Near-Surface Data Assimilation in the NCEP Regional GSI system: Use of Mesonet Data & a New Forward Operator

1. Near-Surface Data Assimilation in the NCEP Regional GSI system:Use of Mesonet Data & a New Forward Operator Seung-Jae Lee

2. Introduction • The NCEP analysis system- many changes since 1978- 3DVAR-Gridpoint Statistical-Interpolation (GSI) System - can assimilate diverse kinds of obs. • Importance of and demand for Real-Time Mesoscale Analysis (RTMA)- near-surface data assimilation is one of the challenges to conquer • U.S. meso-network systems measure and provide information on the environment at the size and duration of mesoscale wx events.

3. Purposes • assimilation of the sfc mesonet data in the NCEP regional GSI system- test of regional background error are also considered • understanding the characteristics of innovations of the sfc mesonet data

4. Mesonet data: QC • We at NCEP currently do not perform any automated platform-specific QC on any surface data and simply honor quality markers provided by the FSL- MADIS. • NCEP could (but not currently doesn’t) place manual quality markers on the mesonet data. This would be done by either putting reports on the rejectlist which might be updated monthly or on a report-by-report basis by the NCEP Senior Duty Meteorologists. • We perform some gross checks and flag data that are outside reasonable limits, data with mising lat/lon etc.

5. Mesonet data: PrepBUFR file • all mesonet data are included but the observation error for all mass and wind observations is set to missing In this study, we modify the observation error file to assign the mesonet observations the same observation error as for METAR mass and wind observations • The time window over which data collection is performed is +/- 1.5 hours, and the 3DVAR analysis experiment is valid for t=0h. • Near-surface data used:Mesonet data, METAR data, Synoptic land data, Synoptic sea data + minor sfc data

6. Modification to background error statistics • Until now, the background error for regional assimilation has been a downscaled version from the background error derived from the global model ( Default) • In the existing approach, horizontal scales are estimated from derivatives, but in the new approach, these are estimated using auto-covariances (W.-S. Wu 2005, personal communication)

7. eastern western central First Guess • WRF-NMM 8 km • There are three domains available for the WRF-NMM 8 km model: Initial fields for the western, central, eastern domains are at 06, 12 and 18 UTC, respectively. • Experiments are carried out for each model domain: Single time analyses

8. experimental design

9. Case 1: western USA, 0600 UTC 14 Feb 2005 Nighttime (around midnight) Clear sky; large-scale nighttime sfc inversion

10. analysis increments (case 1) A -G < 0 over much of the domain

11. Mean values of (Oi – Gi) at each category of dP The obs are consistently colder than the model, leading to the large negative anal inc. at the sfc. Part of the reason this is undesirable is that T inc are coupled with W incin approximate geos. balanceand large wind inc are created at middlelevels in the troposphere.

12. Case 2: central USA, 1200 UTC 10 Mar 2005 Early morning (around 6-7 am) - sfc inversion by clear weather- A low pressure system in the northern-central region

13. Analysis Increments (case 2) 6th-7th : 920-900hPa - Smaller and detailed str- Positive in the N-easternregion where the L is located

15. Case 3: eastern USA,1800 UTC 23 Mar 2005 Daytime (2 pm) A large low-pressure system over the eastern coast

16. Analysis increments (case 3) Unstable sfc

17. Small bias Even in the east coast case,the A-G is large.It just has smaller scales.

18. Accumulated Statistics of (O-G) • Understanding the characteristics of innovations (observed–guess) of the near-surface data • Long-term statistics: 1 month • Scatter diagram of (O-G) during May 2005 for nighttime (0600 UTC), early morning western (1200 UTC),daytime eastern (1800 UTC) domains. • Linear regression, bias, rmse etc.

19. Western domain (0600 UTC) • Sfc mesonet T data have a considerable amount of outliers compared with other land sfc T data. • Bad obs or local effects Some stations can be seen to produce the same values regardlessof model fcsts.

20. Central domain (1200 UTC) Very few outliers in METAR possibility of local effect Many outliers This can mean quality markers placed on the data by the FSL-MADIS QC are of little valuenot only for wind but also T data.  Proper QC is required.

21. Eastern domain (1800 UTC) Slope and correlation coeff. of land sfc T data are good and similar in all three domains. However,in the case of synoptic sea sfc data, they show a peculiar pattern in the western domain: a steep slope andvery low correlation coeff. (<0.5)  Ocean wave effect etc.

22. The nighttime western and central domains indicate a model warm bias. The western domain, in particular, shows a model warm bias of about +2.2 C at nighttime when compared with the eastern domain which did not show any obvious bias. The o-g statistics as a function of surface pressure difference in the eastern domain seems to indicate that the mesonet observations have small temperature bias (about +0.5 C).

23. Horizontal distribution of stations with large (O-G) Unlike the other two types of sfc stations, surface mesonet stations are very dense, particularly around large cities. Stations with large innovations are distributed uniformly in the nighttime western and central domains, while are mainly located in the large cities in the daytime eastern domain. In the case of the eastern domain, it is 14 LST and the synoptic situation is characterized by many local and unstable situations with small-scale variation in daytime. In the case of the western (central) domain, it is midnight (dawn) and frequent large-scale inversion situation prevail.

24. The number of stations as a function of surface pressure difference Western (0600 UTC) Central (1200 UTC) Eastern (1800 UTC) The western domain revealed an asymmetric distribution that implies that there are many stations where the model surface is higher than the observation surface. This could partly account for the model warm bias in the western domain at nighttime. In the other two domains, the distributions are comparatively axisymmetric.

