using FINECAST Some preliminary results

ICE-POP 2018 International Collaborative Experiments for Pyeongchang 2018 Olympic & Paralympic winter games) Co-sponsored by KMA and WMO RDP/FDP kick-off meeting Oct 2015; 3 workshops since then Participants: 27 agencies from 12 countries Australia, Austria, Canada, China, Finland, Russia, Korea, Spain, Swiss, Taiwan, UK, USA Pyeong Chang 150 km Seoul South Korea

Three Scientific Issues Main Themes of ICE-POP 2018 Three working groups Observation NWP Evaluation observation network observation campaign Nowcasting & NWP model To operate model for FDP Model verification & evaluation Three Scientific Issues Seamless prediction (0-12h) Predictability over Mountainous, Complex Terrain of snow storms Toward Advanced Physics in NWP

ICE-POP 2018 Fields Campaign December 2017 – March 2018 S

CHALLENGES FOR NWP Mechanism for East Snow Storm(ESS) 2014. 1. 21 case 1) Updraft by terrain 2) Convergence between land(Mountain) and sea Advection of Cold air 4) Complex flows and phase changes 3) Heat/moisture flux by sea East Snow Storm caused by Cold outbreak over East Sea - Low predictability in NWP, few observation in Ocean Scale Interaction, Scale Transition - Air-Sea interaction, Synoptic to Mesco scale transition Microphysical Phase change in Ocean – Coastal Area – Steep Topography – Complex

NCAR’s involvement in ICE-POP 2018 Wind and precipitation analysis and nowcast over complex terrain using FINECAST (update every 10 min) Scientific questions: Do FINECAST work for winter weather? How to estimate precipitation from radar observations in wintertime? What is the quality of KMA’s radar network? How shall we deal with the complex terrain? Will the X-band radars provide additional value? How to produce reliable surface wind and temp nowcast with advanced post-processing

Overview of FINECAST (VDRAS) A rapid update (10min) analysis and nowcast system 4DVar Radar and surface data assimilation WRF model data as first guess A cloud-resolving model used for analysis and nowcast Operational capability MPI parallel processing for real time rapid computation Installed over many regional domains Applications Nowcasting severe weather Process study of convective system Initializing NWP models Wind energy prediction Winter weather

Technical Development for ICE-POP FINECAST for ICE-POP2018 16 Site Profile format: ascii 6 KMA operational Radars + 1-3 X-band: Radial vel & reflectivity KMA Verification FINECAST Analysis & Nowcast 2D product: lev: Surface & 2km format: GRIB AWS surface: Wind, Temp, Pres, Humidity, Rainfall Standard FINECAST output and diagnosis WRF 3DVar RUC forecasts In-house Verification Outputs: prep, type, Temp, RH, hori/vert wind, visibility, qc, qr, qs Lead time: 0--2hr Frequency: 10 min cycle with 10 min forecast output

Domain & Configuration Domain and Configuration Domain & Configuration PyeongChang area terrain Size : 280km X 210km Resolution: hori 2km, vert 200m Vert Levels: 27, model top 5km Center: 37.46/128.53 Radars: GNG, GDK, KWK, IIA, MYN, KSN Case study: 00--06 UTC, Jan 29, 2016 ⛷

The 2016-01-29 snow storm The 2016-01-29 case Reflectivity mosaic from 01UTC to 07UTC every 10 Min ⛷

Comparison with WRF (Qr & DIV) Comparison with WRF for Qr & DIV WRF (5h FCST) FINECAST analysis Qr Qr ⛷ DIV DIV Convergence Divergence

Comparison with WRF (Qr & DIV) Comparison with WRF and OBS WRF (5h FCST) FINECAST analysis Qr Qr ⛷ 1h rainfall Gauge + radar Radar REFf

1H accumulated Rainfall Nowcast 1H precipitation forecast 20160129_05:00UTC OBS (Radar + gauge) FINECAST nowcast OBS

1H accumulated Rainfall Nowcast 1H precipitation forecast 20160129_06:00UTC OBS (Radar + gauge) FINECAST nowcast OBS FINECAST 1H FCST OBS FINECAST 1H FCST OBS

1H accumulated Rainfall Nowcast 1H precipitation forecast 20160129_07:00UTC Full model 1H FCST OBS (Radar + gauge) FINECAST nowcast OBS

RMSE of Radial Velocity (B-O vs. A-O) Wind RMSE against radial velocity (BKG-OBS) The errors are larger than the typical error ~ 2m/s in previous studies The largest RMSE is on the 1st elevation angle of KWK The largest error occurs in areas far from the radar (horizontal distance >100km) Poor radar coverage might have caused the poor fit to observations B-O WRF - OBS A-O High elevation angle, 资料太少，没有统计意义， need to remove FINECAST - OBS

Surface wind and T analysis/nowcast Gridded Surface obs FINECAST analysis 05 UTC Gridded Surface obs FINECAST 1h nowcast 06 UTC

RMSE against surface OBS V T Qv

Verification of rainfall FCST Verification of precipitation nowcast FSS with 10km ROI

Lessons learned so far Summary Nowcasting at the scale of Olympics venues presents great challenges for NWP models KMA’s operational radar network have a poor coverage in the PyeongChang area and therefore the QPE uncertainty is an issue; the additional X-bands are expected to provide some help FINECAST analyzed and predicted some mesoscale features that were reasonable in terms of general precipitation patterns; but uncertainties for high precipitation are quite obvious FINECAST improved over WRF on surface wind and temperature forecasts; a terrain adjustment scheme can further improve surface temperature forecast The dense observations from ICE-POP 2018 field campaign create a valuable dataset for more in-depth study on terrain-induced winter storms; we will seek opportunities for continued collaboration with KMA.