Data Assimilation and Observing Systems

Transcription

1 Data Assimilation and Observing Systems Prof. Sharan Majumdar University of Miami, USA & WMO/WWRP DAOS Working Group & Bob Atlas, Joe Cione, Jim Doyle, Mike Fiorino, Paul Gregory, Julian Heming, Vijay Tallapragada, Ryan Torn Special Focus Session 1c IWTC-VIII, Jeju Island, South Korea December 2, 2014

2 WMO/WWRP DAOS Working Group

3 DAOS Mission Provide guidance to the WWRP on international efforts to optimise the use of the current WMO Global Observing System (GOS) and to advise on the strategy for its evolution. It will also provide guidance on which data assimilation methods may provide the highest-quality analysis products possible from the GOS. Assist WWRP projects and WMO working groups in achieving their scientific objectives by providing expert advice on the use of observations and DA.

4 Recent DAOS Activities Monitoring and communicating on status of global DA and OS Inter-comparison of forecast sensitivity to observations across NWP centers WMO publication on targeted observations Standardized DA terminology Exploit field campaigns Organized 6 th WMO DA symposium (2013) Sessions and White Papers at WWOSC (2014) Tropical session at 7 th WMO DA symposium (2016)?

5 This Special Focus Session vs 2014: Summary 2. Targeted Observations (IWTC-VII SF1a) 3. Advances in DA schemes since Advances in assimilation of obs since 2010 Global models Regional models 5. Observing Systems: Current and Future 6. Observing System Simulation Experiments (OSSEs) 7. Challenges 8. Potential Recommendations

6 Global models of resolution >16 km 2. Global assimilation systems: mostly 3d-Var and 4d-Var 3. Intensity forecasting models largely statistical 4. Satellite and aircraft data used in global models

7 Global models of resolution >12 km 2. Global assimilation systems: mostly 4d-Var and EnKF (or hybrids or EnVar ) 3. Dynamical intensity forecasting models showing improved skill, with assimilation 4. Satellite and aircraft data also used in regional models; new datasets & processing

8 2. Targeted Observations

9 ? 9 th 11 th 13 th t i t a t v Initialization Analysis (targeting) Verification

10 Targeted Observations IWTC-VI (Session Leader: C.-C. Wu) IWTC-VII (Session Leaders: Wu and Majumdar) Recommendations made in 2006 and 2010 Limited progress since 2010 T-PARC completed (>60 papers on TY Sinlaku!) DOTSTAR, NOAA missions operational Review IWTC-VII recommendations

11 IWTC-VII final report: pp Improved use of existing observations e.g. Satellite radiances, AMVs, rawinsondes 2. Frequently review programmes 3. New strategies Methods for targeting Evaluation strategies: OSEs, data impact, OSSEs

12 IWTC-VII final report: pp New observing platforms 5. Coordination of the use of observations EURORISK Preview data targeting system 6. Explore mechanisms by which data assimilation affects the forecasts 7. New focus on TC formation, structure, intensity Most of these recommendations have been followed through in the broader context of DAOS

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14 WMO Report: Conclusions Almost exclusively evaluated for track Case studies: observations targeted in selected areas are mostly beneficial Aircraft are often unable to cover target areas Sensitivity to model and DA scheme Marginal benefit of new targeted observations decreasing (with more routine data)? Observations are important both within and outside the TC, varies from case to case

15 WMO Report: Recommendations Preserve memory of observations Strategies consistent with DA scheme Target areas of high uncertainty and/or areas where errors are expected to grow fastest Devise and assess techniques on mesoscale

16 Targeting on the mesoscale: ensemble sensitivity Torn (2014, MWR)

17 3. Data Assimilation Methodologies 3d-Var vs EnKF vs Hybrid: NOAA 4d-Var vs 4d-Hybrid vs 4d-EnVar: Met Office Terminology: A. Lorenc report in WGNE Blue Book

18 Single 850 hpa u observation (3 m/s O-F, 1m/s error) in Hurricane Ike 3d-Var EnKF hybrid Whitaker, Hamill, Kleist, Parrish, Derber, Wang

19 Whitaker, Hamill, Kleist, Parrish, Derber, Wang

20 Operational global DA at the Met Office : Standard 4DVar Standard 4DVar Background error covariance at beginning of window: B B c B propagated in time by linear Perturbation Forecast (PF) model. Response to a single wind observation:

21 Operational global DA at the Met Office present: Hybrid 4DVar Hybrid 4DVar MOGREPS-G Ensemble (MOGREPS-G, based on localised ETKF) Background error covariance at beginning of window: B B B 2 c c 2 e e B propagated in time by linear Perturbation Forecast (PF) model. Response to a single wind observation:

22 Operational global DA at the Met Office Beyond Hybrid 4DVar: 4DEnVar? Hybrid 4DEnVar MOGREPS-G Ensemble No PF model more appropriate DA for coupled ESM 4DEnVar ~1/11 cost of 4DVar for fixed M afford higher res/more members. I/O and memory challenges. Full use of ensemble information would include its use in quality control (background check)

23 Bogusing / Vortex Initialization / Assimilation of TCVitals? Bogus observations based on TC warning centers estimates of central pressures have improved 1-3 day forecasts at UK Met Office (better tracks, reduced weak bias) Real time estimation of TC state: location, intensity, structure information TCVitals Much uncertainty about how best to use this valuable information in DA

24 M. Fiorino

25 Handling position errors First guess Adjusted first guess First guess - truth Position-shifted analysis - truth Nehrkorn et al., 2015, MWR, also studies by Hodyss (2011), Aksoy (2013), Navarro and Hakim (2014)

26 Vortex Spin-Down: analysis produced by DA does not lie on model attractor? NOAA/NCEP/EMC

27 4a. Data Impact Experiments: Global Models

28 Methods to evaluate data impact Observing System Experiments (OSE) Data denial or addition Forecast Sensitivity to Observations Adjoint-based Ensemble-based (New terminology being developed by WWRP DAOS)

29 120 h Importance of 96 h polar orbiter data (ECMWF) 72 h Denial of all polar orbiting data and effect on position of Hurricane Sandy (2012). McNally et al. (2014, MWR)

30 Supplemental dropwindsondes and rawinsondes Small but positive improvements to NCEP GFS track forecasts of Irene (2011) from dropwindsondes. Similar results for Sandy (NCEP study). In both cases, largest uncertainty >3 days from US landfall. Majumdar, Brennan and Howard (2013, WAF)

31 Adjoint-based observation impact Ob-Impact by Type: Difference (48hr 24hr) Higher Impact at 48hrs Lower Impact at 48hrs Wind Ob-Impact by Level: Difference (48hr 24hr) AMVs from geostationary satellites impose a larger impact between the 24hr and 48hr forecast than other observation types. Wind observations in general impose this larger impact in the 400 hpa 100 hpa layer Hoover and Velden Lower Impact at 48hrs Higher Impact at 48hrs

32 4b. Data Impact Experiments: Regional Models

33 Mesoscale and Convective-scale DA Represent realistic structure of TCs Many studies since 2010 Largely driven by NOAA Hurricane Forecast Improvement Project (HFIP) Grid spacing 1-90 km Convection permitting ensembles feasible Mostly ensemble Kalman filters

34 Inner-core DA: Tail Doppler Radar NOAA s Hurricane Forecast Improvement Project: successful transitions from Research to HWRF Operations (e.g. Doppler Radar DA initiated by Fuqing Zhang) Moist upright vortex Observed tilted vortex Dry and tilted vortex 34 V. Tallapragada

35 J. Doyle NASA Hurricane Severe Storm Sentinel (HS3) Impact of HS3 Dropwindsondes for Nadine (2012) HS3 Global Hawk Flight Tracks: Nadine 76 Drops Intensity: Min. SLP Error (hpa) No drops No drops 58 Drops HS3 drops 70 Drops HS3 75 drops Drops 30 Drops 34 Drops Bias (dash) Sensitive regions in Nadine were often well observed by HS3 dropwindsondes Dropwindsonde impact experiments performed for Sep. (3 flights) HS3 drops No drops with synthetics COAMPS-TC intensity and track skill are markedly improved using HS3 drops.

36 Mesoscale satellite DA Assess influence of assimilating specially processed data by CIMSS on WRF/EnKF ensemble analyses and forecasts: Hourly and rapid-scan AMVs derived by CIMSS Selected subsets of these AMVs AIRS clear sky T and Q soundings AMSR-E TPW T.-C. Wu, Velden, Majumdar et al.

37 37 T.-C. Wu, Velden, Majumdar et al.

38 Mesoscale satellite DA Specially processed AMVs produced improved analyses and forecasts of track and structure (cf. operational AMVs used at NOAA). AMVs in the TC vicinity are important. Upper-level and environmental AMVs influence forecasts. AIRS T and AMSR-E TPW can be beneficial. Now being tested in NOAA s operational HWRF system T.-C. Wu, Velden, Majumdar et al.

