Assimilation of Mesoscale Observations for use in Numerical Weather Prediction

Transcription

1 Assimilation of Mesoscale Observations for use in Numerical Weather Prediction Steve Koch Thermodynamic Profiling Technologies Workshop Boulder April 2011

2 Important Questions (from 2003 USWRP Mesoscale Observing Networks Workshop) Is it more effective to sample the upper troposphere with fewer observing systems than to sample the boundary layer with more observing systems for mesoscale modeling? The Committee envisions a distributed adaptive network of networks (NoN) serving multiple environmental applications near the Earth s surface, jointly provided and used by government, industry, and the public.

3 Important Questions (from 2003 USWRP Mesoscale Observing Networks Workshop) Is it more cost effective to have intermittent, targeted observations at the mesoscale than to enhance the present operational networks to provide additional data in a continuous manner for improving mesoscale prediction? The committee finds that, overall, the status of U.S. surface meteorological observation capabilities is energetic and chaotic, driven mainly by local needs without adequate coordination an overarching national strategy is needed to integrate disparate systems from which far greater benefit could be derived and to define the additional observations required to achieve a true multi-purpose network that is national in scope

4 Important Questions (from 2003 USWRP Mesoscale Observing Networks Workshop) What kinds of observations are best for deriving all the other variables not directly observed? The highest priority observations needed to address current inadequacies are: PBL height (useful as retrieval constraint) Soil moisture and temperature profiles High-resolution vertical profiles of humidity Measurements of air quality and related chemical composition above the surface layer Just below the aforementioned highest priorities are quantities for which some capabilities currently exist but fail to meet a serviceable national standard for one or more reasons: Vertical profiles of wind (can be used to derive T v ) Vertical profiles of temperature

5 Important Questions (from 2003 USWRP Mesoscale Observing Networks Workshop) What mix of radiometric, lidar, interferometric, and active radar systems should be used to obtain the greatest improvement in forecasting severe weather? Federal agencies and their partners should deploy lidars and radio frequency profilers nationwide at approximately 400 sites to continually monitor lower tropospheric conditions. Humidity, wind, and diurnal boundary layer structure profiles are the highest priority for a network, the sites for which should have a characteristic spacing of ~125 km but could vary between 50 and 200 km based on regional considerations Emerging technologies for distributed-collaborativeadaptive sensing should be employed by observing networks, especially scanning remote sensors such as radars and lidars.

6 Important Questions (from 2003 USWRP Mesoscale Observing Networks Workshop) What role can field experiments play in determining the optimal mix of observations needed to realize the greatest improvements in mesoscale data assimilation and prediction? The national network architecture should be sufficiently flexible and open to accommodate auxiliary research-motivated observations and educational needs, often for limited periods in limited regions...federal agencies and partners should employ testbeds for applied research and development to evaluate and integrate national mesoscale observing systems, networks thereof, and attendant data assimilation systems.

7 Stan Benjamin: Observation Sensitivity Experiments (OSEs) Used state-of-the-art assimilation/modeling system Used all available observations for relative impacts 2 types of OSEs Data denial experiments (more expensive) Can show actual effect on forecast skill Adjoint-sensitivity experiments (less exp but requires adj) Provides difference magnitude in forecasts but not actual difference in forecast skill Note: It is easier to show positive impact from certain observation systems when using older assimilation systems or without all available observations. But those results will be misleading. Benjamin et al. (2010) results for 8 observing systems to follow

8 RUC Hourly Assimilation Cycle Cycle hydrometeor, soil temp/moisture/snow plus atmosphere state variables 1-hr fcst Background Fields 3dvar Obs 1-hr fcst Analysis Fields 3dvar Obs 1-hr fcst Time (UTC) Hourly obs in 2009 NCEP RUC Data Type ~Number Rawinsonde (12h) 80 NOAA profilers 30 VAD winds PBL profiler/rass ~25 Aircraft (V,temp) TAMDAR (V,T,RH) Surface/METAR Buoy/ship GOES cloud winds GOES cloud-top pres 10 km res GPS precip water ~300 Mesonet (temp, dpt) ~7000 Mesonet (wind) METAR-cloud-vis-wx ~ d Radar reflectivity 2km Obs sensitivity exps

