Soil moisture analysis at DWD

Transcription

1 Soil oisture analysis at DWD Martin Lange, DWD

2 Outline NWP odel suite - past to present Soil oisture analysis at regional and global scale Fro 2d Var to EnKF Model soil oisture vs Observation at validation site Suary and conclusions Martin Lange DWD

3 Regional odelling in COSMO fraework Courtesy R. Potthast Martin Lange DWD

4 Nine NWP generations at DWD fro 1966 until today (1) 1966: BP: Δ ~ 381 k (A ~ k 2 ); 1 layer; barotropic (Area of D: k 2 ) (2) 1967: BKL: Δ ~ 381 k (A ~ k 2 ); 5 layers; heispheric odel, barocl., dry (3) 1978: BKF: Δ ~ 254 k (A ~ k 2 ); 9 layers; heispheric odel, barocl., oist (4) 1991: GM: Δ ~ 190 k (A ~ k 2 ); 19 layers; global spectral odel (5) 1999: GME: Δ ~ 60 k (A ~ k 2 ); 31 layers; icosahedral hexagonal grid (6) 2004: GME: Δ ~ 40 k (A ~ k 2 ); 40 layers (7) 2010: GME: Δ ~ 30 k (A ~ 778 k 2 ); 60 layers (8) 2012: GME: Δ ~ 20 k (A ~ 346 k 2 ); 60 layers (9) 2015: ICON: Δ ~ 13 k (A ~ 173 k 2 ); 90 layers; nonhydrostatic odel; triang. grid factor 29 factor 839 he NWP generations are also characterized by increasing coplexity of the physical paraeterizations, nuerical ethods and software design. Moreover, the progress in data assiilation (algoriths as well as types of data used, esp. satellite data) significantly contributes to the steady iproveent of forecast quality.

5 Forecast quality endency correlation of surface pressure for North Atlantic and Central Europe PBPV 03/2010

6 EMP-Verification 850 hpa NH fro 2014 to 2017 ICON EnVar ICON EnVar +24h +48h UKMO Met.Fr. ECMWF DWD UKMO Met.Fr. ECMWF DWD 6

7 Motivation for SMA at DWD J.F. Mahfouf (1991) who showed that soil oisture can be inferred fro syonop observations given an appropriate SVA schee in an NWP environent. Andreas Rhodin et al,. (1999) who showed positive ipact of soil oisture assiilation with a 2D var syste on the forecast of 2 teperature and rel. huidity in a stand alone version of the short range weather forecast odel fro DWD (Rhodin et al.,.1999). Based on the previous work, Reinhold Hess ipleented the 2d variational SMA schee in a Kalan cycled analysis for the regional LM odel which becae operational in his schee was able to assiilate not only synoptic observations as the OI, but also satellite easureents. he explicit calculation of grad J by finite differences obtained fro perturbed forecasts was an additional advantage which saved recalculation of OI coefficients in case of odel changes. herefore it was adopted by ost other european weather centers. PBPV 03/2010

8 2d var (z,t) soil oisture analysis applied in LM / COSMO-EU ) ( ) ( ) ( ) ( obs obs b b O w w B w w J fc error b obs w A b ana w O B O w w (12:00.15:00) )) ( ( ) ( 2 Cost function penalizes deviations fro observations and initial soil oisture content Analysed soil oisture depends on 2 forecast error and sensitivity 2/w 0 J he B atrix is consistently updated daily in a Kalan filter cycled analysis Q MAM B t 1

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10 Evaluation in the eldas project he SMA schee was extensively evaluated in the EU funded ELDAS project which started in 2002 and aied at the Developent of a European Land Data Assiilation Syste to predict Floods and Droughts A series of odel experients were conducted using observations of 2, Rh2 and analysed precipitation which revealed interesting results. Positive ipact was shown with for both assiilation of 2 and Rh2, but these results were outperfored by including analysed precipitation. his iproved not only the forecast of near surface variables but also on precipitation. It was shown that the SMA syste can copensate for forecast errors in precipitation by bringing soil oisture back closer to reality.

11 Strong ipact of SMA on 2 and Rh2, but also on precipitation forecast Europe, onthly ean Rse 2 (12:00, 15:00) Europe, onthly ean Rse Rh2 (12:00, 15:00) rue skill score for 24hr accu. precip (prec>1 ) rue skill score for 24hr accu. precip (prec>5 ) adopted fro Hess, Lange, Wergen (2008)

12 Global soil oisture analysis For the global syste the variational analysis was not considered as an appropriate solution due to the coputational cost of the additional forecast runs and as it is cubersoe to aintain in an operational fraework. It also led to liits with the operational schedule herefore an SMA schee was developed which paraeterizes the sensitivity of near surface teperature on soil oisture variations. An analytical expression was derived fro the relations for evaporation fro plants and bare soil.

13 Paraeterisation of 2(12:00,15:00)/w(0:00) with Lhfl/w fro penan eqn. at obs tie Surface energy balance equation 0 Rn Lhfl Shfl G Soil oisture variation: Lhfl Shfl a ( s 2 Shfl c p ra c p Lhfl 2 r Lhfl f (1 f )(1 f ) E pl i ) snow pot r a Lhfl( 12 : 00) ~ 2 2 (10*K) ra r f Lhfl (W/2) (12 : 00)

14 Paraeterisation for Sensitivity d2/dw Senstivity as derived fro Surface energy balance and Penan type equation w 2 ra Lhfl 1 rs rs k, root (1 ) k c p ( r r ) f r w w a f LAI s,ax root pwp dz z root d2/dw Para / explicit variation No further need for additional odel runs!

