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Tropical weather systems within a global data assimilation and forecasting framework

Tropical weather systems within a global data assimilation and forecasting framework

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Tropical weather systems within a global data assimilation and forecasting framework

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  1. Tropical weather systems within a global data assimilation and forecasting framework Oreste Reale NASA Goddard Laboratory for Atmospheres and GEST/UMBC

  2. Motivation • The deadliest natural events are related to tropical weather systems (500,000 people died because of 1970 cyclone in Bangla Desh) • Almost 40 years later (2008): Tropical Cyclone Nargis killed at least 140,000 people • Numerical weather forecasts in the tropics have improved at a slower pace than mid-latitude weather forecasts • Acknowledging intrinsic predictability limitations, there is vast room for improvement

  3. Outline • Part I: the global analysis in the tropics • Part II: the representation of tropical cyclones in global models • Part III: improvements stemming from the assimilation of AIRS-derived products • Part IV: other improvements • Conclusionsand future

  4. Outline of Part I • AEJ representation in state-of-the-art reanalyses • AEJ representation on weather-time-scales in operational analyses during SOP-3 NAMMA (2006) • Vertical soundings during SOP-3 NAMMA (2006) • Mid-tropospheric flow over the entire tropical Pacific in August 2010 in NCEP operational, ECMWF operational, and MERRA

  5. AEJ representation in state-of-the-art reanalyses • Previously published work (Wu et al., 2009) shows substantial differences between reanalyses in the monthly mean representation of the African Easterly Jet (AEJ) • ERA-40, NCEP-R2, JRA-25 provide very different descriptions of the AEJ structure, and of thehorizontal shearin thecyclonically-sheared portion of the AEJ M.-L. C. Wu, Reale, O., S. Schubert, M. Suarez, R. Koster, P. Pegion, 2009: African Easterly Jet: Structure and Maintenance. J. Climate, 22, 4459-4480.

  6. Large differences in AEJ SHAPE, INTENSITY VERTICAL STRUCTURE and distribution of the horizontal shear in a 22-year average performed on ERA-40, NCEP-R2, and JRA-25. From Wu et al. (2009) Fig 2 July zonal wind (m s−1, contours every 1 m s−1, 0 omitted, solid: positive, dashed: negative) climatology (1980–2001) based on (top to bottom) ERA-40, NCEP R2, and JRA-25 data: (left) meridional horizontal shear of the zonal wind at 600 hPa and (right) meridional cross section at 0° longitude. (Wu et al., 2009, J.Climate)

  7. AEJ and its instability properties in state-of-the-art reanalyses • Work recently submitted (Wu et al., 2011) shows differences in the representation of the African Easterly Jet (AEJ)seasonalinstability properties between reanalyses across a 22-year average • Despite revealing some instability property of the AEJ that appear data-independent, ERA-40, NCEP-R2, JRA-25 and MERRA provide very different descriptions of the AEJ horizontal structure, intensity, and of some properties that control wave instability on a seasonal scale (JAS). M.-L. C. Wu, Reale, O., S. Schubert, M. Suarez, C. Thorncroft, 2011: African Easterly Jet: barotropic instability, waves and cyclogenesis. Submitted to: J. Climate.

  8. The analyses differ in terms of strength and intensity of the low-level monsoonal flow, slope of the barotropically unstable part of the AEJ, horizontal shear distribution. All Figures show a 22-year JAS average From Wu et al. (2010) Fig 2

  9. Unexpected discrepancies between snapshots of analyzed representation of the African Monsoon-Eastern Tropical Atlantic regions • The African Easterly Jet at about 600hPa,the low-level monsoonal flow (predominantly southwesterly between 1000 and 800 hPa)and the Tropical Easterly Jet (between 200 and 100 hPa)are the critical players in Atlantic tropical development. • Comparison between operational NCEP analyses and GEOS-5-produced analyses reveal serious discrepancies • Validation agains the only vertical sounding in the area at Cape Verde (15N, 23.5W) during the 2006 NAMMA campaign, show that both analyses have large errors

  10. Huge discrepancies between GEOS-5 and NCEP operational analyses Wind at 5-15N, 500-600 hPa, has opposite direction! Only in the tropics the two analyses differ substantially Section at 23.5W

  11. Largest differences between reanalyses are in the tropics, at about 15N (on the order of 12m/s)larger even thandiscrepancies in the southern hemisphere jet stream NCEP GEOS-5

