1. Science Panel II: TC NWP and Data Assimilation (90 min) Panel Members Sim Aberson HRD/AOML Scott Braun GSFC/NASA Shuyi Chen, RSMAS, U. Miami Gregory Tripoli, U. Wisc. William Lewis, U. Wisc. Sharan Majumdar, RSMAS, U. Miami George Halliwell, AOML Ming Xue, SOM/CAPS, U. of Oklahoma. Develop consensus concerning specific uses and value that NIS would have on TC NWP and data assimilation paradigm. Slides prepared by Ming Xue (mxue@ou.edu)

2. Specific Questions: Given that operational TC NWP model(s) are expected to be running at cloud-resolving resolutions (~km) and possibly in ensemble mode during the lifetime of the NIS, how will its observations facilitate the initialization of TCs and their precursors via advanced data assimilation? What are the most desirable observed parameters (e.g., radial velocity, reflectivity, polarimetric variables, Doppler spectra/spectrum width, differential reflectivity) and what unique information do they provide for the cloud-resolving TC NWP purpose? What unique information will NIS provide to enhance data assimilation for TCs in global and other non-TC specific models that will most likely to be running at non-cloud-resolving resolutions? What is the expected duration of NIS impact on both deterministic and probabilistic TC forecasts? What are the expected impacts of NIS observations on the overall intensity/structure prediction problem?

3. Specific Questions - continued Given that operational TC NWP model(s) are expected to be running at What are the expected impacts of NIS observations on TC surge and hydrological forecasting derived from TC model forecasts? Within the proposed technological constraint (~spiral scans of 5000 km diameter area every hour, at 12 km resolution without over-sampling), what might be the preferred scanning configurations for the cloud-resolving TC NWP purpose? Should or can NIS operate in coarse grain (e.g., tracking TC vortex) or fine-grain (e.g., changing scanning configurations for different parts of the TC on the fly and choosing scanning modes based on expected TC development) adaptive scanning mode? What happens when multiple TCs are in view?

4. Additional charge: Given proposed NIS instrument design (i.e., frequency, Doppler acuity, polarimetric diversity, resolution, scan strategy, orbit) and issues relevant to future TC NWP and data assimilation (i.e., cloud resolving deterministic and probabilistic models), develop consensus on: aspects of design that are particularly useful, critical weaknesses that must be addressed, enhancements to design that should be considered.

5. Nowcasting v.s. Short-range NWP of Convection/Precipitation The quoted NWP models are not initialized with radar and/or other high-res data and/or their resolution is too low (Jim Wilson 2006, US-Korea Workshop) NWP should be here! Properly initialized NWP models with complete, dynamically consistent IC should out-perform extrapolation/statistical nowcasting models from time zero Current ICs of TC prediction are no where close to being complete/dynamically consist  spin-up problem

6. CAPS and CASA CAPS was established as one of the 1 st NSF Science & Technology Centers in 1989 at University of Oklahoma CAPS's mission was and still is to develop and demonstrate methods/techniques for the numerical analysis and prediction of high-impact local weather conditions, with emphasis on the assimilation of observations from Doppler radars and other advanced in-situ and remote sensing systems. CASA – NSF ERC for Collaborative Adaptive Sensing of Atmosphere. Develop low-cost dense network of radars to dynamically adaptively sensing the lower atmosphere.

7. US WSR-88D (NETRAD) Doppler Radar Network

8. WSR-88D radar coverage at 1 km AGL - low-level coverage is poor

9. Center for Adaptive Sensing of the Atmosphere (CASA) – an NSF Engineering Research Center (~ $50million/10 year) http://www.casa.umass.edu

10. Radar Data Assimilation Advanced data assimilation techniques essential for radar data – a problem of both analysis and retrieval! Vr and Z only, more with polarimetric radars, usually only in precipitation regions All other state variables have to be ‘retrieved’ Leading methods: 3DVAR, 4DVAR and EnKF methods

11. NIS Data NIS Data, while unprecedented, and extremely important in filling the ‘gap’, are by no means complete (no direct observations of state variables) Advanced 4-D data assimilation (DA) methods essential Need to closely couple with model dynamics and physics

12. DA cycles on 1-km Grid 3DVAR+Cloud Analysis Forecast 2030 UTC 2140 UTC 4 nested grids May 8 th, 2003 OKC tornado case

13. ARPS 1-km-grid forecast using 3DVAR + Cloud Analysis cycles over 1 hour 30-min forecast 40-min forecast Reflectivity at 1.45º elevation

14. 50-m Grid Forecast v.s. Observation Forecast Low-level Reflectivity Observed Low-level Reflectivity 43 minute forecast

15. Ensemble Kalman Filter DA Method Obs Assimilation t t+1 t=0

16. For radar data assimilation Radar data: V r only @ Z>10dBZ V r @ Z>10dBZ & Z K: Kalman gain calculated from background and obs error covariances (Tong and Xue 2005 MWR, Xue et al. 2006, JTech.) Very good results with OSSEs

17. OSS Experiments (Tong and Xue 2005 MWR) EnKF system based on ARPS OSSE experiments (using simulated data) for a supercell storm dx = 2km, dz=500m Using 100 members Assimilate V r and/or Z data Include 5 water/ice species

18. Low level cold pool (  ') and reflectivity Truth Vr Vr & Z

19. Analysis of Microphysical Variables at T=65 min Truth Vr + Z q c, q r, q i, q s, q h

20. RMS Errors for Ensemble Mean Analysis and Forecast (Tong and Xue 2005) VrP (black; Vr only) VrPZF (red; both Vr and Z data)

21. Ensemble forecast Ensembles of forecasts (e.g., 3 hourly) are launched periodically from the ensemble analyses produced at much shorter intervals (e.g., every 15 minutes) Probabilistic info is provided by the ensembles Optimal scanning strategies can be chosen to maximize future forecast error reduction, using, e.g., ETKF method.