39 5. Observing Systems: Current and Future

40 Satellite data usage at ECMWF: past, present and near future Millions of observations assimilated per 24h period POES Suomi-NPP DMSP METOP ERS-1/2 ENVISAT COSMIC COSMIC-2 CNOFS GRACE GCOM-W/C TRMM Megha Tropiques AQUA AURA FY-3A/B QuikSCAT JASON-1/2/3 Oceansat HY-2A METEOSAT AMV GOES AMV GMS/MTSAT AMV FY-2C/D AMV METEOSAT Rad GOES Rad GMS/MTSAT Rad TERRA/AQUA AMV Cryosat SMOS EarthCARE ADM Aeolus GOSAT Sentinel 1 Sentinel 3

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53 Geostationary clear-sky radiances

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56 Current Satellites Operational geostationary satellites USA: GOES-13 (East), -15 (West), GOES-14 spare EUMETSAT: METEOSAT-10 (0 Deg.) & -7 (Indian Ocean); & -9 (R/S) India/ISRO: Kalpana & INSAT-3D CMA:FY-2E & F; KMA: COMS; Russia: Electro-L N1; JMA: MTSAT-2 Operational polar satellites Suomi-NPP used operationally in NWP, good impacts. METOP-A/B IASI and AMSU-A/MHS data being assimilated. GCOM-W1 (Japan) AMSR-2 good replacement for AMSR-E FY-3C (China) New MW channels. Good quality ASCAT and Oceansat-2 providing scatterometer-derived winds. Cosmic constellation providing GPS-RO bending angles R. Saunders / C. Velden

57 Future Geostationary Satellites EUMETSAT Meteosat Imager/ Sounder Launch in 2017/20, six satellites in series NESDIS GOES-R Series -R launch slipped to 2016 (West), -S launch in 2017 (East) JMA Himawari-8 operational 2015; -9 launch in 2016 CMA FY-4 series (IR, MW Sounders): KMA COMS-Next: environmental monitoring (~2018) ISRO INSAT-3D operational, AMVs available Russia HydroMet Electro-L N2 launch in Dec 14 R. Saunders / C. Velden

58 Future Polar Satellites ESA: Aeolus Doppler wind lidar launch-ready in JPSS: JPSS-1 2Q 2017 launch in PM orbit followed by JPSS-2. GPM-core: Launched to replace aging TRMM for precip NASA: RapidScat on Int l Space Station in COSMIC-2 GPS-RO (6 low-inclination satellites approved, highinclination awaiting funding from US side) Canada: PCW, Status? EUMETSAT:-2 nd Generation polar orbiter close to approval. CMA: FY-3D (2015), FY-3E (2017), FY-3F (2019) JMA: GCOM-C (2014) R. Saunders / C. Velden

59 Atmospheric Motion Vectors from GOES-R Proxy: AMVs from special GOES-14 super-rapid-scan ops during Hurricane Sandy 1800 UTC 25 Oct, UTC 26 Oct, hpa hpa ABOVE: VIS/IR AMVs from 5-min image intervals derived using the current CIMSS/NESDIS AMV algorithm Tests using the new GOES-R tracking algorithm are underway (with NESDIS/STAR) Data assimilation and hurricane model forecast impact experiments are planned C. Velden (CIMSS)

60 MetOp B/C (EUMETSAT) JPSS-1 (NOAA) GPM Constellation Concept Suomi NPP (NASA/NOAA) GPM Core Observatory (NASA/JAXA, 2014 ) DPR (Ku & Ka band) GMI ( GHz) 65 o Inclination 407 km altitude 5 km best footprint mm/hr and snow Megha-Tropiques (CNES/ISRO) TRMM (NASA/JAXA) NOAA 18/19 (NOAA) DMSP F17/F18/F19/F20 (DOD) GCOM-W1 (JAXA) Next-Generation Unified Global Precipitation Products Using GPM Core Observatory as Reference Precipitation rates everywhere in the world every three hours 60

61 Activation of Special Observations Rapid-Scanning mode on USA GOES satellites NOAA G-IV, P-3 and USAF C-130 aircraft DOTSTAR (Taiwan) From Late 2016: CYGNSS enhanced sampling Other?

