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Advanced meteorological pre-processing for the real-time emergency response systems dealing with the atmospheric dispersion in complex terrain I. Kovalets (IPMMS NAS of Ukraine), S. Andronopolous (NCSR Demokritos, Greece), J. Bartzis (Thessaloniki University, Greece)

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The situation Real-Time On-Line Decision Support System for Nuclear Emergency Management In Europe (RODOS) Atmospheric Dispersion Model (ADM) Meteorological Pre-processor (MPP) Other Modules … Measurement data from meteorological stations NWP prognostic Meteorological data ADMs: Key Role in DSSs – determine the current, and predict the future spatial distribution of radionuclides after an accidental release of radioactivity to the atmosphere MPPs: Interface between the ADMs and the incoming meteorological data Meteorological data: measurements from one or more stations in the vicinity of the NPP / prognostic data from Numerical Weather Prediction (NWP) models of National Weather Services

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Example of RODOS calculations during nuclear emergency trainings on Zaporizzhe NPP 22.08.2002 a)Integral concentration of I-131 in air, calculated by RODOS with the use of NWP data b)calculated by RODOS with the use of single meteorological observation in the point of release c) Wind streamlines in domain of RODOSs calculations, calculated by the NWP model MM5, operated in IPMMS NASU a) b) c) Time of release: 11-00 UTC Time of NWP Analyses: 6-00

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Measurements: past and current local conditions NWP data: wide range in space and future time, where no measurements exist Simultaneous use by MPP Consistency Methodology for reconciliation The problem Objective The introduction of data assimilation (DA) techniques in the MPP of the RODOS, acting as diagnostic meteorological model to reconcile the NWP data with the local meteorological stations observations

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Choosing method of solution Strong need in real-time applicability MPP acts as diagnostic wind model Only three dimensional data assimilation (3DDA): Statistical or variational ? Applicability to domains with complex geometry Variational preferable Method of solution: Multivariate optimal interpolation combined with various meteorological parameterizations of atmospheric boundary layer (ABL) and with variational divergence minimizing procedure Statistical preferable

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1. Calculating first guess field Calculated from the NWP data by 1/r 2 interpolation: 1)3D fields of velocity, pressure, temperature, humidity 2)2D fields of precipitation, mixing layer height, sensible heat flux, cloud cover, net radiation (if available from the NWP data) 2. Pre-processing of observations 1)calculation of the net radiation/cloud cover and sensible heat flux in the points of observations from measured values of surface temperature and cloud cover/netradiation (S. Hanna, J. Chang, 1993, van Ulden, Holstag, 1985) 2)calculation of the friction velocity and Monin Obukhov length from the measured values of wind velocity and values of sensible heat flux (iterative procedure) 3)vertical extrapolation of the measurements of the wind velocity to the vertical levels of MPP up to the lower 200 m. of the atmosphere (Monin-Obukhov theory, van Ulden, Holstag, 1985) Cycle of data assimilation 3. Data assimilation 1)assimilation of the measured values of cloud cover/net radiation, surfece temperature, precipitation 2)assimilation of the measured (and vertically extrapolated) values of the wind velocities 4. Post-processing 1)applying variational divergence minimizing procedure (Sasaki, 1952, Bartzis, et.al., 1998) 2)calculation of all other variables needed for ADM using standard meteorological parameterizations

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General optimal interpolation algorithm (Daley, 1991) Background field : r i r k - Observations "a" - analyzed (improved forecast) field; "b" - background (unimproved forecast) field; "T" - true field, "o" - observations (1) forward interpolation operator (2) – form of correction, W ik - unknown matrix (3), assumptions: (4) Squaring (3), taking expected values and minimizing with respect to W i gives: (5) Observation error covariance matrix (CV) Forward interpolation error CV Background field error CV Vector of background field RMS errors Procedure (1)-(5) is equivalent to minimizing functional:

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Assumed statistical structure of the background and measurement errors Errors of the background field: isotropy, constant rms of each variable Scalar field:(6) µ - correlation function, R 0 – radius of influence σ B – root mean square error Isotropic vector field, Batchelor, 1953: Each isotropic homogeneous vector field can be represented as sum of the isotropic homogeneous potential and non-divergent non-correlating vector fields (Obukhov, 1954), Let ψ – correspondent stream function, χ – correspondent potential with isotropic distributions: ν- ratio of divergent kinetic energy to the total horizontal kinetic energy, R - radius of influence in (r), then (Daley, 1991): In current work =0 (7) For all RMS errors of the background field B assumed: B = B (z), assumed also Bu = Bv (9) (8) Observation error covariance matrix is assumed to be diagonal with RMS error: O = O (r k ); assumed also: Ou = Ov (10)

