Development of snow parameterization: definition of initial snow density and accounting of fractional snow coverage within a cell

Transcription

1 User Seminar 11, COLOBOC Workshop Development of parameterization: definition of initial density and accounting of fractional coverage within a cell Ekaterina Kazakova Inna Rozinkina Hydrometeorological Centre of Russia

2 User Seminar 11, COLOBOC Workshop Methods and data model COSMO-Ru 1km resolution, version. Integration period 78h (from UTC) Data: - SYNOP measurements (3 s) - decade measurements of survey on Roshydromet s s (33 s)

3 User Seminar 11, COLOBOC Workshop Area of study: European Part of Russia north centre south

4 User Seminar 11, COLOBOC Workshop Problem: an overestimation (twice as much) of initial water equivalent (SWE) from GME (DWD) à too much mass in the model in comparison with the reality à underestimation of Tm forecasts (up to 1ºC) during melt period

5 User Seminar 11, COLOBOC Workshop SWE forecasts and measurements (green dots) Tm, 3h forecast. 9 March 1

6 User Seminar 11, COLOBOC Workshop см north С осадки снег, мм осадки дождь, мм станция 7ч-нов 7ч-стар (Т+1С), UTC декабрь январь февраль H см centre С осадки снег, мм осадки дождь, мм станция 7ч-нов 7ч-стар (Т+1С), UTC декабрь январь февраль см south декабрь январь февраль С осадки снег, мм осадки дождь, мм станция 7ч-нов 7ч-стар (T+1C), UTC

7 User Seminar 11, COLOBOC Workshop Cell heating

8 User Seminar 11, COLOBOC Workshop Surface fraction covered by (in TERRA within COSMO-Ru) f = W Max(.1; Min(1., δ s )) δ s =.15 parameter W - water equivalent (SWE)

9 User Seminar 11, COLOBOC Workshop Possibilities for improving Tm during melt period: To change SWE (there is a feedback in Tm) Thus it s necessary to define density (ρ ) correctly (since prognostic height corresponds to measurements, and SWE=H *ρ ) To take into account fractional covering

10 User Seminar 11, COLOBOC Workshop Different approaches for determination density: As constant (for example, 5 kg/m 3 ) Precipitation microphysics Laboratorial researches Hydrological-historical approach: density is a function of meteorological parameters during the whole period (first of all temperature)

11 User Seminar 11, COLOBOC Workshop Snow density (kg/m 3 ): initial model values for h, 7h forecasts (two schemes) and measurements kg/m Verhnaya Toyma Krasnoborsk Lalsk Medvezhegorsk North region Pinega COSMO h EM EH december january february Centre region kg/m december january february Anna Bologoe Buzuluk Buy Verhny Baskunchak Vetluga Gotnya Dmitrov Inza Kamishin Karabulak Karachev Kolomna Michurinsk Mozhga Morshansk Ponyri Ribinsk Rilsk Spas-Demensk Urupinsk Frolovo EM EH COSMO h Initial and following density overestimation of the model COSMO-Ru

12 User Seminar 11, COLOBOC Workshop Average density (g/cm 3 ) at maximum height

13 User Seminar 11, COLOBOC Workshop The dependece of density on sum of positive average daily temperatures density, kg/m 3 formula the sum of positive Tm (ºC) ρ = As + 3, 7 SumT temperature method As empirical coefficient, As=13 See Dmitrieva N. Snow density calculation using meteorological data // Meteorology and Hydrology, 195,, pp.39-

14 User Seminar 11, COLOBOC Workshop Snow density, kg/m 3 :measurement and formula calculations North region South region density, kg/m forest field formula kg/m forest formula density, kg/m field formula the sum of positive Tm ( C) the sum of positive Tm ( C) the sum of positive Tm ( C) Centre region density, kg/m forest field formula density, kg/m field formula kg/m field formula the sum of positive Tm ( C) the sum of positive Tm ( C) the sum of positive Tm ( C) kg/m forest field formula kg/m forest field formula the sum of positive Tm ( C) the sum of positive Tm ( C)

15 User Seminar 11, COLOBOC Workshop Influencing factors: - Presence of thaws; - Length of existence; - The amount of precipitation; - Compaction due to wind

16 User Seminar 11, COLOBOC Workshop Snow density depends on native zones Average density (g/cm3) at maximum ten-day height,8-,3 tundra, forest-tundra; south - steppe,-,8 north - forest-tundra; steppe and broadleaf forests (with predominance of birch),-, taiga (spruce; spruce with a touch of oak); south - steppe Types of vegetation,-, swamp vegetation, taiga (spruce); west broadleaf forests (with predominance of oak and hornbeam) <. taiga (with predominance of larch)

