Real-Time Mesoscale Analysis (RTMA) - A First Step Towards

an Analysis of Record

Geoff DiMego, Ying Lin, Manuel Pondeca, Seung-Jae Lee,Wan-Shu Wu, David Parrish, and Stacie Bender

Mesoscale Modeling BranchNational Centers for Environmental Prediction

Geoff.DiMego@noaa.gov301-763-8000 ext 7221

NOAA Science Center - Room 2055200 Auth Road

Camp Springs, MD 20746-4304

Topics Background Three Phase Plan Phase I: RTMA

Whys + wherefores RUC first guess 2DVar / GSI Precip Sky

Future Phases

I promise to try to avoid achieving this result.

Analysis of RecordA 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

Glahn-Livesey Verification Meeting:Need to Verify NDFD – Grid vs Analysis

Insufficient density of obs for grid vs point verification of NDFD alone

Need centrally produced “analysis of record” (DiMego’s application of a term used in FGGE)

No funds available No 00 hr analysis in NDFD

National Digital Forecast Database (NDFD)

A national database of digital weather forecast informationDesigned to meet the basic weather information needs of industry, media, commercial weather services, academia, and the public 3 hour to 7 day lead time

Opinions/Concerns Expressed

Cliff Mass (2003), Weather and Forecasting WR SOO/DOH IFPS White Paper provided

recommendations: Develop a national real-time, gridded verification system Provide full-resolution NCEP model grids Produce objective, bias-corrected model grids for WFO

use Implement methods to objectively downscale forecast grids Incorporate climatology grids into the GFE process Deliver short and medium-range ensemble grids Produce NDFD-matching gridded MOS Modify the GFE software to ingest real-time data Optimize ways to tap forecaster expertise

First Steps Toward an AOR A Community Meeting on Real-time and Retrospective

Mesoscale Objective Analysis Convened by NWS’ Brad Colman & UofUtah’s John Horel June 2004 in Boulder Strawman – 3DVar based 4DDA

AOR program should develop and implement suite of consistent sensible weather analysis products using current and future technologies.

Mesoscale Analysis Committee (MAC) established August 2004 by Director, NWS Office of Science and Technology

Robert Aune, NOAA/NESDIS University of Wisconsin Space Sciences and Engineering Center Stanley Benjamin, Forecast Systems Laboratory Craig Bishop, Naval Research Laboratory Keith A. Brewster, Center for Analysis and Prediction of Storms The University of Oklahoma Brad Colman (Committee Co-chair), NOAA/National Weather Service -- Seattle Christopher Daly, Spatial Climate Analysis Climate Service Oregon State University Geoff DiMego, NOAA/ National Weather Service National Centers for Environmental

Prediction Joshua P. Hacker, National Center for Atmospheric Research John Horel (Committee Co-chair), Department of Meteorology, University of Utah Dongsoo Kim, National Climatic Data Center Steven Koch, Forecast Systems Laboratory Steven Lazarus, Florida Institute of Technology Jennifer Mahoney, Aviation Division Forecast Systems Laboratory Tim Owen, National Climatic Data Center John Roads, Scripps Institution of Oceanography David Sharp, NOAA/National Weather Service -- Melbourne

Ex Officio: Andy Edman, Science & Technology Committee representative LeRoy Spayd, Meteorological Services Division representative Gary Carter, Office of Hydrology representative Kenneth Crawford, COOP/ISOS representative

Mesoscale Analysis Committee (MAC)

Steps Toward an AOR Strategy

MAC Committee meeting in Silver Spring in October 2004 to define needs and development strategy for AOR

Distinct requirements become clear: Real-time for forecasters – hourly within ~30

min

Best analysis for verification – time is no object

Long-term history for local climatology

Three Phases of AOR

Phase I – Real-time Mesoscale Analysis Hourly within 30 minutes Prototype for AOR NCEP and FSL volunteer to build first

phase

Phase II – Analysis of Record Best analysis possible Time is no object

Phase III – Reanalysis Apply mature AOR retrospectively 30 year time history of AORs

NWS’ Integrated Work Team (IWT)

Lee Anderson (co-chair), OST PMB Brad Colman (co-chair), WFO  SEA Fred Branski, OCIO Geoff DiMego, NCEP EMC Brian Gockel, OST MDL Dave Kitzmiller, OHD Chuck Kluepfel, OCWWS Performance

Branch Art Thomas, OCWWS Al Wissman, OOS

Subsequent Context

Admiral Lautenbacher “Observations alone are often meaningless

without the actions that provide economic and societal benefit.”

