DEVELOPMENT OF THE NCEP UNIFIED GLOBAL COUPLED SYSTEM (UGCS) FOR WEATHER AND CLIMATE PREDICTION

Transcription

1 DEVELOPMENT OF THE NCEP UNIFIED GLOBAL COUPLED SYSTEM (UGCS) FOR WEATHER AND CLIMATE PREDICTION Xingren Wu, Suranjana Saha, Jiande Wang, Jack Woollen, Patrick Tripp, Malaquias Pena, Shrinivas Moorthi, David Behringer, Partha Bhattacharjee, Jun Wang, Arun Chawla, Avichal Mehra, Mike Ek, Jiarui Dong, Xu Li, Sarah Lu, Daryl Kleist, Steve Penny 1

2 Coupled Data Assimilation and Forecasting System A good prediction system incorporates both a good data assimilation (DA) system and a good forecast model. 2

3 A Historical Perspective ONE DAY OF CFS Analysis or Reanalysis 12Z GDAS 18Z GDAS 0Z GDAS 6Z GDAS 0Z GLDAS 12Z GODAS 18Z GODAS 0Z GODAS 6Z GODAS 9-hr coupled T574L64 forecast guess (GFS + MOM4 + SeaIce+ Noah) 3

4 R2 (1997) CDASv2 (2011) Vertical coordinate Sigma Sigma/pressure Spectral resolution T62 T574 Horizontal resolution ~210 km ~27 km Vertical layers Top level pressure ~3 hpa hpa Layers above 100 hpa ~7 ~24 Layers below 850 hpa ~6 ~13 Lowest layer thickness ~40 m ~20 m Analysis scheme SSI GSI Satellite data NESDIS temperature retrievals (2 satellites) Radiances (all satellites) Bob Kistler, EMC 4

5 GODAS 3DVAR Version Changing from a version based on MOM V3 to one based on MOM4p0d As was the case with MOM4p0d, the code has been completely rewritten from Fortran 77 to Fortran 90. Domain and Resolution Changing to a global domain. Increasing resolution to match the MOM4p0d configuration used in the CFS: 1/2 o x1/2 o (1/4 o within 10 o of the equator); 40 Z-levels. Functionality Adding the ability to compile the 3DVAR analysis in either an executable combining the analysis with MOM or an executable containing only the analysis. The latter formulation is used with the CFS where it reads the forecast from a restart file produced by the coupled CFS, does the analysis, and updates the restart file. Adding relaxation of surface temperature and salinity to observed fields under the control of the 3DVAR analysis. Data The data sets that can be assimilated remain unchanged from the current operational GODAS: XBTs, tropical moorings (TAO, TRITON, PIRATA, RAMA), Argo floats, CTDs), altimetry (JASON-x). Dave Behringer, EMC 5

6 Global Land Data Assimilation System (GLDAS) GLDAS (running Noah LSM under NASA/Land Information System) forced with CFSv2/GDAS atmospheric data assimilation output and blended precipitation in a semicoupled mode, versus no GLDAS in CFSv1, where CFSv2/GLDAS ingested into CFSv2/GDAS once every 24-hours. In CFSv2/GLDAS, blended precipitation a function of satellite (CMAP; heaviest weight in tropics), surface gauge (heaviest in middle latitudes) and GDAS (modeled; high latitude), vs use of model precipitation comparison with CMAP product and corresponding adjustment to soil moisture in CFSv1. Snow cycled in CFSv2/GLDAS if model within 0.5x to 2.0x of the observed value (IMS snow cover, and AFWA snow depth products), else adjusted to 0.5 or 2.0 of observed value. GDAS-CMAP precip Gauge locations IMS snow cover AFWA snow depth Mike Ek, EMC 6

7 The NCEP Climate Forecast System Reanalysis Suranjana Saha, Shrinivas Moorthi, Hua-Lu Pan, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, Robert Kistler, John Woollen, David Behringer, Haixia Liu, Diane Stokes, Robert Grumbine, George Gayno, Jun Wang, Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H. Juang, Joe Sela, Mark Iredell, Russ Treadon, Daryl Kleist, Paul Van Delst, Dennis Keyser, John Derber, Michael Ek, Jesse Meng, Helin Wei, Rongqian Yang, Stephen Lord, Huug van den Dool, Arun Kumar, Wanqiu Wang, Craig Long, Muthuvel Chelliah, Yan Xue, Boyin Huang, Jae-Kyung Schemm, Wesley Ebisuzaki, Roger Lin, Pingping Xie, Mingyue Chen, Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou, Quanhua Liu, Yong Chen, Yong Han, Lidia Cucurull, Richard W. Reynolds, Glenn Rutledge, Mitch Goldberg Bulletin of the American Meteorological Society Volume 91, Issue 8, pp doi: /2010BAMS

