ATMOSPHERIC PROCESSES in SPACE-ATMOSPHERE-SEA/LAND system.

ATMOSPHERIC PROCESSES in SPACE-ATMOSPHERE-SEA/LAND system

Submodels

WMO WEATHER FORECASTING RANGES

Nowcasting A description of current weather parameters and 0 -2 hours description of forecasted weather parameters

Very short-range Up to 12 hours description of weather parameters

Short-range Beyond 12 hours and up to 72 hours description of weather parameters

Medium-range Beyond 72 hours and up to 240 hours description of weather parameters

Extended-range Beyond 10 days and up to 30 days description of weather parameters, usually averaged and expressed as a departure from climate values for that period.

Long-range

Monthly outlook

Three month or 90 day outlook

Seasonal outlook

From 30 days up to two years

Description of averaged weather parameters expressed as a departure (deviation, variation, anomaly) from climate values for that month (not necessarily the coming month).

Description of averaged weather parameters expressed as a departure from climate values for that 90 day period (not necessarily the coming 90 day period).

Description of averaged weather parameters expressed as a departure from climate values for that season.

Climate forecasting

Climate variability prediction

Climate prediction

Beyond two years

Description of the expected climate parameters associated with the variation of inter-annual, decadal and multi-decadal climate anomalies.

Description of expected future climate including the effects of both natural and human influences.

SYNOPSYNOP

AIROLOGICAL DATAAIROLOGICAL DATA

AIROCRAFSAIROCRAFS

RadarRadar

RADARS,RADARS,

r = 100kmr = 100km

The system of equations (conservation laws applied to individual parcels of air)

(from E.Kalnay)

• conservation of the 3-dimensional momentum (equations of motion),

• conservation of dry air mass (continuity equation),

• the equation of state for perfect gases,

• conservation of energy (first law of thermodynamics),

• equations for the conservation of moisture in all its phases.

They include in their solution fast gravity and sound waves, and therefore in their space and time discretization they require the use of smaller time steps, or alternative techniques that slow them down. For models with a horizontal grid size larger than 10 km, it is customary to replace the vertical component of the equation of motion with its hydrostatic approximation, in which the vertical acceleration is neglected compared with gravitational acceleration (buoyancy). With this approximation, it is convenient to use atmospheric pressure, instead of height, as a vertical coordinate.

V. Bjerknes (1904) pointed out for the first time that there is a complete set of 7 equations with 7 unknowns that governs the evolution of the atmosphere:

/a adm

dt

vF

.( )t

v RTp

dt

dp

dt

dTCQ p

.( ) ( )q

q E Ct

v

pC

R

p

pT

0

1p

ds d QC

dt dt T

2

(2 )( sin cos )cos cos

(2 ) sincos

(2 ) coscosr

du p uF v w

dt r r

dv p u vwF u

dt r r r

dw p u vg F u

dt r r r

ECMWF: T511L60 – 40 km; EPS: T255L60 – 80 km; DWD: GME (L41) – 40 km; LM (L3550) – (2.8)7 km; France: ARPEGE(L41)-23-133km; ALADIN (L41)– 9 km;

HIRLAM: -------------- (L16-31) – 5-55 km;

UK: UM(L30) – 60 km; (L38) – 12 km; USA: AVP (T254L64) – 60 km; ETA (L60) – 12 km; Japan: GSM(L40) – 60 km; MSM(L40) – 10 km.

RusFed.: T85L31 – 150 km; (L31) – 75 km. Moscow region (300kmx300km) - 10 km.