  12. Huge differences in the entire tropical zonal flow from 20S to 20N at all levels

  13. Largest mid-tropospheric wind difference is in the tropics, at 0-10N GEOS-5 analyses produce a weaker easterly flow than NCEP GEOS-5 NCEP

  14. Largest low-tropospheric wind difference is in the tropics, between 10S and Equator Opposite-sign discrepancy with respect to previous slide: GEOS-5 analyses produce stronger easterly flow than NCEP) NCEP GEOS-5

  15. Additional vertical soundings at Cape Verde during SOP-3 provided the chance to validate operational analyses in 2006 Both NCEP and GEOS-5 miss the AEJ maximum at 600hPa. Error larger than 10 m/s at AJE level!!! One of the rare cases in which NCEP and GEOS-5 differ less than 5 m/s) obs NCEP vs GEOS-5 obs

  16. Catastrophic non-systematic differences NCEP provides a good representation of low-level and upper-level flows but misses the AEJ. GEOS-5 has huge errors at all levels except at 600hPa. NCEP and GEOS-5 both miss the low-level flow, with NCEP having larger errors. obs NCEP vs GEOS-5 obs

  17. Catastrophic non-systematic differences NCEP produces a stronger AEJ. GEOS-5 produces a stronger AEJ. NCEP vs GEOS-5

  18. Huge differences between operational ECMWF, NCEP and MERRA over the entire tropical Pacific during strong La Nina conditions (Aug 2010) • Weather prediction over the tropical Pacific is controlled by a good representation of the predominantly easterly flow and periodic westerly bursts along the Equator • Large errors in the equatorial flow propagate away from the Equator affecting TC genesis prediction, and TC track forecast as far as 30N/S

  19. Huge600hPa zonal wind difference affects the entire tropical Pacific in 2010 Speeds are very comparable away from the tropics. Difference of about 10m/s over Eq.Pacific

  20. Huge600hPa zonal wind difference affects the entire tropical Pacific in 2010 50% speed Difference Over Eq. Pacific Opposite sign wind over, and NE of, Hawaii

  21. Huge600hPa zonal wind difference affects the entire tropical Pacific in 2010involving all 3 data sets

  22. The largest 600hPa wind difference at 165W occurs in the tropics, between 20S and 10N ECMWF NCEP MERRA

  23. Part IIThe representation of tropical cyclones in global models • The overall forecast quality is a blend of the impacts of initial conditions produced by the Data Assimilation System -and- the forecast model capability • It is important to separate the intrinsic model capability from the impact of the analysis • Less-than-optimal model performance with respect to TCs can be somewhat improved with very good TC initialization • BUT less-than-optimal TC initializationcan be somewhat compensated by a very good model representation of the large scale forcing • What past and currentmodels can produce in `free-running mode’or in`weather-forecasting mode’concerningTropical Cyclone verticalstructure,scale,intensity,track realism,genesis process, large-scale forcing • Model comparison in forecast mode:NMC MRF (1998), NASA GEOS-4 (2004), NCEP GFS (2004); NASA GEOS-5 v2 (2009) • Long simulations (so as to free the model from the memory of the ICs, can be performed to assess the intrinsic model capabilities with respect to TC structure and realism) :ECMWF T511 Nature Run, NASA GEOS-5 (2009) • The problem of missing TCs in the operational ANALYSIS can deteriorate the forecast of any good model

  24. TCs in high-resolution global models • It has been empirically noted in the operational wx forec. community that at hor. res. of 1 degree one can start seeing vertically aligned structures and an eye-like feature, at 0.5 degree the maximum winds begin to develop in the lower levels (instead of the mid-troposphere, as observed in lower resolution global models), at resolutions of few tens of kilometers global models start displaying realistic radii of maximum wind (e.g., Atlas et al., 2005; Shen et al., 2006; Reale et al., 2007), • But it takes cloud-resolving models at resolution of few kilometers to detect eye-wall replacement cycles • Accepting the limitation imposed by global models, it is interesting to follow the representation of tropical cyclones in global forecast models over the last 12 years.

