ecmwf international session 2003 db-1 weather prediction and the use of weather data the european...

International Session 2003DB-1 ECMWF

Weather Predictionand the use of weather data

The European Centre forMedium-Range Weather Forecasts

ECMWF

Weather Predictionand the use of weather data

The European Centre forMedium-Range Weather Forecasts

ECMWF

David Burridge with help from A. Simmons, G. Kelly, J-N Thepaut,D. Marbouty, A. Thorpe, A. Lorenc, T. Palmer ……


Annual means of the forecast range at which theanomaly correlation of 500 hPa forecasts first reaches The 60%, 655, 70%, 80%, 85% and the 95% levels for the Northern hemisphere (left panel) and theSouthern hemisphere (right panel).


• Peaks beyond 500mm/day (670mm at Anduze)

• A large area with more than 200mm in 24h

• Early warnings were available


NOAA16 9 Sept. 2002 1220UTC


Le Gardon à Collias

9 Sept 2002

11 Sept


Forecast consistencyfive consecutive T511 forecasts valid at the same time


T511 precipitation forecastscumulated over 24h and valid for: 20020908 18z to 20020909 18z


CTRL

Prob.RRR>50mm

Prob. (area)RRR>50mm

Sa 7/9 Fr 6/9 Th 5/9 We 4/9 Tu 3/9

EPS precipitation probabilitiesvalid for TP cumulated over 24h: 20020908 18z to 20020909 18z


S-France flood: Summary

A stationary convective system generates huge amounts of precipitation in a few hours ( ~ 600 mm/24h in some places) over a limited area.

The medium-range deterministic forecast is very consistent. The synoptic pattern is correctly forecasted already 5 days in advance. Good ability of the model to simulate the basic ingredients for severe convection.

EPS supports the deterministic forecast showing unusual high probability values. EFI index gives early warning but spread a large area.


Precipitation accumulated over the Po' river catchmentarea predicted by the 51 EPS members started on2 November 1994 and run for 10 days (grey lines, cyan for the control)and the 6 nested LEPS members (violet lines, green for the control)started on 3 November and run for 3 days. The red line depicts thearea-average over the river-catchment computed from synoptic observations.


Global Weather Prediction – A Triumph

for science and computing

Today we have global operational forecasts with useful skill varying between 7 and 8 days;

During the satellite data era there has been a gain of more than 2 days and 4 day gain since Miyakoda’s experimental forecasts which he carried out in the late 1960s (published in 1972); the hemispheric skill that can be achieved with the 1950s observing system is around 6 days – truly a triumph for science and computing


Number of used observational data per 12 UTC cycle inECMWF's operational assimilation system, 1997-2002.


N.HEM LAT 20.000 TO 90.000 LON -180.000 TO 180.000

ANOMALY CORRELATION FORECAST

1000hPa GEOPOTENTIAL

FORECAST VERIFICATION 12UTC

%

10DECEMBER

2002

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 1 2 3 4 5 6 7 8 9JANUARY

2003

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no_sat_w T+ 96control_w T+ 96no_upper_w T+ 96


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Wednesday 25 December 2002 12UTC ECMWF Forecast t+96 VT: Sunday 29 December 2002 12UTC 1000hPa geopotential height

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ECMWF Analysis VT:Sunday 29 December 2002 12UTC 1000hPa geopotential height

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Wednesday 25 December 2002 12UTC ECMWF Forecast t+96 VT: Sunday 29 December 2002 12UTC 1000hPa geopotential height

A F1

F2

Four-day forecasts for Sunday 29 December 2002A – analysisF1 – full systemF2 - without satellite data


Real-time data from new research satellites

ENVISAT from ESA, flying a range of instruments to measure ocean waves and ozone - LAUNCHED

AQUA from NASA, flying AIRS the first of a new generation of high-resolution infrared instruments - LAUNCHED


Major enhancements of the operational satellite observing system

SEVIRI on MSG, the new generation of EUMETSAT geostationary satellites

- 2003

IASI and other instruments on the first generation of EUMETSAT polar-orbiting satellites

- LATE 2005


THORPEX?THORPEX?

• significant failures in forecasting high-impact weather

• the inability to extend the range of skillful predictions beyond 7 days

• poor prediction of tropical-extratropical interactions

• inadequate skill in predicting specific aspects of mesoscale weather, e.g., precipitation

A combination of inaccurate initial conditions and errors in model formulation contribute to:


Rossby Wave Dispersion: 6-28 November 2002Time/Long. Diagram: 250-mb Meridional Wind : 35-60 N

UK Japan* *

UKCal.**

Cyclogenesis

TornadoesOil Tanker

Alps FloodFoehn Wind

Cyclogenesis

Snow/Ice Storm

Moroccan Flood

Alps flood

Cyclogenesis

India/T.C.

Cyclogenesis

Cold-Air

Cold-Air

6 Nov6 Nov

9 Nov9 Nov

12 Nov12 Nov

15 Nov15 Nov

18 Nov18 Nov

21 Nov21 Nov

24 Nov24 Nov

27 Nov27 Nov


THORPEXTHORPEX ObjectivesObjectives• advance basic knowledge of global-to-regional influences on

the predictability of high-impact weather

• contribute to the development of a dynamically-controlled interactive forecast system

• consider short-range (0-3 days), medium-range (3-7 days) and week-two forecast problems: the “middle ground” between weather and climate

• develop methods for assessing the economic and societal value of improved weather forecast skill


Interactive Forecast SystemInteractive Forecast System

• optimal design of fixed and adaptive observing

system

• use of targeting techniques for observing and data

assimilation

• two-way flow of information between model and

observing system


ATLANTIC OCEAN

Zero-pressureB alloon

G ondola(24 sonde capacity)

6 hoursbetw eendrops

NORTHAMERICA

EUROPE

O rbC om mLEO S ate llite

Driftsonde System Driftsonde System

High-resolution vertical profiles of Temperature, Wind, Moisture, Pressure

Ground Station

Hourly data at flight level


An exampleAn example of of possible possible Driftsonde Driftsonde deployment from Japandeployment from Japan


Numerical Weather Prediction

• Errors in initial conditions and forecast model dominate.