9 RUC Wind forecast Accuracy Analysis ~ truth Sept-Dec 2002 Verification against rawinsonde data over RUC domain RMS vector difference (forecast vs. obs) RUC is able to use recent obs to improve forecast skill down to 1-h projection for winds

10 Retrospective runs an excellent test bed for measuring the impact of observing systems All RUC data were saved for two 10-day period Winter 26 Nov - 5 Dec 2006 Summer 5-15 August 2007

11 WINTER No-aircraft - control No-profiler - control No-VAD - control No-RAOB - control No-surface - control No-GPS-PW control No-mesonet control No-AMV - control RH - national hpa #1 obs type = Raobs #2 = GPS-PW SUMMER

12 WINTER No-aircraft - control No-profiler - control No-VAD - control No-RAOB - control No-surface - control No-GPS-PW control No-mesonet control No-AMV - control Temp - national hpa Tie for #1 = Aircraft, RAOBs Aircraft more at 3h, RAOB-12h SUMMER

13 WINTER No-aircraft - control No-profiler - control No-VAD - control No-RAOB - control No-surface - control No-GPS-PW control No-mesonet control No-AMV - control Wind - national hpa #1 = Aircraft #2 = RAOBs SUMMER

14 Dave Turner, et al.: Observing System Simulation Experiment (OSSE) Study of Impact of Lower Tropospheric Temperature, Moisture, and Winds Observing System Simulation Experiment (OSSE) of a single wintertime case of 4 observing systems: Doppler Wind Lidar (DWL) Microwave Radiometer (MWR) Atmospheric Emitted Radiance Interferometer (AERI): infrared Scanning Raman Lidar (SRM): a research-only system Synthetic ground-based remote sensors placed at each of the 140 existing WSR-88D radar sites (to minimize installation and operation costs) not the 400 sites recommended by NAS report Used 18-km WRF model and DART DA system Results limited to just this one case, and did not consider wind profilers or other proven systems

15 OSSE Thermo/Wind Profiler Study Results The best analysis was achieved when both DWL wind observations and thermodynamic (temperature and moisture) profile observations from the SRM, AERI, and MWR were assimilated simultaneously Impact of these systems was limited to ~4 km (PBL) Joint AERI+MWR approach recommended AERI provides needed vertical resolution, but MWR provides both allweather operations and a reasonable first guess for AERI retrievals Assimilating thermodynamic data alone without DWL data did not produce strong enough moisture transport, thus failed to predict the heaviest precipitation Turner et al. (2011)

16 CAPS: Value of Radar Reflectivity and Radial Velocity Data Assimilation for QPF 4-km forecasts initialized using radar observations yield improved shortrange forecasts of convective activity (Kain et al. 2010). Particularly helpful for looking at convective mode and evolution. Courtesy Jack Kain and Ming Xue

17 Tornadoes of 8-9 May 2007 El Reno tornado Patrick Marsh Union City tornado Minco Tornado 10:54pm (0354Z) 30 km range 7:21pm (0021Z) Lawton Tornado Need to sample the PBL fully (75% lost by WSR-88D): Collaborative Adaptive Sensing of the Atmosphere (CASA) X-Band Radar Network

18 EnKF analysis and ensemble forecasts for May tornadic mesoscale convective system (MCS) Ensemble forecast Reported tornadoes 1 hr. spin-up period Assimilation period Deterministic forecast 0:00Z 0:30Z 1:00Z 1:30Z 2:00Z 2:30Z 3:00Z 3:30Z 4:00Z 4:30Z 5:00Z Experiment contained 40 Ensemble members. Reflectivity and radial velocity observations from 5 WSR-88D radars as well as the 4 CASA radars were assimilated every 5 minutes over a 1 hour window. Analysis and probabilistic ensemble forecasts were generated for three experiments to test effect of assimilated CASA data and use of a mixedmicrophysics ensemble using three single moment ice microphysics schemes and 2-moment scheme. Snook, Jung and Xue 2011a,b. Putnam et al. (mostly CASA supported)

19 Effects of assimilated CASA data and mixed-microphysics ensemble Analyzed reflectivity fields using CASA and WSR-88D radar data (top left) compare well with radar observations (top right); reflectivity structure of the main convective line is well-captured. CASA + WSR-88D EnKF Composite Radar Reflectivity Analysis Final analysis (0200 UTC ) Reflectivity WSR-88D Observed Composite Radar Reflectivity Inclusion of CASA data improves representation of a low-level mesoscale vortex and gust front observed by CASA and WSR-88D. Near-surface winds and potential temperature (0140 UTC) KCYR Vr 0141 UTC CASA + WSR- 88D WSR-88D Only