8 TOWARDS BUILDING A PROTOTYPE OF THE NEXT GENERATION UNIFIED GLOBAL COUPLED ANALYSIS AND FORECAST SYSTEM AT NCEP (UGCS) 8

9 UNIFIED GLOBAL COUPLED SYSTEM DATA ASSIMILATION Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere Data Assimilation cycle Analysis frequency Hourly Forecast length 9 hours Resolution 10 km Ensemble members 100 Testing regime 3 years (last 3 years) Upgrade frequency 1 year 9

10 UNIFIED GLOBAL COUPLED SYSTEM WEATHER FORECASTS Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere Weather Forecasts Forecast length Resolution Ensemble members Testing regime Upgrade frequency 10 days 10 km 10/day 3 years (last 3 years) 1 year 10

11 UNIFIED GLOBAL COUPLED SYSTEM SUB-SEASONAL FORECASTS Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere Sub-seasonal forecasts Forecast length Resolution Ensemble members Testing regime Upgrade frequency 6 weeks 30 km 20/day 20+ years (1999-present) 2 year 11

12 UNIFIED GLOBAL COUPLED SYSTEM SEASONAL FORECASTS Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere Seasonal Forecasts Forecast length Resolution Ensemble members Testing regime Upgrade frequency ~1 year 50 km 40 (lagged) 40+ years (1979-present) 4 year 12

13 UNIFIED GLOBAL COUPLED SYSTEM NEW PARADIGM Atmosphere, Land, Ocean, Seaice, Wave, Chemistry, Ionosphere Predictions for all spatial and temporal scales will be ensemble based. There will be a continuous process of making coupled Reanalysis and Reforecasts for every implementation (weather, sub-seasonal and seasonal) Since the resolution of all parts of the system is usually increased with every new implementation (in proportion to the increased computing power currently available), we need to explore cheaper cloud computing and storage options for making these reanalysis/reforecasts. 13

14 Whole Atmosphere Model NGGPS Unified Global Coupled Model NGGPS Unified Global Coupled Model GFS GEFS CFS Actionable weather Week 1 through 4-6 Seasonal & annual Application = Ensemble + Reanalysis + Reforecast 1 y 2 y 4 y Update cycle 3 y present present Reanalysis 6h 6h 6h cycling WCOSS WCOSS WCOSS where 14

15 NGGPS Planned Components Atmospheric Components Atm Dycore (FV3) Atm Physics (GFS) Aerosols (GOCART) Atm DA (GSI) NEMS/ESMF Land Surface (NOAH) Ocean (HYCOM/MOM) Wave (WW3) Sea Ice (CICE/SIS2/KISS) 15

16 PROOF OF CONCEPT Atmosphere: Hybrid 4D-EnVAR approach using a 80-member coupled forecast and analysis ensemble, with Semi-Lagrangian dynamics, and 128 levels in the vertical hybrid sigma/pressure coordinates. Will test with the new GFDL FV3 dynamic core when it is ready. Ocean/Seaice: GFDL MOM5.1/MOM6 and/or HYCOM for the ocean and CICE/SIS2/KISS for sea-ice coupling, using the NEMS coupler. Aerosols: Inline GOCART for aerosol coupling. Waves: WAVEWATCH III for wave coupling. Land: Noah Land Model for land coupling. Ionosphere: Inline Whole Atmosphere Model (WAM) up to 600km. 16

17 ATMOS AERO LAND WAVE SEA-ICE OCEAN NCEP Coupled Hybrid Data Assimilation and Forecast System NEMS NEMS OCEAN SEA-ICE WAVE LAND AERO ATMOS Coupled Model Ensemble Forecast Coupled Model Ensemble Forecast Coupled Ensemble Forecast (N members) Ensemble Analysis (N Members) INPUT OUTPUT 17

18 NEMS The NOAA Environmental Modeling System is being built to unify operational systems under a single framework, in order to more easily share common structures/components and to expedite interoperability. The first two systems under NEMS (NAM/NMMB and GOCART) have been implemented into NCEP operations with others to follow in the next few years, such as the GFS, etc. The NUOPC (National Unified Operational Prediction Capability, NOAA, Navy and Air Force) layer will be used to make collaboration with other groups less difficult, when building/coupling modeling systems. Incorporation of a NUOPC physics driver can help standardize the often complex connections to physics packages, thereby enhancing their portability. Mark Iredell, EMC 18