2003, December

Coordinate systems: p, sigma, z, eta, hybrid

Models of atmosphere: Steps: global - 40-60 km, local 7-12 km; Methods: splitting, semi-Lagrangian scheme (23), ensembles, nonhydrostatic, grids

Data assimilation: 3(4)D-Var, Kalman filter

Reanalyses NCEP / NCAR USA 50-years (1948-…; T62L28~210km) Reanalyses-2 (ETA RR 32 km, 45 layers) ECMWF ERA-15 (TL106L31~150km, 1979-1993), ERA-40 (TL159L60~120km, 3D-Var, mid1957-2001)

FEATURES OF INFORMATION AND COMPUTATIONAL TECHNOLOGIES IN ATMOSPHERIC SCIENCES

One method (which used by ECMWF forecast system) based on the finding grand with help of the part of the eigenvectors of the linear operator L

which received after linearization of the operator N from finite-difference scheme of the system of the using forecasting thermo- hydrodinamic equations

)(1 jh

jh

jh N

,

where jh is grid vector-function

Tjh

jh

jh

jh

jh

jh Tpwvu ,...),.,,( , other

notations in this formula are usual. Plus of this method is good physical meaning but minus consist in first of all in

necessary finding eigenvectors of the linearization L and then barest necessity of the making sufficiently big quality of the additional forecasts.

Modern and Possible further development computational technologies

ensemble simulation

ECMWF: FORECASTING SYSTEM - DECEMBER 2003

Model:Smallest half-wavelength resolved: 40 km (triangular spectral truncation 511)Vertical grid: 60 hybrid levels (top pressure: 10 Pa)Time-step: 15 minutesNumerical scheme: Semi-Lagrangian, semi- implicit time-stepping formulation.Number of grid points in model: 20,911,680 upper-air, 1,394,112 in land surface and sub- surface layers. The grid for computation of physical processes is a reduced, linear Gaussian grid, on which single- level parameters are available. The grid spacing is close to 40km.Variables at each grid point (recalculated at each time-step):Wind, temperature, humidity, cloud fraction and water/ ice content, ozone content (also pressure at surface grid-points)Physics: orography (terrain height and sub-grid-scale), drainage, precipitation, temperature, ground humidity, snow-fall, snow-cover & snow melt, radi ation (incoming short-wave and out-going long-wave), friction (at surface and in free atmosphere), sub-grid-scale orographic drag - gravity waves and blocking effects, evaporation, sensible & latent heat flux, oceanic waves.

ECMWF: FORECASTING SYSTEM - DECEMBER 2003

Data Assimilation: Analysis: Mass & wind (four-dimensional variational multi- variate analysis on 60 model levels) Humidity (four-dimensional variational analysis on model levels up to 250 hPa) Surface parameters (sea surface temperature from NCEP Washington analysis, sea ice from SSM/I sat ellite data), soil water content, snow depth, and screen level temperature and humidityData used: Global satellite data (SATOB/AMV, (A)TOVS, Quikscat, SSM/I, SBUV, GOME, Meteosat7 WV radiance), Global free-atmosphere data (AIREP, AMDAR, TEMP, PILOT, TEMP/DROP, PROFILERS), Oceanic data (SYNOP/SHIP, PILOT/SHIP, TEMP/SHIP, DRIBU), Land data (SYNOP). Data checking and validation is applied to each parameter used. Thinning procedures are applied when observations are redundant at the model scale.

the Penn State/NCAR Mesoscale Model (e.g., Dudhia, 1993),

the CAPS Advanced Regional prediction System (Xue et al, 1995),

NCEP's Regional Spectral Model (Juang et al, 1997),

the Mesoscale Compressible Community (MCC) model (Laprise et al, 1997),

the CSU RAMS Tripoli and Cotton (1980),

the US Navy COAMPS (Hodur, 1997).