A comprehensive weather and climate observing system requires integration and synthesis of observations into gridded analyses of current and past states of the atmosphere

NWS has critical need to produce real-time and retrospective analyses at high resolution to help create and verify gridded forecasts as part of NWS Digital Services Program

Societal Benefits

Climate Variability and

ChangeOcean

Resources

Disasters

Energy

Health

Agriculture

Ecosystem

Water Resources

GEOSS - Creating a “System of Systems”

Global Observing Systems

GCOSGOOSGTOS

WHYCOSWorld Weather

IGBPIOOSCEOSIGOS

Global Observing Systems

GCOSGOOSGTOS

WHYCOSWorld Weather

IGBPIOOSCEOSIGOS

National/Multinational Observing Systems

Satellites

Surface Obs.

Radar

Aircraft

Ocean Observations

Paleo-data

National/Multinational Observing Systems

Satellites

Surface Obs.

Radar

Aircraft

Ocean Observations

Paleo-data

Private Sector Observing Systems

Satellites

Mesonets

Lightning

Commercial Aircraft

Private Sector Observing Systems

Satellites

Mesonets

Lightning

Commercial Aircraft

14

Proposed Ongoing Analysis of the Climate System/Analysis of Record

P. Arkin

Global Earth Observing System of Systems

Diverse Needs For AOR Forecaster situational awareness,

gridded forecast preparation and verification

Aviation and surface transportation Environmental issues from the coastal zone to

fire management Mesoscale modeling (AOR must be 3D) Homeland security

Dispersion modeling for transport of hazardous materials and pollutants

Impacts of climate change on a regional scale

Phase I: The Real-Time Mesoscale Analysis

(RTMA)

Real-Time Mesoscale Analysis

The RTMA is a fast-track, proof-of-concept effort intended to: leverage and enhance existing analysis capabilities in

order to generate experimental CONUS-scale hourly NDFD-matching analyses

OPERATIONAL at NCEP and on AWIPS with OB7 by end of FY2006 establish a real-time process that delivers a sub-set of

fields to allow preliminary comparisons to NDFD forecast grids

Hourly temp, wind, moisture plus precip & sky on 5 km NDFD grid also provide estimates of analysis uncertainty establish benchmark for future AOR efforts build constituency for subsequent AOR development

activities

RTMA Procedure Temperature & dew point at 2 m & wind at

10 m RUC forecast/analysis (13 km) is downscaled

by FSL to 5 km NDFD grid Downscaled RUC used as first-guess in NCEP’s

2DVar analysis of ALL surface observations Estimate of analysis error/uncertainty

Precipitation – NCEP Stage II analysis Sky cover – NESDIS GOES sounder

effective cloud amount

RTMA Logistics Hourly within ~30 minutes 5 km NDFD grid in GRIB2 Operational at NCEP Q3 FY2006 Distribution of analyses and estimate

of analysis error/uncertainty via AWIPS SBN as part of OB7 upgrade – end of FY2006

Archived at NCDC

Why not another solution?

