Transcript THE NCEP REAL-TIME MESOSCALE ANALYSIS (RTMA)

THE NCEP REAL-TIME MESOSCALE ANALYSIS
(RTMA)
Manuel Pondeca, Geoff Manikin , David Parrish,
James Purser, Geoff DiMego, Stan Benjamin,
John Horel, Lee Anderson, Brad Colman, Greg
Mann, and Greg Mandt
Mesoscale Modeling Branch
National Centers for Environmental Prediction
[email protected]
301-763-8000 ext 7734
NOAA Science Center-Room 207
5200 Auth Road
Camp Springs, MD 20746-4304
Topics
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The need for an Analysis of Record (AOR) and the
Proposed Three Phase Implementation Plan
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The Real-Time Mesoscale Analysis (RTMA):
Phase-I of the AOR
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The Mechanics of the RTMA
 GSI-2DVar
 Precipitation fields
 Sky cover fields
Current and Future Work
Analysis of Record
A comprehensive set of the best possible analyses
of the atmosphere at high spatial and temporal
resolution with particular attention placed on
weather and climate conditions near the surface
Critical need for AOR at
NOAA NWS!
Analysis of Record (AOR)
Summary of Need
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In part needed to:
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Meet the NDFD production requirement --a minimum
analysis with a grid spacing of 5-km and temporal frequency
of one hour.
Provide analyses to verify NDFD gridded forecasts
Enhance Mesoscale Modeling efforts
Establish a benchmark climate analysis for use in regional and
local climate change studies
Enhance Representativeness of Physical Driving in
Dispersion modeling (eg. for transport of hazardous
materials).
Enhance Aviation and sfc transportation management efforts
Enhance Coastal zone and fire management efforts
AOR: Three Phase Program
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August 2004: Mesoscale Analysis Committee (MAC)
established by Director, NWS Office of Science and
Technology to implement AOR program.
MULTI-PHASE APPROACH ADOPTED:
Phase I – Real-time Mesoscale Analysis
 Analyses produced hourly within 30 minutes. Time
constraint is a factor.
 Prototype for AOR.
Phase II – Analysis of Record
 Use state-of-the-art methods to obtain best analysis
possible
 Time constraint lifted
Phase III – Reanalysis
 Apply mature AOR retrospectively
 30 year time history of AORs
Real-Time Mesoscale Analysis (RTMA)
Fast-track, proof-of-concept of the AOR program.
Intended to:
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Enhance existing analysis capabilities and generate near
real-time hourly NDFD grid matching analysis of surface
parameters and clouds.
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Also provide estimates of analysis uncertainty
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Establish benchmark for future AOR efforts
Developed by NCEP and ESRL
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Running since August 2006 for CONUS. Extended to
Alaska in 2007, and to Hawaii and Puerto Rico in 2008.
CONUS RTMA PROCEDURE
RUC 13 Downscaled
to 5km NDFD grid
NDFD terrain
Observations
Stage-II Precip
Interpolated to
5km NDFD grid
Sky Cover
First guess
2m-T, 2m-q,
10m-u and
v, psfc
GSI 2DVar
Analysis
Uncertainty
OUTPUT
in GRIB2
FILE 1: 2m T, q
10m u, v, psfc
FILE 2: Precip
FILE 3: Sky Cover
AWIPS
FTP Server
RTMA Website
Summary of the Conus RTMA
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GSI-2DVar analysis of near surface parameters: Currently,
T and SPH at 2m, wind at 10m, and surface pressure.
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Hourly 5km resolution analysis
First guess: One hour 13km RUC forecast downscaled to NDFD
grid
Univariate analysis
Terrain following background error covariances
Estimate of analysis error/uncertainty
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Precipitation – NCEP Stage II analysis
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Sky cover – NESDIS GOES sounder effective cloud amount
RTMA First Guess / 2m T
Original 13 km
Downscaled 5 km
RTMA First Guess / 10m Wind
Original 13 km
Downscaled 5 km
Observations and Quality Control
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Surface Land (SYNOPTIC and METAR)
Surface marine (Ship, Buoy, C-MAN, Tide Gauge)
Splash-level dropsonde over ocean
Surface Mesonets
SSM/I Superobed wind speed over ocean
QUICKSCAT winds over ocean
DATA FEED
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Conventional through TOC
Mesonets through MADIS. In Future Also Through
MesoWest
Observations and Quality Control
PRE- GSI QC:
1.
