Post on 16-May-2020
TRAINING SEMINARS 1/6/2012-31/5/2013
Konstantin Rubinstein from RHMC.
Seminar 1..- Introduction to the Weather Research & Forecasting Model (WRF) and the two dynamical solvers: ARW (Advanced Research WRF) core and the NMM (Nonhydrostatic Mesoscale Model).
Seminar 2..-Short-range regional forecast model WRF-ARW. Preprocessing I.
Seminar 3..-Short- range regional forecast model WRF-ARW. Three-dimensional variational assimilation (3D-Var. Preprocessing II.
Seminar 4..-Dynamical core of the short-range regional forecast model WRF-ARW, v.3.4.
Seminar 5..-Parameterization of clouds and precipitation in the short-range regional forecast model WRF-ARW.
Seminar 6..-Planetary boundary layer (PBL) and soil processes in the WRF-ARW parameterizations.
Seminar 7..-Postprocessing for the short-range regional forecast model WRF-ARW.
Seminar 8..-Estimation of the quality of the results of the short-range regional forecast model WRF-ARW.
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INTRODUCTION to description Short- range regional Forecast model
WRF-ARW, WRF-NMM
•Konstantin Rubinstein•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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Regional numerical Forecasting
• Now about all Weather Forecast offices and many Universities have some forecast models from Global scale to Meso scale.
• Most part of models has near the same quality of Forecasts.
• Girona University now have WRF-ARW version 3.3 in frame of GRANT
• FP7-IRSES – “CLIMSEAS”
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Weather Research and Forecasting (WRF) modeling system can:
• - Idealized simulations in atmosphere (e.g. convection, baroclinic waves)- Regional and global applications - Parameterizations research- Data assimilation research- Forecast research- Hurricane research - Coupled-model applications- Teaching
It can runing from hundreds meters to thousands kilometers
• Hurricane MAWAR 2005
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The WRF currently supports two dynamical cores,
•the Advanced Research WRF (ARW), whose development has•been led by the National Center for Atmospheric Research (NCAR), •and the Non-hydrostatic Mesoscale Model (NMM) core developed by the National Centers for Environmental•Prediction (NCEP).
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1 - Short- range regional Forecast
2 - Cities meteorology Modeling
3 – Forecast of comfort degree
4 – Forecast in polar regions
5 - Forecast of Danger meteorological Events
6 – New data assimilation
7 - Supplying by meteorological data some transport models
TASKS which solves by WRF-ARW in our Laboratory HydroMeteoCentre of Russia
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Flow-Chart of numerical Forecast
• 1. Preprocessing
•2. MODEL CALCULATION
•3 Postprocessing
•4. Estimation of results quality
USERS OF FORECAST
• INPUT DATA
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Flow chart of WRF-ARW•1.Preprocessing
•3.Postprocessing
• 2 WRF-ARW AMODEL
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1. Preprocessing WRF-ARW• WPS (WRF Preprocessing System)
• (4 projections – Lambert, polar stereography, Mercator and Latitude - Longitude)
• 1. Metgrid - Definition of Area of forecast and horizontal structure
In Girona University there are now 4 areas in Lambert projection:
• ARAL sea – 5 km • Caspian sea– 20 km Black sea 5km • Girona area-5 km
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METGRID It is reading and interpolated fields:
• 1.Orography• 2.Land use• 3.Tipe of soils• 3. Temperature of soil in
2 m• 4. Monthly data of
vegetation • 5. Max albedo of snow• 6. Monthly albedo without
snow• 7. Slopes of relief and so
on
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Gridded data
• Ungrib, REAL• To take data from GRIB format• Horisontal interpolation in dots for
calculations. It is possible to choose method of interpolation (namelist 4 methods).
• Vertical interpolation data from p levels to – vertical coordinate of WRF (namelist 6 methods).
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3DVAR
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WRF-MODEL CORE
• Dynamic
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WRF-MODEL COREPhysics Radiation
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WRF-MODEL COREPhysics Clouds and Microphysics
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WRF-MODEL COREPhysics Planetary Boundary Layer and Soil
processes
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WRF POSTPROCESSINGVisualization
• Many functions and diagnostics in NCL libraries
• � RIP4, ARWpost, etc.
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2 WRF POSTPROCESSINGVisualization by GRADS
• North
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WRF POSTPROCESSINGVisualization GRADS
• Central region
• Sea Level Pressure and precipitation
• Temperature and snow depth
• Velocity of wind
• Precipitation and temperature in Moscow
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WRF POSTPROCESSINGVisualization GRADS
• South
• Temperature on 2m and Snow depth
• Pressure and precipitation
• WRF POSTPROCESSINGVisualization
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Schedule of lectures
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PREPROCESSINGShort- range regional Forecast model
WRF-ARW
•Konstantin Rubinstein•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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Flow-Chart of numerical Forecast
• 1. Preprocessing
•2. MODEL CALCULATION
•3 Postprocessing
•4. Estimation of results quality
USERS OF FORECAST
• INPUT DATA
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Flow chart of WRF-ARW•1.Preprocessing
• 2 WRF-ARW AMODEL
WPS
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WPS• The WRF Preprocessing System (WPS) is a set
of four programs whose collective role is to prepare input to the real program for real-data simulations.
