From LAPS to VLAPS multiscale hot-start analysis NOAA ESRL/GSD/FAB Y. Xie, S. Albers, H. Jiang, D. Birkenheuer, J. Peng, H. Wang, and Z. toth Global Systems Division

Outline Review of LAPS features; Multigrid variational analysis (Space and Time Multiscale Analysis System, STMAS); Modernizing LAPS using STMAS: Multigrid variational analysis; Variational cloud analysis; Balance and constraints; Use of remote sensing data; Future plan and collaboration with KMA

LAPS review An objective analysis (modified Barnes) scheme; Meteorological states are analyzed sequentially and dynamic balance is applied afterward; Hot-start: Analysis of microphysics; Temperature adjustment; Vertical velocity; Analysis of water vapor; Efficiency; Ease of use, particularly with local data.

Transition from Traditional to Fully Variational LAPS state vars, wind (u,v) clouds / precip balance and constraints in multi-scale variational analysis Wind analysis Temp/Ht analysis Humidity analysis Cloud analysis balance Traditional LAPS analysis: Wind, Temp, Humidity, Cloud, Balance Ultimately Temporary hybrid system: Traditional LAPS cloud analysis and balance Numerical Forecast model Large Scale Model First Guess Cycling Option Var. LAPS

LAPS assimilates a wide range of datasets and local data

6 LAPS USER BASE NOAA – ~120 WFOs (via AWIPS), ARL, NESDIS Other US Agencies – DHS, DoD, FAA, CA DWR, GA Air Qual. Academia – Univ of HI, Athens, Arizona, CIRA, UND, McGill Private Sector – Weather Decision Tech., Hydro Meteo, – Vaisala, Greenpower Labs International agencies (10+ countries) – KMA, CMA, CWB, Finland (FMI), Italy, Spain, – BoM (Australia), Canary Islands, HKO, – Greece, Serbia

Cloud analysis vs. all sky camera Demonstrates high resolution analysis of hydrometeors, aerosols, land surface Check 3-D cloud placement and microphysical properties Forecasts can also be visualized Data assimilation a future possibility

Multigrid variational analysis STMAS Inherit traditional LAPS multiscale (Barnes) analysis by a multigrid technique (wavelet and recursive filter were also tested and yielded similar results); Improvement of standard 3dvar; Enhance the analysis by a fully variational analysis with simultaneous balance and constraints; Improvement of traditional LAPS; Better assimilate remotely sensed observation data, such as satellite IR/VIS, cloud optical depth and radar; Improvement of traditional LAPS.

OAR/ESRL/GSD/Forecast Applications Branch Sequence of 3-4DVARs with proper balances – need for covariance information reduced Similar to traditional LAPS Standard 3-4DVAR With banded covariance Possible ensemble Filter application Long wavesShort waves Xie et al. “A Space–Time Multiscale Analysis System: A Sequential Variational Analysis Approach”, MWR 2011 Analysis and model initialization may end at different multigrid levels MULTISCALE VARIATIONAL ANALYSIS

Humidity Analysis resolving discontinuity LAPS STMAS

VLAPS (STMAS) bound constraints VLAPS uses the L-BFGSB in its variational analysis and this quasi-Newton software allows users to use bound constraints; VLAPS can use cloud and/or reflectivity information to constrain its humidity analysis: Currently, if an area is covered with cloud and reflectivity, VLAPS constrains its humidity to 100% RH. An on-going evaluation is to make it as weak one for accommodating other obs (e.g., GPS); Such bound constraints are considered for variational cloud analysis.

A real time example Possible collaboration: improving covariance

VLAPS assimilation of remote sensing observations with collaborators A long list of datasets: AMSU-A and B for Taiwan now; GPS (TPW now and slant delay next); GOES sounder IPW; Cloud mask and optical depth (testing now); Dual Pol radar (Serbia Meteorological Agency); GOES IR and visible imagery (with CRTM); GOES-R cloud cooling and over-shooting; ……

AMSU-B Up Air Impact No AMSU-BAMSU-B all channels

GPS TPW data impact

General methodology of VLAPS analysis of remote sensing data A forward operator mapping analysis variables to observations: F(X)≈Y; An adjoint of this operator, F’(X); An additional term in the minimization cost function: (F(X)-Y) T O -1 (F(X)-Y); Minimization is done with added gradient term from the remote sensing data.

GPS TPW forward operator vertically Surface grid box Domain top

GPS Examples: A forward operator for GPS TPW(specific humidity): An integration of vertical specific humidity along a given GPS zenith path; A forward operator for GPS slant delay: An integration of refractivity along the GPS slant path; Both are differentiable in terms of the control variables, sh for the former and sh, T and p for the later.

Variational cloud analysis Currently, use the traditional LAPS cloud analysis as an initial guess; Cloud mask as constraints of cloud ice, liquid, rain and snow (possible graupel), including ; Cloud phase products are also used; Cloud optical depth is being tested; IR and visible data will be assimilated; Temperature is used to constrain cloud ice and liquid; Variationalization of LAPS cloud components, e.g., estimated cloud from RH; Sophisticated covariance is needed for filling the data void regions.

Cloud optical depth vs. cloud ice analyses VLAPS LAPS Cloud Optical Depth OBS

VLAPS (1km) without GPS and COD

Future Plan Identify and assimilate important observation data sources, dual pol radar, IR and visible; Improve balance and constraints, particularly on the hydrometer state variables; Continuity (already in), hydrostatic, etc; WRF FDDA collaborating with US Army; WRF adjoint for short 4DVAR assimilation window. Improve forecast model parameters, e.g., snow cover, land types etc; Parallelization of VLAPS; Object-oriented design of VLAPS.

Collaboration with KMA Forecast model for 200-m resolution run with tuned model parameters and topography; Local observation datasets, in-situ and remotely sensed data, including all-sky images; Observation forward operators and their adjoint; Variational cloud analysis; Hydrometeor constraints; Terrain following VLAPS code development; GIT LAPS software sharing; Object-oriented VLAPS development.