WRF Development TeamsNumerics and

Software (J. Klemp)

Data Assimilation (T. Schlatter)

Analysis and Validation

(K. Droegemeier)

Community Involvement

(W. Kuo)

Operational Implementation

(G. DiMego)

Ensemble Forecasting

(D. Stensrud)

Analysis and Visualization (L. Wicker)

Model Testing and Verification

(C. Davis)

Wor

kin

g G

rou

ps

Standard Initialization (J. McGinley)

3-D Var (J. Derber)

Advanced Techniques (D. Barker)

Dynamic Model Numerics

(W. Skamarock)

Software Architecture,

Standards, and Implementation (J. Michalakes)

Data Handling and Archive (G. DiMego)

Regional Climate Modeling

(proposed)

Workshops, Distribution, and Support

(J. Dudhia)

Model Physics (J. Brown)

Atmospheric Chemistry (P. Hess)

Land Surface Models

(J. Wegiel)

Operational Requirements

(G. DiMego)

Operational Forecaster

Training (T. Spangler)

Courtesy NCAR

WG1

WG2

WG3

WG4

WG10

WG7

WG6

WG13

WG5

WG8

WG11

WG14

WG12

WG9

WG15

WG16

1 10 100 km

Cumulus ParameterizationResolved Convection

LES PBL Parameterization

Two Stream Radiation3-D Radiation

Model Physics in High Resolution NWP

Physics“No Man’s Land”

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

Weather Research and Forecasting Model

Goals: Develop an advanced mesoscale forecast and assimilation system, and accelerate research advances into operations

36h WRF Precip Forecast

Analyzed Precip

27 Sept. 2002

• Collaborative partnership, principally among NCAR, NOAA, DoD, OU/CAPS, FAA, and university community• Multi-agency WRF governance; development conducted by 15 WRF Working Groups • Software framework provides portable, scalable code with plug-compatible modules• Ongoing active testing and rapidly growing community use

– Over 1,400 registered community users, annual workshops and tutorials for research community– Daily experimental real-time forecasting at NCAR , NCEP, NSSL, FSL, AFWA, U. of Illinois

• Operational implementation at NCEP and AFWA in FY04

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

Hurricane Isabel

NOAA –17 AVHRR 13 Sep 03 14:48 GMTFrom Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

Hurricane Isabel Track

18/1700Z

10 km WRFInitialized 15/1200Z

4 km WRFInitialized 17/0000Z

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

Hurricane Isabel 3 h Precip Forecast

Initialized:12 UTC 15 Sep 03

WRF Model10 km grid

5 day forecast

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

48 h Hurricane Isabel Reflectivity Forecast

4 km WRF forecastRadar Composite

Initialized 00 UTC 17 Sep 03

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

Hurricane Isabel Reflectivity at Landfall

Radar Composite

18 Sep 2003 1700 Z

41 h forecast from 4 km WRF

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

Hurricane Isabel Surface-Wind Forecast

Initialized:00 UTC 17 Sep 03

WRF Model4 km grid

2 day forecast

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

• Terrain-following hydrostatic pressure vertical coordinate

• Arakawa C-grid, two-way interacting nested grids (soon)

• 3rd order Runge-Kutta split-explicit time differencing

• Conserves mass, momentum, dry entropy, and scalars using flux form prognostic equations

• 5th order upwind or 6th order centered differencing for advection

• Physics for CR applications: Lin microphysics, YSU PBL, OSU/MM5 LSM, Dudhia shortwave/RRTM longwave radiation, no cumulus parameterization

WRF Mass Coordinate Core

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

Model Configuration for 4 km Grid

• Domain

– 2000 x 2000 km, 501 x 501 grid– 50 mb top, 35 levels– 24 s time step

• Initialization

– Interpolated from gridded analyses– BAMEX: 40 km Eta CONUS analysis– Isabel: 1o GFS global analysis (~110 km)

• Computing requirements

– 128 Processors on IBM SP Power 4 Regatta– Run time: 106 min/24h of forecast

From Joe Klemp, NCAR (Bad Orb, 23-27.10.03 2003)

North American Early Guidance System

5/31/20096 km aerosols in radiative transfer & reflectivity

6 km WRF aerosols

5/31/20087 km absorption scattering in radiative transfer

7 km WRF improved physics

5/31/20059 km AIRS, GOES imagery & move top to 2mb

9 km NMM top @ 2mb hourly output

5/31/20068 km WRF 4DDA8 km WRF

5/31/20105 km NPP, advanced 4DDA, NPOESS, IASI & air quality

5 km WRF L100

2/28/200410 km hourly update & improved

background error cov.10 km Meso Eta improved physics

9/30/200212 km 3DVAR radial velocity12 km Meso Eta

DateData AssimilationPrediction Model

Global Forecast System (GFS)