Overarching Constraints: No funding available – must rely on volunteered resources Need to generate centrally as part of NCEP operations

RSAS, OI, SCM or Barnes – legacy techniques with known issues where obs density changes

ADAS, LAPS or STMAS – built around non-NCEP environments, too long & costly to adapt, no expertise at NCEP (for O&M and evolution)

Ensemble Kalman Filter – brand new technique (risk), does not scale to large number of obs, too long & costly to adapt, no expertise at NCEP

Why 2DVar solution? 2DVar is subset of NCEP’s more general 3DVar

Grid-point Statistical Interpolation (GSI) Connected to state-of-the-art unified GSI

development at NCEP / JCSDA 2DVar is already running in NAM (low risk) Anisotropy built into 2DVar provides way to

restrict influence of obs based on Elevation (terrain height – NAM & ADAS in WR) Flow (wind) Air mass (potential temperature)

2DVar is more than fast enough to run overtop of hourly RUC in tight NCEP Production suite

Can provide estimate of analysis error as well as “accuracy” via built-in cross-validation

NCEP Production SuiteWeather, Ocean & Climate Forecast Systems

Version 3.1 October 20, 2004

0102030405060708090

100

0:00 0:30 1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00 5:30 6:00

6 Hour Cycle

Perc

ent U

sed

RR/RTMAFireWXWAVESHUR/HRWGFSfcstGFSanalGFSensNAMfcstNAManalSREFAir QualityGlblOceanMonthlySeasonal

RTMA Background/First Guess

Hourly RUC 1 hr forecast (or analysis) Downscale from 13 km RUC to 5 km NDFD

using simple techniques like SMARTINIT Ten (10) parameters currently provided

Temperature at 2m Dew point and specific humidity at 2m Wind components at 10 m Surface pressure and elevation Wind gust at 10 m Cloud base Visibility at 2 m

Primary contact: Stan Benjamin (FSL) Implemented by Geoff Manikin (EMC/NCEP)

Downscaled 2 m Temperature

Original 13 km

Downscaled 5 km

Downscaled 10 m Wind

Original 13 km

Downscaled 5 km

Terrain Grid Variants (issue?)

MDL WRF SI

NCEP’s Gridpoint Statistical Interpolation (GSI)

http://wwwt.emc.ncep.noaa.gov/gmb/treadon/gsi/documents/presentations/1st_gsi_orientation/

1st GSI User Orientation 4-5 January 2005

NCEP obtains full complement of observations Conventional through TOC Mesonets through MADIS (FSL) MesoWest will be critical alternate

path to MADIS during AOR due to their ability to store and forward “old” data transmitted in bursts from some sites/networks (may have better QC as well)

ALL Surface Obs = 89126 total

SFCSHP+ADPSFC Obs = 10843

of which METAR Obs = 8543

All Mesonet Obs = 78283

of which AWS Obs = 35565

Mesonet Issues Mesonets comprise majority of obs but they

are not as “good” as the other conventional sfc ob sources

Winds not used in current RUC due to biases

FSL has constructed a “Uselist” of acceptable networks based on overall siting strategies etc.

Seung-Jae Lee (KMA visitor at NCEP) working with these mesonet data in GSI context

Seung-Jae Lee Projects Sensitivity to regional background

error Understanding the characteristics

of innovations (guess-obs) of the sfc mesonet data

A complex forward operator

Mesonet data: QC NCEP currently honors quality markers

provided by the FSL-MADIS. Bad quality marks can be put on the mesonet

data based on the SDM’s reject-list. Subjective QC is too costly. NCEP will convert FSL’s use-list to reject-list.

Some gross checks result in bad quality marks for data that are outside reasonable limits, data with mising lat/lon etc.

Background error treatment Old: Downscaled version of

background error derived from NCEP global model

New: background error computed from auto-covariances based on 8 km forecasts of WRF-NMM (W.-S. Wu 2005, personal communication)

Experimental design

analysis

use ofmesonet

data

background error

Note

AN1 No global operation (control run)

AN2 Yes global impact of mesonet data

AN3 Yesregional

(WRF-NMM)impact of new regional

background error statistics

Analysis increments (case 1)

A -G < 0 over much of the domain

Analysis Increments (case 2)

- Smaller and detailed str- Positive in the N-easternregion where Low is located

6th-7th : 920-900hPa

Analysis increments (case 3)

Unstable sfc

Characteristics of innovations (observed–guess) of the near-surface data

Long-term statistics: 1 month Scatter diagram of (O-G) during

May 2005 for nighttime (0600 UTC), early morning western (1200 UTC),daytime eastern (1800 UTC) domains.

