Application of Numerical Weather Prediction Models for Drought Monitoring. Gregor Gregorič Jožef Roškar Environmental Agency of Slovenia

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1 Application of Numerical Weather Prediction Models for Drought Monitoring Gregor Gregorič Jožef Roškar Environmental Agency of Slovenia

2 Contents 1. Introduction 2. Numerical Weather Prediction Models - NWP 3. Application of NWP for Draught Monitoring 4. Verification of Products using Decadal Bulletins

3 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 How to get space information? Indicators Horizontal interpolation Remote sensing Numerical Weather Prediction Models

4 Numerical Weather Prediction Models - NWP Initial and Boundary Conditions (data archive) Integration Simulated/ Predicted Athmospheric Variables/Fields Numerical resolving of mathematical equations describing development of athmospheric variables in time Air Pressure Wind Temperature Humidity Cloudiness Precipitation Evaporation Soil Moisture Etc.

5 Numerical Weather Prediction Models - NWP Real continous space is by the model distributed into equidistant discrete grid points Distance between grid points - space resolution defines the smallest structures seen by a NWP: Low resolution (~200 km) simulation of basic structures (planetary waves, big frontal systems) used for climate modeling and studies of global mechanisems; 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

6 Numerical Weather Prediction Models - NWP Integration Weather Observing System System Atmospheric Physics Surface Physics Surface Processes Simulated Fields Fields Data DataAnalysis Data DataAsimilation Initialization Data Collecting over Observing Network Numerical Methods Numerical Model Postprocessing Vizualization Data and Model Results Presentation

7 Numerical Weather Prediction Models - NWP To run a LAM, a a LAM, access to global archives of weather patterns is needed Data in regular grid, internally consistent, without errors (+) Not directly related to actual situation on ground (-)

8 Numerical Weather Prediction Models - NWP Relief presentation depends on horizontal resolution Very important over montainous areas Example: slope and precipitation: Wind This is why majority of NWP models underestimate precipitation

9 Numerical Weather Prediction Models - NWP (Non)Predictability Physical Laws Co-existence of various scales Interaction of all variables Exchange of energy among various scales Chaos

10 Numerical Weather Prediction Models - NWP Lorentz butterfly

11 Numerical Weather Prediction Models - NWP (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

12 Numerical Weather Prediction Models - NWP Uncertainty Taking into account (Non)Predictability, we have to consider that by the model simulated parameters and fields are not directly related to actual parameters, in particular to the situation on ground. How to reduce uncertainty and use the NWP products for drought monitoring? to choose the model and setup it in the way that the output best fits the observations (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.

13 Numerical Weather Prediction Models - NWP Limited Area Model Main Idea: Take data from global archive Choose area and grid points Re-simulate weather patterns from global model to obtain more details on a regional scale

14 Application of NWP for drought monitoring Limited Area NMM (NCEP) Non-Hydrostatic Meso-scale Model Area: 461 x 289 x 92 = points ( 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

15 Application of NWP for drought monitoring

16 Application of NWP for drought monitoring OUTPUT: Simulated and averaged variables (air and soil) daily aggregates precip daily sum (00-24h), mm evaporation (and transpiration) daily sum (00-24h), mm potential evaporation (and transpiration) daily sum (00-24h), mm liquid soil moisture volumetric fraction 0-10cm (24h) liquid soil moisture volumetric fraction 10-40cm (24h) liquid soil moisture volumetric fraction cm (24h) liquid soil moisture volumetric fraction cm (24h) total soil moisture volumetric fraction 0-10cm (24h) total soil moisture volumetric fraction 10-40cm (24h) total soil moisture volumetric fraction cm (24h) total soil moisture volumetric fraction cm (24h) total soil moisture content (kg/m2) in 0-200cm layer (24h) geopotential height on 850hPa (12h), m temperature on 850hPa (12h), K daily average 10m wind, m/s daily average 10m wind gusts, m/s daily min. temperature, K daily max. temperature, K daily min. relative humidity, % daily max. relative humidity, %

17 Application of NWP for drought monitoring DROUGHT RELATED VARIABLES Soil moisture? DROUGHT RELATED TIME SCALE Not daily! Decade? DROUGHT RELATED INTERPRETATION Not absolute values, deviation from normals

18 Application of NWP for drought monitoring GOAL: To re-compute reanalyses data over SE Europe in dense grid to obtain climatology for drought situations interpretation ECMWF ERA Interim Model Integration Area (SE Europe) Limited Area Model NNM (NCEP)

19 Application of NWP for drought monitoring CLIMATOLOGY: Accumulated Water Balance over 70 days Mean for the period 21 May 29 July, data period

20 Application of NWP for drought monitoring POSSIBLE PRODUCT: Accumulated Water Balance over 70 days Anomaly

21 Application of NWP for drought monitoring POSSIBLE PRODUCT: 10 days Soil Moisture Index - Anomaly

22 Application of NWP for drought monitoring POSSIBLE PRODUCT: 10 days Mean Temperature - Anomaly

23 Verification of Products using Decadal Bulletins How to verify the products: The best way would be comparison with in-situ data Your help is urgently needed cooperation all over the region

24 Verification of Products using Decadal Bulletins Ten days Bulletins based on the NWP products and verifyed with your help might 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!

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