22. Capturing and Variationally Analyzing Tornadic Circulation via Azimuthal Over-sampling (2 degree beams) (Xue et al 2007 JTech) Sampled Vr with azimuthal increments of (a) 2 o, (b) 1 o and (c) 0.125 o (c) 0.125 o 1o1o 2o2o Over-sampling alone, without wind retrieval, is not much of a help beyond a factor of two over-sampling Variational ‘de-convolution’ for sub-beam width structures

23. Illustration of the simulation of radial velocity data from a gridded wind field. Sample Volume Beam pattern weighted Vr sampling Importance of Observation Operator when observations are integrated quantities (e.g., radar Z and Vr, sat. radiance data)

24. Impact of azimuthal increments of sampling “truth” & analyses from radial velocity data sampled at azimuthal increments of 0.125 o and 2 o Beamwidth = 2 o Distance of radars = 15 km truth O/S 0.12 5 o CC=0.9 N/OS 2 o CC=0.7 u v wind

25. Vr from KTLX and KOUN for May 10 th, 2003 OKC tornado case KTLX KOUN 1.0 o inc. KOUN.125 o inc. 8 times oversampling with KOUN no oversampling u v V 60 35

26. Comments The observation operator did not include reflectivity weighting Reflectivity weighting essential for 12 km beams Attenuation is not considered Attenuation can and should be built in the observation operator and used in DA system!

27. Simulated reflectivity before and after attenuation (2 nd tilt) for a supercell storm

28. EnKF Assimilation without attenuation correction Data without attenuation (black) Data with attenuation, no attenuation correction

29. Assimilation with attenuation correction Data without attenuation (black) Data with attenuation, attenuation correction is included in the forward operator (red)

30. Comments Complete attenuation still a problem – complete loss of signal (single 94 GHz probably not desirable) – low level obs most important. Both attention and reflectivity strongly dependent on microphysics (phase, size and shape of hydrometeors) – need better (e.g., two-moment) MP schemes Fall speeds of hydrometeors are equally strongly dependent on MP – can contaminating the most-important vertical velocity information. DSD info via dual-frequency reflectivity/attenuation ratio may be valuable Reflectivity weight depends on model-resolved convective cell structures and distribution Model forecast used as the analysis background cannot deviate too much from the truth – hence the need for cycle length < ½ cell life cycle Important to retain sub-beam-scale structures in the background forecast rather than suppressing them in the analysis process – otherwise spinup problem remains (though to a less extent than when without NIS at all)

31. Bulk Microphysics Parameterization Bulk microphysics schemes assume either  drop/particle size distribution (DSD) or inverse exponential (  =0) for species x: can be uniquely determined if three moments of DSD are known:

32. 2 hr forecast @  x=3km for May 3, 1999 OKC F-5 Tornado Case Base Reflectivity and q v at 0000 UTC Lin Lin w/ reduced N0r WSM6WSM6 (diag N0r) MY3MY1MY2 MY3 = Milbrandt and Yau 3-moment scheme

33. Single-Sounding Simulations (  x=500 m) Surface Reflectivity and q v at 1 hr

34. Comments Need additional and more sophisticated OSSEs (Observing System Simulation Experiments) that realistically simulate observation data from sub-km resolution simulations (can include side lobes) assimilate the data at expected TC NWP resolutions (~ 1 km) assess impact and help refine NIS design and scanning strategies yes, prediction model needs to be an integral part of the signal-processing (from Chandra - can't believe it came from a radar engineer!)

35. One of such examples See also Simone Tanelli and Mike Biggerstaff’s presentations.

36. Scanning Strategies 15-min volume scans (4 times the frequency) A factor of 2 over-sampling (~6 km increments, 4 times number of samples) 1250 km diameter region for established TCs (1/16 th scan area) Achievable without second arm Data assimilated continuously as they come in, not necessarily at 15-min discrete intervals Assimilated together with e.g., airborne radar data that better capture horizontal winds, and other data defining environmental wind, T, and qv. Cycles startied from pre-genesis stage Larger area without over-sampling at pre- and genesis stages? Fine-grained adaptive sampling?

37. Expected length of impact at individual cell, organized rainband/eyewall and TC scales, on deterministic and probabilistic forecasting? At individual cell level, no more 1 hour At rainband/eyewall scale ~ a few to tens of hours? At TC scale – several days? Definitely the accumulated impact via continuous cycles Difference between deterministic and probabilistic forecasts?

38. Data Quality Info/Error Estimate? Spectrum width info useful in DA? What error estimation can be provided for the data? Is signal-to-noise ratio available for estimating sampling errors? Need to define R – obs error covariance matrix – do not want correlated errors. Other QC issues (unfolding, clutter removal, attenuation correction, beam pattern uncertainty, etc.)