Linear regression, bias, rmse etc.

Western domain (0600 UTC)

Sfc mesonet T data have a considerable amount of outliers compared with other land sfc T data. Bad obs or local effects

Some stations can be seen to produce the same values regardlessof model fcsts.

Many outliers

This can mean quality markers placed on the data by the FSL-MADIS QC are of little valuenot only for wind but also T data.

Better QC is needed.

Very few outliers in METAR possibility of local effect

Central domain (1200 UTC)

Eastern domain (1800 UTC)

Slope and correlation coeff. of conventional surface land T data are good and similar in all three domains.

Motivation for Forward Model

)]()()([2

1 11 yHxFOyHxxBxJ TT

x : an N-component vector of analysis incrementB : the N by N previous forecast-error covariance matrixO : the M by M observational (instrumental)-error covariance matrixF : the M by M representativeness (forward operator)-error covariance matrixH : a linear or nonlinear transformation operator (aka forward model) y : an M-component vector of observational residuals; that is, M : the number of degrees of freedom in the analysis; andN : the number of observations.

guessobs Hxyy

Cost function

H, the forward model Converts the analysis variables to the observation

type and location (May include variable transformations in case of, for example, radar or satellite data assimilations).

The quality of simulated observation, the model state converted to “observation units”, depends on the accuracy of the forward operator: Inaccurate pseudo-observations give rise to errors in observation increments leading to bad analyses.

The accuracy of analysis is dependent on the effectiveness of algorithm used to match obs with the background values

New Forward Model Uses a similarity theory based on Dyer and

Hicks formula which has been adopted in model PBL parameterization such as surface layer process in MRF PBL scheme (Hong and Pan 1996).

realized in the MM5-3DVAR system (Barker et al. 2004). It has been used for surface data assimilation in operational analysis systems and has proven to be encouraging in many contexts (e.g., Shin et al. 2002; Hwang 2005).

Number of sites with large innovations is reduced

Oldsimpleinterpol.

Newfwdmodel

GSI: Nonlinear Quality Control

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Next to be tried will be the GSI’s nonlinear QC procedure

Anisotropic covariance functions

Error Correlations for Valley Ob (SLC) Location Plotted Over Utah Topography

Anisotropic Correlation:obs' influence restricted to areas of similar elevation

Isotropic Correlation:obs' influence extends up mountain slope

NCEP GSI-2DVAR

Terrain-following auto-correlation function for temperature for a test point located at (x=360,y=420). Terrain shown as color

contours. Units on x and y axis are “grid units.”

Example of 2DVar/RTMA analysis increment for temp

Isotropic

Anisotropic

Wind-following auto-correlation function for moisture for a test point located at (x=140,y=180)

Example of 2DVar/RTMA analysis increment for moisture

Isotropic

Anisotropic

Estimation of Observation Error

Wu, W.-S.,, and S.-W. Joo, 1996: The change of the observational errors in the NCEP SSI analysis. Pp84-85 in Proceedings of the 11th Conference on Numerical Weather Prediction, 19-23 August 1996, Norfolk, Virginia.

Desroziers, G., and S. Ivanov, 2001: Diagnosis and adaptive tuning of observation-error parameters in a variational assimilation. Q. J. R. Meteorol. Soc., 127, 1433-1452