5/31/2009NPP, integrated SST analysis40 km / L80

5/31/2008Aerosols in radiative transfer, GIFTS40 km / L80

5/31/20053-D Background error covariance, cloud analysis, minimization

45 km / L64

5/31/2006Absorption / scattering in radiative transfer

45 km / L64 + improved microphysics

5/31/2010Advanced 4DDA, NPOESS, IASI + air quality

35 km / L100

2/28/2004Grid point version, AIRS, GOES imagery

T-254 / L64 add 2 passive tracers

9/30/20023D-VAR, AMSU-B, QuikscatT-254 / L64

DateData AssimilationPrediction Model

Timeline for WRF at NCEP

• North American WRF: Operational in FY05

• Hurricane WRF: Operational in FY06

• Rapid Refresh (RUC) WRF (hourly): Operational in FY07

• WRF SREF : Operational in FY07

• Others? (Fire Wx, Homeland Security, etc.) using best WRF deterministic model

The Unified Model

The Unified Model is the name given to the suite of atmospheric and oceanic numerical modelling software developed and used at the Met Office. The formulation of the model supports global and regional domains and is applicable to a wide range of temporal and spatial scales that allow it to be used for both numerical weather prediction and climate modelling as well as a variety of related research activities. The Unified Model was introduced into operational service in 1992. Since then, both its formulation and capabilities have been substantially enhanced.

New DynamicsA major upgrade to the Met Office Global Numerical Weather Prediction model was implemented on 7th August 2002.

SubmodelsThe Unified Model is made up of a number of numerical submodels representing different aspects of the earth's environment that influence the weather and climate. Like all coupled models the Unified Model can be split up in a number of different ways, with various submodel components switched on or off for a specific modelling application.

The Portable Unified Model (PUM)A portable version of the Unified Model has also been developed suitable for running on workstations and other computer systems.

http://www.metoffice.com/research/nwp/numerical/unified_model/index.html

The Met Office Global Numerical Weather Prediction model was implemented on 7th August 2002. The package of changes was under trial for over a year and is known as "New Dynamics". This document details some of the key changes that are part of the New Dynamics package.

Non-hydrostatic model with height as the vertical co-ordinate. Charney-Philips grid-staggering in the vertical, Arakawa C-grid staggering in the horizontal, Two time-level, semi-Lagrangian advection and semi-implicit time stepping. Edwards-Slingo radiation scheme with non-spherical ice spectral files Large-scale precipitation includes prognostic ice microphysics. Vertical gradient area large-scale cloud scheme. Convection with convective available potential energy (CAPE) closure, momentum transports and convective anvils. A boundary-layer scheme which is non-local in unstable regimes. Gravity-wave drag scheme which includes flow blocking. GLOBE orography dataset. The MOSES (Met Office Surface Exchange Scheme) surface hydrology and soil model scheme.Predictor-corrector technique with no extraction of basic state profile. Three-dimensional Helmholtz-type equation solved using GCR technique.

http://www.metoffice.com/research/nwp/numerical/unified_model/new_dynamics.html

The operational forecast system at Météo-France is based on two different numerical applications of the same code 1. ARPEGE-IFS, 2. additional code to build the limited area model ALADIN.

The ARPEGE-IFS has been developed jointly by Météo-France and ECMWF (ARPEGE is the usual name in Toulouse and IFS - in Reading): ECMWF model for medium range forecasts (4-7 days) a Toulouse variable mesh version in for short range predictions (1-4 days)

The ALADIN library has been developed jointly by Météo-France and the national meteorological or 14 hydrometeorological services: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Hungary,Moldova, Morocco, Poland, Portugal, Romania, Slovakia,Slovenia, Tunisia.