• Each of the programs performs one stage of the preparation:
• geogrid defines model domains and horizontally interpolates static geographical data to the grids;
• ungrib extracts meteorological fields from GRIB-formatted files; and
• metgrid horizontally interpolates the meteorological fields extracted by ungrib to the model grids defined by geogrid.
• real - the program for vertically interpolating meteorological fields to WRF eta levels.
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Flow chart and program components of WPS
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WPS
data flow between the programs of the WPS
•Data flow between the programs of the WPS
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GEOGRID• geogrid define the simulation domains, and
horizontally interpolate various terrestrial data sets to the model grids:
1. soil categories,2. land use category,
3. terrain height, 4. annual mean deep soil temperature,5. monthly vegetation fraction, 6. monthly albedo,7. maximum snow albedo, 8. slope category
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1. Preprocessing WRF-ARW• WPS (WRF Preprocessing System)
• (4 projections – Lambert, polar stereography, Mercator and Latitude - Longitude)
• 1. Metgrid - Definition of Area of forecast and horizontal structure
In Girona University there are now 4 areas in Lambert projection:
• ARAL sea – 5 km • Caspian sea– 20 km Black sea 5km • Girona area-5 km
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Lambert projectin
PRECIPITATION AND WIND OVER SPAINIn three proejctions
LAT- LONprojectin
North Stereographicprojectin
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NAMELIST OF GEOGRID• XLAT_M 1 0 Latitude on mass grid (degrees latitude)• XLONG_M 1 0 Longitude on mass grid (degrees longitude)• CLAT 1 0 Computational latitude on mass grid (degrees
latitude)• CLONG 1 0 Computational longitude on mass grid (degrees
longitude)• MAPFAC_M 1 0 Mapfactor on mass grid (none)• MAPFAC_MX 1 0 Mapfactor (x-dir) on mass grid (none)• MAPFAC_MY 1 0 Mapfactor (y-dir) on mass grid (none)• E 1 0 Coriolis E parameter (-)• F 1 0 Coriolis F parameter (-)• SINALPHA 1 0 Sine of rotation angle (none)• COSALPHA 1 0 Cosine of rotation angle (none)• LANDMASK 1 0 Landmask : 1=land, 0=water (none)• LANDUSEF 21 0 Noah-modified 21-category IGBP-MODIS landuse
(category)• LU_INDEX 1 0 Dominant category (category)• HGT_M 1 0 Topography height (meters MSL)• SLPX 1 0 df/dx (-)• SLPY 1 0 df/dy (-)• HGT_U 1 0 Topography height (meters MSL)
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NAMELIST OF GEOGRID• HGT_V 1 0 Topography height (meters MSL)• SOILTEMP 1 0 Annual mean deep soil temperature (Kelvin)• SOILCTOP 16 0 16-category top-layer soil type (category)• SCT_DOM 1 0 Dominant category (category)• SOILCBOT 16 0 16-category top-layer soil type (category)• SCB_DOM 1 0 Dominant category (category)• ALBEDO12M 12 0 Monthly surface albedo (percent)• GREENFRAC 12 0 Monthly green fraction (fraction)• SNOALB 1 0 Maximum snow albedo (percent)• SLOPECAT 1 0 Dominant category (category)• CON 1 0 orographic convexity (-)• VAR 1 0 stdev of subgrid-scale orographic height (m)• OA1 1 0 orographic asymmetry (-)• OA2 1 0 orographic asymmetry (-)• OA3 1 0 orographic asymmetry (-)• OA4 1 0 orographic asymmetry (-)• OL1 1 0 effective orographic length (fraction)• OL2 1 0 effective orographic length (fraction)• OL3 1 0 effective orographic length (fraction)• OL4 1 0 effective orographic length (fraction)
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Nested greed areas
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METGRID It is reading and interpolated static geographical fields from the different bases:
• As surface characteristics can change in time it is possible rare mistakes.Left is one example of such rare mistake in Aral area description
Now from Satellite
• Now from • WRF DATA BASE
From model
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Gridded data
• Ungrib, REAL• To take data from GRIB format• Horisontal interpolation in dots for
calculations. It is possible to choose method of interpolation (namelist 4 methods).
• Vertical interpolation data from p levels to – vertical coordinate of WRF (namelist 6 methods).