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Multivariate optimal interpolation algorithm for assimilation of wind velocities Derived using standard OI algorithm (1)-(5) and assumptions (8)-(10) (11) (1) (12) (5) Note, that in (12) included are only relative errors: being the key parameter tuning between the observations and background field

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Determination of Link can be established with the approach for weighting coefficient used in the MPP CALMET of CALPUFF system (Scire, et. al., 1999) In CALMET: From statistics for one-point measurements: (12) H ORI H FINE H COARS E Terrain height

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Domain of calculations for ETEX experiment (300x300 km.)

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Statistical characteristics of wind field improvement For comparison effect of 4DDA in some models (Seaman, 2000)

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Vertical wind profiles Vertical profiles of the wind velocity a1)-b1) and of the wind direction a2)-b2), calculated by the MPP with the use of observations (), with the use of the ECMWF data only (), measured by the sodar (,line) Sodar measurements were not used in data assimilation a1), a2) – 12-00 UTC 24/10/1994. b1), b2) – 18-00 UTC 24/10/1994. a1) a2) b1) b2)

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Vertical wind profiles Vertical profiles of the wind velocity a1)-b1) and of the wind direction a2)-b2), calculated by the MPP with the use of observations (), with the use of the ECMWF data only (), measured by the sodar (,line) Sodar measurements were not used in data assimilation a1), a2) – 00 UTC 25/10/1994. b1), b2) – 06-00 UTC 25/10/1994.

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Comparison of friction velocity and kinematic heat flux U *, m/s, mK/s a)Time dependence of the friction velocity. b)Time dependence of the kinematic heat flux Dots - measured values (sonic anemometer at the Monterfil), solid black line - calculated data with the use of DA procedures, dashed line - calculated with the use of the ECMWF data only Measurements of sonic anemometer were not used in DA procedure

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a)b) d) c) Ground level wind fields a)Background from ECMWF b) IOS c) IO d) Measured

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2D fields of net radiation and cloud cover

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Further developments 1. DA developed need more enhanced capability to deal with flows in complex geometries Now we rely on: 1) quality of the NWP model; 2) relations for B 2 / O 2 ; 3)divergence minimizing procedure What further can be done? 1)Advanced meteorological parameterizations for pre-processing of observations in complex geometries: i.e., for calculation of flux parameters (sensible heat flux and other, Barlow, Belcher, et. al., 2000), for estimating mixing height and vertical extrapolation of wind/temperature measurements in complex geometries (e.g., Zilitinkevich, 2004) 2)Revising correlation functions (6), (8) to account for anisotropy introduced by complex geometries 2.1) simplest approach (used in DA of the some mesoscale models, e.g. MM5, Seaman, 1998) is to use form: µ(r i, r j ) =µ 1 (|r j -r i |)µ 2 ( z)µ 3 ( z b ) 2.2) ensemble method (Zupanski, and other) 2.3) may be something will be known from nature ? 3) Minimizing abovementioned cost functional with constraints: variational approach (Penenko and other)

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Variational approach for 3DDA Minimize functional: (1) with constraints (2) For instance, divergence minimizing: minimizing Lagrangian: B =0; Generally, very few cases, when Lagrangian simplifies situation, one more is: adjustment of the wind velocities perturbations in the outer region of the canopy flow: when z>>l

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Variational approach for 3DDA General case for 3DDA: minimize functional (the same as in OI): (3) with constraints: (4) Problem (3)-(4) usually can be solved numerically using standard approaches (e.g., penalty + descent algorithms or other more advanced). The main complexity of the problem is caused by the choice of the constraints (4)

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Conclusions 1.Methodologies for the assimilation of the observations of wind velocities and other in the MPP of the ERS system RODOS were developed 2.The multivariate optimal interpolation scheme combined with the relations for the weighting coefficient used in MPP CALMET was for the first time implemented as a 3DDA scheme in the MPP of the real-time ERS system 3.Comparisons of the model results with the meteorological measurements performed in the ETEX experiments showed good agreement of calculated values with measurements and improvement of the first guess field produced using the NWP results with the use of the 3DDA procedures 4. Further development of the data assimilation procedures for the MPPs of the ERS should be performed for producing more physically consistent meteorological fields when applied in complex geometries

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Acknowledgements The present work has been fully supported by the European Commission through the EURATOM grant in connection to the European Project "RODOS Migration".

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