3535

325325

32532540(35)40(35)

the hydrostatic model , 41(31) layers and horizontal resolution ~ 40(60) km, prognostic equations: horizontal wind components, temperature, specific

humidity, specific cloud water content and surface pressure, physical processes: a comprehensive representation of the precipitation

process, a massflux convection parameterisation, a radiation model with cloud-radiation interaction, turbulent exchange in the free atmosphere based on a level 2 scheme, surface layer fluxes based on a bulk approach, a two layer soil model including energy and mass budget equations for snow cover and the representation of sub-grid scale orographic effects,

the topography of the earth's surface.

nonhydrostatic model,

resolution ~ 2.8 (7) km, GME + 3 additional

prognostic equations: vertical wind speed, pressure deviation, turbulent kinetic energy (TKE),

the vertical turbulent diffusion (2.5 scheme), a laminar sublayer at the earth's surface.

Forecast variables ),/,,( tzpf

Data supply from DWD’s spwvu ,/,,

LM or GME forecast models Numerical scheme Euler-Cauchy with iteration Interpolation 1st order in time, 2nd or 3rd order in space.

Daily routine (ca. 1500 trajectories) 1. LM trajectories (7 km, Central and western Europe):

48h forward trajectories for 36 nuclear and chemical installations.

2. GME trajectories (60km resolution, worldwide): 120h forward trajectories for 60 European nuclear sites, 120h backward trajectories for 37 German radioactivity measuring sites, backward trajectories for the international GAW stations, backward trajectories for 5 African cities in the METEOSAT-MDD program, dis-

seminated daily via satellite from Bracknell, backward trajectories for the German research polar stations Neumayer (An-

tarctica) and Koldewey (Spitzbergen) and the research ships 'Polarstern' and 'Meteor'.

Operational emergency trajectory system(Trajectory system for scientific investigations)

1. LM or GME trajectory models2. Data supply from LM or GME forecasts or analyses from current

database or archives3. Foreward and backward trajectories for a choice of offered or

freely eligible stations at optional heights and times in the currenttime period of 7 - 12 days

4. Interactive menue to be executed by forecasters, operational 24h.

Further Development of the Local Systems LME and LMK 2003 to 2006

LME: Local model LM for whole of Europe; mesh size 7 km and 40 layers; 78-h forecasts from 00, 12 and 18 UTC.

LMK: LM-”Kürzestfrist”; mesh size < 3 km and 50 layers; 18-h forecasts from 00, 03, 06, 09, 12, 15, 18, 21 UTC for Germany with explicit prediction of deep convection.

1. Data assimilation

• 2 Q 2005 Use satellite (GPS) and radar data (reflectivity, VAD winds)

• 1 Q 2006 Use European wind profiler and satellite data

Further Development of the Local Systems LME and LMK 2003 to 2006

2. Local modelling

• 2 Q 2004 Increase model domain (7 km mesh) from 325x325 up to

753x641 gridpoints (covering whole of Europe), 40 layers

• 3 Q 2005 New convection scheme (Kain-Fritsch ?)

Europa

LMK: LM-Kürzestfrist

Model-based system for nowcasting and very short range forecasting

Goals:Prediction of severe weather on the mesoscale.Explicit simulation of deep convection.Method:18-h predictions of LM initialised every three hours,

mesh size < 3 km

Usage of new observations:SYNOP: Every 60 min, METAR:Every 30 min,GPS: Every 30 min, VAD winds: Every 15 min,Reflectivity: Every 15 min, Wind profiler: Every 10 min,Aircraft data.

