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Development of NWP-based Nowcasting at the Met Office -The Nowcasting Demonstration ProjectWorkshop on Use of NWP for Nowcasting October 2011

Susan P Ballard, Zhihong Li, David Simonin, Jean-Francois Caron, Cristina

Charlton-Perez, Nicolas Gaussiat, Lee Hawkness-Smith, Helen Buttery, Graeme Kelly, Robert Tubbs, Dingmin Li +DAE Exeter + Humphrey Lean, Emilie Carter, Nigel Roberts….

Contents

• Introduction and Plans

• NWP based Nowcasting compared to UKPP conventional nowcasts

• Impact of Doppler Winds and Observation Errors

• Impact of Observations and Background Errors

• Impact of Analysis Increments and Boundary Conditions

• Conclusions and future work

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Aims

• Hourly cycling 1.5km resolution NWP for southern England - NDP

• 6-12hour forecasts (12hours would allow 6 member lagged ensembles)

• Needs new supercomputer

• Olympics demonstration

• Compare UKV (UK 1.5km 3hourly 3D-Var), UKPP (conventional nowcast) and NDP (nowcasting demonstration project)

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Nowcasting Model Domain

Model Resolution VAR Time Window Cycling Forecast

Length

UK4 / UKV 4 km / 1.5km 3D-Var 4/3km 3 hr 3 hr T+36

South UK Fixed 1.5 km 3D/4D-Var 1.5/3km 1 hr 1 hr T+6 or T+12

Techniques:

3DVAR, 4DVAR, latent heat and moisture nudging

Needs:

Hourly analyses and forecasts to customer within 15mins of data time

Therefore data must be in Met Office in real time

With 4D-Var can exploit high spatial and temporal resolution

High temporal resolution eg every 5 -15mins may help to offset poor or limited horizontal resolution

Exploit more observations – type and time frequency eg GPS, AMDAR, Meteosat imagery (clear and cloudy) use of radar Doppler winds, reflectivity and refractivity data

Nowcasting Data Assimilation Strategy

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UKPP – Met Office Nowcast– forecasts of surface rain rate valid at 21Z 3/6/2007

radar

T+3 T+2

T+1 T+0

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Impact of hourly 4D-Var data assimilation Including latent heat nudging of 15minRadar derived rain rates – forecasts of surface rain rate valid at 21Z 3/6/2007

radar

T+3 T+2

T+1 T+0 Zhihong Li

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Impact of Doppler Radial Winds on Fractional Skill Score for rainfall >0.2mm accumulation – scale 55km

Forecast skill is improved for rain accumulations with low threshold – effectively all rain (~ 1 hour gain)

FSS – Roberts and Lean

ΔFSS – 0.2 mm acc – scale 55km

ΔF

SS

Impact of Doppler Radial Wind Assimilation on RMSE differences in fit to surface winds and Satwinds

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RepError= error derived from O-B statistics approx 2-3m/sObs Error = standard deviation of superobs approx 1-2m/s

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Doppler Wind Observation Error

• 4 months of data O-B

• Increase with range

• Range from 2.4 to 3.2 m/s

Observation Error

Distance

σ2o

σ2b

Background error covariance

Hollingsworth–Lonnberg

•Rely on the use of departure between the background and observation (innovations)

• Construct a histogram of background departure covariance against distance

15Z 18th May 2011

UKV T+6 radar UKV T+0

Impact of Observations T+0 NDP

No obs All obs

No GPSNo doppler wind

Zhihong Li

Background ErrorsJean-Francois Caron

• NMC method – Met Office - SOAR horizontal correlation function

• UKV T+24/T+12, 30 cases

• 180km for streamfunction, 130km for velocity potential and unbalanced pressure and 90km for humidity and logm

• UKV T+6/T+3

• 130 to 30km for velocity potential and stream function

• 60 to 5km for unbalanced pressure and 40 to 5km for humidity

• NDP T+6/T+3 every 6 hours, 75

• 60 -10km vel pot, stream fn , 30-2km unbalanced pressure and 30-2km for humidity

• Ensembles – Meteo France

• Brousseau et al 2011

• 6 members, 26 days, 3hour range

Impact of new Cov Stats and MOPS/LHN in NDP T+0

radarAll obs Zhihong Li/Jean-Francois Caron

Impact of background errors on screen Temperature

Balance and Boundary Updates- rms pstar tendency - NDP

4D-Var Analysis increments at T-30mins, start of window T-30mins

Top – 15Z, 9Z lbc

Middle – 16Z, 15Z lbc

Bottom – 17Z, 15Z lbc

Zhihong Li

Conclusions• Operational convective scale NWP beating

advection type precipitation nowcast from about T+2.5hours

• Good progress being made in use of radar data in NWP-based nowcasting

• However many challenges still to extract the full benefit from the radar data and other observations

• Only just getting access to observations to test real benefit in NWP-based nowcasting

• Modifications to background errors, linear model and other options in DA likely to improve skill

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Other Scientific Challenges

• Matching the observations during the assimilation cycle and first 2 hours of forecast

• Balance and control variables, adaptive vertical grid

• Precipitation bias in rates and area – data assimilation and modelling problems. Model too cellular and can miss some convection – maybe need for more 3dimensional parametrizations?