62 Low-level unmanned aircraft: Coyote When: September 16 and Where: Hurricane Edouard boundary layer inflow channel UAS flight duration: 68 minutes Minimum (controlled) Altitude: 400m Maximum Wind Speed: 62 J. Cione

63 6. Observing System Simulation Experiments (OSSEs)

64 OSSEs: Motivation Costs of developing, maintaining & using new space-based observing systems often exceed $ M / instrument Significant time lags between instrument deployment and eventual operational NWP use Quantitative information on observing system impacts New instruments (e.g. trade-offs on design) Alternative mix of current instruments DA diagnosis and improvement Optimize Global Observing System Lead to better planning and decisions R. Atlas

65 OSSEs for future observations (NASA/NOAA) Manned and Unmanned Aircraft Airborne and Spaceborne Wind Lidar GNSS Satellites (COSMIC, CYGNSS) Hyperspectral Sounders (geo and polar) Geostationary Microwave Sounders R. Atlas

66 OSSEs: Concept R. Atlas

67 NASA GEOS-5 7 km global nature run

68 WRF/ARW 1 km Regional Nature Run One 13-day run through the life cycle of a fictitious hurricane. Saved every 6 minutes. 1 hour = 16 GB Day 3 David Nolan

69 WRF/ARW 1 km Regional Nature Run One 13-day run through the life cycle of a fictitious hurricane. Saved every 6 minutes. 1 hour = 16 GB Day 4 David Nolan

70 WRF/ARW 1 km Regional Nature Run One 13-day run through the life cycle of a fictitious hurricane. Saved every 6 minutes. 1 hour = 16 GB Day 6 David Nolan

71 WRF/ARW 1 km Regional Nature Run One 13-day run through the life cycle of a fictitious hurricane. Saved every 6 minutes. 1 hour = 16 GB Day 8 David Nolan

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77 NASA CYGNSS OSSEs using HWRF Experiments: 1. Control (C) includes all data except CYGNSS 2. C + Perfect CYGNSS Wind Speeds 3. C + Perfect CYGNSS Wind Vectors 4. C + CYGNSS Wind Vectors from Variational Analysis 5. C + Realistic CYGNSS Wind speeds 6+. C + CYGNSS with Wind lidar and GOES R AMVs 7+. Trade Studies TBD by CYGNSS Science Team 8+. Ocean OSSEs forced with and without CYGNSS DA Systems: GSI, HEDAS, Hybrid, Operational.

78 NASA CYGNSS OSSEs using HWRF NATURE CONTROL CYGNSS NOAA/AOML and U. Miami OSSE teams; CYGNSS Team

79 7. Challenges

80 Challenges: Predictability Track Approaching limit of predictability? Intensity and Structure High sensitivity to small changes in local shear or moisture, environmental flow, Predictability Focus on wavenumber 0 and 1?

81 Challenges: DA Model errors and biases can dominate Microphysics, planetary boundary layer Initial position errors, even after assimilating position Sensitivity to satellite thinning algorithms Linearity assumed in DA schemes Handling rapid (hours) timescales of evolution Vortex spin-down

82 Challenges: Number of Cases Small sample of TC cases Results dependent on type of TC Large TCs: less sensitive, environment a priority? Small TCs, weak TCs, sheared TCs: more sensitive? Variability in basins Need many cases for satellite bias corrections E.g. SSMI/S

83 8. Draft Recommendations

84 Statistical Robustness; new metrics From 2012 WMO Workshop on the impact of various observing systems on NWP Studies extending over several seasons in order to build up statistically robust results and to draw relevant and unequivocal conclusions. Evaluate observation impact in terms of tropical cyclone damage, intensity, precipitation, risk of flooding.

85 DA Mitigate / account for model bias Vortex position and basic structure TCVitals / position DA / bogus data / other? Improve B matrix to represent TC structure Will also decrease # rejected obs near TC Adaptive localization and inflation Improve dispersiveness of ensemble

86 DA Focus on wavenumber 0 and 1 primary circulation, then secondary circulation Improved understanding of issues Inadequacies in DA processes (e.g. resolution) Quality of observations in/near TCs Usage / thinning of observations in/near TCs Frequency of assimilation

87 Short-range forecasts: Inner-core DA HFIP Scientific Review Committee (2014) The SRC surmises that concentration on the h intensity forecast problem, with keen attention to the effective assimilation of inner core observations, is the best avenue to approach HFIP goals.

88 Observing Systems Need to observe key parameters MSLP Lower tropospheric winds and moisture Assessing optimal mix of observations Trade studies for single and multiple data types Identify types of cases that are a higher priority for additional data Adaptive sampling of satellite data Priority to scatterometer, AMV, cloudy radiances, other types?

89 Code Sharing / Data Archiving Community DA Assimilation algorithms Observation operators Nature runs, synthetic data etc for OSSEs Emphasis in 3 new WWRP Post-THORPEX Legacy Projects