00 03 (UTC)00211815120906

+3h

+6h

+9h

+12h

+18h

+15h

LMK: A new 18-h forecast every three hours

High-resolution Regional Model HRM

• Operational NWP Model at 13 services worldwide• Hydrostatic, (rotated) latitude/longitude grid • Operators of second order accuracy• 7 to 28 km mesh size, various domain sizes• 20 to 35 layers (hybrid, sigma/pressure)• Prognostic variables: ps, u, v, T, qv, qc, qi

• Same physics package as GME• Programming: Fortran90, OpenMP/MPI for

parallelization• From 00 and 12 UTC: Forecasts up to 78 hours• Lat. bound. cond. from GME at 3-hourly intervals

General structure of a regional NWP system

Topographical data

Initial data (analysis)

Lateral boundary data

RegionalNWPModel

Direct modeloutput (DMO)

GraphicsVisualization

MOSKalman

Applications Wave model,Trajectories

VerificationDiagnostics

Short Description of the High-Resolution Regional Model (HRM)

Hydrostatic limited-area meso- and meso- scale numerical weather prediction model

Prognostic variables

Surface pressure ps

Temperature T

Water vapour qv

Cloud water qc

Cloud ice qi

Horizontal wind u, v

Several surface/soil parameters

Diagnostic variables

Vertical velocity Geopotential Cloud cover clc

Diffusion coefficientstkvm/h

Current operational users of the HRM• Brazil, Directorate of

Hydrography & Navigation

• Brazil, Instituto Nacional de Meteorologia

• Bulgaria, National Meteoro-logical & Hydrological Service

• China, Guangzhou Regional Meteorological Centre

• India, Space Physics Lab.

• Israel, Israel Meteorological Service

• Italy, Italian Meteorological Service

• Kenya, National Meteorological Service

• Oman, National Meteoro-logical Service (DGCAM)

• Romania, National Meteoro-logical & Hydrological Service

• Spain, National Met. Institute

• United Arab Emirates, National Met. Institute

• Vietnam, National Meteoro-logical & Hydrological Service; Hanoi University

Numerics of the HRM• Regular or rotated latitude/longitude grid• Mesh sizes between 0.25° and 0.05° (~ 28 to 6 km)• Arakawa C-grid, second order centered differencing• Hybrid vertical coordinate, 20 to 35 layers• Split semi-implicit time stepping; t = 150s at = 0.25°• Lateral boundary formulation due to Davies• Radiative upper boundary condition as an option• Fourth-order horizontal diffusion, slope correction• Adiabatic implicit nonlinear normal mode initialization

Physical parameterizations of the HRM -two stream radiation scheme (Ritter and Geleyn, 1992)

including long- and shortwave fluxes in the atmosphere and at the surface; full cloud - radiation feedback; diagnostic derivation of partial cloud cover (rel. hum. and convection)

• Grid-scale precipitation scheme including parameterized cloud microphysics (Doms and Schättler, 1997)

• Mass flux convection scheme (Tiedtke, 1989) differentiating between deep, shallow and mid-level convection

• Level-2 scheme of vertical diffusion in the atmosphere, similarity theory (Louis, 1979) at the surface

• Two-layer soil model including snow and interception storage; three-layer version for soil moisture as an option

Computational aspects of the HRM

• Fortran 90 and C (only for Input/Output: GRIB code)

• Multi-tasking for shared memory computers based on standard Open-MP

• Efficient dynamic memory allocation

• NAMELIST variables for control of model

• Computational cost: ~ 3100 Flop per grid point, layer and time step

• Interface to data of the global model GME available providing initial and/or lateral boundary data

• Build-in diagnostics of physical processes

• Detailed print-out of meteographs

Total wallclock time (min for 24 h) for HRM - Africa (361x321, 31 layers, 28 km) on an IBM RS/6000 SP

242,57

128,00

97,5173,19 62,93 53,48 47,63 42,87

0

50

100

150

200

250

300

0 2 4 6 8 10 12 14 16 18

nproc

t(m

in)

Total Wallclock time (min)

Time distribution (%) of the main processes of HRM on an IBM RS/6000 SP

2,4 4,3

39,5

25,2

7,9

4,1

3,2

3,6

9,8

Start up of HRM

l.b.c. update

Diabatic processes

Explicit forecast

SI Scheme

Asselin filtering

Condensation/evaporation

Diagnostics/meteographs

Post-processing GRIB files

Further Development of the HRM 2003 to 2006

• An MPI version of HRM for Linux PC Clusters, developed by Vietnam, is available to all HRM users since July 2003.