20 Forecast of Minco Mesovortex at 400m resolution OKC TDWR obs Predicted Vr with CASA Predicted Vr with 88D only Radial velocity at 0340 UTC Schenkman et al. (2011b MWR)

21 Hybrid Ensemble Kalman Filter (EnKF) 4DVAR Data Assimilation Xue et al. (2006) and Yussouf and Stensrud (2010) demonstrated the benefit of rapid scan radar data assimilated via EnKF But, high-frequency EnKF Data Assimilation is costly Data I/O can cost 80% of total CPU to read & write ensembles 4D extension of EnKF requires fewer cycles while still using observations at their correct times Asynchronous EnKF can achieve this Hybrid 4D Variational and Ensemble Data Assimilation allows VAR methods to use flow-dependent background error covariance and dynamical constraints

22 Sampling the nocturnal stable boundary layer (Bob Banta, NOAA/CSD) Must have good enough Δz throughout SBL to define its structure, and to determine depth, strength, and rate of growth of SBL SBL depth h, a fundamental quantity difficult to measure Problem coarse vertical resolution, precision of available measurements POSTER [Banta, Pichugina, et al]: hi-res wind profile data from Doppler lidar able to address this issue, significantly reduce uncertainty in h estimates Use of velocity variance profiles to estimate SBL instead of more traditional aerosol concentration profiles

23 HRDL measurements of the nocturnal Stable Boundary Layer Examples of hourly profiles of LLJ development for one night using NOAA High-Resolution Doppler Lidar (HRDL) Time-height cross sections of measured HRDL mean wind U(z) and turbulence σ u2 (z) profile 1 min LLJ structure to winds (* = max), symbols indicate top of SBL using several indicators NOTE: Consistency among diagnostics, continuity in time ( confidence in estimates of h)

24 Thermodynamic retrieval from HRDL wind measurements of the nocturnal SBL Techniques to retrieve 3D wind and thermodynamic fields from scanning Doppler lidar are similar to those developed for Doppler radar (e.g., Sun and Crook (1997, 1998). 4DVAR technique is used to fit the output of a prognostic model (dry, shallow Boussinesq) to the lidar measurements, which requires development of an adjoint model to compute the gradient of the cost function with respect to the initial state of the forward model. Because of poor temporal sampling of eddies, and the disparity with excellent spatial sampling by HRDL, the measured values are not interpolated to the model grid to avoid smoothing. Retrievals are quite sensitive to changes in the gradients of the base-state virtual temperature profile Newson and Banta, 2004a,b JAOS

25 Lidar-Radar Analyses of Convection Initiation by Gravity Currents, Bores, and Solitons Evolution of a density current into a bore Evolution of a bore into a soliton Inversion surface Amplitude-ordered solitary waves Density current Bore Evolution of an undular bore from an advancing density current. Fluid enveloping the current is similar to what happens to warm air underneath a low-level inversion as a density current intrudes into the stable layer.

26 Two-dimensional circulation system relative to the bore and gravity current-like cold front derived from 915 MHz wind profiler shows need for dual lifting to initiate convection Max = 1 m s -1 Gravity Current lifting Koch and Clark, 1999 JAS Bore lifting

27 Lifting by bore and gravity current-like cold front destabilizes and moistens sounding: strong convection was initiated Max displacement = 1.25 km Lifting depth = 2.5 km (bore + front combined) Koch and Clark, 1999 JAS

28 AERI time-height displays show sudden and deep moistening and adiabatic cooling aloft following bore passages A B Water vapor Potential temperature Koch et al MWR

29 Some Issues for this Workshop The NAS report states that the boundary layer is critically under-observed, but how strong is the scientific support for this assertion, and what cost-effective technology solutions are available to fill this apparent gap? Existing studies that have systematically studied the relative impacts of various observing systems are incomplete in terms of seasonal coverage, phenomena predicted, number and type of observing systems, and DA technology. What specific recommendations can be made to address this? What techniques are available to estimate impact of changes to both current observing system configurations and future combinations of observing systems?