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Application of Numerical WeatherPrediction Models forDrought Monitoring
Gregor GregoričJožef Roškar
Environmental Agency of Slovenia
Contents
1. Introduction
2. Numerical Weather Prediction Models - NWP
3. Application of NWP for Draught Monitoring
4. Verification of Products using Decadal Bulletins
Introduction
Basic condition for any drought management is good knowledge
of drought state
Drought monitoring
(Hydrological cycle,
state of Vegetation)
In-situ measurement
Remote sensing
Indices Indicators
How to get space information?Horizontal interpolation
Remote sensing
Numerical Weather Prediction Models
Numerical Weather Prediction Models - NWP
Simulated/
Predicted
Athmospheric
Variables/Fields
Initial and
Boundary
Conditions
(data archive)
Integration
Numerical resolving of
mathematical equations
describing development of
athmospheric variables in time
Air Pressure
Wind
Temperature
Humidity
Cloudiness
Precipitation
Evaporation
Soil Moisture
Etc.
Real continous space is by the model distributed into equidistant discrete grid
points
Distance between grid points - space resolution defines the smallest structures seenby a NWP:
• Low resolution (~200 km) – simulation of basic structures (planetary waves, big frontal systems) – used for climate modeling and studies of globalmechanisems;
• Medium resolution (50 - 10 km) – simulation of sinoptic and mesoscale systems– used for general weather forecast;
• High resolution (< 10 km) – simulation of local systems (wind, fog, tunderstorms, etc.)
Regardless the resolution, there are
• Global models covering entire globe and
• Limited Area Models simulating weather over choosed smaller area
Numerical Weather Prediction Models - NWP
Weather Observing
System
Weather Observing
System
Data Analysis
Data Asimilation
Initialization
Data Analysis
Data Asimilation
Initialization
Integration
Atmospheric Physics
Surface Physics
Surface Processes
Numerical Methods
Simulated FieldsSimulated Fields
Postprocessing
Vizualization
Postprocessing
Vizualization
Data Collecting
over
Observing Network
Numerical ModelData and Model
Results Presentation
Numerical Weather Prediction Models - NWP
NumericalNumericalNumericalNumerical WeatherWeatherWeatherWeather PredictionPredictionPredictionPrediction ModelsModelsModelsModels ---- NWPNWPNWPNWP• To run a LAM, aa LAM, aa LAM, aa LAM, accessccessccessccess to global archives of weather patterns to global archives of weather patterns to global archives of weather patterns to global archives of weather patterns is is is is neededneededneededneeded• Data in regular grid, internally Data in regular grid, internally Data in regular grid, internally Data in regular grid, internally consistentconsistentconsistentconsistent, , , , withoutwithoutwithoutwithout errorserrorserrorserrors (+)(+)(+)(+)• Not Not Not Not directlydirectlydirectlydirectly relatedrelatedrelatedrelated to to to to actualactualactualactual situationsituationsituationsituation ““““on on on on groundgroundgroundground”””” ((((----))))
Very important over montainous areas
Relief presentation depends on horizontal resolution
Example: slope and precipitation:
Wind
Numerical Weather Prediction Models - NWP
This is why majority of NWP models underestimate precipitation
(Non)Predictability
• Physical Laws– Co-existence of various scales– Interaction of all variables– Exchange of energy among various
scalesChaos
NumericalNumericalNumericalNumerical WeatherWeatherWeatherWeather PredictionPredictionPredictionPrediction ModelsModelsModelsModels ---- NWPNWPNWPNWP
NumericalNumericalNumericalNumerical WeatherWeatherWeatherWeather PredictionPredictionPredictionPrediction ModelsModelsModelsModels ---- NWPNWPNWPNWP
Lorentz butterfly
NumericalNumericalNumericalNumerical WeatherWeatherWeatherWeather PredictionPredictionPredictionPrediction ModelsModelsModelsModels ---- NWPNWPNWPNWP
(Non)Predictability• Physical Laws
– Co-existence of various scales– Interaction of all variables– Exchange of energy among various
scales• Discretization of continous space• Limited computing power• Incomplete knowledge of initial state
NumericalNumericalNumericalNumerical WeatherWeatherWeatherWeather PredictionPredictionPredictionPrediction ModelsModelsModelsModels ---- NWPNWPNWPNWP
UncertaintyUncertaintyUncertaintyUncertaintyTaking into account (Non)Predictability, we have to consider that by themodel simulated parameters and fields are not directly related to actual
parameters, in particular to the situation “on ground”.