• A 3D-Var data assimilation scheme developed by Italy will be available to experienced HRM users early 2004.

• The physics packages in GME and HRM will remain exactly the same.

• The interaction between the different HRM groups should be intensified.

• A first HRM User’s Meeting will take place in Rio de Janeiro (Brazil) in October 2004.

Univ LancasterUniv. BristolECMWF

WL|Delft,RIZA

SMHI

JRC IspraUniv. Bologna

DWDDMI

GRDC

1) Run the complete assimilation-forecast system for GME and LM for the three historical flood events for a period of roughly 2 weeks for each flood event.

2) Perform for the three flood events high resolution analyses of 24h precipitation heights on the basis of surface observations.

3) Develop a prototype-scheme for near real-time 24h precipitation analysis on the basis of Radar-data and synoptic precipitation observations.

In addition to these tasks the operational model results according to task 1) for the period of the Central European flood were retrieved from the archives and supplied to the project ftp-server.

Deutscher Wetterdienst (DWD) meteorological data set

for the development of a flood forecasting systemDWD prepared data sets which include all meteorological fields necessary asinput fields to hydrological models. Four flood cases in different Europeanriver basins for different seasons (autumn, winter and summer) wereinvestigated:

a) Po – 1994, November, Autumn,b) Rhine, Meuse – 1995, January, Winter,c) Odra – 1997, July, Summer,d) Elbe – 2002, August, Summer.

The fields are based on the analysis of observed precipitation and on model forecasts:

48 h forecasts by DWD's limited area model LM (ca. 7 km resolution, model area is Central Europe, data provided at hourly intervals);

156 h forecasts by DWD's global model GME (model resolution ca. 60 km, data provided at 6 hourly intervals on a 0.75o

0.75o grid with NW-corner at 75o N, 35o W and SE-corner at 30o N, 45o E);

analyses of 24 h precipitation observations for the LM area in ca. 7 km resolution.

Maps of the constant fields for GME and LM.

a) b)

c)

c) LM model prediction(18 to 42 hours forecast).

PRECIPITATION DISTRIBUTION (kg/m2)for 05 Nov, 1994, 06 UTC to 06 Nov, 1994, 06 UTC:

a) analysis basedon synoptic (631) stations b) analysis based

on synoptic (631) and MAP (5173) stations

• Austria 263

• Czech Republic 800

• Germany 4238

• Poland 1356

• Switzerland 435

• Alltogether 7092

2002 2003 2004 2005 2006

ECMWF 0.96 Tf

TL511 (40km) L60

10 Tf 20 Tf TL511(40km) L60

TL799(25km) L91

DWD 1.92 Tf

60km L31

7 km L35

2.88 Tf

40km L40

7 / 2 km L35

18-28 Tf

30km L45

NCEP 7.3 Tf

T170(80km) L42

12km L60

T254(50km) L64

15.6 Tf

TL611(40km) L42

8 km

28 Tf 2007:

G 30km

L 5 km

JMA Japan 0.768 Tf

T106(120km) L40

20 / 10 km L40

TL319(60km) L42

6 Tf

5 km L50

20 Tf 2007: TL959(20km) L60

CMA China 0.384 Tf

T213(60km) L31

25 km L20

3.84 Tf ?

15 km 2008: 5 km

HMC Russia 35 Gf

T85(150km) L31

75 km L30

T Tf ?

T169(80km) L31

Machine Processors Memory Storage Tape2 IBM Cluster 1600 1820 2500 GB 12 TB5 x IBM p660 nodes 26 40 GB 20 TB 73 3 HP K580 mashines 18 22 GB 0.4 TB

Computer equipment being readied for operational use

ECMWF: EQUIPMENT IN USE (end of 2003)

Central Computer System (CCS)

2500 TB84 TB2752 MB

2752 1.8+1.3GHz

Phase II

6/2004

1250 TB42 TB1408 MB

1408 1.3GHz

Phase I

9/2002

200 TB30 TB1216 MB

2432 375MHz

Current 2001

Tape Storage

Disk Space

MemoryProcessors

Clock SpeedPhase /

Date

But what are we going to do if we have not CCS?