19 Atmospheric Data Assimilation Upgrades Develop prototype, Hybrid 4D-EnVAR data assimilation system using the GSI and a NEMS-based GSM at SL T574/L64 resolution with a 80 member ensemble. Test and validate system using UGCS coupled model as background for atmospheric DA. Determine impacts of each domain for atmospheric prediction and DA. Prototype system will be initial NEMS-based code set for atmospheric component of NGGPS and CFSv3 development. System will generate more accurate background field for the atmosphere and other domains. Daryl Kleist, UMD and NCEP 19

20 Atmospheric Model Physics Upgrades CONVECTION Improve the representation of small-scale phenomena by implementing a scaleaware PDF-based subgrid-scale (SGS) turbulence and cloudiness scheme replacing the boundary layer turbulence, the shallow convection, and the cloud fraction schemes in the GFS and CFS. Improve the treatment of deep convection by introducing a unified parameterization that scales continuously between the simulation of individual clouds when and where the grid spacing is sufficiently fine and the behavior of a conventional parameterization of deep convection when and where the grid spacing is coarse. Improve the representation of the interactions of clouds, radiation, and microphysics by using the additional information provided by the PDF-based SGS cloud scheme. Alternatively, upgrade the clouds and boundary layer parameterization, using the approach of moist Eddy Diffusion and Massflux (EDMF) approach by considering several microphysics schemes available in WRF package and a simple pdf based clouds and improved scale aware convection by extending SAS. S. Moorthi, EMC 20

21 Atmospheric Model Physics Upgrades RADIATION AND OTHER: Improve cloud radiation interaction with advanced radiation parameterization based on the McICA-RRTMG from AER Inc. with interactive aerosol (both direct and indirect) affects. Include radiative effects due to additional (other than CO2) green-house gases from near real-time observations. Improve the representation of aerosol-cloud-radiation interaction in the model. Implement a double-moment cloud microphysics scheme and a multimodal aerosol model. Develop an interface to link cloud properties and aerosol physiochemical properties, consistent coupling of cloud micro and PDF-based macro physics, and the modification of RRTM to support the new cloud-aerosol package. Consistent clouds, convection, radiation and microphysics interactions Enhance the parameterization of gravity wave drag, particularly the nonstationary, propagating type. As shown by ECMWF, this is important for the proper simulation of QBO in the stratosphere. Yu-Tai Hou, Sarah Lu 21

22 Hybrid GODAS Hybrid Method: The Hybrid-Gain method of Penny (2014) EnKF Component: The Local Ensemble Transform Kalman Filter (LETKF) developed by Hunt et al. (2007) at the University of Maryland (UMD) Variational Component: NCEP s operational 3DVar used in the Global Ocean Data Assimilation System (GODAS) described by Derber and Rosati (1989) and Behringer (2007) Penny, S.G., 2014: The Hybrid Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 142, doi: /MWR-D Hunt, B.R., E.J. Kostelich, and I. Szunyogh, 2007: Efficient Data Assimilation for Spatiotemporal Chaos: A Local Ensemble Transform Filter. Physica D, 230, Derber, J. D., and A. Rosati, 1989: A global oceanic data assimilation system. J. Phys. Oceanogr., 19, Behringer, D. W., 2007: The Global Ocean Data Assimilation System at NCEP. Preprints, 11th Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans and Land Surface, San Antonio, TX, Amer. Meteor. Soc., Steve Penny, UMD and EMC Kalman 22

23 Hybrid 3DVar/LETKF GODAS The following slide shows the results from an 8-year Reanalysis using temperature and salinity profile data as used in the CFSR. All experiment conditions are identical except for the DA system (3DVar vs. Hybrid) The Hybrid-GODAS eliminates growing biases in temperature and salinity, and gives an overall reduction in RMSD and BIAS. This ocean data assimilation approach is also being tested with the HYCOM ocean model Steve Penny, UMD and EMC 23

24 Depth (m) BIAS (ºC) RMSD (ºC) Historical Reanalysis, RMS Deviations are reduced uniformly by the hybrid, for both temperature and salinity (solid line = 3 mo. moving ave. dashed line = linear regression) Temperature 3DVar-GODAS Hybrid-GODAS Salinity Growth in temperature and salinity biases using 3DVar are eliminated by the Hybrid The magnitude of all biases are generally reduced The reduction in RMS deviations for temperature and salinity are fairly uniform throughout the upper ocean Steve Penny, UMD 24

