NCEP non-hydrostatic regional model and surface scheme LAPS: A dynamical scaling tool for use in agricultural models

Transcription

1 NCEP non-hydrostatic regional model and surface scheme LAPS: A dynamical scaling tool for use in agricultural models D.T. Mihailović and B. Lalić Faculty of Agriculture, University of Novi Sad, Novi Sad, SERBIA guto@uns.ns.ac.yu Center for Meteorology and Environmental Predictions

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3 Downscaling is a method for obtaining high-resolution climate or climate change information from relatively coarse-resolution global climate models (GCMs). Dynamical downscaling uses a limited-area, highresolution model (a regional climate model, or RCM) driven by boundary conditions from a GCM to derive smaller-scale information. RCMs generally have a domain area of 106 to 107 km2 and a resolution of 20 to 60 km. Dynamical downscaling provides very small scale meteorological information useful for agricultural models for different purposes IT WIIL BE CONSIDERED IN THIS CONTEXT

4 Content 1) Short background of modelling tools a) LAPS land surface scheme b) WRF Modelling System (NMM) 2) Forecasting the occurrence of plant diseases 3) Simulation (forecasting) of evaporation over crops and orchards 4) Hydrological simulation (forecasting) 5) Forecasting of UV radiation for risk assessment in agriculture 6) Climate simulation using climate regional model 7) Conslusions

5 1a) LAPS land surface scheme

6 LAPS: Soil-Vegetation-Atmosphere- Transfer model Forcing data Air temperature Short wave radiation Prognostic variables Temperatures: leaf, ground and deep soil temperature Soil moisture content at three soil layers Long wave radiation Leaf wetness Humidity Wind speed Precipitation Initial conditions Aerodinamic ch. L A P S Main outputs Sensible and latent heat fluxes vegetation - canopy air space ground - canopy air space canopy air space - reference level Momentum flux Wind within the canopy Morphological ch.

7 LAPS: resistance representation Mihailovic et al. (1992), J. App. Meteor. Mihailovic et al. (1993), J. App. Meteor. Mihailovic (1994), Workshop, McQuary Univ., Sydney Mihailovic et al. (1995), J. App. Meteor. Mihailovic (1996), Global and Planetary Chang. Mihailovic and Kallos (1997), Bound. Layer Met. Mihailovic et al. (1999), Bound. Layer Met. Mihailovic et al., (2006), J. App. Meteor.

8 LAPS: submodels used Soil three layer model Vegetation-single layer model

9 Governing equations in the LAPS The net radiation absorbed by the canopy and soil is assumed to be partitioned into sensible heat, latent heat, and storage terms, as R R nf ng T f = λe f + H f + C f (1) Prognostic t Tg = λeg + H g + Cg (2) Prognostic t Deep soil temperature T t d = ( net λ )/( 365 ) 2 R H E C π g g g g (3) Prognostic

10 Governing equations in the LAPS Water stored on the canopy w t f = P f E wf / ρw Prognostic The volumetric soil water content w t w t w t E g + Etf, l = Pl F1,2 R0 R Dl ρ w { ρ } 2 = F1,2 F 2,3 Etf,2 / w R2 D2 3 1 = D 3 { } F 2,3 F 3 R 3 Prognostic 1

11 1b) WRF Modelling System

12 NMM non-hydrostatic mesoscale model # Fully compressible, non-hydrostatic or hydrostatic # Mass based sigma-pressure hybrid terrain following coordinate with constant pressure above 400 hpa # Arakawa E-grid staggering # Adams-Bashorft and Cranck-Nickolson time integration schemes # High-order advection scheme

13 NMM non-hydrostatic mesoscale model # Scalar and energy conservation # Coriolis, curvature and mapping terms # One-way nesting # Lateral boundary conditions suitable for real-data and one-way nesting # Full physics options to represent atmospheric radiation, surface and boundary layer, and cloud and precipitation processes

14 2) Forecasting the occurrence of plant diseases

15 BAHUS biometeorological system for predicting the occurrence of diseases in plant production (Center for Meteorology and Envoronmental Predictions) 1) Clamatological data 2) Automatic weather station 3) NCEP outputs (mesoscale) 4) LAPS surface scheme Venturia inequalis Ervinia amylovora Plasmopra vitiocola (Mihailovic et al., Envir. Modell. Soft., 2001) FP6 project ADAGIO

16 Ten-day variation (8-17 July 2002) of within-canopy air temperature simulated by the LAPS and observed inside a sunflower canopy at the Rimski Sancevi (SERBIA) (Mihailovic and Eitzinger, Ecol. Modell., 2007) NCEP LAPS Air temperature in canopy, T a ( o C) Simulated (thin) Observed (thick) Julian day