• Computer resources – should be able to produce forecast within 1 hour

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Questions and answers

6 March 2011 15UTC – Robert Tubbs

SEVIRI Channel 5

SEVIRI Channel 9

UKV Analysis simulated Ch 5

UKV Analysis simulated Ch 9

Thunderstorms 28th June 201112UTC

radarUK4 T+3UKV T+3

Thunderstorms 28th June 201112UTC

radarUK4 T+3UKV T+9

Radar Rain Rates 28/6/2011

9UTC

11UTC

10UTC

12UTC

Hourly cycling NDP – 12UTCoriginal version

T+2T+3

T+1 T+0

Zhihong Li

NDP forecasts for 12UTC 28 June 2011latest UKV-type version

T+2

T+1

T+3

T+0

Zhihong Li

UKPP forecasts for 12UTC 28 June 2011

T+0T+1

T+2T+3

Background

Aim: to replace current UKPP nowcasting system

Hazardous weather , especially flood risk

Boscastle, North Cornwall16th August 2004 Estimated Cost £500million

Data Assimilation/analysis vital for these short period forecastsof 0-6 hours

Current Nowcasts - UKPP

• UKPP analysis of surface rain rate every 5 mins at 2km

• Radar composite plus 2DVAR of UK4 and MSG outside radar area

• Nowcasts every 15mins to 7 hours using T-30, T-15 and T+0 rain analyses to derive field of motion

• Blend UK4 and nowcast using STEPS

• 8 member ensemble

• Hourly temperature, precip type, cloud, wind, visibility etc

• Start 1min after DT but waits 7mins for radar rates and satellite imagery

• Available within 15mins

Comparison of NWP forecasts and STEPS (advection) Nowcasts of Precipitation – RMSF error of 1hour accumulation > 1mm

RMSF=

Exp(Sum/N)0.5

Sum= sum(i-1,N)

Log(Fi/Oi)2

where O=radar estimate

Both smoothed to6km

Nowcasting System

• NWP-based nowcasting system is non-hydrostatic model that resolves convection explicitly.

• Nonlinear Unified Model (UM) is 1.5 km resolution (360 x 288 x 70 levels), has model top at 40 km, and uses 50 s timestep.

• Linear Perturbation Forecast (PF) model and its adjoint: 3 km resolution (180 x 144 x 70 levels) and 100 s timestep.

• 4D-Var uses hourly assimilation windows with linearization states for PF model updated every 10 minutes and UKV every 10mins in OPS

• Observations are extracted in the observation time window T-30 to T+30 minutes. Might change to T-60 to T+0.

• 4D-Var increments are added to UM at initial forecast time T-30 mins (first UM time step). Might change to T-60.

• Runs using latest UKV as boundary conditions, 45min cutoff time

Hourly 3D/4D-Var DA cycle

• Assimilation/Obs window T-0.5 to T+0.5

• LH and cloud (RH) nudging from T-1 to T+0

Schematic diagram of 3D/4D-Var DA cycle with MOPS cloud and LH nudging in SUKF 1.5km hourly nowcasting system

Observations currently available • Surface fields (u, v, p ,T, RH, visibility) [1 min] 1 hr

• Wind profiler (u, v) [15 min] 15min

• Doppler radar radial winds [5 min] 3 per hr NDP*

• Radar reflectivity both direct and indirect [5 min] not used

• Rain rates (radar-derived) [15 min] 15 min NDP

• GPS (integrated humidity path) [10 min] 10min

• Scatterometer winds [~5 min] 1 hr

• Cloud-track winds (Atmospheric Motion Vectors AMVs)

[low res. 30 min, high res. 15 min] low res. only used 1 hr

• VAD Velocity Azimuthal Display winds [1 hr] 1 hr NDP*

• Radiosondes [12 hours or when reported] when reported NDP

• 3D cloud analysis (MOPS) [1 hr] 1 hr NDP

• AMDAR aircraft-derived T, u, v [1 hour batches] 1 hr NDP

• Geostationary Satellite Imagery (clear radiances) [15 min] 1 hr NDP

Observations coming soon

Potential for assimilation of following observations:

• Cloud track winds (u, v) or Atmospheric Motion Vectors (AMVs) at high resolution available but not yet assimilated operationally

• Radar reflectivity (indirect and direct assimilation)

• Satellite-derived cloud top

• Surface cloud cover and cloud base (manual and automatic reporting)