  25. Is high resolution always exploited? • At any resolution, a wind speed vertical cross-section of a mature tropical cyclone should present two approximately symmetric maxima around a wind minimum. • The compactness of this eye-like feature increases with resolution but often high resolution models display structures that are much broader and moredilutedthan what could be expected at that resolution. • Unrealistically large eye-like features (on the order of hundreds of km, encompassing several gridpoints) are common in GCMs even when horizontal resolution is of a quarter of a degree. • The optimal, theoretical representation that should be possible at a given resolution, is NOT always reached. • It is important to perform proper diagnostics that allow to assess the quality of the representation of a TC at any given resolution

  26. TCs in Global Operational forecasting modelstrack versus intensity forecast • Forecast track failures in earlier global operational models were generally assessed only from the point of view of large-scale forcings, irrespective of how the TC structure was represented • In the latest global operational models forecast, structure realism and good forecasttrack appear to be connected (unlike the past, where track and intensity were treated as completely separate problems) • The quality of the representation of some large-scale forcings (i.e. ITCZ position) appear to control part of the weather forecasting scales involved with TC motion • In the past (>10 years ago), TC representation in global operational models was sporadic and very poor • Bogusing was a necessity (now replaced by vortex relocation)

  27. 13 years ago: Bonnie (1998) as seen by the MRF (ancestor of NCEP GFS) 850 hPa wind Sea level pressure NMC state-of-the-art representation of TCs in 1998: no more than 25 m/s, excessively large scale (~1000km); center pressures above 1000 hPa (despite containing Hurricane Hunters flight data). TCs away from operational HH flights were often absent from analyses and forecasts.

  28. Bonnie (1998) cont. MRF (NMC-now NCEP) state-of-the-art representation of TCs in 1998: no more than 25 m/s, unrealistically wide eye-like feature (r~100km); very weak warm core

  29. TC Structure: NASA GEOS-4 in 2004 Isidore (2002) Modeled with GEOS-4 in 2004 Realistic deepening (center down to 960 hPa, unseen in any un-bogused GCMs). The NCEP Analyses confirm the position but are not as deep with respect to observations. wind speed, temp, vort Atlas, R., O. Reale, B.-W. Shen, S.-J. Lin, J.-D. Chern, W. Putman, T. Lee, K.-S. Yeh, M. Bosilovich, and J. Radakovich, 2005: Hurricane forecasting with the high-resolution NASA finite-volume general circulation model. Geophysical Research Letters, 32, L03807, doi:10.1029/2004GL021513.

  30. Example of a very realistic NASA GEOS4 simulationin which track and intensity forecast go side-by-sideIvan (2004)

  31. Example of hurricane vertical structure as modeled by the GEOS-4 (2004): Ivan • The GEOS4 could produce a very compact eye-like feature throughout the troposphere, a prominent warm core; wind maxima located at about 850-900 hPa and a radius of maximum wind of about 50-100 km • In this 66 hour forecast of hurricane Ivan for 18z12Sep2004, initalized at 00z10September, the 900 hPa wind is higher than 55 m/s Wind speed, temp

  32. Realistic Cyclogenesis in a global operational model (NASA GEOS-4, 2004) WITHOUT BOGUSING • Frances (2004): early phase • Example of rapid deepening and good forecast track despite poor analyzed intensity • One run reaches the correct intensity (IC: 00z30Aug) and produces the best forecast track as well • It takes four days for the model to compensate the deficient initialization

  33. In free-running mode, the TCs are spontaneously produced by the model without memory of ICs. • Seasonal runs or long runs exceed forecast capability but statistical behavior of TC activity over months/seasons/years, and realism of TC structure can be inferred • TC activity is controlled only by global forcings (SST) • TC cyclogenesis and structure are produced by the model alone without any contribution of Initial Conditions • These aregood tests to assess the intrinsic model capability with respect to TC processes

  34. T511 ECMWF Nature Run (2007) • Free running model – no memory of initial conditions – no additional data • A long simulation is the only way to assess the capability of a forecasting model – as opposed as a DAS+forecasting model. No bogusing,vortex relocation, targeted obs, can be added. • 13-month run, initialized May 2005 • Only SST (2005) and Sea-Ice as boundary forcings • Analysis published in Reale et al. (2007) Reale, O., J. Terry, M. Masutani, E. Andersson, L. P. Riishojgaard, J. C. Jusem, 2007: Preliminary evaluation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Nature Run over the Tropical Atlantic and African Monsoon region. Geophysical Research Letters, 34, L22810, doi:10.1029/2007GL31640.

