From LAPS to VLAPS multiscale hot-start analysis NOAA ESRL/GSD/FAB Y. Xie, S. Albers, H. Jiang, D....
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Transcript of From LAPS to VLAPS multiscale hot-start analysis NOAA ESRL/GSD/FAB Y. Xie, S. Albers, H. Jiang, D....
From LAPS to VLAPSmultiscale hot-start analysisFrom 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
OutlineReview 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 reviewAn 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 constraintsin multi-scale variational
analysis
Windanalysis
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
NumericalForecast
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 analysisSTMAS
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 3-4DVARWith banded covariance
Possible ensembleFilter application
Long waves Short waves
Xie et al. “A Space–Time Multiscale Analysis System: A Sequential Variational Analysis Approach”, MWR 2011
Analysis and model initialization may endat different multigrid levels
MULTISCALE VARIATIONAL ANALYSIS
Humidity Analysis resolving discontinuity
LAPS
STMAS
VLAPS (STMAS) bound constraintsVLAPS 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-B AMSU-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 analysisCurrently, 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 PlanIdentify 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 KMAForecast 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.