HowHowHowHow to to to to reducereducereducereduce uncertaintyuncertaintyuncertaintyuncertainty andandandand useuseuseuse thethethethe NWP NWP NWP NWP productsproductsproductsproducts forforforfor droughtdroughtdroughtdroughtmonitoringmonitoringmonitoringmonitoring????to choose the model and setup it in the way that the output best fits theobservations (might be difficult for precipitation!);
to compute model climatology for some drought related variables using
historical reanalyzes and to develop a tool for drought monitoring like
METEOALARM.
Limited Area Model
Main Idea:
•Take data from global archive
•Choose area and grid points
•Re-simulate weather patterns fromglobal model to obtain more details on a regional scale
NumericalNumericalNumericalNumerical WeatherWeatherWeatherWeather PredictionPredictionPredictionPrediction ModelsModelsModelsModels ---- NWPNWPNWPNWP
Application of NWP for droughtmonitoring
LimitedArea
NMM (NCEP)Non-Hydrostatic
Meso-scaleModel
• Area: 461 x 289 x 92 = 12.257.068 points
(133.229 points “on ground”)
• Top Level: 1 hPa (~ 60 km)
• Horizontal resolution: ~8.5 km
• Time Step: 30 sec.• Integration Time: 36 h starting at 12 UTC to get
simulation for the entire next day
Application of NWP for droughtmonitoring
Application of NWP for droughtmonitoring
OUTPUT:
Simulated and averaged variables (air and soil) – daily aggregates 1989-2008
precip daily sum (00-24h), mmevaporation (and transpiration) daily sum (00-24h), mmpotential evaporation (and transpiration) daily sum (00-24h), mmliquid soil moisture volumetric fraction 0-10cm (24h)liquid soil moisture volumetric fraction 10-40cm (24h)liquid soil moisture volumetric fraction 40-100cm (24h)liquid soil moisture volumetric fraction 100-200cm (24h)total soil moisture volumetric fraction 0-10cm (24h)total soil moisture volumetric fraction 10-40cm (24h)total soil moisture volumetric fraction 40-100cm (24h)total soil moisture volumetric fraction 100-200cm (24h)total soil moisture content (kg/m2) in 0-200cm layer (24h)geopotential height on 850hPa (12h), mtemperature on 850hPa (12h), Kdaily average 10m wind, m/sdaily average 10m wind gusts, m/sdaily min. temperature, Kdaily max. temperature, Kdaily min. relative humidity, %daily max. relative humidity, %
Application of NWP for droughtmonitoring
DROUGHT RELATED VARIABLES
Soil moisture?
DROUGHT RELATED TIME SCALE
Not daily! Decade?
DROUGHT RELATED INTERPRETATION
Not absolute values, deviation from normals
Application of NWP for droughtmonitoring
GOAL:
To re-computereanalyses dataover SE Europe
in dense grid
to obtain“climatology”
for droughtsituationsinterpretation
ECMWF ERA – Interim
1989 - 2008
Model Integration
Area (SE Europe)
Limited Area Model
NNM (NCEP)
Application of NWP for droughtmonitoring
CLIMATOLOGY:
AccumulatedWater Balanceover 70 days
Mean for theperiod 21 May –29 July, dataperiod 1989 -2008
Application of NWP for droughtmonitoring
POSSIBLE PRODUCT:
AccumulatedWater Balanceover 70 days
Anomaly
Application of NWP for droughtmonitoring
POSSIBLE PRODUCT:
10 days SoilMoisture Index -
Anomaly
Application of NWP for droughtmonitoring
POSSIBLE PRODUCT:
10 days MeanTemperature -
Anomaly
How to verify theproducts:
The best waywould becomparison within-situ data
Your help is urgently needed – cooperation all over the region
Verification of Products using DecadalBulletins
Verification of Products using DecadalBulletins
Ten days Bulletins based on the NWP products and verifyed with your helpmight be useful.
Shall we prepare the metodology during the coming 2010 season and put
the bulletins to the internet for your inspection and intevention?
Thank you!