• NWP centres concentrate on

– sophisticated data assimilation schemes,– built round high-resolution models.

• Error growth is important:

• Sensitivity to initial conditions,• Chaotic error growth - limits to

predictability,• THORpex international research

programme.


Assimilation of cloud & precipitation

Weather systems (In order of priority + possibility?)

1. Resolved, dynamically-forced, vertical motion (eg depressions)

2. Horizontally (but not vertically) resolved forcings and feedback on parametrized boundary-layer processes (eg Strato-Cu)

3. Resolved forcings and feedback on parametrized deep convection (eg MCS, embedded convection in fronts)

4. Scattered convection.


Sources of errors in model predictions

• Initial conditions

• Forecast model

• External forcing

Errors in all these accumulate,

and in some circumstances grow,

to limit the predictability.


Seasonal Prediction

• Chaotic growth Detailed initial conditions of the atmosphere are not important.

• Coupled systems have components without this rapid growth, e.g. ocean, land surface.

• Seasonal forecasts do depend on initial conditions for these components.

Data assimilation for coupled models.


ACC: 1987 – 1994 (32 start dates) DEMETER multi-model ECMWF CNRM UKMO LODYC Persistence

Niño-3 SST hindcasts


Real-time dynamical multi-model seasonal forecast

Risk of wet / dry winter 2002/03


Climate Prediction

• Changes in external forcing dominate.

• Model validation and development is crucial

• Data assimilation is important for this.


Coupled Data Assimilation

• Skill in seasonal predictions comes from slowly evolving sea and land surface.

• We do not have enough observations for these; the atmosphere is relatively better known.

Need atmosphere in coupled Data Assimilation, even though atmosphere Initial Conditions are not important.

• But don’t need fully coupled Data Assimilation:

– the insertion of obs into each model is independent– with coupling, via fluxes, in the forecast steps.


Bias

• If a model has bias, it is impossible for Data Assimilation to give consistent bias-free fields and fluxes.

• Exacerbated by biased obs & Data Assimilation methods.

Coupled Data Assimilation must give higher priority to coping with bias than does NWP Data Assimilation.


Summary

• Non-NWP Data Assimilation has different priorities

• Biases are unavoidable, and must be allowed for in coupled Data Assimilation

• Re-analyses useful to validate seasonal prediction systems


Prospects for NWP and Seasonal Forecasting

• Comprehensive Earth-System forecast facilities

• Comprehensive Earth-System assimilation facilities

• Strong Satellite Capabilities

• Improving ground-based observing systems with experimental

systems to provide guidance

• Robust and efficient Numerical methods

• Thoroughly Validated Parametrizations

• Extensive Ensemble Capabilities

• Strong Computing

• We may confidently expect

– Increasing forecast quality– Further spin-offs and new products


The Weather Community can advance Global monitoring of the

environment in:• SYSTEMS FOR RISK ASSESSMENT

– delivering operational support to weather-related risk management (early warning, impact assessment and reaction) in sensitive areas for: floods; forest fires; oil spills; and support for humanitarian aid

• GLOBAL ATMOSPHERE MONITORING

– delivering regular assessments of state of the atmosphere with particular attention to aerosols, ozone, UV radiation and specific pollutants

• GLOBAL OCEAN MONITORING

– in support of seasonal weather predictions, global change research, commercial oceanography and defence.

• GLOBAL VEGETATION MONITORING

– to assess carbon fluxes to/from the biosphere.


Areas for GMES Collaboration Global Monitoring / Forecasting of

Greenhouse Gases2.1 Map the seasonal

variations of total column amounts of Greenhouse Gases

2.2 Model and assimilate ocean colour data, to estimate ocean carbon uptake.

2.3 Model and assimilate global aerosol information (to improve weather forecasts & the use of ocean colour data)

2.4 Model and assimilate information on the Land Biosphere and carbon cycle.


Global Monitoring / Forecasting of Reactive Gases: The Chemical Weather Forecast

Current operational ozone monitoring capability is a good basis for developing a global capability to monitor reactive gases and associated aerosols

3.1 Integrate chemical modules with weather models, to provide global assimilation & forecasts of the distributions of

•ozone and its precursors•sulphate aerosol•other aerosol

The global models can drive regional chemistry / air quality models.

Ozone

CO


Environmental PredictionsEnvironmental Predictions can be very effective if environmental models are driven by

Global Earth-System Assimilations

Global Deterministic & Ensemble Forecasts

Inverse Model for Carbon attribution

Atmosphere Regional Weather Model

Chemical & Aerosol Transport Model

Trajectory Model

Hydrological Model

Land Crop Model

Fire Model

Disease Model

Ocean Oil-spill Model

Storm Surge Model

Coastal Zone Model

Regional Ocean Model

ecmwf international session 2003 db-1 weather prediction and the use of weather data the european...

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