25 NSST (Near-Surface Sea Temperature) The ocean model does not produce an actual SST. The NSST determines a T-Profile just below the sea surface, where the vertical thermal structure is due to diurnal thermocline layer warming and thermal skin layer cooling. c z w Mixed Layer Thermocline T f f T mix T T(z) T Diurnal Warming Profile T ' w ( z) (1 z / z ) T ' w ' T ' ' w ) ' w, t) Tc ( z, ) T w ( z T( z, t) Tf ( t0 t z ) z w z w w ~ O(5m) (0) Deeper Skin Layer Cooling Profile z Ocean ' ' ' TT(0, ( zt ) T(1 ( z, t/ ) ) T(0, (0) t) 0 c c c ' T c c ( z) c c ' T c T( z, t) T ' T ( z, t) T f ( z ' w w, t) T ( z, t) T ' ' c ( z, t), ( z, t) z [ 0, zw] z c ~ O(1mm) Xu Li, EMC 25

26 Incorporation of the NSST into an air-sea coupled data assimilation and prediction system ANALYSIS Hybrid ENKF GSI with Tf analysis built in an an [ X a, T ] Observation innovation in Tf analysis f Get skin-depth dependent T(z) bg with T f and NSST T-Profile ' T bg ( z) IC ( X an a, X an o, T an f X : Atmospheric state a vector SST(z) = SST fnd + T(z) T w (z) = T- f T(z c (z) w ) + T (z) ) FORECAST Atmospheric FCST Model with NSST Model built in bg ' bg [ X, T ( z)] T bg f Get and then bg for the coupling with NSST T-Profile [ T ' bg ( z)] T-Profile a and Oceanic model [ T bg ( z)] o SST Hybrid GODAS ( an X o ) BG X, X, T bg a bg o bg f X : Oceanic state vector Oceanic FCST Model o NSST algorithm is part of both the analysis and forecast system. In the analysis, the NSST provides an analysis of the the interfacial temperature ( SST ) in the hybrid ENKF GSI using satellite radiances. In the forecast, the NSST provides the interfacial temperature (SST) in the free, coupled forecast instead of the temperature at 5-meter depth that is predicted by the ocean model. 26 ( bg X o )

27 Proposed Upgrades to Land Surface Methodology upgrades: NASA s Land Information System (LIS) integrates NOAA NCEP s operational land model, high-resolution satellite and observational data, and land DA tools (EnKF). Global observed precipitation forcing from 0.5 o to 0.25 o resolution (CPC daily, 1979-present). Model upgrades: Noah 3.x, Noah-MP (e.g, dynamic vegetation, explicit canopy, CO2-based photosynthesis, groundwater, multilayer snow pack, river routing). J. Dong, M. Ek, H. Wei, J. Meng, EMC 27

28 Proposed Upgrades to Land Surface (cont.) Land use dataset upgrades: Near-realtime GVF (global 16-km, 1981-present, and 25-year climatology), land-use/vegetation (IGBP-MODIS, global 1km), soil type (STATSGO-FAO, global 1km), MODIS snow-free albedo (global 5km), maximum snow albedo (U. Arizona, global 5km). SMOPS soil moisture (AMSR-E ( ), SMOS (2010- present), ASCAT, AMSR2 (2012-present), SMAP (launched 2014). Snow cover: IMS (global 24km (1997-present) & 4km (2004- present). Snow water equivalent: SMMR ( ), SSM/I ( ), AMSR-E ( ), AMSR2 (2012-present). J. Dong, M. Ek, H. Wei, J. Meng, EMC 28

29 Aerosol Data Assimilation Observations Aerosol Optical Depth (satellite & in-situ) Smoke Emissions (satellite) INPUT INPUT Dust Sea salt Sulfate Black carbon Organic carbon In OUTPUT AOD data assimilation (EnKF/GSI) Sarah Lu, SUNY Albany and EMC 29

30 The Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulates major tropospheric aerosol components, including sulfate, dust, black carbon (BC), organic carbon (OC), and sea-salt aerosols. GOCART considers emission, wet removal and dry removal processes and simple sulfate chemistry. Model upgrades: To introduce aerosol-cloud interaction (indirect effect) in the GFS Methodology upgrades Proposed upgrades to data assimilation and modeling of aerosols To use much higher resolutions for NGAC (NEMS GFS Aerosol Component (NGAC) to couple with higher resolution of the GFS. Upgrade the Hybrid DA system to enable assimilation of AOD observations. Sarah Lu, SUNY Albany and EMC 30