LINUX (Red Hat 7.3)PGI Workstation 4.0 (Portland Group Fortran and C++) HRM DWD (hydrostatic High Resolution Model) 93 x 73, 31 Layers, 0.1250 grid spacing (14 km), forecast for 48 hours

AMD Duron 1300MHz 384 Mb PC 133 SDRAM 96 minAMD Athlon XP 1800+ MHz 256 Mb DDR266 RAM 81 minPentium 4 2.4 GHz 512 Mb DDR333 SDRAM 70 minIntel Xeon Workstation 1 processor 2.4 GHz 2048 Mb RDRAM PC 800 60 min 2 processors 2.4 GHz 2048 Mb RDRAM PC 800 33 min

Result of V.Galabov (Bulgaria) experiments with different PC

program TestOMPinteger k, n, tid, nthreads, max_threads, procs logical dynamic, dynamic double precision d (5000)===== call gettim (hrs1,mins1,secs1,hsecs1) call getdat (year,month,day)

max_threads = OMP_GET_MAX_THREADS() procs = OMP_GET_NUM_PROCS() dynamic = OMP_GET_DYNAMIC() nested = OMP_GET_NESTED()

!$OMP PARALLEL PRIVATE (NTHREADS, tid, n, k) tid = OMP_GET_THREAD_NUM() nthreads = OMP_GET_NUM_THREADS()!$OMP DO SCHEDULE (STATIC, 5000) do n = 1 , 10000 do k = 1, 5000 d(k) = sin (dble(k+n))**2 + cos (dble(k+n))**2 end do end do!OMP END DO !$OMP END PARALLEL===== call gettim (hrs2,mins2,secs2,hsecs2) call getdat (year,month,day)

end program TestOMP

OS BIOS Compiler OpenMP Time

Windows XPThreads

DISABLE

Visual

Fortan 6.5 - 3.59 s

Windows XPHyper

Threadings

Visual

Fortan 6.5 - 3.63 s

Linux (Mandrake9.2)

Threads

DISABLE

Intel

Fortran 8.0 + & - 3.59 s

Linux (Mandrake9.2)

Hyper

Threadings

Intel

Fortran 8.0 + 2.38 s

Pentium 4 3.06 GHz; 2 Gb DDR DIMM PC3200; 120 Gb Seagate

The future (from E.Kalnay)An amazing improvement in the quality of the forecasts based on NWP guidance.From the active research currently taking place, one can envision that the next decade will continue to bring improvements, especially in the following areas:• Detailed short-range forecasts, using storm-scale models able to provide skillful predictions of severe weather.• More sophisticated methods of data assimilation able to extract the maximum possible information from observing systems, especially remote sensors such as satellites and radars.• Development of adaptive observing systems, where additional observations are placed where ensembles indicate that there is rapid error growth (low predictability).• Improvement in the usefulness of medium-range forecasts, especially through the use of ensemble forecasting.• Fully coupled atmospheric-hydrological systems, where the atmospheric model precipitation is appropriately downscaled and used to extend the length of river flow prediction. • More use of detailed atmosphere-ocean-land coupled models, where the effect of long lasting coupled anomalies such as SST and soil moisture anomalies leads to more skillful predictions of anomalies in weather patterns beyond the limit of weather predictability (about two weeks).• More guidance to government and the public on areas such as air pollution, UV radiation and transport of contaminants, which affect health.• An explosive growth of systems with emphasis on commercial applications of NWP, from guidance on the state of highways to air pollution, flood prediction, guidance to agriculture, construction, etc.

1. Observing system

2. Telecommunication system

3. Computer system

4. Data assimilation

5. Model

6. Postprocessing