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An example of list initial input datafor UNGRIB (NCAR final analysis)
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An example of initial field for UNGRIB program
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PREPROCESSING_23dvar
Short- range regional Forecast model WRF-ARW
•Konstantin Rubinstein,Maria Smirnova,
•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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3DVAR
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Types of DATA in 3DVAR
3DVAR use 17 types of DATA:
1. SYNOP - data from synoptically stations,2. SHIPS - data from ships,3. METAR -4. TEMP - data from aerologic stations5.AIREP, 6. PILOT data from aviations , 7.AMDAR,8.PROFL,9.SATOB,10.SATEM,11.SSMT1,12.SSMT2,13.SSMI,14.GPSPW/GPSZD,15.GPSRF,16.QSCAT, 17.BOGUS – satellites' data
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An example of map with synoptic stationswhich are using in NCEP analysis (black dots) and it is possible to use (open dots) in European part of Russia
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An example (1Jul 2009 ) of difference between NCEP analysis near surface temperature and measurements on stations for 3
regions of Russia
European Partof Russia
Murmansk region
Moscow region
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Случай сильного мороза в Мурманской области
Разность между начальным полем температуры и наблюдениями на станциях 05 января 2010 г.
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Mean for areas difference in temperature between analysis and measurements ºС
Murmansk district European area of
Russia
January2010
July2009
Moscow district
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Mean for areas difference in wetness between analysis and measurements g/kg
январь 2010
июль 2009
Murmansk district Moscow districtEuropean areaof Russia
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Ветерсредняя по станциям разность, м/с
январь 2010
июль 2009
Начальные данные
Мурманская область ЕТР Московская область
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Some results of 3dVarin Temperature
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Some results of 3dVarIn wetness
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Прогноз метеоситуации вокруг АЭС ФукусимаРазность между начальным полями и наблюдениями на
станциях
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Some results of 3dVarin WIND
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Осадкипрогноз на 12 ч
base 3DVAR
мм/12ч
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Результаты расчета переноса радионуклидов
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Dynamic COREShort- range regional Forecast model
WRF-ARW v.3.4
•Konstantin Rubinstein•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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The WRF currently supports two dynamical cores,
•the Advanced Research WRF (ARW), whose development has•been led by the National Center for Atmospheric Research (NCAR), and whose history begins from MM5 model•and the Non-hydrostatic Mesoscale Model (NMM) core developed by the National Centers for Environmental•Prediction (NCEP), and whose history begins from “ETA” model
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Flow chart of WRF-ARW•1.Preprocessing
•3.Postprocessing
• 2 WRF-ARW AMODEL
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Governing Equations
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Map Projections• The ARW solver currently supports four
projections to the sphere - the Lambert conformal,
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Mercator, and Cylindrical Equidistant
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Model Discretization
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Model Discretization
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Generating Lateral Boundary Data
• .
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Nesting Options
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Description of clouds and precipitation inShort- range regional Forecast model
WRF-ARW
•Konstantin Rubinstein•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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Physics Interaction in models
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Microphysics of Cloud Interactions
•Atmospheric heat and moisture tendencies
•Microphysical rates
•Surface rainfall
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Microphysics of Cloud Interactions• Cloud microphysical schemes have to describe the
formation,growth and sedimentation of water particles (hydrometeors).
• Cloud microphysics is difficult due tocomplexity, non-linearity, multi-scale interactions,
• lack of observations and • lack of fundamental knowledge
• gaseous liquid solid
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• Graupel (also called soft hail or snow pellets )1 refers to precipitation that forms when supercooled droplets of water are collected and freeze on a falling snowflake, forming a 2–5 mm (0.079–0.197 in) ball of rime. Strictly speaking, graupel is not the same as hail or ice pellets, although it is sometimes referred to as small hail . However, the World Meteorological Organization defines small hail as snow pellets encapsulated by ice, a precipitation halfway between graupel and hail.2
• The term graupel is the German word for the described meteorological phenomenon.3 Its METAR code is GS.
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•MP_PHYSICS=1
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Изменение со временем температуры на 2-х метрах (°C) в Казани для различных параметризаций микрофизики. Прогноз на 120 часов.
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08.06.2012 09.06.2012 10.06.2012 11.06.2012 12.06.2012 13.06.2012
Дата
°С
Факт
Без парам.KesslerLinWSM 3WSM 5FerrierWSM 6GoddarThomsonMilbrandtMorrisonSBU_YLINWDM 5WDM 6
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Изменение со временем скорости ветра на 10 метрах (м/с) в Казани для различных параметризаций микрофизики. Прогноз на 120 часов.