MADIS QC control flags honored
2.
GSD Mesonet Wind “Provider-Uselist”
3.
GSD Mesonet Wind “Station-Uselist”
4.
Rejectlists from WFOs
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Dynamic Rejectlists
QC Within GSI:
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Gross-error Check
Mesonet Issues:
Mesonets comprise majority of obs but they are not as
``good’’ as the other conventional surface ob sources.
SURFACE TEMPERATURE OBSERVATIONS
Total = 11911 ; METARS = 1678 (14. 1%)
MESONETS = 9914 (83.2%)
Others = 319 (2.6%)
Cycle 2008081517
Anisotropic Background Error Covariance
Functions
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Background error covariances mapped to
smoothed version of the NDFD topography
=> Restrict ob influence based on elevation
differences.
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Implementation is based on the use of Recursive
filters in grid-point space. Covariance model is a
variant of the Riishojgaard model (1998, Tellus, V50A,
42-57)
For details, see:
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Purser, Wu, Parrish and Roberts, 2003, MWR, Vol 131, p1524-1535
Purser, Wu, Parrish and Roberts, 2003, MWR, Vol 131, p1536-1548
Error Correlations for Valley Ob (SLC)
Location Plotted Over Utah Topography
Isotropic Correlation:
obs' influence extends up
mountain slope
Anisotropic Correlation:
obs' influence restricted to
areas of similar elevation
RTMA Analysis Uncertainty
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METHOD USED TO ESTIMATE THE ANALYSIS ERROR IS
ADAPATION TO THE GSI OF THE LANCZOS METHOD
DESCRIBED BY FISHER AND COURTIER (1995, Tech.
Memorandum 220, ECMWF).
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ANALYSIS ERROR COVARIANCE MATRIX=INVERSE OF
HESSIAN MATRIX IN INCREMENTAL VARIATIONAL
ANALYSIS.
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USE BI-PRODUCTS OF THE CONJUGATE-GRADIENT
ALGORITHM OF THE GSI (gradient vectors and stepsizes)
TO COMPUTE SUBSET OF EIGENVECTORS AND
EIGENVALUES OF HESSIAN AND RECONSTRUCT LOWERRANK REPRESENTATION OF THIS MATRIX (OR OF ITS)
INVERSE.
TEMP INCREMENTS + ANALYSIS ERROR FOR
14Z 22 FEB 2008
ANAL INCREMENTS
ANAL ERROR
Temperature Analysis for 12 Z 6 Oct 2008
http://www.emc.ncep.noaa.
gov/mmb/rtma/para
NCEP RTMA Precipitation Analysis
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NCEP Stage II (real-time) and Stage IV (delayed) precipitation analyses are
produced on the 4-km Hydrologic Rainfall Analysis Project grid
The existing multi-sensor (gauge and radar) Stage II precipitation analysis
available 35 minutes past the hour
RTMA is mapped to the 5 km NDFD grid and converted to GRIB2
Upgrade plan including OHD analysis + improved gauge QC from FSL
Primary contact: Ying Lin, NCEP/EMC
http://wwwt.emc.ncep.noaa.gov/mmb/ylin/pcpanl/
ORIGINAL
NDFD GRIB2
Hourly Gages Available for Stage II
Precipitation Analysis
Sky Cover: Effective Cloud Amount
(a)
• Effective Cloud Amount (ECA, %)
• Derived from GOES sounder
• Mapped onto 5-km NDFD grid
• Converted to GRIB2 for NDGD
• Contact: Robert Aune, Advanced
Satellite Products Branch,
NESDIS (Madison, WI)
(b)
Derived ECA from GOES-12
GOES-12 IR image (11um)
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ECA from GRIB2 file – 5km grid
FUTURE PLANS
• EXPAND NUMBER OF ANALYZED PARAMETERS: ADD
CLOUD BASE, VISIBILITY AT 2m, WIND GUST AT
10m, PBL HEIGHT, etc.
• TURN ON BIAS CORRECTION FOR FIRST-GUESS
• IMPROVE FIRST GUESS, eg. HURRICANE TREATMENT
• TURN ON VARIATIONAL QC for Obs
• RUN RTMA CONUS AT 2.5km RESOLUTION
• EXPAND BAKCGROUND ERROR COVARIANCE SHAPES