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Modelización de la
Atmósfera en Alta
Resolución
High Resolution Numerical
Weather Prediction
Isabel Martínez Marco
Jefe del Área de Aplicaciones
AEMET
[email protected]
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
OUTLINE
• Introduction
• Forecast Ranges
• Operational Activities
•
•
•
•
Observations
Data Assimilation
Numerical Formulation
Physical Parametrizations
• Numerical Models of the Atmosphere
• ECMWF Model
• HIRLAM Models
• HARMONIE Model
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Numerical weather prediction
• The behaviour of the atmosphere is governed by a set of physical laws
which express how the air moves, the process of heating and cooling, the
role of moisture, and so on.
• Equations cannot be solved analytically, numerical methods are needed.
• Given a description of the current state of the atmosphere, numerical
models can be used to propagate this information forwards to produce a
forecast for future weather.
• Additionally, knowledge of initial conditions of the system is necessary.
• An incomplete picture from observations can be completed by data
assimilation.
• The resolution of the model is determined by available computing
resources. It does not correspond to any natural scale separation.
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Numerical Weather Prediction
• Processes not resolved by the model must be ‘parametrized’.
• Effective resolution is not the same as model grid spacing.
• Numerical algorithms are compromise between accuracy and speed; care
needed to ensure numerical stability.
• Interactions between atmosphere and land/ocean are important.
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Forecast ranges
• Short-range weather forecast (0-2 days ahead)
• Detailed prediction - regional forecasting system
• It produces forecast few hours after observations are made
• Medium-range weather forecast (2 days - 2 weeks ahead)
• Less detailed prediction - global forecasting system
• It produces forecast up to several hours after
observations are made
• Long-range weather forecast (more than 2 weeks ahead)
• It predicts statistics of weather for the coming month or
season
• Climate prediction
• It predicts the climate evolution on the basis of pre-defined
scenarios (CO2, O3, …)
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Operational activities
• Observations
• Acquisition/Pre-processing/Quality control/Bias
correction
• Data assimilation
• Dynamical fit to observations
• Forecasts
• Product dissemination and archiving
• Verification
• Operational/Pre-operational validation
• Data Monitoring
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Observations
• Conventional
•
•
•
•
Surface: Weather stations (land+sea)
Profiles: Radiosondes, UHF/VHF profilers
Altitude: Aircraft
(Ocean: TAO/PIRATA)
• Satellite
• Imagery -> Winds
• Radiances -> Temperature, Humidity, Sea wind
(Precipitation)
• Ozone
• Scatterometers -> Sea wind
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Conventional observations used
SYNOP/METAR/SHIP:
MSL Pressure, 10m-wind, 2m-Rel.Hum.
DRIBU: MSL Pressure, Wind-10m
Radiosonde balloons (TEMP):
Wind, Temperature, Spec. Humidity
PILOT/Profilers: Wind
Note: We only use a limited
number of the observed
variables; especially over land.
Aircraft: Wind, Temperature
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Satellite data
13 Sounders: NOAA AMSU-A/B, HIRS, AIRS, IASI, MHS
3 Scatterometer sea winds: ERS, ASCAT, QuikSCAT
5 imagers: 3xSSM/I, AMSR-E, TMI
Geostationary, 4 IR and 5 winds
4 ozone
2 Polar, winds: MODIS
6 GPS radio occultation
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Significant increase in the number of observations
assimilated
Conventional and satellite data assimilated at ECMWF 1996-2010
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Data Assimilation
• Observations measure the current state, but provide an incomplete picture
Observations made at irregularly spaced points, often with large gaps
Observations made at various times, not all at ‘analysis time’
Observations have errors
Many observations not directly of model variables
• The forecast model can be used to process the observations and produce a
more complete picture (data assimilation)
Start with previous analysis
Use model to make short-range forecast for current analysis time
Correct this ‘background’ state using the new observations
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Data Assimilation
Background
values =
Observations=
Analysis
values =
Analysis
12 UTC
00 UTC
12 UTC
00 UTC
5 May
6 May
6 May
5 May
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Data assimilation for weather
prediction
The FORECAST is computed on a grid over
the globe.
The meteorological OBSERVATIONS can be
taken at any location in the grid.
The computer model’s prediction of the
atmosphere is compared against the
available observations, in near real
time.
A short-range forecast provides an
estimate of the atmosphere that
is compared with the
observations.
The two kinds of information are
combined to form a corrected
atmospheric state: the analysis.
Corrections are computed and
applied twice per day. This
process is called ‘Data
Assimilation’.
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
4D-Var implementation
All observations within a 12-hour period (~9,000,000) are used
simultaneously in one global (iterative) estimation problem
• Observation minus model
differences are computed at
the observation time using the
full forecast model.
• 4D-Var finds the 12-hour
forecast evolution that
optimally fits the available
observations. A linearized
forecast model is used in the
minimization process based on
the adjoint method.
• It does so by adjusting surface
pressure, the upper-air fields
of temperature, wind,
specific humidity and ozone.
09Z
12Z
15Z
18Z
21Z
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
4D-Var is using more a-synoptic data than 3D-Var
4D-Var SYNOP Screening
4D-Var is using more data from
frequently reporting stations.
The plots show the use of SYNOP
surface pressure observations.
Column height gives the number of
observations available, while the black part
displays those actually used in the
assimilation.
3D-Var SYNOP Screening
3D-Var is like 4D-Var without the time
dimension. The analysis is performed
at synoptic times only (0000, 0600,
1200 and 1800 UTC). Mostly only data
valid a synoptic time is used.
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
4D-Var versus
3D-Var and Optimum Interpolation
• 4D-Var compares observations with background model fields at the
correct time.
• 4D-Var can use observations from frequently reporting stations.
• The dynamics and physics of the forecast model are in an integral part
of 4D-Var, so observations are used in a meteorologically more
consistent way.
• 4D-Var combines observations at different times during the 4D-Var
window in a way that reduces analysis error.
• 4D-Var propagates information horizontally and vertically in a
meteorologically more consistent way.
• 4D-Var more complex: it needs linearized perturbation forecast model
and its adjoint to solve the cost function minimization problem
efficiently.
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Numerical Formulation
• The Model equations
•
•
•
•
•
•
•
Gas Law
Hydrostatic equation
Equation of Continuity
Equations of Motion
Thermodynamic equation
Conservation Equation for moisture
Conservation Equation for liquid water
• The Numerical Formulation
• Horizontal grid: finite differences, finite elements or spectral
representations
• Vertical grid: finite differences or finite elements schemes
• Advection scheme: Semi-Lagrangian
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Orography
HIRLAM 0.16 (~16km)
HIRLAM 0.05 (~5km)
HARMONIE 2.5km
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Vertical Resolution
L60
L91
0.01
1
Position of levels and pressure layer
thickness of L60 (blue) and L91 (red)
0.02
0.03
2
0.05
0.07
3
0.1
4
5
0.2
6
7
0.5
0.7
1
8
9
10
2
12
3
14
5
7
10
16
Level number
Pressure (hPa)
0.3
18
20
20
30
25
50
70
100
30
35
40
45
50
55
60
65
70
200
300
500
700
1000
91
60 levels
91 levels
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Why parametrization
•Small scale processes are not resolved by large scale models,
because they are sub-grid.
•The effect of the sub-grid process on the large scale can only be
represented statistically.
•The procedure of expressing the effect of sub-grid process is
called parametrization.
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
What is parametrization and why
it is needed
•The standard Reynolds decomposition and averaging leads
to co-variances that need “closure” or “parametrization”
•Radiation absorbed, scattered and emitted by molecules,
aerosols and cloud droplets play an important role in the
atmosphere and need parametrization.
•Cloud microphysical processes need “parametrization”
•Parametrization schemes express the effect of sub-grid
processes in resolved variables.
•Model variables are U,V,T,q, (l,a)
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Reynolds decomposition
e.g. equation for potential temperature:




 2  2  2
u
v
w
 Q  ( 2  2  2 )
t
x
y
x
x
y
z
advection
source molecular diffusion
Reynolds decomposition: U  u  u ' , V  v  v' ,
W  w  w' ,      '.
Averaged (e.g. over grid box):




t
u
x
v
y
w
x







Q  (u ' '   )  (v' '   )  ( w' '   )
x
x
y
y
z
z
Q : source term (e.g. radiation absorption/emission or condensation)
w' ' : subgrid (Reynolds) transport term (e.g. due to turbulence,
convection)
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Physical processes
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Importance of physical processes
•General
•Tendencies from sub-grid processes are substantial and
contribute to the evolution of the atmosphere even in the short
range.
• Diabatic processes drive the general circulation.
•Synoptic development
•Diabatic heating and friction influence synoptic development.
•Weather parameters
•Diurnal cycle
•Clouds, precipitation, fog
•Wind, gusts
•T and q at 2m level.
•Data assimilation
•Forward operators are needed for observations.
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Numerical Models of the Atmosphere
Horizontal
resolution
Vertical
resolution
Time range
Climate models
200 km
500 m
100 years
Global weather
prediction
20 km
200 m
10 days
Limited area
weather pred.
10 km
200 m
2 days
Cloud Resolving
Models (CRM)
500 m
200 m
1 day
Large Eddy
Simulation (LES)
50 m
10-50 m
5 hours
Different models need different level of parametrization
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Importance of physical processes and
initial conditions
-
+
• Climate Models
• General Circulation Global Numerical
Models
• Limited Area Numerical Models
• High-Resolution Meso-scale Models
Initial
Conditions
+
-
External
Forcings
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
The ECMWF Numerical Weather
Prediction (NWP) Model
• High-resolution model
• T1279 spectral
resolution
• 16 km global grid
• 91 hybrid levels from
the surface to a height
of 80km
• Variables at each grid
point
•
•
•
•
Wind
Temperature
Humidity
Cloud water, ice, cloud
fraction
• Ozone
• Pressure at surface
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
HIRLAM (High Resolution Limited Area Model)
• Model Formulation:
• Horizontal resolution: 0.16º latxlon (ONR) and 0.05º latxlon
(HNR)
• Boundary Conditions:
• ONR: from ECMWF with 0.25º
• HNR and CNN: from ONR with 0.16º (nesting models)
• Analysis: 3-dimensional variational method (3D-VAR)
• The Resolution in space
• Vertical Resolution: 40 hybrid levels
• Horizontal grid: regular rotated longitude/latitude
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Integration Domains
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
HIRLAM (High Resolution Limited Area Model)
• In development: HARMONIE
Hirlam Aladin Regional/Meso-scale Operational NWP In Europe
• A new model formulation:
•
•
•
•
•
•
Horizontal resolution: 2.5km
Vertical resolution: 65 hybrid levels
Analysis: 4-dimensional variational method (4D-VAR)
Horizontal grid: spectral representation
Vertical grid: finite differences
Non-hydrostatic dynamical kernel from ALADIN Model
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011
Productos de Predicción: 2011
www.aemet.es
"La Meteorología de las Energías Renovables" 2ª Jornada. Cátedra de Aprendizaje Automático en Modelado y Predicción, 30/11/2011

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