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08.06.2012 09.06.2012 10.06.2012 11.06.2012 12.06.2012 13.06.2012
Дата
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Без парам.KesslerLinWSM 3WSM 5FerrierWSM 6GoddarThomsonMilbrandtMorrisonSBU_YLINWDM 5WDM 6
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Изменение со временем давления на уровне моря (гПа) в Казани для различных параметризаций микрофизики. Прогноз на 120 часов.
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1002
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1008
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08.06.2012 09.06.2012 10.06.2012 11.06.2012 12.06.2012 13.06.2012
Дата
гПа
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Без парам.KesslerLinWSM 3WSM 5FerrierWSM 6GoddarThomsonMilbrandtMorrisonSBU_YLINWDM 5WDM 6
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Изменение со временем суточных сумм осадков в Казани для различных параметризаций микрофизики. Прогноз на 120 часов.
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08.06.2012 09.06.2012 10.06.2012 11.06.2012 12.06.2012
Дата
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Факт
Без парам.KesslerLinWSM 3WSM 5FerrierWSM 6GoddarThomsonMilbrandtMorrisonSBU_YLINWDM 5WDM 6
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WRF-MODEL COREPhysics Clouds and Microphysics
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PBL, SOIL Processes in WRF-ARWPARMETRIZTIONS
•Konstantin Rubinstein•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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WRF-MODEL COREPhysics Planetary Boundary Layer and Soil
processes
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Изменение со временем скорости ветра на 10 метрах (м/с) в Казани для различных параметризаций планетарного пограничного слоя. Прогноз до 120 часов.
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08.06.2012
08.06.2012
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Дата
мм
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YSUBouLac+M-OMY+M-O-JBouLac+M-O-JQNSEMYNN 2.5Pleim
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Изменение со временем температуры на 2-х метрах в Казани для различных параметризаций планетарного пограничного слоя. Прогноз до 120 часов.
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08.06.2012 09.06.2012 10.06.2012 11.06.2012 12.06.2012 13.06.2012
Дата
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YSUBouLac+M-OMY+M-O-JBouLac+M-O-JQNSEMYNN 2.5Pleim
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Изменение со временем давления на уровне моря (гПа) в Казани для различных параметризаций планетарного пограничного слоя. Прогноз до 120 часов.
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1002
1004
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1008
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1016
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08.06.2012 09.06.2012 10.06.2012 11.06.2012 12.06.2012 13.06.2012
Дата
гПа
Факт
YSUBouLac+M-OMY+M-O-JBouLac+M-O-JQNSEMYNN 2.5Pleim
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Изменение со временем суточных сумм осадков в Казани для различных параметризаций планетарного пограничного слоя. Прогноз до 120 часов.
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08.06.2012 09.06.2012 10.06.2012 11.06.2012 12.06.2012
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YSUBouLac+M-OMY+M-O-JBouLac+M-O-JQNSEMYNN 2.5Pleim
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YSU
• YSU • MYJ
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• BouLac • QNSE
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Postprocessing for Short- range regional Forecast model
WRF-ARW
•Konstantin Rubinstein•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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WRF POSTPROCESSINGVisualization
• Many functions and diagnostics in NCL libraries
• � RIP4, ARWpost, etc.
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GRADS
• The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is used for easy access, manipulation, and visualization of earth science data. GrADS has two data models for handling gridded and station data. GrADS supports many data file formats, including binary (stream or sequential), GRIB (version 1 and 2), NetCDF, HDF (version 4 and 5), and BUFR (for station data). GrADS has been implemented worldwide on a variety of commonly used operating systems and is freely distributed over the Internet.
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2 WRF POSTPROCESSINGVisualization by GRADS
• North
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WRF POSTPROCESSINGVisualization GRADS
• Central region
• Sea Level Pressure and precipitation
• Temperature and snow depth
• Velocity of wind
• Precipitation and temperature in Moscow
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WRF POSTPROCESSINGVisualization GRADS
• South
• Temperature on 2m and Snow depth
• Pressure and precipitation
• WRF POSTPROCESSINGVisualization
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Estimation of running WRFin August – September 2011
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Estimation of running WRFin August – September 2011
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ESTIMATION ofShort- range regional Forecast model
WRF-ARW results
•Konstantin Rubinstein•k.g.rubin@gmail.com
http://weatherlab.ruHydroMetCentre of RUSSIA
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There is a big problem to understand quality of forecast?
• The method of “beautiful pictures”
• Time change of wind direction (°) in 4- meteorological;-stations from00 hours of 22 of November till 00 24 of Novembers 2011
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The method of statistical estimations
• Average absolute errors of scalar variable of forecast:
•
∑=
−=N
ii
TфTпN 1
)(1δ
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Средняя векторная ошибка прогноза ветра:
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Estimation of running WRFin August – September 2011
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Estimation of running WRFin August – September 2011
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Thank you very much!!