25. A complex forward operator

26. Motivation Cost function x : an N-component vector of analysis increment B : the N by N previous forecast-error covariance matrix O : the M by M observational (instrumental)-error covariance matrix F : the M by M representativeness (forward operator)-error covariance matrix H : a linear or nonlinear transformation operator y : an M-component vector of observational residuals; that is, M : the number of degrees of freedom in the analysis; and N : the number of observations.

27. H, the forward model • Converts the analysis variables to the observation type and location (May include variable transformations in case of, for example, radar or satellite data assimilations). • The quality of simulated observation, the model state converted to “observation units”, depends on the accuracy of the forward operator: Inaccurate pseudo-observations give rise to errors in observation increments leading to bad analyses.

28. The accuracy of anl is dependent on the effectiveness of algorithm used to match obs with the bg values. • Currently, there does NOT exist any forward model for near-surface variables in the NCEP GSI system: Just involves simple interpolation of the background value to the location of the observation.

29. the new fwd operator • Uses a similarity theory based on Dyer and Hicks formula which has been adopted in model PBL parameterization such as surface layer process in MRF PBL scheme (Hong and Pan 1996). • realized in the MM5-3DVAR system (Barker et al. 2004). It has been used for surface data assimilation in operational analysis systems and has proven to be encouraging in many contexts (e.g., Shin et al. 2002; Hwang 2005).

30. assumptions • All the surface observation sites are assumed to be located at the model surface, regardless of the actual difference in elevation between the surface measurement and the model surface. • The different types of surface observations are directly assimilated without any modification. The observed surface pressure is still reduced to the model’s lowest level. • Near-surface wind and mass variables are obtained at different heights using the surface layer similarity theory: Near-surface wind is obtained at 10 m, while temperature and specific humidity are obtained at 2 m.

31. 2nd Lowest ps2, ts2, qs2 EL ps, ts, qs, us,vs, hs Lowest 10 m SL u10, v10 2 m t2, q2 psfc, tgroughness, xland surface Forward model description • Monin-Obukhov similarity theory Input & output Atmosphere = PBL(SL+EL) + FA

32. near-surface variables wind temperature specific humidity Z = 2 or 10 meter

33. stability functions Turbulence regime determines function type

34. (= Ribc) 0 0.2 (1) Stable turbulence regimes (4) Free convection (2) Damped mechanical (3) Forced convection

35. Stability-dependent functions (1) Stable (2) Damped (3) Forced convection where

36. (4) Free convection

37. Implementation and tests • Incorporated into the latest GSI version with some modification  Jun 2005 • Application - eastern domain, 1800 UTC 23 Jun 2005 • Parallel runs (during early July 2005)- western domain: Jul 7, 11-13, 18 - central: Jul 2-14, 17-18- eastern: Jul 6-12, 14,15,17

38. Case: eastern domain,1800 UTC 23 Jun 2005

39. Large innovation sites Newfwdmodel Oldsimpleinterpol.

40. Innovations as a function of dp Worse over sea, why? - Because of setting z=2 m (not the height of buoy) over water also.- Ocean wave effect -> roughness is uncertain. - Physics etc.  Despite this, the total bias was reduced from 2.2 to 0.9.

41. Parallel run results: Early July, 2005 old Western, 0600 UTC new

42. old new Central, 1200 UTC

43. old Eastern, 1800 UTC new

44. old Western, 0600 UTC new

45. old Central, 1200 UTC new

46. old new Eastern, 1800 UTC

47. Summary and Conclusions • We have assimilated sfc mesonet data in the NCEP regional GSI using the same observation error as that adopted for METAR data within the WRF-NMM (8 km) 3DVAR system. • In the single-time anl experiments, the anl field was shown to contain mesoscale (smaller and detailed) structures as mesonet data are added. • When the regional bg err statistics are used, the overall pattern is similar to the downscaled global version but the amplitude is somewhat intensified. It is believed to be the results of smaller (shallower) vertical structures in the regional bg err covar.

48. sfc mesonet T data were found to have a considerable amount of outliers compared with other land sfc T data. • The nighttime western and central domains indicated a model warm bias. The western domain, in particular, showed a model warm bias of about +2.2 C at nighttime when compared with the eastern domain which did not show any obvious bias. • Stations with large innovations are distributed uniformly in the nighttime western and central domains, while they are mainly located in the large cities in the daytime eastern domain.  These differences could also be the result of urban heat island effects (not contemplated in the model) or erroneous station groups.

49. The current near-surface observation operator in the NCEP GSI system was improved from a simple linear interpolation to a more complex similarity model. • In the intercomparison of the old and new forward operators using case experiments and long term runs, the complex forward model is shown to improve the innovation statistics substantially.  This is due to the realistic considerations of surface characteristics (e.g., roughness length) and atmospheric stability within surface layer.

50. Sources of disagreement between observations and the model include observation errors and errors related to the model. It is shown that the latter was reduced by introducing a more complex forward model in the NCEP regional GSI analysis system. • The new operator has significant and practical potential to assimilation of diverse surface data in the NCEP regional GSI system. It can be applied to not only regional but also global GSI system. • It has diverse applications to atmospheric and oceanic data assimilation and can be used for not only surface data over land and but also satellite data, such as QuikSCAT sea surface wind.