31 Proposed upgrades to data assimilation and modeling of aerosols (contd) Emissions dataset upgrades: Use satellite-based smoke emissions (MODIS fire radiative power) for organic carbon, black carbon and sulfate. Data assimilation upgrades: The AOD observations will be from MODIS (satellite) and AERONET (in-situ). Aerosol data assimilation is proposed for the EOS era (2002 to present). Sarah Lu, SUNY Albany and EMC 31

32 Sea Ice Data Assimilation Observations (remote & in-situ) (real time or climatology) IN Sea Ice Data Assimilation INPUT Sea Ice fraction & thickness OUTPUT LETKF Xingren Wu, EMC 32

33 Wave Coupling GFS SST Charnock U c R air Q air U ref τ air Ocean τ diff τ coriol λ U c WAVEWATCH III GFS model air-sea fluxes depend on sea state (roughness -> Charnock). WAVEWATCH III model forced by wind from GFS and currents from Ocean. Ocean model forced by heat flux, sea state dependent wind stress modified by growing or decaying wave fields and Coriolis-Stokes effect. 33

34 WAVEWATCH III Planned Upgrades o Drive the wave model in a coupled mode with atmospheric winds (for wave dependent boundary conditions in atmospheric models) o Include wave - ocean coupling (currents from ocean model to wave model and wave induced langmuir mixing and Stokes drift from wave model to ocean model) o Data assimilation of significant wave heights to develop a wave analysis GSI approach LETKF approach o Planned sources of data Spectral data from ocean buoys Satellite data from altimeters A. Chawla, H. Alves and H. Tolman, EMC

35 IMPROVE ANALYSIS COUPLING The operational CFSR uses a coupled atmosphere-ocean-land-sea ice forecast for the analysis background but the analysis is done separately for each of the domains. In the next reanalysis, the goal is to increase the coupling so that, e.g., the ocean analysis influences the atmospheric analysis (and vice versa). This will be achieved mainly by using a coupled ensemble system to provide the background and the EnKF to generate structure functions that extend across the seaatmosphere interface. The same can be done for the atmosphere and land, because assimilation of land data will be improved for soil temperature and soil moisture content. 35

36 LETKF: Perform assimilation locally at each point Courtesy: University of Maryland The state estimate is updated at the central grid red dot All observations (purple diamonds) within the local region are assimilated Observations Observation operator ensemble observations LETKF ensemble analyses ensemble forecasts Model 36

37 Coupled LETKF: Sharing of Departures Observations Atmos. LETKF ensemble analyses Observation operator ensemble observations Ocean LETKF Land LETKF Coupled Model ensemble forecasts Each grid point is solved independently, so by passing observation departures (O-F) and model sub-state (land, ocean, atmosphere), one can solve LETKF equations independently (on different grids if desired) and decide (spatial/variable localization) how strongly to couple. For example, can pass atmospheric O-F to ocean LETKF! Equivalent to strong coupling. 37

38 The way ahead work on building the CFSv3 repository in EMC subversion work on using Ricoto/ecflow for running the global parallel scripts in coupled mode work on coupled data assimilation work on improving the physics in the coupled system work on verification of the coupled system 38

39 Simple Test Harness for Scale-Aware Physics Weather (0-10 days) Sub-seasonal (11-45 days) Seasonal (2-6 months)

40 Weather coupled experiments T day forecasts Initial conditions: Summer July 2015 Winter January 2016 Hurricane period? siganl/sfcanl from operational/parallel runs ocnanl from CFSv2 ocean initial states

41 Sub seasonal coupled experiments T day forecasts Initial conditions: Strong MJO cases over the period from (4-member ensemble for each). Study onset/mature/decay phase. siganl/sfcanl from CFSR ocnanl from CFSR

42 Seasonal coupled experiments T126 6-month forecasts Initial conditions (over the period ): Strong El-Nino (warm) cases (4-member ensemble each) Strong La-Nina (cold) cases ((4-member ensemble each) Neutral ENSO cases (4-member ensemble each) Summer start from May 15 for JJA (108 cases) Winter start from Nov 15 for DJF (108 cases) siganl/sfcanl from CFSR ocnanl from CFSR.

43 THANK YOU! 43