  35. EC T511 NR: realistic activity (9 strong TCs) From Reale et al. (2007) GRL

  36. EC T511: Realistic Variability of Atl. TC tracks Looping and Binary vortex interaction 4 systems: Looping, Binary vortex Interaction, Extratropical Transitions and Extra-tropical Re-intensification Singuarities, binary vortex Interactions, Intensity fluctuations Due to large-scale forcing fluctuations A long simulation must produce complex tracks

  37. GEOS-5 with Stocastic Tokioka (2009)Simulations by Myong-In Lee, PI S. Schubert (NASA): Same experiment settings of ECMWF Nature Run Behavior comparable to the EC T511 Control Run GEOS-5 0.25 (with rel. Arakawa-Schubert) GEOS-5 (with Tokioka) No cyclone reaches 1000hPa in the Control during September. At least 7 cyclones below 1000 hPa in the GEOS-5 w.Tok. One hurricane goes below 960hPa. Very realistic track variability, scale. Even non-developing waves are well captured.

  38. EC T511: Multiple simultaneous tropical cyclones can be present in the Atlantic in very active seasonsAnother important –realistic- capability of the ECMWF NR 500 hPa geop (m) and 900 hPa rel vort (s-1)

  39. 3 TCs simultaneously present in the GEOS-5 w. Tokioka 11SepSimulations by Myong-In Lee, PI S. Schubert (NASA): Slp (hPa) and 925 hPa wind (m/s)

  40. Intensity • In the operational forecasting environment, 10m observed wind and center pressure are currently used • PROBLEM: excessively high drag in the marine boundary layer seems to occur in global models when winds exceed 30m/s: 10m wind often about 60% of the 850hPa wind (unlike 90% in real world) • Possibly due to unrealistically high roughness length over oceans with wind speeds exceeding 30m/s • As a consequence, it may better to use 850hPa or 900hPa wind as intensitydiagnostics in global models • One simple way of assessing comprehensively the TC intensity reached in a simulation is to produce the max wind at 850hPa throughout the system’s lifespan

  41. Example of Intensity inferred from 850hPa wind max (Isabel, 2003) Operational GFS and GEOS-4 have comparable intensity Different degree of compactness

  42. A possible metrics to assess how well the horizontal scale is represented Horizontal Compactness, ratio of radius of maximum wind (rmw) over radius of wind greater than the environmental wind of a given threshold, which we can consider the radius of the tropical cyclone (TC) in the model (rtc). The wind magnitude of a modeled tropical cyclone decreases from the center and is not distinguishable from the large-scale wind at a certain distance. This distance could be considered the tc-influenced domain in the model and can be compared with the rmw. The smaller rmw with respect to the rtc the more realistic the modeled cyclone is.In low-resolution global models, the radius of maximum wind occupies a large fraction of the domain affected by the cyclone.

  43. Example from older GEOS-5 v.2: how compact is this 0.5 simulation of Helene?

  44. Compactness evaluated in GEOS-5 simulation at .5 for Helene (2006) 850 hPa wind at 18.5N Despite being a relatively weak simulation, the representation of the system is quite compact in the above sense [RMW(l)+RMW(r)] / [RTC(l)+RTC(r)]=0.27

  45. Compactness in the GEOS-5 w. Tokioka at 0.25a much better rpresentation of a TC At ~60W, a RAINBAND RMW RTC(l) RTC(r) [RMW(l)+RMW(r)] / [RTC(l)+RTC(r)]=0.07

  46. Very clear evidence of a rainband at 61W RAINBAND

  47. Warm Core Structure One immediate, effective way of assessing if a model produces a vertically aligned and symmetric system, is to measure the strenght of its warm core. One way is simply to subtract a standardized zonal mean intersecting the center of the storm. Examples: GEOS-5 versus NCEP GFS

  48. Examples of warm core (Helene, 2006) 48-h Fc 72-h Fc GEOS-5 (0.25) GEOS-5 (0.25) GFS GFS 48-h Fc 72-h Fc Ms. M. Fuentes, Ph.D. Thesis

  49. Vertical Structure inferred through zonal and meridional vertical cross-sections of wind speed and temperature of mature TCs in the deep tropics Desirable features: • Wind maximum at 900hPa or lower • Small radius of maximum wind • Perfectly vertically aligned low-speed column • Vorticity column with maximum in the lower levels • Low-level convergence confined below 800 hPa • Upper-level divergence confined above 200 hPa

  50. Side by side comparison ECT511 vs GEOS5 with TokiokaGEOS runs by Myong-In Lee, PI S. Schubert (NASA): EC T511 (2007) GEOS-5 with Tokioka (2009) Zonal Meridional Zonal Meridional GEOS-5 has slightly sharper warm core, better-defined eye, max wind at lower elevation, slightly smaller radius of max wind. Intensity is about the same.