Obs Error Tuning results

aircft 3.6 3.29

asdar 3 5.65

acars 2.5 2.32

Metar 3.5 1.58

sfcshp 2.5 2.13

synop 4 1.50

Nopsfc

7 2.63

metar 6 2.26

Wind oberr Temp oberr

RH 20% 5.9%

Psfc 1.6 0.54

Ob type 180-189

Impact of oberr tuning on forecasts3 hr forecast RMS fit to obs at end of 12-hr cycle (cntl/exp)blue blue : positive: redred : negative

psfc q t u/v

1.84/1.711.71 8.49/7.747.74 2.25/2.112.11 4.19/4.19

1.12/1.041.04 13.37/12.9412.94 2.18/2.162.16 4.05/4.034.03

1.68/1.371.37 12.41/12.1712.17 2.12/2.072.07 4.18/4.114.11

1.75/1.391.39 11.33/10.8210.82 2.12/2.042.04 4.36/4.294.29

1.50/1.281.28 13.79/13.2313.23 2.04/1.991.99 4.52/4.504.50

1.75/1.581.58 12.63/12.2112.21 2.15/2.072.07 4.31/4.244.24

1.45/1.461.46 13.76/13.5013.50 2.10/2.082.08 4.74/4.644.64

1.72/1.501.50 11.72/11.5511.55 2.15/2.132.13 4.29/4.314.31

1.65/1.391.39 11.64/11.5211.52 2.13/2.072.07 4.24/4.144.14

1.60/1.281.28 13.03/12.5812.58 2.21/2.192.19 4.58/4.614.61

1.69/1.251.25 12.54/11.9811.98 2.22/2.182.18 4.72/4.694.69

1.35/1.121.12 13.24/12.7112.71 2.19/2.162.16 4.43/4.484.48

1.69/1.321.32 12.10/11.7511.75 2.11/2.052.05 4.81/4.764.76

1.20/1.201.20 13.53/12.1512.15 2.13/1.991.99 4.61/4.504.50

Estimates of RTMA Analysis Error / Uncertainty Reflect Obs density, Obs quality and

Background quality Not direct from GSI but it will be possible to

estimate it:

Estimates of RTMA Analysis “Accuracy” - Cross-validation NWP data assimilation gauges quality of initial

conditions via model forecast skill Cross-validation is really only way:

Withhold observations from analysis Measure ability of analysis system to reproduce their

values GSI will have Cross-Validation built in

Withhold ~10% of each type of obs for 1st ~85% iterations Measure fit of solution to withheld obs Add obs back in for another ~35% iterations Plan to do this periodically (not routinely)

Evaluation plans (Nov-Jun) NCEP will establish webpage displaying

2DVar/RTMA NCEP will attempt to acquire gridded form

of: ADAS from UofUtah / WR STMAS from FSL Steve Koch MatchOb from AWIPS

Steve Lazarus (ADAS expert) to help with intercomparison of 2DVar with other analyses

NCEP RTMA Precipitation Analysis 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

Derived ECA from GOES-12 ECA from GRIB2 file – 5km grid

GOES-12 IR image (11um)

• 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)

(a)

(c)

Sky Cover: Effective Cloud Amount

Phase II: Analysis of Record Moving Beyond the Prototype RTMA

Best possible analysis requires 4-Dimensional Data Assimilation

Model to move info through space & time – WRF Analysis capable of using ALL observations – GSI More data collection time than RTMA to acquire all obs – 18-

48 hours Should use obs precip to drive evolution of land-states lower

boundary condition – EDAS/NDAS 4DVar might be feasible but only with MAJOR funding

increase and essentially new computer hence $10M Three levels scoped out $2.5M (4DDA w/ PBL

emphasis only), $4.5M (4DDA w/ 3DVar or equivalent, and $10M (4DVar or equivalent – will take years longer too)

RTMA will remain in place to support near-real-time needs of forecaster, but support ($100K) needed for O&M and extra efforts to include all NDFD parameters

Phase II: Analysis of Record Moving Beyond the Prototype RTMA

Support for an AOR suite of mesoscale analyses needs to be broadened (established!)

Considerable research, development, and operational infrastructure (computing) required for complete suite of analysis products of sufficient accuracy to verify NDFD

Initial steps underway to address NWS AOR funding requirements for FY 2008 and beyond, emphasizing critical role played by EMC/NCEP and partners (OSIP, PPBES, etc.)

Additional funding sources for broader community efforts to develop next-generation AOR need to be identified