17 User Seminar 11, COLOBOC Workshop Snow fractional covering: experiments δ s f = =.15 parameter W W Max(.1; Min(1., δ s )) - water equivalent f = Max(.1; Min(1.,.)) f = Max(.1; Min(1.,.5))

18 ΔTm (9.3.1) cf _ =. 5м если T, C, то cf _ =. 1м

19 User Seminar 11, COLOBOC Workshop Snow height (m), 3h forecast According to model 3h forecast of height (see fig.), -cm exists on a wide territory in model as well as in the reality. However model mass is more big then according to observations (due to overestimated initial model SWE) and measured Tm is above ºC

20 User Seminar 11, COLOBOC Workshop It was decided to change longwave radiation and total forcing at surface as they gave the main effect on the instability in calculating a forecast. So, only in these terms fractional cover was calculated as usual : f W = Max(.1; Min(1., δ For the rest of terms (net radiation, latent and sensible heat fluxes for and soil as well as evaporation and evapotranspiration) fractional cover was: s )) δ s =.15 f = Max(.1;Min(1., H b s )) b s =.

21 User Seminar 11, COLOBOC Workshop G c Total forcing at surface G = f + p H rad net ( c p H + LE + Qrad, net G p total forcing at surface LE Q, net radiation at surface sensible and latent heat fluxes at surface ) or G G f = L p P r = L p P freezing rain melting fall W = Max(.1; Min(1., δ s )) surface fraction covered by

22 User Seminar 11, COLOBOC Workshop Longwave radiation Q = σ ( 1 α) ((1 f ) T + f T ) + lw s Q gr Qlw σ α f Ts Q T gr downward longwave radiation Boltzmann-constant thermal albedo ( of all soil types ) W = Max(.1; Min(1., δ soil surface temperature )) surface temperaure thermal radiation at the ground s surface fraction covered by

23 User Seminar 11, COLOBOC Workshop Tm(experiment)-Tm(ctrl) 3h forecast 8h forecast Tm temperature raised up to 1ºC on the territory where according to model data cover was below cm

24 User Seminar 11, COLOBOC Workshop Day Tm temperature (ºC): measurements (blue), 3h forecasts experiment (pink), ctrl model (orange) C Poniry model 3h experiment model 3h C Vetluga model 3h experiment model 3h Moscow C Rilsk model 3h experiment model 3h C model 3h experiment model 3h Kaluga C C Gotnya model 3h experiment model 3h Anna model 3h experiment model 3h C Karachev model 3h experiment model 3h C C Kursk model 3h experiment model 3h model 3h experiment model 3h

25 User Seminar 11, COLOBOC Workshop For h forecast the tendency remains as for 3h Kaluga Moscow C model 3h experiment model 3h C model 3h experiment model 3h Poniry C model 3h experiment model 3h

26 User Seminar 11, COLOBOC Workshop AE experiment AE ctrl model Vetluga -1,1 -,5 Moscow -,7-9,3 Kaluga -1,5 -,9 Karachev, -,1 Rilsk -, -, Anna, -,

27 User Seminar 11, COLOBOC Workshop Tm (K), 3h forecast experiment Ctrl model Changes also occured to northern regions, where height was more then cm: areas with extreme Tm were reduced

28 User Seminar 11, COLOBOC Workshop Night Tm temperature (ºC): measurements (blue), h forecasts experiment (pink), ctrl model (orange) Vetluga C Poniry model h experiment model h C model h experiment model h C Rilsk model h experiment model h C Moscow model h experiment model h Gotnya C model h experiment model h C Karachev model h experiment model h C Kaluga model h experiment model h Anna C model h experiment model h C Kursk model h experiment model h

29 User Seminar 11, COLOBOC Workshop SWE(experiment)-SWE(ctrl), mm 3h forecast 8h forecast

30 User Seminar 11, COLOBOC Workshop 7h forecast SWE(experiment)-SWE(ctrl), mm H(experiment)-H(ctrl), m

31 User Seminar 11, COLOBOC Workshop Conclusions SWE used as initial condition for COSMO-Ru run is overestimated, correspondingly, density is significantly overestimated as well It impacts the predicted near surface air temperature (Tm) during melt period caused by the excessive mass, which prevents air to get warmer The more realistic algorithm of density calculation considers meteorological conditions during the period of presence ( temperature method) Constants are in the empirical dependence of temperature method, and have geographical distribution close to configuration of vegetation-native zones Changes in algorithm of fractional coverage (SWE H) lead to possibility to simulate air warming more realistically (COSMO-Ru, version.) It is necessary further investigations of fractional coverage impact in the new COSMO-versions (.13,.17)

32 User Seminar 11, COLOBOC Workshop Thank you for your attention!