17 Daily variations (19 July 1998) of the sunflower foliage temperature: (1) simulated by the LAPS model and (2) measured (Rimski Sancevi, SERBIA) (Mihailovic and Eitzinger, Ecol. Modell., 2007) Foliage temperature, T f ( o C) Sunflower (NS-Velja) Rimski Sancevi (Serbia) Simulated (solid line) Observed (square) NCEP LAPS (b) Hours (LST)

18 Air temperature ( o C) Relative humidity (%) Appearance of wet period Date Date Date Values measured by ADAS weather station (dots) and simulated using LAPS model (L) during last decade of April 2003 (Lalic et al., Idojaras, 2007)

19 3) Simulation (forecasting) of evaporation over crops and orchards

20 Surface fluxes (Wm -2 ) Latent heat flux (Wm -2 ) NEW OLD Observed Day of Year 0 SIMULATED Sensible Latent OBSERVED Sensible Latent Apple orchard 16 May 2002 Chlewiska (Poland) Surface fluxes (Wm -2 ) (a) Temporal variation of latent heat flux and obtained by the LAPS scheme compared with the observations over a soybean field in Caumont site (France) during its growing season in 1986 (Mihailovic et al., 2006, J. App. Meteor.) SIMULATED Diurnal variations of latent and sensible heat fluxes over (a) apple orchard, (b) rapeseed in Chlewiska (Poland) for 16 May 2002 (Mihailovic et al.,ther. App. Climat. in revision) Measured values provided by the Poznan Agric. University Group Sensible Latent OBSERVED Sensible Latent Rapeseed 16 May 2002 Chlewiska (Polan (a) Hours (UTC) Hours (UTC) (b)

21 3) Hydrological simulation (forecasting)

22 Water transfer modelling: Soil (WRF Modelling System - NMM) + LAPS (forcing data) Daily averages of total soil water content (mm) over a depth of 1.6 m simulated by the LAPS and observed beneath a soybean canopy in HAPEX experiment at the Caumont (France) (Mihailovic et al., 1998, J. Hydrol.) 600 Soil moisture, θ t (mm) 400 Field capacity (480 mm) Observed (squares) Calculated (solid line) Julian day

23 5) Forecasting of UV radiation and index for risk assessment in agriculture

24 NEOPLANTA computes the: 1) solar direct and diffuse UV irradiances under cloud free conditions for the wavelength range nm and UV index 2) effects of O3, SO2, NO2, aerosols, and nine different ground surface types on UV radiation are included. 3) instantaneous spectral irradiance for a given solar zenith angle 4) UV index for the whole day at half-hour intervals from sunrise to sunset Modelling details: 5) atmosphere is divided into several parallel layers (maximum 40) in the model 6) it is assumed that the layers are homogeneous with constant values of meteorological parameters 7) vertical resolution of the model is 1 km for altitudes below 25 km and 5 km above this height. 8) upper boundary of the highest layer in the model is 100 km 9) model uses standard atmosphere meteorological profiles 10) there is also an option of including the real-time meteorological data profiles from the high-level resolution mesoscale

25 (WRF Modelling System - NMM) + LAPS + LAPS (forcing data) NEOPLANTA: A Short Description of the First Serbian UV Index Model Authors: Malinovic, S., Mihailovic, D.T., Kapor, D., Mijatovic, Z., Arsenic, I.D., Publication: Journal of Applied Meteorology and Climatology, vol. 45, Issue 8, p

26 6) Climate simulation using regional climate model

27 1) Two-way coupled model, grid point, primitive equations, hydrostatic approximation. 2) Atmospheric component is Eta model and ocean component is POM 3) Models exchange atmospheric surface fluxes and SST every physical time step of atmospheric model (~180s). 4) grid boundaries: -10W to 40E, and, 28S to 52N for atmosphere, and ocean model covers Mediterranean sea. 5) ECHAM boundary conditions (Rajkovic and Djurdjevic)

28 Climate change experiment set up Present climate integration: , Future climate integration: (A1B experiment). Atmospheric model: 0.25 horizontal resolution / 32 vertical levels 6h lateral boundary condition from SINTEX integration, Annual cycle of vegetation fraction, New radiation scheme (Pérez et al., 2006) SST bottom boundary condition from SINTEX over uncoupled seas. Ocean model: 0.2 horizontal resolution / 21 vertical levels (Mediterranean sea), Initial condition: MODB for 1961 / SINTEXG for 2071

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32 Conclusions 1) There are a lot of agrometeorological models requiring sophisticated meteorological inputs 2) Downscaling can provide them but with a very comprehensive physics and biology 3) Problem of model equations and parameterisation 4) New generation of mesoscale models providing approaches that take care about non-linearity and complexity