15 Strong ipact through the whole boundary layer ep Verification June 2009 Europe

16 Satellite obs require new SMA syste Present global SMA has been introduced as efficient alternative ethod to the 2d var analysis used in COSMO-EU and runs in the global syste since However it suffers fro its liitation to conventional observations. For satellite observations the relation between easured brightness teperature and soil oisture content cant be siply calculated analytically due to the coplex forward operator. For assiilation of b fro satellite observations the syste has to be replaced. Solution which becae practically available with introduction of global LEKF: Developent of Enseble based SMA schee.

17 Enseble Kalan Filter Analysis update equation Approxiation of B with deviations fro enseble ean Linear scenario: HBH can be derived fro covariance of obs equivalents

18 Algorith coputational efficient Analogue - No horizontal correllations in SMA, - R diagonal Kalan gain can be calculated siply fro enseble forecast at affordable cost Upscaling of K for the deterinistic grid using scaling by soil oisture index to account for changing soil type.

19 Algorith coputational efficient Calculation of increents on deterinistic grid Present state at DWD: SMA is alost coded, first tests are outstanding

20 Can we benefit fro realistic soil oisture obs? top soil layers botto soil layers GME 2014 GME 2014 top soil layers botto soil layers ICON 2016 ICON 2016

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22 Satellite obs require new SMA syste DWD runs variational soil oisture analysis since 17 years operationally using screen level observations. By design it helps to reduce screen level forecast errors by adjustent of surface fluxes. But it also adjusts soil oisture in case of wrong precip forecast. Satellite observations can not be assiilated with the present global syste. An enseble based SMA is developed which is able to solve this gap. ELDAS results showed strong positive ipact of analysed precipitation on weather forecast. Soil oisture changes derived fro consecutive overpasses of SMOS or SMAP satellite ay help to correct for precipitation errors on the global scale.

23 Satellite obs require new SMA syste However at least daily analysis of local soil oisture changes are needed to get used by the SMA syste. So teporal disaggregation is required using odel inforation or inforation fro other observing systes. Soil texture is an issue in the odels as external soil databases does not always atch. Paraeter estiation for soil texture ay alleviate the proble. Soil texture based dielectric properties transfer this uncertainty to the foreward operator. Eissivity aps for L-band are required to inversely derive the appropriate soil paraeter. Model syste has iproved the last years so it becoes difficult to get positive ipact of SMA on weather forecast as it copensates for odel biases. However it is the only way to bring soil oisture closer to reality.

24 hank you for your attention!

25 Is the odel able to benefit fro realistic soil oisture? Measureent site Lindenberg additional obs for Suisse, Sweden, Czech., Roania Obs Lindenberg GME 20k (gpt 67456) ICON 13 k (gpt ) Geogr. longitude 14, ,11 Geogr. latitude 52, Soil type sand loa (5) sand (3) Plant cover (July) 54 % LAI (July) 3.5

26 Suary -However at least daily analysis of local soil oisture changes are needed to get used by the SMA syste. So teporal disaggregation is required using odel inforation or inforation fro other observing systes. -Global radar database would be useful. Fostering exchange between different countries is topic of other working groups. -Soil texture is an issue in the odels as external soil databases does not always atch. Paraeter estiation for soil texture ay alleviate the proble. Soil texture based dielectric properties transfer this uncertainty to the foreward proble. Eissivity aps for L-band are required to reverse the order and to inversely derive the appropriate soil conditions. Model syste has iproved the last years so ipact of SMA to reduce odel bias has reduced.

27 Enseble Kalan Filter Analysis update equation Approxiation of B with deviations fro enseble ean Linear scenario: HBH derived fro covariance of obs equivalents

28 Notes Iproved odel and data assiilation syste is basic for use of satellite data. In earlier ties uch efforts in assiilation didn t succeed for this reason, this changed the with the latest syste we are prepared to ake use of indirect observations. Cost workshop, Offenbach

29 Modelsyste present and next 50 layers 30 k top layers, 75 k top GME, 20 k Icon, 13k 40 layers 22 k top 60 layers, 30 k top C-EU, 7k Icon Nest, 6.5 k 50 layers 22 k top 65 layers, 22 k top C-DE, 2.8 k C-2 extended, 2.2 k Model / doain Spatial resolution Replaced by Spatial resolution Operational ipleentation at DWD GME / global 20 k ICON 13 k January 2015 COSMO EU / Europe link to doain figures (orografie) Gerany 7 k 40 layers 2.8 k 50 layers ICON Nest Europe extended COSMO 2 Gerany extended to west 6.5 k 60 layers 2.2 k, 65 layers 2015 End 2015

30 Verbesserung der Vorhersagequalität endency correlation of surface pressure for North Atlantic and Central Europe 30

31 EMP-Verification Europe fro 2014 to 2016 ICON EnVar ICON EnVar +24h +48h UKMO Met.Fr. ECMWF DWD UKMO Met.Fr. ECMWF DWD 31

32 IFS (ECMWF) is not always better than ICON RH 2, Central Europe, January 2017 red: IFS (ECMWF), black: ICON 32

33 Verbesserung der Vorhersagequalität ICON 33

34 PBPV 03/2010

35 2d var (z,t) soil oisture analysis applied in COSMO-EU ) ( ) ( ) ( ) ( obs obs b b O w w B w w J fc error b obs b ana w O B O w w )) ( ( ) ( Cost function penalizes deviations fro observations and initial soil oisture content Analysed soil oisture depends on 2 forecast error and sensitivity 2/w 0 J 00) (0 : 00) 00,15: (12 : 2 w he B atrix is consistently updated daily in a Kalan cycled analysis

36 PBPV 03/2010

37 PBPV 03/2010