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© Crown copyright 2007

Rainfall predictionDr. Brian Golding

Head of Meteorology Research & DevelopmentMet Office

June 2007


Outline

Extreme rainfall eventsRadar observationExtrapolation nowcastingNWP nowcasting


Extreme rainfall events


Extreme rainfall totals of the UK, Japan & the world

Takara, １９９６, 97, 2004

Maximum Precipitation records of the world

Preston 1893

Eskdalemuir1953

Maidenhead 1901

Halifax 1989

Martinstown1955

From Takara (2004)

UK record (mm)



Extreme rainfall events, 1900-2005, by weather type

Convection

Organised Convection

Front with embedded convection

Front

OrographicBoscastle, Cornwall, 2004

Kent, 1973

Lynmouth, Devon, 1952

Ffestiniog, Wales, 1973

Hampstead, London,1975

Halifax, Yorkshire, 1989

Horncastle, Lincolnshire, 1960

Martinstown, Dorset, 1955

+ = convective

X = convective with frontal forcing

ж = orographic

∆= frontal with embedded convection

□ = frontal

Carlisle, 2005



Scales of Motion & Predictability

Hail shaft

Thunderstorm

Front

Extratropical Cyclone

Space Scale

Lifetime

Predictability

Nowcast

10mins 1 hr 12hrs 3 days

30mins 3 hrs 36hrs 9 days

2mins 15min 3hrs 18hrs

1000km

100km

10km

1km

MCS*

* Mesoscale Convective System


Boscastle: Radar and Gauge Accumulations

200.4 mm


Hampstead, 14th August 1975

Extreme rainfall, 170.8mm in 3 hrs - largest record in LondonStorm consisted of successive clouds, forming to south then drifting north along a stationary convergence lineHigh ground may have been triggerRapid runoff due to hard urban surfaceMajor disruption and several deaths

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40

60

80

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180

0 60 120 180minutes

Cumulative rainfall (mm)Rainfall rate (mm/hr)


West London flood, 3rd August 2004

Silverlink rail services affected between Willesden & Wembley Signals out of action at Charing Cross & London Bridge due to lightning strikeShop ceilings collapsed in StainesDisruption at Heathrow

Parts of Metropolitan, District, Piccadilly and Central Lines closed by floods.


Rain rate profile on 3rd August 2004 at High Wycombe

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200

14:52

14:54

14:56

14:58

15:00

15:02

15:04

15:06

15:08

15:10

15:12

15:14

15:16

15:18

15:20

15:22

15:24

15:26

15:28

Time (UTC)

Rain

rat

e in

mm

/hr

(1 minute average rates)

41mm total


Nowcasting


InformationInformationContentContent

Forecast LengthForecast Length

LogLogScaleScale

Log Scale (days)Log Scale (days)0.010.01 0.10.1 11 1010

NowcastNowcast(Extrapolation Forecast)(Extrapolation Forecast)

LimitLimit((““Perfect ForecastPerfect Forecast””))

NWPNWP(Model Forecast)(Model Forecast)

Blending extrapolation & NWP


The role of Nimrod

A rapid update cyclefor

very short range forecasting of

hazardous weather variables using

high resolution observationsmerged with

NWP forecasts


Outline structure of Nimrod processing

Each cycle (15 mins)Image processing (radar & satellite)Data fusion (images, lightning fixes, in situ obs)Extrapolation (to T+6 hours)Merging with downscaled NWP (4km grid)Diagnosis


Planned radar coverage

1km resolution

2km

5km


Apply gauge adjustment factor

Correct for variations in the vertical reflectivity

profile

Remove spurious echoes

Check for corrupt imageInput Image

Quality controlled and

corrected image

Radar data processing


Raw radar data Radar data (qualitycontrolled and corrected)

25 50 75 100 150 200 250 300 500 mm

Rain gauges(3494 gauges used)

Monthly rainfall for February 1997


Raw Radar data Radar data (qualitycontrolled and corrected)

25 50 75 100 150 200 250 300 500 mm

Rain gauges (3463 gauges used)

Monthly rainfall for June 1997


Generate analysis for full domain by blending:

Corrected radar compositeSatellite visible and infrared imagery calibrated against radarLightning fixes (minimum rain rate)A very short range forecast Surface in situ weather reports

Radar is used where it has good visibilityA variational analysis, combining the

remaining data sources, is used elsewhere

Producing a rain analysis


Rain analysis


“Optic Flow” extrapolation method

Given the “Optic flow” equationdp/dt = ∂p/∂t + u.∂p/∂x + v.∂p/∂y = 0with p the precipitation rate

Calculate estimates of ∂p/∂t, ∂p/∂x & ∂p/∂y in each nxn pixel block containing precipitationFind values of u,v that minimise the residualSmooth the results and extend to non-precipitating areas


3-hr Gandolfforecast

Observed 06:45

Object forecast

Observed 09:45

Optic flow forecast


Merge with NWP model forecast

NWP provides forecast information in areas where the extrapolation forecast has areas of “missing data”Introduces an element of growth and/or decay into the forecastsEnsures continuity between forecast products


Accuracy of 15km rain accumulation vs radarover UK, Oct 03 - Sep 04

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3

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6

7

0 60 120 180 240 300 360 420 480Lead tim e in m inutes

RMSF

mesoscale model

advection

Nimrod

Persistence


Convective scale NWP


Performance Improvements

RMS surface pressure error over the NE Atlantic



Influence of gridlength on Boscastle forecasts

60km forecast from 00UTC

Forecast rainfall accumulations for

1200-1800UTC 16/8/2004

12km forecast from 00UTC 4km forecast from 00UTC 1km forecast from 00UTC

5km radar actual

Clark, 2006


3rd August 2004

1500-1700UTC12km modelvs 1km modelvs radar actual


Lower scale limit of skill 3rd Aug 2004

Uncertainty scale above which forecast of exceedanceof 8mm has useful skill: 12km (blue) and 1km (red) models initialised at 0900UTC 3rd August 2004


13th Aug 2006

1500-1700UTC12km modelvs 1.5km modelvs radar actual


Lower scale limit of skill for 13/8/2006

Uncertainty scale above which forecast of exceedance of 4mm has useful skill: 12km (blue)

and 1.5km (red) models initialised at 0900UTC 13th August 2006


Summary

Nowcasting, based on extrapolation, can provide useful very short range forecasts of high impact weather variables

Convective scale Numerical Weather Prediction should be able to provide more accurate nowcasts at longer lead times

Success will require fundamental scientific research, as well as bigger computers. In particular:

New approaches to data assimilation using new data sources such as radar

Improved models, which represent both boundary layer and convective cloud processes more accurately

Better products which convey the information required by the user

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